diff --git a/Assets/Editor/MLHelper.cs b/Assets/Editor/MLHelper.cs
index 96269c8..db45461 100644
--- a/Assets/Editor/MLHelper.cs
+++ b/Assets/Editor/MLHelper.cs
@@ -67,7 +67,7 @@ private void OnGUI ()
BuildPlayerOptions buildPlayerOptions = new BuildPlayerOptions();
buildPlayerOptions.options = BuildOptions.ShowBuiltPlayer;
buildPlayerOptions.scenes = new[] { "Assets/Scenes/Training.unity" };
- buildPlayerOptions.target = BuildTarget.StandaloneOSXUniversal;
+ buildPlayerOptions.target = BuildTarget.StandaloneOSX;
buildPlayerOptions.locationPathName = "python/ML-Roguelike";
BuildPipeline.BuildPlayer(buildPlayerOptions);
}
diff --git a/Assets/Plugins.meta b/Assets/JsonDotNet.meta
similarity index 62%
rename from Assets/Plugins.meta
rename to Assets/JsonDotNet.meta
index 8ad03d7..06e25c1 100644
--- a/Assets/Plugins.meta
+++ b/Assets/JsonDotNet.meta
@@ -1,8 +1,6 @@
fileFormatVersion: 2
-guid: e6e3aa2960c934f319eafc63f5207868
+guid: 9678c27ce23c223419ea9d8d1b81599d
folderAsset: yes
-timeCreated: 1508965819
-licenseType: Pro
DefaultImporter:
externalObjects: {}
userData:
diff --git a/Assets/JsonDotNet/Assemblies.meta b/Assets/JsonDotNet/Assemblies.meta
new file mode 100644
index 0000000..59ac16b
--- /dev/null
+++ b/Assets/JsonDotNet/Assemblies.meta
@@ -0,0 +1,9 @@
+fileFormatVersion: 2
+guid: 577d9725f58264943855b8ac185531fe
+folderAsset: yes
+timeCreated: 1466788344
+licenseType: Store
+DefaultImporter:
+ userData:
+ assetBundleName:
+ assetBundleVariant:
diff --git a/Assets/JsonDotNet/Assemblies/AOT.meta b/Assets/JsonDotNet/Assemblies/AOT.meta
new file mode 100644
index 0000000..f9dba64
--- /dev/null
+++ b/Assets/JsonDotNet/Assemblies/AOT.meta
@@ -0,0 +1,9 @@
+fileFormatVersion: 2
+guid: 14f21d7a1e53a8c4e87b25526a7eb63c
+folderAsset: yes
+timeCreated: 1466788345
+licenseType: Store
+DefaultImporter:
+ userData:
+ assetBundleName:
+ assetBundleVariant:
diff --git a/Assets/JsonDotNet/Assemblies/AOT/Newtonsoft.Json.XML b/Assets/JsonDotNet/Assemblies/AOT/Newtonsoft.Json.XML
new file mode 100644
index 0000000..9a914af
--- /dev/null
+++ b/Assets/JsonDotNet/Assemblies/AOT/Newtonsoft.Json.XML
@@ -0,0 +1,8015 @@
+
+
+
+ Newtonsoft.Json
+
+
+
+
+ Represents a reader that provides fast, non-cached, forward-only access to serialized JSON data.
+
+
+
+
+ Gets or sets a value indicating whether binary data reading should compatible with incorrect Json.NET 3.5 written binary.
+
+
+ true if binary data reading will be compatible with incorrect Json.NET 3.5 written binary; otherwise, false.
+
+
+
+
+ Gets or sets a value indicating whether the root object will be read as a JSON array.
+
+
+ true if the root object will be read as a JSON array; otherwise, false.
+
+
+
+
+ Gets or sets the used when reading values from BSON.
+
+ The used when reading values from BSON.
+
+
+
+ Initializes a new instance of the class.
+
+ The stream.
+
+
+
+ Initializes a new instance of the class.
+
+ The reader.
+
+
+
+ Initializes a new instance of the class.
+
+ The stream.
+ if set to true the root object will be read as a JSON array.
+ The used when reading values from BSON.
+
+
+
+ Initializes a new instance of the class.
+
+ The reader.
+ if set to true the root object will be read as a JSON array.
+ The used when reading values from BSON.
+
+
+
+ Reads the next JSON token from the stream.
+
+
+ true if the next token was read successfully; false if there are no more tokens to read.
+
+
+
+
+ Changes the to Closed.
+
+
+
+
+ Represents a writer that provides a fast, non-cached, forward-only way of generating JSON data.
+
+
+
+
+ Gets or sets the used when writing values to BSON.
+ When set to no conversion will occur.
+
+ The used when writing values to BSON.
+
+
+
+ Initializes a new instance of the class.
+
+ The stream.
+
+
+
+ Initializes a new instance of the class.
+
+ The writer.
+
+
+
+ Flushes whatever is in the buffer to the underlying streams and also flushes the underlying stream.
+
+
+
+
+ Writes the end.
+
+ The token.
+
+
+
+ Writes out a comment /*...*/ containing the specified text.
+
+ Text to place inside the comment.
+
+
+
+ Writes the start of a constructor with the given name.
+
+ The name of the constructor.
+
+
+
+ Writes raw JSON.
+
+ The raw JSON to write.
+
+
+
+ Writes raw JSON where a value is expected and updates the writer's state.
+
+ The raw JSON to write.
+
+
+
+ Writes the beginning of a JSON array.
+
+
+
+
+ Writes the beginning of a JSON object.
+
+
+
+
+ Writes the property name of a name/value pair on a JSON object.
+
+ The name of the property.
+
+
+
+ Closes this stream and the underlying stream.
+
+
+
+
+ Writes a value.
+ An error will raised if the value cannot be written as a single JSON token.
+
+ The value to write.
+
+
+
+ Writes a null value.
+
+
+
+
+ Writes an undefined value.
+
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a [] value.
+
+ The [] value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a [] value that represents a BSON object id.
+
+ The Object ID value to write.
+
+
+
+ Writes a BSON regex.
+
+ The regex pattern.
+ The regex options.
+
+
+
+ Represents a BSON Oid (object id).
+
+
+
+
+ Gets or sets the value of the Oid.
+
+ The value of the Oid.
+
+
+
+ Initializes a new instance of the class.
+
+ The Oid value.
+
+
+
+ Converts a binary value to and from a base 64 string value.
+
+
+
+
+ Writes the JSON representation of the object.
+
+ The to write to.
+ The value.
+ The calling serializer.
+
+
+
+ Reads the JSON representation of the object.
+
+ The to read from.
+ Type of the object.
+ The existing value of object being read.
+ The calling serializer.
+ The object value.
+
+
+
+ Determines whether this instance can convert the specified object type.
+
+ Type of the object.
+
+ true if this instance can convert the specified object type; otherwise, false.
+
+
+
+
+ Create a custom object
+
+ The object type to convert.
+
+
+
+ Writes the JSON representation of the object.
+
+ The to write to.
+ The value.
+ The calling serializer.
+
+
+
+ Reads the JSON representation of the object.
+
+ The to read from.
+ Type of the object.
+ The existing value of object being read.
+ The calling serializer.
+ The object value.
+
+
+
+ Creates an object which will then be populated by the serializer.
+
+ Type of the object.
+ The created object.
+
+
+
+ Determines whether this instance can convert the specified object type.
+
+ Type of the object.
+
+ true if this instance can convert the specified object type; otherwise, false.
+
+
+
+
+ Gets a value indicating whether this can write JSON.
+
+
+ true if this can write JSON; otherwise, false.
+
+
+
+
+ Provides a base class for converting a to and from JSON.
+
+
+
+
+ Determines whether this instance can convert the specified object type.
+
+ Type of the object.
+
+ true if this instance can convert the specified object type; otherwise, false.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Converts a to and from JSON.
+
+
+
+
+ Writes the JSON representation of the object.
+
+ The to write to.
+ The value.
+ The calling serializer.
+
+
+
+ Reads the JSON representation of the object.
+
+ The to read from.
+ Type of the object.
+ The existing value of object being read.
+ The calling serializer.
+ The object value.
+
+
+
+ Determines whether this instance can convert the specified object type.
+
+ Type of the object.
+
+ true if this instance can convert the specified object type; otherwise, false.
+
+
+
+
+ Converts a to and from JSON and BSON.
+
+
+
+
+ Writes the JSON representation of the object.
+
+ The to write to.
+ The value.
+ The calling serializer.
+
+
+
+ Reads the JSON representation of the object.
+
+ The to read from.
+ Type of the object.
+ The existing value of object being read.
+ The calling serializer.
+ The object value.
+
+
+
+ Determines whether this instance can convert the specified object type.
+
+ Type of the object.
+
+ true if this instance can convert the specified object type; otherwise, false.
+
+
+
+
+ Converts a to and from JSON and BSON.
+
+
+
+
+ Writes the JSON representation of the object.
+
+ The to write to.
+ The value.
+ The calling serializer.
+
+
+
+ Reads the JSON representation of the object.
+
+ The to read from.
+ Type of the object.
+ The existing value of object being read.
+ The calling serializer.
+ The object value.
+
+
+
+ Determines whether this instance can convert the specified object type.
+
+ Type of the object.
+
+ true if this instance can convert the specified object type; otherwise, false.
+
+
+
+
+ Converts an to and from its name string value.
+
+
+
+
+ Gets or sets a value indicating whether the written enum text should be camel case.
+
+ true if the written enum text will be camel case; otherwise, false.
+
+
+
+ Gets or sets a value indicating whether integer values are allowed.
+
+ true if integers are allowed; otherwise, false.
+
+
+
+ Initializes a new instance of the class.
+
+
+
+
+ Initializes a new instance of the class.
+
+ true if the written enum text will be camel case; otherwise, false.
+
+
+
+ Writes the JSON representation of the object.
+
+ The to write to.
+ The value.
+ The calling serializer.
+
+
+
+ Reads the JSON representation of the object.
+
+ The to read from.
+ Type of the object.
+ The existing value of object being read.
+ The calling serializer.
+ The object value.
+
+
+
+ Determines whether this instance can convert the specified object type.
+
+ Type of the object.
+
+ true if this instance can convert the specified object type; otherwise, false.
+
+
+
+
+ Json Converter for Vector2, Vector3 and Vector4. Only serializes x, y, (z) and (w) properties.
+
+
+
+
+ Default Constructor - All Vector types enabled by default
+
+
+
+
+ Selectively enable Vector types
+
+ Use for Vector2 objects
+ Use for Vector3 objects
+ Use for Vector4 objects
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Converts a to and from a string (e.g. "1.2.3.4").
+
+
+
+
+ Writes the JSON representation of the object.
+
+ The to write to.
+ The value.
+ The calling serializer.
+
+
+
+ Reads the JSON representation of the object.
+
+ The to read from.
+ Type of the object.
+ The existing property value of the JSON that is being converted.
+ The calling serializer.
+ The object value.
+
+
+
+ Determines whether this instance can convert the specified object type.
+
+ Type of the object.
+
+ true if this instance can convert the specified object type; otherwise, false.
+
+
+
+
+ Converts a to and from the ISO 8601 date format (e.g. 2008-04-12T12:53Z).
+
+
+
+
+ Gets or sets the date time styles used when converting a date to and from JSON.
+
+ The date time styles used when converting a date to and from JSON.
+
+
+
+ Gets or sets the date time format used when converting a date to and from JSON.
+
+ The date time format used when converting a date to and from JSON.
+
+
+
+ Gets or sets the culture used when converting a date to and from JSON.
+
+ The culture used when converting a date to and from JSON.
+
+
+
+ Writes the JSON representation of the object.
+
+ The to write to.
+ The value.
+ The calling serializer.
+
+
+
+ Reads the JSON representation of the object.
+
+ The to read from.
+ Type of the object.
+ The existing value of object being read.
+ The calling serializer.
+ The object value.
+
+
+
+ Converts a to and from a JavaScript date constructor (e.g. new Date(52231943)).
+
+
+
+
+ Writes the JSON representation of the object.
+
+ The to write to.
+ The value.
+ The calling serializer.
+
+
+
+ Reads the JSON representation of the object.
+
+ The to read from.
+ Type of the object.
+ The existing property value of the JSON that is being converted.
+ The calling serializer.
+ The object value.
+
+
+
+ Converts XML to and from JSON.
+
+
+
+
+ Gets or sets the name of the root element to insert when deserializing to XML if the JSON structure has produces multiple root elements.
+
+ The name of the deserialize root element.
+
+
+
+ Gets or sets a flag to indicate whether to write the Json.NET array attribute.
+ This attribute helps preserve arrays when converting the written XML back to JSON.
+
+ true if the array attibute is written to the XML; otherwise, false.
+
+
+
+ Gets or sets a value indicating whether to write the root JSON object.
+
+ true if the JSON root object is omitted; otherwise, false.
+
+
+
+ Writes the JSON representation of the object.
+
+ The to write to.
+ The calling serializer.
+ The value.
+
+
+
+ Reads the JSON representation of the object.
+
+ The to read from.
+ Type of the object.
+ The existing value of object being read.
+ The calling serializer.
+ The object value.
+
+
+
+ Checks if the attributeName is a namespace attribute.
+
+ Attribute name to test.
+ The attribute name prefix if it has one, otherwise an empty string.
+ True if attribute name is for a namespace attribute, otherwise false.
+
+
+
+ Determines whether this instance can convert the specified value type.
+
+ Type of the value.
+
+ true if this instance can convert the specified value type; otherwise, false.
+
+
+
+
+ Specifies how constructors are used when initializing objects during deserialization by the .
+
+
+
+
+ First attempt to use the public default constructor, then fall back to single paramatized constructor, then the non-public default constructor.
+
+
+
+
+ Json.NET will use a non-public default constructor before falling back to a paramatized constructor.
+
+
+
+
+ Specifies how dates are formatted when writing JSON text.
+
+
+
+
+ Dates are written in the ISO 8601 format, e.g. "2012-03-21T05:40Z".
+
+
+
+
+ Dates are written in the Microsoft JSON format, e.g. "\/Date(1198908717056)\/".
+
+
+
+
+ Specifies how date formatted strings, e.g. "\/Date(1198908717056)\/" and "2012-03-21T05:40Z", are parsed when reading JSON text.
+
+
+
+
+ Date formatted strings are not parsed to a date type and are read as strings.
+
+
+
+
+ Date formatted strings, e.g. "\/Date(1198908717056)\/" and "2012-03-21T05:40Z", are parsed to .
+
+
+
+
+ Date formatted strings, e.g. "\/Date(1198908717056)\/" and "2012-03-21T05:40Z", are parsed to .
+
+
+
+
+ Specifies how to treat the time value when converting between string and .
+
+
+
+
+ Treat as local time. If the object represents a Coordinated Universal Time (UTC), it is converted to the local time.
+
+
+
+
+ Treat as a UTC. If the object represents a local time, it is converted to a UTC.
+
+
+
+
+ Treat as a local time if a is being converted to a string.
+ If a string is being converted to , convert to a local time if a time zone is specified.
+
+
+
+
+ Time zone information should be preserved when converting.
+
+
+
+
+ Specifies float format handling options when writing special floating point numbers, e.g. ,
+ and with .
+
+
+
+
+ Write special floating point values as strings in JSON, e.g. "NaN", "Infinity", "-Infinity".
+
+
+
+
+ Write special floating point values as symbols in JSON, e.g. NaN, Infinity, -Infinity.
+ Note that this will produce non-valid JSON.
+
+
+
+
+ Write special floating point values as the property's default value in JSON, e.g. 0.0 for a property, null for a property.
+
+
+
+
+ Specifies how floating point numbers, e.g. 1.0 and 9.9, are parsed when reading JSON text.
+
+
+
+
+ Floating point numbers are parsed to .
+
+
+
+
+ Floating point numbers are parsed to .
+
+
+
+
+ Specifies formatting options for the .
+
+
+
+
+ No special formatting is applied. This is the default.
+
+
+
+
+ Causes child objects to be indented according to the and settings.
+
+
+
+
+ Provides an interface for using pooled arrays.
+
+ The array type content.
+
+
+
+ Rent a array from the pool. This array must be returned when it is no longer needed.
+
+ The minimum required length of the array. The returned array may be longer.
+ The rented array from the pool. This array must be returned when it is no longer needed.
+
+
+
+ Return an array to the pool.
+
+ The array that is being returned.
+
+
+
+ Instructs the to use the specified constructor when deserializing that object.
+
+
+
+
+ Instructs the how to serialize the collection.
+
+
+
+
+ Initializes a new instance of the class.
+
+
+
+
+ Initializes a new instance of the class with the specified container Id.
+
+ The container Id.
+
+
+
+ The exception thrown when an error occurs during JSON serialization or deserialization.
+
+
+
+
+ Initializes a new instance of the class.
+
+
+
+
+ Initializes a new instance of the class
+ with a specified error message.
+
+ The error message that explains the reason for the exception.
+
+
+
+ Initializes a new instance of the class
+ with a specified error message and a reference to the inner exception that is the cause of this exception.
+
+ The error message that explains the reason for the exception.
+ The exception that is the cause of the current exception, or a null reference (Nothing in Visual Basic) if no inner exception is specified.
+
+
+
+ Initializes a new instance of the class.
+
+ The that holds the serialized object data about the exception being thrown.
+ The that contains contextual information about the source or destination.
+ The parameter is null.
+ The class name is null or is zero (0).
+
+
+
+ Instructs the to deserialize properties with no matching class member into the specified collection
+ and write values during serialization.
+
+
+
+
+ Gets or sets a value that indicates whether to write extension data when serializing the object.
+
+
+ true to write extension data when serializing the object; otherwise, false. The default is true.
+
+
+
+
+ Gets or sets a value that indicates whether to read extension data when deserializing the object.
+
+
+ true to read extension data when deserializing the object; otherwise, false. The default is true.
+
+
+
+
+ Initializes a new instance of the class.
+
+
+
+
+ Instructs the to always serialize the member, and require the member has a value.
+
+
+
+
+ Specifies how JSON comments are handled when loading JSON.
+
+
+
+
+ Ignore comments.
+
+
+
+
+ Load comments as a with type .
+
+
+
+
+ Specifies how line information is handled when loading JSON.
+
+
+
+
+ Ignore line information.
+
+
+
+
+ Load line information.
+
+
+
+
+ Represents a view of a .
+
+
+
+
+ Initializes a new instance of the class.
+
+ The name.
+
+
+
+ When overridden in a derived class, returns whether resetting an object changes its value.
+
+
+ true if resetting the component changes its value; otherwise, false.
+
+ The component to test for reset capability.
+
+
+
+
+ When overridden in a derived class, gets the current value of the property on a component.
+
+
+ The value of a property for a given component.
+
+ The component with the property for which to retrieve the value.
+
+
+
+
+ When overridden in a derived class, resets the value for this property of the component to the default value.
+
+ The component with the property value that is to be reset to the default value.
+
+
+
+
+ When overridden in a derived class, sets the value of the component to a different value.
+
+ The component with the property value that is to be set.
+ The new value.
+
+
+
+
+ When overridden in a derived class, determines a value indicating whether the value of this property needs to be persisted.
+
+
+ true if the property should be persisted; otherwise, false.
+
+ The component with the property to be examined for persistence.
+
+
+
+
+ When overridden in a derived class, gets the type of the component this property is bound to.
+
+
+ A that represents the type of component this property is bound to. When the or methods are invoked, the object specified might be an instance of this type.
+
+
+
+
+ When overridden in a derived class, gets a value indicating whether this property is read-only.
+
+
+ true if the property is read-only; otherwise, false.
+
+
+
+
+ When overridden in a derived class, gets the type of the property.
+
+
+ A that represents the type of the property.
+
+
+
+
+ Gets the hash code for the name of the member.
+
+
+
+ The hash code for the name of the member.
+
+
+
+
+ Specifies the settings used when loading JSON.
+
+
+
+
+ Gets or sets how JSON comments are handled when loading JSON.
+
+ The JSON comment handling.
+
+
+
+ Gets or sets how JSON line info is handled when loading JSON.
+
+ The JSON line info handling.
+
+
+
+ Specifies the settings used when merging JSON.
+
+
+
+
+ Gets or sets the method used when merging JSON arrays.
+
+ The method used when merging JSON arrays.
+
+
+
+ Gets or sets how how null value properties are merged.
+
+ How null value properties are merged.
+
+
+
+ Specifies how JSON arrays are merged together.
+
+
+
+ Concatenate arrays.
+
+
+ Union arrays, skipping items that already exist.
+
+
+ Replace all array items.
+
+
+ Merge array items together, matched by index.
+
+
+
+ Specifies how null value properties are merged.
+
+
+
+
+ The content's null value properties will be ignored during merging.
+
+
+
+
+ The content's null value properties will be merged.
+
+
+
+
+ Represents a raw JSON string.
+
+
+
+
+ Initializes a new instance of the class from another object.
+
+ A object to copy from.
+
+
+
+ Initializes a new instance of the class.
+
+ The raw json.
+
+
+
+ Creates an instance of with the content of the reader's current token.
+
+ The reader.
+ An instance of with the content of the reader's current token.
+
+
+
+ Represents a collection of objects.
+
+ The type of token
+
+
+
+ Gets the with the specified key.
+
+
+
+
+
+ Compares tokens to determine whether they are equal.
+
+
+
+
+ Determines whether the specified objects are equal.
+
+ The first object of type to compare.
+ The second object of type to compare.
+
+ true if the specified objects are equal; otherwise, false.
+
+
+
+
+ Returns a hash code for the specified object.
+
+ The for which a hash code is to be returned.
+ A hash code for the specified object.
+ The type of is a reference type and is null.
+
+
+
+ Contains the LINQ to JSON extension methods.
+
+
+
+
+ Returns a collection of tokens that contains the ancestors of every token in the source collection.
+
+ The type of the objects in source, constrained to .
+ An of that contains the source collection.
+ An of that contains the ancestors of every token in the source collection.
+
+
+
+ Returns a collection of tokens that contains every token in the source collection, and the ancestors of every token in the source collection.
+
+ The type of the objects in source, constrained to .
+ An of that contains the source collection.
+ An of that contains every token in the source collection, the ancestors of every token in the source collection.
+
+
+
+ Returns a collection of tokens that contains the descendants of every token in the source collection.
+
+ The type of the objects in source, constrained to .
+ An of that contains the source collection.
+ An of that contains the descendants of every token in the source collection.
+
+
+
+ Returns a collection of tokens that contains every token in the source collection, and the descendants of every token in the source collection.
+
+ The type of the objects in source, constrained to .
+ An of that contains the source collection.
+ An of that contains every token in the source collection, and the descendants of every token in the source collection.
+
+
+
+ Returns a collection of child properties of every object in the source collection.
+
+ An of that contains the source collection.
+ An of that contains the properties of every object in the source collection.
+
+
+
+ Returns a collection of child values of every object in the source collection with the given key.
+
+ An of that contains the source collection.
+ The token key.
+ An of that contains the values of every token in the source collection with the given key.
+
+
+
+ Returns a collection of child values of every object in the source collection.
+
+ An of that contains the source collection.
+ An of that contains the values of every token in the source collection.
+
+
+
+ Returns a collection of converted child values of every object in the source collection with the given key.
+
+ The type to convert the values to.
+ An of that contains the source collection.
+ The token key.
+ An that contains the converted values of every token in the source collection with the given key.
+
+
+
+ Returns a collection of converted child values of every object in the source collection.
+
+ The type to convert the values to.
+ An of that contains the source collection.
+ An that contains the converted values of every token in the source collection.
+
+
+
+ Converts the value.
+
+ The type to convert the value to.
+ A cast as a of .
+ A converted value.
+
+
+
+ Converts the value.
+
+ The source collection type.
+ The type to convert the value to.
+ A cast as a of .
+ A converted value.
+
+
+
+ Returns a collection of child tokens of every array in the source collection.
+
+ The source collection type.
+ An of that contains the source collection.
+ An of that contains the values of every token in the source collection.
+
+
+
+ Returns a collection of converted child tokens of every array in the source collection.
+
+ An of that contains the source collection.
+ The type to convert the values to.
+ The source collection type.
+ An that contains the converted values of every token in the source collection.
+
+
+
+ Returns the input typed as .
+
+ An of that contains the source collection.
+ The input typed as .
+
+
+
+ Returns the input typed as .
+
+ The source collection type.
+ An of that contains the source collection.
+ The input typed as .
+
+
+
+ Represents a JSON constructor.
+
+
+
+
+ Gets the container's children tokens.
+
+ The container's children tokens.
+
+
+
+ Gets or sets the name of this constructor.
+
+ The constructor name.
+
+
+
+ Gets the node type for this .
+
+ The type.
+
+
+
+ Initializes a new instance of the class.
+
+
+
+
+ Initializes a new instance of the class from another object.
+
+ A object to copy from.
+
+
+
+ Initializes a new instance of the class with the specified name and content.
+
+ The constructor name.
+ The contents of the constructor.
+
+
+
+ Initializes a new instance of the class with the specified name and content.
+
+ The constructor name.
+ The contents of the constructor.
+
+
+
+ Initializes a new instance of the class with the specified name.
+
+ The constructor name.
+
+
+
+ Writes this token to a .
+
+ A into which this method will write.
+ A collection of which will be used when writing the token.
+
+
+
+ Gets the with the specified key.
+
+ The with the specified key.
+
+
+
+ Loads an from a .
+
+ A that will be read for the content of the .
+ A that contains the JSON that was read from the specified .
+
+
+
+ Loads an from a .
+
+ A that will be read for the content of the .
+ The used to load the JSON.
+ If this is null, default load settings will be used.
+ A that contains the JSON that was read from the specified .
+
+
+
+ Represents a token that can contain other tokens.
+
+
+
+
+ Occurs when the list changes or an item in the list changes.
+
+
+
+
+ Occurs before an item is added to the collection.
+
+
+
+
+ Gets the container's children tokens.
+
+ The container's children tokens.
+
+
+
+ Raises the event.
+
+ The instance containing the event data.
+
+
+
+ Raises the event.
+
+ The instance containing the event data.
+
+
+
+ Gets a value indicating whether this token has child tokens.
+
+
+ true if this token has child values; otherwise, false.
+
+
+
+
+ Get the first child token of this token.
+
+
+ A containing the first child token of the .
+
+
+
+
+ Get the last child token of this token.
+
+
+ A containing the last child token of the .
+
+
+
+
+ Returns a collection of the child tokens of this token, in document order.
+
+
+ An of containing the child tokens of this , in document order.
+
+
+
+
+ Returns a collection of the child values of this token, in document order.
+
+ The type to convert the values to.
+
+ A containing the child values of this , in document order.
+
+
+
+
+ Returns a collection of the descendant tokens for this token in document order.
+
+ An containing the descendant tokens of the .
+
+
+
+ Returns a collection of the tokens that contain this token, and all descendant tokens of this token, in document order.
+
+ An containing this token, and all the descendant tokens of the .
+
+
+
+ Adds the specified content as children of this .
+
+ The content to be added.
+
+
+
+ Adds the specified content as the first children of this .
+
+ The content to be added.
+
+
+
+ Creates an that can be used to add tokens to the .
+
+ An that is ready to have content written to it.
+
+
+
+ Replaces the children nodes of this token with the specified content.
+
+ The content.
+
+
+
+ Removes the child nodes from this token.
+
+
+
+
+ Merge the specified content into this .
+
+ The content to be merged.
+
+
+
+ Merge the specified content into this using .
+
+ The content to be merged.
+ The used to merge the content.
+
+
+
+ Gets the count of child JSON tokens.
+
+ The count of child JSON tokens
+
+
+
+ Represents a collection of objects.
+
+ The type of token
+
+
+
+ An empty collection of objects.
+
+
+
+
+ Initializes a new instance of the struct.
+
+ The enumerable.
+
+
+
+ Returns an enumerator that iterates through the collection.
+
+
+ A that can be used to iterate through the collection.
+
+
+
+
+ Returns an enumerator that iterates through a collection.
+
+
+ An object that can be used to iterate through the collection.
+
+
+
+
+ Gets the with the specified key.
+
+
+
+
+
+ Determines whether the specified is equal to this instance.
+
+ The to compare with this instance.
+
+ true if the specified is equal to this instance; otherwise, false.
+
+
+
+
+ Determines whether the specified is equal to this instance.
+
+ The to compare with this instance.
+
+ true if the specified is equal to this instance; otherwise, false.
+
+
+
+
+ Returns a hash code for this instance.
+
+
+ A hash code for this instance, suitable for use in hashing algorithms and data structures like a hash table.
+
+
+
+
+ Represents a JSON object.
+
+
+ /*...*/ containing the specified text.
+
+ Text to place inside the comment.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a [] value.
+
+ The [] value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Represents an abstract JSON token.
+
+
+
+
+ Gets a comparer that can compare two tokens for value equality.
+
+ A that can compare two nodes for value equality.
+
+
+
+ Gets or sets the parent.
+
+ The parent.
+
+
+
+ Gets the root of this .
+
+ The root of this .
+
+
+
+ Gets the node type for this .
+
+ The type.
+
+
+
+ Gets a value indicating whether this token has child tokens.
+
+
+ true if this token has child values; otherwise, false.
+
+
+
+
+ Compares the values of two tokens, including the values of all descendant tokens.
+
+ The first to compare.
+ The second to compare.
+ true if the tokens are equal; otherwise false.
+
+
+
+ Gets the next sibling token of this node.
+
+ The that contains the next sibling token.
+
+
+
+ Gets the previous sibling token of this node.
+
+ The that contains the previous sibling token.
+
+
+
+ Gets the path of the JSON token.
+
+
+
+
+ Adds the specified content immediately after this token.
+
+ A content object that contains simple content or a collection of content objects to be added after this token.
+
+
+
+ Adds the specified content immediately before this token.
+
+ A content object that contains simple content or a collection of content objects to be added before this token.
+
+
+
+ Returns a collection of the ancestor tokens of this token.
+
+ A collection of the ancestor tokens of this token.
+
+
+
+ Returns a collection of tokens that contain this token, and the ancestors of this token.
+
+ A collection of tokens that contain this token, and the ancestors of this token.
+
+
+
+ Returns a collection of the sibling tokens after this token, in document order.
+
+ A collection of the sibling tokens after this tokens, in document order.
+
+
+
+ Returns a collection of the sibling tokens before this token, in document order.
+
+ A collection of the sibling tokens before this token, in document order.
+
+
+
+ Gets the with the specified key.
+
+ The with the specified key.
+
+
+
+ Gets the with the specified key converted to the specified type.
+
+ The type to convert the token to.
+ The token key.
+ The converted token value.
+
+
+
+ Get the first child token of this token.
+
+ A containing the first child token of the .
+
+
+
+ Get the last child token of this token.
+
+ A containing the last child token of the .
+
+
+
+ Returns a collection of the child tokens of this token, in document order.
+
+ An of containing the child tokens of this , in document order.
+
+
+
+ Returns a collection of the child tokens of this token, in document order, filtered by the specified type.
+
+ The type to filter the child tokens on.
+ A containing the child tokens of this , in document order.
+
+
+
+ Returns a collection of the child values of this token, in document order.
+
+ The type to convert the values to.
+ A containing the child values of this , in document order.
+
+
+
+ Removes this token from its parent.
+
+
+
+
+ Replaces this token with the specified token.
+
+ The value.
+
+
+
+ Writes this token to a .
+
+ A into which this method will write.
+ A collection of which will be used when writing the token.
+
+
+
+ Returns the indented JSON for this token.
+
+
+ The indented JSON for this token.
+
+
+
+
+ Returns the JSON for this token using the given formatting and converters.
+
+ Indicates how the output is formatted.
+ A collection of which will be used when writing the token.
+ The JSON for this token using the given formatting and converters.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to [].
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from [] to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Creates an for this token.
+
+ An that can be used to read this token and its descendants.
+
+
+
+ Creates a from an object.
+
+ The object that will be used to create .
+ A with the value of the specified object
+
+
+
+ Creates a from an object using the specified .
+
+ The object that will be used to create .
+ The that will be used when reading the object.
+ A with the value of the specified object
+
+
+
+ Creates the specified .NET type from the .
+
+ The object type that the token will be deserialized to.
+ The new object created from the JSON value.
+
+
+
+ Creates the specified .NET type from the .
+
+ The object type that the token will be deserialized to.
+ The new object created from the JSON value.
+
+
+
+ Creates the specified .NET type from the using the specified .
+
+ The object type that the token will be deserialized to.
+ The that will be used when creating the object.
+ The new object created from the JSON value.
+
+
+
+ Creates the specified .NET type from the using the specified .
+
+ The object type that the token will be deserialized to.
+ The that will be used when creating the object.
+ The new object created from the JSON value.
+
+
+
+ Creates a from a .
+
+ An positioned at the token to read into this .
+
+ An that contains the token and its descendant tokens
+ that were read from the reader. The runtime type of the token is determined
+ by the token type of the first token encountered in the reader.
+
+
+
+
+ Creates a from a .
+
+ An positioned at the token to read into this .
+ The used to load the JSON.
+ If this is null, default load settings will be used.
+
+ An that contains the token and its descendant tokens
+ that were read from the reader. The runtime type of the token is determined
+ by the token type of the first token encountered in the reader.
+
+
+
+
+ Load a from a string that contains JSON.
+
+ A that contains JSON.
+ A populated from the string that contains JSON.
+
+
+
+ Load a from a string that contains JSON.
+
+ A that contains JSON.
+ The used to load the JSON.
+ If this is null, default load settings will be used.
+ A populated from the string that contains JSON.
+
+
+
+ Creates a from a .
+
+ An positioned at the token to read into this .
+ The used to load the JSON.
+ If this is null, default load settings will be used.
+
+ An that contains the token and its descendant tokens
+ that were read from the reader. The runtime type of the token is determined
+ by the token type of the first token encountered in the reader.
+
+
+
+
+ Creates a from a .
+
+ An positioned at the token to read into this .
+
+ An that contains the token and its descendant tokens
+ that were read from the reader. The runtime type of the token is determined
+ by the token type of the first token encountered in the reader.
+
+
+
+
+ Selects a using a JPath expression. Selects the token that matches the object path.
+
+
+ A that contains a JPath expression.
+
+ A , or null.
+
+
+
+ Selects a using a JPath expression. Selects the token that matches the object path.
+
+
+ A that contains a JPath expression.
+
+ A flag to indicate whether an error should be thrown if no tokens are found when evaluating part of the expression.
+ A .
+
+
+
+ Selects a collection of elements using a JPath expression.
+
+
+ A that contains a JPath expression.
+
+ An that contains the selected elements.
+
+
+
+ Selects a collection of elements using a JPath expression.
+
+
+ A that contains a JPath expression.
+
+ A flag to indicate whether an error should be thrown if no tokens are found when evaluating part of the expression.
+ An that contains the selected elements.
+
+
+
+ Creates a new instance of the . All child tokens are recursively cloned.
+
+ A new instance of the .
+
+
+
+ Adds an object to the annotation list of this .
+
+ The annotation to add.
+
+
+
+ Get the first annotation object of the specified type from this .
+
+ The type of the annotation to retrieve.
+ The first annotation object that matches the specified type, or null if no annotation is of the specified type.
+
+
+
+ Gets the first annotation object of the specified type from this .
+
+ The of the annotation to retrieve.
+ The first annotation object that matches the specified type, or null if no annotation is of the specified type.
+
+
+
+ Gets a collection of annotations of the specified type for this .
+
+ The type of the annotations to retrieve.
+ An that contains the annotations for this .
+
+
+
+ Gets a collection of annotations of the specified type for this .
+
+ The of the annotations to retrieve.
+ An of that contains the annotations that match the specified type for this .
+
+
+
+ Removes the annotations of the specified type from this .
+
+ The type of annotations to remove.
+
+
+
+ Removes the annotations of the specified type from this .
+
+ The of annotations to remove.
+
+
+
+ Represents a JSON property.
+
+
+
+
+ Gets the container's children tokens.
+
+ The container's children tokens.
+
+
+
+ Gets the property name.
+
+ The property name.
+
+
+
+ Gets or sets the property value.
+
+ The property value.
+
+
+
+ Initializes a new instance of the class from another object.
+
+ A object to copy from.
+
+
+
+ Gets the node type for this .
+
+ The type.
+
+
+
+ Initializes a new instance of the class.
+
+ The property name.
+ The property content.
+
+
+
+ Initializes a new instance of the class.
+
+ The property name.
+ The property content.
+
+
+
+ Writes this token to a .
+
+ A into which this method will write.
+ A collection of which will be used when writing the token.
+
+
+
+ Loads an from a .
+
+ A that will be read for the content of the .
+ A that contains the JSON that was read from the specified .
+
+
+
+ Loads an from a .
+
+ A that will be read for the content of the .
+ The used to load the JSON.
+ If this is null, default load settings will be used.
+ A that contains the JSON that was read from the specified .
+
+
+
+ Specifies the type of token.
+
+
+
+
+ No token type has been set.
+
+
+
+
+ A JSON object.
+
+
+
+
+ A JSON array.
+
+
+
+
+ A JSON constructor.
+
+
+
+
+ A JSON object property.
+
+
+
+
+ A comment.
+
+
+
+
+ An integer value.
+
+
+
+
+ A float value.
+
+
+
+
+ A string value.
+
+
+
+
+ A boolean value.
+
+
+
+
+ A null value.
+
+
+
+
+ An undefined value.
+
+
+
+
+ A date value.
+
+
+
+
+ A raw JSON value.
+
+
+
+
+ A collection of bytes value.
+
+
+
+
+ A Guid value.
+
+
+
+
+ A Uri value.
+
+
+
+
+ A TimeSpan value.
+
+
+
+
+ Represents a value in JSON (string, integer, date, etc).
+
+
+
+
+ Initializes a new instance of the class from another object.
+
+ A object to copy from.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Gets a value indicating whether this token has child tokens.
+
+
+ true if this token has child values; otherwise, false.
+
+
+
+
+ Creates a comment with the given value.
+
+ The value.
+ A comment with the given value.
+
+
+
+ Creates a string with the given value.
+
+ The value.
+ A string with the given value.
+
+
+
+ Creates a null value.
+
+ A null value.
+
+
+
+ Creates a undefined value.
+
+ A undefined value.
+
+
+
+ Gets the node type for this .
+
+ The type.
+
+
+
+ Gets or sets the underlying token value.
+
+ The underlying token value.
+
+
+
+ Writes this token to a .
+
+ A into which this method will write.
+ A collection of which will be used when writing the token.
+
+
+
+ Indicates whether the current object is equal to another object of the same type.
+
+
+ true if the current object is equal to the parameter; otherwise, false.
+
+ An object to compare with this object.
+
+
+
+ Determines whether the specified is equal to the current .
+
+ The to compare with the current .
+
+ true if the specified is equal to the current ; otherwise, false.
+
+
+ The parameter is null.
+
+
+
+
+ Serves as a hash function for a particular type.
+
+
+ A hash code for the current .
+
+
+
+
+ Returns a that represents this instance.
+
+
+ A that represents this instance.
+
+
+
+
+ Returns a that represents this instance.
+
+ The format.
+
+ A that represents this instance.
+
+
+
+
+ Returns a that represents this instance.
+
+ The format provider.
+
+ A that represents this instance.
+
+
+
+
+ Returns a that represents this instance.
+
+ The format.
+ The format provider.
+
+ A that represents this instance.
+
+
+
+
+ Compares the current instance with another object of the same type and returns an integer that indicates whether the current instance precedes, follows, or occurs in the same position in the sort order as the other object.
+
+ An object to compare with this instance.
+
+ A 32-bit signed integer that indicates the relative order of the objects being compared. The return value has these meanings:
+ Value
+ Meaning
+ Less than zero
+ This instance is less than .
+ Zero
+ This instance is equal to .
+ Greater than zero
+ This instance is greater than .
+
+
+ is not the same type as this instance.
+
+
+
+
+ Specifies metadata property handling options for the .
+
+
+
+
+ Read metadata properties located at the start of a JSON object.
+
+
+
+
+ Read metadata properties located anywhere in a JSON object. Note that this setting will impact performance.
+
+
+
+
+ Do not try to read metadata properties.
+
+
+
+
+ Represents a trace writer that writes to the application's instances.
+
+
+
+
+ Gets the that will be used to filter the trace messages passed to the writer.
+ For example a filter level of Info will exclude Verbose messages and include Info,
+ Warning and Error messages.
+
+
+ The that will be used to filter the trace messages passed to the writer.
+
+
+
+
+ Writes the specified trace level, message and optional exception.
+
+ The at which to write this trace.
+ The trace message.
+ The trace exception. This parameter is optional.
+
+
+
+ Provides methods to get attributes.
+
+
+
+
+ Returns a collection of all of the attributes, or an empty collection if there are no attributes.
+
+ When true, look up the hierarchy chain for the inherited custom attribute.
+ A collection of s, or an empty collection.
+
+
+
+ Returns a collection of attributes, identified by type, or an empty collection if there are no attributes.
+
+ The type of the attributes.
+ When true, look up the hierarchy chain for the inherited custom attribute.
+ A collection of s, or an empty collection.
+
+
+
+ Represents a trace writer.
+
+
+
+
+ Gets the that will be used to filter the trace messages passed to the writer.
+ For example a filter level of Info will exclude Verbose messages and include Info,
+ Warning and Error messages.
+
+ The that will be used to filter the trace messages passed to the writer.
+
+
+
+ Writes the specified trace level, message and optional exception.
+
+ The at which to write this trace.
+ The trace message.
+ The trace exception. This parameter is optional.
+
+
+
+ Contract details for a used by the .
+
+
+
+
+ Gets or sets the default collection items .
+
+ The converter.
+
+
+
+ Gets or sets a value indicating whether the collection items preserve object references.
+
+ true if collection items preserve object references; otherwise, false.
+
+
+
+ Gets or sets the collection item reference loop handling.
+
+ The reference loop handling.
+
+
+
+ Gets or sets the collection item type name handling.
+
+ The type name handling.
+
+
+
+ Initializes a new instance of the class.
+
+ The underlying type for the contract.
+
+
+
+ Represents a trace writer that writes to memory. When the trace message limit is
+ reached then old trace messages will be removed as new messages are added.
+
+
+
+
+ Gets the that will be used to filter the trace messages passed to the writer.
+ For example a filter level of Info will exclude Verbose messages and include Info,
+ Warning and Error messages.
+
+
+ The that will be used to filter the trace messages passed to the writer.
+
+
+
+
+ Initializes a new instance of the class.
+
+
+
+
+ Writes the specified trace level, message and optional exception.
+
+ The at which to write this trace.
+ The trace message.
+ The trace exception. This parameter is optional.
+
+
+
+ Returns an enumeration of the most recent trace messages.
+
+ An enumeration of the most recent trace messages.
+
+
+
+ Returns a of the most recent trace messages.
+
+
+ A of the most recent trace messages.
+
+
+
+
+ Provides methods to get attributes from a , , or .
+
+
+
+
+ Initializes a new instance of the class.
+
+ The instance to get attributes for. This parameter should be a , , or .
+
+
+
+ Returns a collection of all of the attributes, or an empty collection if there are no attributes.
+
+ When true, look up the hierarchy chain for the inherited custom attribute.
+ A collection of s, or an empty collection.
+
+
+
+ Returns a collection of attributes, identified by type, or an empty collection if there are no attributes.
+
+ The type of the attributes.
+ When true, look up the hierarchy chain for the inherited custom attribute.
+ A collection of s, or an empty collection.
+
+
+
+ Contract details for a used by the .
+
+
+
+
+ Gets or sets the ISerializable object constructor.
+
+ The ISerializable object constructor.
+
+
+
+ Initializes a new instance of the class.
+
+ The underlying type for the contract.
+
+
+
+ Contract details for a used by the .
+
+
+
+
+ Initializes a new instance of the class.
+
+ The underlying type for the contract.
+
+
+
+ Contract details for a used by the .
+
+
+
+
+ Initializes a new instance of the class.
+
+ The underlying type for the contract.
+
+
+
+ Provides data for the Error event.
+
+
+
+
+ Gets the current object the error event is being raised against.
+
+ The current object the error event is being raised against.
+
+
+
+ Gets the error context.
+
+ The error context.
+
+
+
+ Initializes a new instance of the class.
+
+ The current object.
+ The error context.
+
+
+
+ Resolves member mappings for a type, camel casing property names.
+
+
+
+
+ Initializes a new instance of the class.
+
+
+
+
+ Resolves the name of the property.
+
+ Name of the property.
+ The property name camel cased.
+
+
+
+ Used by to resolves a for a given .
+
+
+
+
+ Gets a value indicating whether members are being get and set using dynamic code generation.
+ This value is determined by the runtime permissions available.
+
+
+ true if using dynamic code generation; otherwise, false.
+
+
+
+
+ Gets or sets the default members search flags.
+
+ The default members search flags.
+
+
+
+ Gets or sets a value indicating whether compiler generated members should be serialized.
+
+
+ true if serialized compiler generated members; otherwise, false.
+
+
+
+
+ Gets or sets a value indicating whether to ignore the interface when serializing and deserializing types.
+
+
+ true if the interface will be ignored when serializing and deserializing types; otherwise, false.
+
+
+
+
+ Gets or sets a value indicating whether to ignore the attribute when serializing and deserializing types.
+
+
+ true if the attribute will be ignored when serializing and deserializing types; otherwise, false.
+
+
+
+
+ Initializes a new instance of the class.
+
+
+
+
+ Initializes a new instance of the class.
+
+
+ If set to true the will use a cached shared with other resolvers of the same type.
+ Sharing the cache will significantly improve performance with multiple resolver instances because expensive reflection will only
+ happen once. This setting can cause unexpected behavior if different instances of the resolver are suppose to produce different
+ results. When set to false it is highly recommended to reuse instances with the .
+
+
+
+
+ Resolves the contract for a given type.
+
+ The type to resolve a contract for.
+ The contract for a given type.
+
+
+
+ Gets the serializable members for the type.
+
+ The type to get serializable members for.
+ The serializable members for the type.
+
+
+
+ Creates a for the given type.
+
+ Type of the object.
+ A for the given type.
+
+
+
+ Creates the constructor parameters.
+
+ The constructor to create properties for.
+ The type's member properties.
+ Properties for the given .
+
+
+
+ Creates a for the given .
+
+ The matching member property.
+ The constructor parameter.
+ A created for the given .
+
+
+
+ Resolves the default for the contract.
+
+ Type of the object.
+ The contract's default .
+
+
+
+ Creates a for the given type.
+
+ Type of the object.
+ A for the given type.
+
+
+
+ Creates a for the given type.
+
+ Type of the object.
+ A for the given type.
+
+
+
+ Creates a for the given type.
+
+ Type of the object.
+ A for the given type.
+
+
+
+ Creates a for the given type.
+
+ Type of the object.
+ A for the given type.
+
+
+
+ Creates a for the given type.
+
+ Type of the object.
+ A for the given type.
+
+
+
+ Creates a for the given type.
+
+ Type of the object.
+ A for the given type.
+
+
+
+ Determines which contract type is created for the given type.
+
+ Type of the object.
+ A for the given type.
+
+
+
+ Creates properties for the given .
+
+ The type to create properties for.
+ /// The member serialization mode for the type.
+ Properties for the given .
+
+
+
+ Creates the used by the serializer to get and set values from a member.
+
+ The member.
+ The used by the serializer to get and set values from a member.
+
+
+
+ Creates a for the given .
+
+ The member's parent .
+ The member to create a for.
+ A created for the given .
+
+
+
+ Resolves the name of the property.
+
+ Name of the property.
+ Resolved name of the property.
+
+
+
+ Resolves the key of the dictionary. By default is used to resolve dictionary keys.
+
+ Key of the dictionary.
+ Resolved key of the dictionary.
+
+
+
+ Gets the resolved name of the property.
+
+ Name of the property.
+ Name of the property.
+
+
+
+ The default serialization binder used when resolving and loading classes from type names.
+
+
+
+
+ When overridden in a derived class, controls the binding of a serialized object to a type.
+
+ Specifies the name of the serialized object.
+ Specifies the name of the serialized object.
+
+ The type of the object the formatter creates a new instance of.
+
+
+
+
+ Provides information surrounding an error.
+
+
+
+
+ Gets the error.
+
+ The error.
+
+
+
+ Gets the original object that caused the error.
+
+ The original object that caused the error.
+
+
+
+ Gets the member that caused the error.
+
+ The member that caused the error.
+
+
+
+ Gets the path of the JSON location where the error occurred.
+
+ The path of the JSON location where the error occurred.
+
+
+
+ Gets or sets a value indicating whether this is handled.
+
+ true if handled; otherwise, false.
+
+
+
+ Used by to resolves a for a given .
+
+
+ /*...*/ containing the specified text.
+
+ Text to place inside the comment.
+
+
+
+ Writes out the given white space.
+
+ The string of white space characters.
+
+
+
+ The exception thrown when an error occurs while reading JSON text.
+
+
+
+
+ Gets the path to the JSON where the error occurred.
+
+ The path to the JSON where the error occurred.
+
+
+
+ Initializes a new instance of the class.
+
+
+
+
+ Initializes a new instance of the class
+ with a specified error message.
+
+ The error message that explains the reason for the exception.
+
+
+
+ Initializes a new instance of the class
+ with a specified error message and a reference to the inner exception that is the cause of this exception.
+
+ The error message that explains the reason for the exception.
+ The exception that is the cause of the current exception, or a null reference (Nothing in Visual Basic) if no inner exception is specified.
+
+
+
+ Initializes a new instance of the class.
+
+ The that holds the serialized object data about the exception being thrown.
+ The that contains contextual information about the source or destination.
+ The parameter is null.
+ The class name is null or is zero (0).
+
+
+
+ The exception thrown when an error occurs while reading JSON text.
+
+
+
+
+ Gets the line number indicating where the error occurred.
+
+ The line number indicating where the error occurred.
+
+
+
+ Gets the line position indicating where the error occurred.
+
+ The line position indicating where the error occurred.
+
+
+
+ Gets the path to the JSON where the error occurred.
+
+ The path to the JSON where the error occurred.
+
+
+
+ Initializes a new instance of the class.
+
+
+
+
+ Initializes a new instance of the class
+ with a specified error message.
+
+ The error message that explains the reason for the exception.
+
+
+
+ Initializes a new instance of the class
+ with a specified error message and a reference to the inner exception that is the cause of this exception.
+
+ The error message that explains the reason for the exception.
+ The exception that is the cause of the current exception, or a null reference (Nothing in Visual Basic) if no inner exception is specified.
+
+
+
+ Initializes a new instance of the class.
+
+ The that holds the serialized object data about the exception being thrown.
+ The that contains contextual information about the source or destination.
+ The parameter is null.
+ The class name is null or is zero (0).
+
+
+
+ Converts an object to and from JSON.
+
+
+
+
+ Writes the JSON representation of the object.
+
+ The to write to.
+ The value.
+ The calling serializer.
+
+
+
+ Reads the JSON representation of the object.
+
+ The to read from.
+ Type of the object.
+ The existing value of object being read.
+ The calling serializer.
+ The object value.
+
+
+
+ Determines whether this instance can convert the specified object type.
+
+ Type of the object.
+
+ true if this instance can convert the specified object type; otherwise, false.
+
+
+
+
+ Gets a value indicating whether this can read JSON.
+
+ true if this can read JSON; otherwise, false.
+
+
+
+ Gets a value indicating whether this can write JSON.
+
+ true if this can write JSON; otherwise, false.
+
+
+
+ Represents a collection of .
+
+
+
+
+ Represents a reader that provides fast, non-cached, forward-only access to serialized JSON data.
+
+
+
+
+ Specifies the state of the reader.
+
+
+
+
+ The Read method has not been called.
+
+
+
+
+ The end of the file has been reached successfully.
+
+
+
+
+ Reader is at a property.
+
+
+
+
+ Reader is at the start of an object.
+
+
+
+
+ Reader is in an object.
+
+
+
+
+ Reader is at the start of an array.
+
+
+
+
+ Reader is in an array.
+
+
+
+
+ The Close method has been called.
+
+
+
+
+ Reader has just read a value.
+
+
+
+
+ Reader is at the start of a constructor.
+
+
+
+
+ Reader in a constructor.
+
+
+
+
+ An error occurred that prevents the read operation from continuing.
+
+
+
+
+ The end of the file has been reached successfully.
+
+
+
+
+ Gets the current reader state.
+
+ The current reader state.
+
+
+
+ Gets or sets a value indicating whether the underlying stream or
+ should be closed when the reader is closed.
+
+
+ true to close the underlying stream or when
+ the reader is closed; otherwise false. The default is true.
+
+
+
+
+ Gets or sets a value indicating whether multiple pieces of JSON content can
+ be read from a continuous stream without erroring.
+
+
+ true to support reading multiple pieces of JSON content; otherwise false. The default is false.
+
+
+
+
+ Gets the quotation mark character used to enclose the value of a string.
+
+
+
+
+ Get or set how time zones are handling when reading JSON.
+
+
+
+
+ Get or set how date formatted strings, e.g. "\/Date(1198908717056)\/" and "2012-03-21T05:40Z", are parsed when reading JSON.
+
+
+
+
+ Get or set how floating point numbers, e.g. 1.0 and 9.9, are parsed when reading JSON text.
+
+
+
+
+ Get or set how custom date formatted strings are parsed when reading JSON.
+
+
+
+
+ Gets or sets the maximum depth allowed when reading JSON. Reading past this depth will throw a .
+
+
+
+
+ Gets the type of the current JSON token.
+
+
+
+
+ Gets the text value of the current JSON token.
+
+
+
+
+ Gets The Common Language Runtime (CLR) type for the current JSON token.
+
+
+
+
+ Gets the depth of the current token in the JSON document.
+
+ The depth of the current token in the JSON document.
+
+
+
+ Gets the path of the current JSON token.
+
+
+
+
+ Gets or sets the culture used when reading JSON. Defaults to .
+
+
+
+
+ Initializes a new instance of the class with the specified .
+
+
+
+
+ Reads the next JSON token from the stream.
+
+ true if the next token was read successfully; false if there are no more tokens to read.
+
+
+
+ Reads the next JSON token from the stream as a .
+
+ A . This method will return null at the end of an array.
+
+
+
+ Reads the next JSON token from the stream as a .
+
+ A . This method will return null at the end of an array.
+
+
+
+ Reads the next JSON token from the stream as a [].
+
+ A [] or a null reference if the next JSON token is null. This method will return null at the end of an array.
+
+
+
+ Reads the next JSON token from the stream as a .
+
+ A . This method will return null at the end of an array.
+
+
+
+ Reads the next JSON token from the stream as a .
+
+ A . This method will return null at the end of an array.
+
+
+
+ Reads the next JSON token from the stream as a .
+
+ A . This method will return null at the end of an array.
+
+
+
+ Reads the next JSON token from the stream as a .
+
+ A . This method will return null at the end of an array.
+
+
+
+ Reads the next JSON token from the stream as a .
+
+ A . This method will return null at the end of an array.
+
+
+
+ Skips the children of the current token.
+
+
+
+
+ Sets the current token.
+
+ The new token.
+
+
+
+ Sets the current token and value.
+
+ The new token.
+ The value.
+
+
+
+ Sets the state based on current token type.
+
+
+
+
+ Performs application-defined tasks associated with freeing, releasing, or resetting unmanaged resources.
+
+
+
+
+ Releases unmanaged and - optionally - managed resources
+
+ true to release both managed and unmanaged resources; false to release only unmanaged resources.
+
+
+
+ Changes the to Closed.
+
+
+
+
+ Provides methods for converting between common language runtime types and JSON types.
+
+
+ /*...*/ containing the specified text.
+
+ Text to place inside the comment.
+
+
+
+ Writes out the given white space.
+
+ The string of white space characters.
+
+
+
+ Releases unmanaged and - optionally - managed resources
+
+ true to release both managed and unmanaged resources; false to release only unmanaged resources.
+
+
+
+ Sets the state of the JsonWriter,
+
+ The JsonToken being written.
+ The value being written.
+
+
+
+ Specifies the state of the .
+
+
+
+
+ An exception has been thrown, which has left the in an invalid state.
+ You may call the method to put the in the Closed state.
+ Any other method calls results in an being thrown.
+
+
+
+
+ The method has been called.
+
+
+
+
+ An object is being written.
+
+
+
+
+ A array is being written.
+
+
+
+
+ A constructor is being written.
+
+
+
+
+ A property is being written.
+
+
+
+
+ A write method has not been called.
+
+
+
+
diff --git a/Assets/JsonDotNet/Assemblies/AOT/Newtonsoft.Json.XML.meta b/Assets/JsonDotNet/Assemblies/AOT/Newtonsoft.Json.XML.meta
new file mode 100644
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+licenseType: Store
+TextScriptImporter:
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diff --git a/Assets/JsonDotNet/Assemblies/AOT/Newtonsoft.Json.dll b/Assets/JsonDotNet/Assemblies/AOT/Newtonsoft.Json.dll
new file mode 100644
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new file mode 100644
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+ executionOrder: {}
+ defineConstraints: []
+ isPreloaded: 0
+ isOverridable: 0
+ isExplicitlyReferenced: 0
+ validateReferences: 1
+ platformData:
+ - first:
+ '': Linux
+ second:
+ enabled: 0
+ settings:
+ CPU: x86
+ - first:
+ '': OSXIntel
+ second:
+ enabled: 0
+ settings:
+ CPU: AnyCPU
+ - first:
+ '': OSXIntel64
+ second:
+ enabled: 0
+ settings:
+ CPU: AnyCPU
+ - first:
+ '': SamsungTV
+ second:
+ enabled: 1
+ settings:
+ STV_MODEL: STANDARD_13
+ - first:
+ '': Tizen
+ second:
+ enabled: 1
+ settings: {}
+ - first:
+ Android: Android
+ second:
+ enabled: 1
+ settings:
+ CPU: AnyCPU
+ - first:
+ Any:
+ second:
+ enabled: 0
+ settings: {}
+ - first:
+ Editor: Editor
+ second:
+ enabled: 0
+ settings:
+ CPU: AnyCPU
+ DefaultValueInitialized: true
+ OS: AnyOS
+ - first:
+ Facebook: WebGL
+ second:
+ enabled: 1
+ settings: {}
+ - first:
+ Facebook: Win
+ second:
+ enabled: 0
+ settings:
+ CPU: AnyCPU
+ - first:
+ Facebook: Win64
+ second:
+ enabled: 0
+ settings:
+ CPU: AnyCPU
+ - first:
+ Standalone: Linux64
+ second:
+ enabled: 0
+ settings:
+ CPU: AnyCPU
+ - first:
+ Standalone: Win
+ second:
+ enabled: 0
+ settings:
+ CPU: AnyCPU
+ - first:
+ Standalone: Win64
+ second:
+ enabled: 0
+ settings:
+ CPU: AnyCPU
+ - first:
+ WebGL: WebGL
+ second:
+ enabled: 1
+ settings: {}
+ - first:
+ Windows Store Apps: WindowsStoreApps
+ second:
+ enabled: 1
+ settings:
+ CPU: AnyCPU
+ DontProcess: False
+ PlaceholderPath: Assets/JsonDotNet/Assemblies/Standalone/Newtonsoft.Json.dll
+ SDK: AnySDK
+ ScriptingBackend: Il2Cpp
+ - first:
+ iPhone: iOS
+ second:
+ enabled: 1
+ settings:
+ CompileFlags:
+ FrameworkDependencies:
+ - first:
+ tvOS: tvOS
+ second:
+ enabled: 1
+ settings: {}
+ userData:
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diff --git a/Assets/JsonDotNet/Assemblies/Standalone.meta b/Assets/JsonDotNet/Assemblies/Standalone.meta
new file mode 100644
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diff --git a/Assets/JsonDotNet/Assemblies/Standalone/Newtonsoft.Json.XML b/Assets/JsonDotNet/Assemblies/Standalone/Newtonsoft.Json.XML
new file mode 100644
index 0000000..4dbcd9e
--- /dev/null
+++ b/Assets/JsonDotNet/Assemblies/Standalone/Newtonsoft.Json.XML
@@ -0,0 +1,8040 @@
+
+
+
+ Newtonsoft.Json
+
+
+
+
+ Represents a reader that provides fast, non-cached, forward-only access to serialized JSON data.
+
+
+
+
+ Gets or sets a value indicating whether binary data reading should compatible with incorrect Json.NET 3.5 written binary.
+
+
+ true if binary data reading will be compatible with incorrect Json.NET 3.5 written binary; otherwise, false.
+
+
+
+
+ Gets or sets a value indicating whether the root object will be read as a JSON array.
+
+
+ true if the root object will be read as a JSON array; otherwise, false.
+
+
+
+
+ Gets or sets the used when reading values from BSON.
+
+ The used when reading values from BSON.
+
+
+
+ Initializes a new instance of the class.
+
+ The stream.
+
+
+
+ Initializes a new instance of the class.
+
+ The reader.
+
+
+
+ Initializes a new instance of the class.
+
+ The stream.
+ if set to true the root object will be read as a JSON array.
+ The used when reading values from BSON.
+
+
+
+ Initializes a new instance of the class.
+
+ The reader.
+ if set to true the root object will be read as a JSON array.
+ The used when reading values from BSON.
+
+
+
+ Reads the next JSON token from the stream.
+
+
+ true if the next token was read successfully; false if there are no more tokens to read.
+
+
+
+
+ Changes the to Closed.
+
+
+
+
+ Represents a writer that provides a fast, non-cached, forward-only way of generating JSON data.
+
+
+
+
+ Gets or sets the used when writing values to BSON.
+ When set to no conversion will occur.
+
+ The used when writing values to BSON.
+
+
+
+ Initializes a new instance of the class.
+
+ The stream.
+
+
+
+ Initializes a new instance of the class.
+
+ The writer.
+
+
+
+ Flushes whatever is in the buffer to the underlying streams and also flushes the underlying stream.
+
+
+
+
+ Writes the end.
+
+ The token.
+
+
+
+ Writes out a comment /*...*/ containing the specified text.
+
+ Text to place inside the comment.
+
+
+
+ Writes the start of a constructor with the given name.
+
+ The name of the constructor.
+
+
+
+ Writes raw JSON.
+
+ The raw JSON to write.
+
+
+
+ Writes raw JSON where a value is expected and updates the writer's state.
+
+ The raw JSON to write.
+
+
+
+ Writes the beginning of a JSON array.
+
+
+
+
+ Writes the beginning of a JSON object.
+
+
+
+
+ Writes the property name of a name/value pair on a JSON object.
+
+ The name of the property.
+
+
+
+ Closes this stream and the underlying stream.
+
+
+
+
+ Writes a value.
+ An error will raised if the value cannot be written as a single JSON token.
+
+ The value to write.
+
+
+
+ Writes a null value.
+
+
+
+
+ Writes an undefined value.
+
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a [] value.
+
+ The [] value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a [] value that represents a BSON object id.
+
+ The Object ID value to write.
+
+
+
+ Writes a BSON regex.
+
+ The regex pattern.
+ The regex options.
+
+
+
+ Represents a BSON Oid (object id).
+
+
+
+
+ Gets or sets the value of the Oid.
+
+ The value of the Oid.
+
+
+
+ Initializes a new instance of the class.
+
+ The Oid value.
+
+
+
+ Converts a binary value to and from a base 64 string value.
+
+
+
+
+ Writes the JSON representation of the object.
+
+ The to write to.
+ The value.
+ The calling serializer.
+
+
+
+ Reads the JSON representation of the object.
+
+ The to read from.
+ Type of the object.
+ The existing value of object being read.
+ The calling serializer.
+ The object value.
+
+
+
+ Determines whether this instance can convert the specified object type.
+
+ Type of the object.
+
+ true if this instance can convert the specified object type; otherwise, false.
+
+
+
+
+ Create a custom object
+
+ The object type to convert.
+
+
+
+ Writes the JSON representation of the object.
+
+ The to write to.
+ The value.
+ The calling serializer.
+
+
+
+ Reads the JSON representation of the object.
+
+ The to read from.
+ Type of the object.
+ The existing value of object being read.
+ The calling serializer.
+ The object value.
+
+
+
+ Creates an object which will then be populated by the serializer.
+
+ Type of the object.
+ The created object.
+
+
+
+ Determines whether this instance can convert the specified object type.
+
+ Type of the object.
+
+ true if this instance can convert the specified object type; otherwise, false.
+
+
+
+
+ Gets a value indicating whether this can write JSON.
+
+
+ true if this can write JSON; otherwise, false.
+
+
+
+
+ Provides a base class for converting a to and from JSON.
+
+
+
+
+ Determines whether this instance can convert the specified object type.
+
+ Type of the object.
+
+ true if this instance can convert the specified object type; otherwise, false.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Converts a to and from JSON.
+
+
+
+
+ Writes the JSON representation of the object.
+
+ The to write to.
+ The value.
+ The calling serializer.
+
+
+
+ Reads the JSON representation of the object.
+
+ The to read from.
+ Type of the object.
+ The existing value of object being read.
+ The calling serializer.
+ The object value.
+
+
+
+ Determines whether this instance can convert the specified object type.
+
+ Type of the object.
+
+ true if this instance can convert the specified object type; otherwise, false.
+
+
+
+
+ Converts a to and from JSON and BSON.
+
+
+
+
+ Writes the JSON representation of the object.
+
+ The to write to.
+ The value.
+ The calling serializer.
+
+
+
+ Reads the JSON representation of the object.
+
+ The to read from.
+ Type of the object.
+ The existing value of object being read.
+ The calling serializer.
+ The object value.
+
+
+
+ Determines whether this instance can convert the specified object type.
+
+ Type of the object.
+
+ true if this instance can convert the specified object type; otherwise, false.
+
+
+
+
+ Converts a to and from JSON and BSON.
+
+
+
+
+ Writes the JSON representation of the object.
+
+ The to write to.
+ The value.
+ The calling serializer.
+
+
+
+ Reads the JSON representation of the object.
+
+ The to read from.
+ Type of the object.
+ The existing value of object being read.
+ The calling serializer.
+ The object value.
+
+
+
+ Determines whether this instance can convert the specified object type.
+
+ Type of the object.
+
+ true if this instance can convert the specified object type; otherwise, false.
+
+
+
+
+ Converts an to and from its name string value.
+
+
+
+
+ Gets or sets a value indicating whether the written enum text should be camel case.
+
+ true if the written enum text will be camel case; otherwise, false.
+
+
+
+ Gets or sets a value indicating whether integer values are allowed.
+
+ true if integers are allowed; otherwise, false.
+
+
+
+ Initializes a new instance of the class.
+
+
+
+
+ Initializes a new instance of the class.
+
+ true if the written enum text will be camel case; otherwise, false.
+
+
+
+ Writes the JSON representation of the object.
+
+ The to write to.
+ The value.
+ The calling serializer.
+
+
+
+ Reads the JSON representation of the object.
+
+ The to read from.
+ Type of the object.
+ The existing value of object being read.
+ The calling serializer.
+ The object value.
+
+
+
+ Determines whether this instance can convert the specified object type.
+
+ Type of the object.
+
+ true if this instance can convert the specified object type; otherwise, false.
+
+
+
+
+ Json Converter for Vector2, Vector3 and Vector4. Only serializes x, y, (z) and (w) properties.
+
+
+
+
+ Default Constructor - All Vector types enabled by default
+
+
+
+
+ Selectively enable Vector types
+
+ Use for Vector2 objects
+ Use for Vector3 objects
+ Use for Vector4 objects
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Converts a to and from a string (e.g. "1.2.3.4").
+
+
+
+
+ Writes the JSON representation of the object.
+
+ The to write to.
+ The value.
+ The calling serializer.
+
+
+
+ Reads the JSON representation of the object.
+
+ The to read from.
+ Type of the object.
+ The existing property value of the JSON that is being converted.
+ The calling serializer.
+ The object value.
+
+
+
+ Determines whether this instance can convert the specified object type.
+
+ Type of the object.
+
+ true if this instance can convert the specified object type; otherwise, false.
+
+
+
+
+ Converts a to and from the ISO 8601 date format (e.g. 2008-04-12T12:53Z).
+
+
+
+
+ Gets or sets the date time styles used when converting a date to and from JSON.
+
+ The date time styles used when converting a date to and from JSON.
+
+
+
+ Gets or sets the date time format used when converting a date to and from JSON.
+
+ The date time format used when converting a date to and from JSON.
+
+
+
+ Gets or sets the culture used when converting a date to and from JSON.
+
+ The culture used when converting a date to and from JSON.
+
+
+
+ Writes the JSON representation of the object.
+
+ The to write to.
+ The value.
+ The calling serializer.
+
+
+
+ Reads the JSON representation of the object.
+
+ The to read from.
+ Type of the object.
+ The existing value of object being read.
+ The calling serializer.
+ The object value.
+
+
+
+ Converts a to and from a JavaScript date constructor (e.g. new Date(52231943)).
+
+
+
+
+ Writes the JSON representation of the object.
+
+ The to write to.
+ The value.
+ The calling serializer.
+
+
+
+ Reads the JSON representation of the object.
+
+ The to read from.
+ Type of the object.
+ The existing property value of the JSON that is being converted.
+ The calling serializer.
+ The object value.
+
+
+
+ Converts XML to and from JSON.
+
+
+
+
+ Gets or sets the name of the root element to insert when deserializing to XML if the JSON structure has produces multiple root elements.
+
+ The name of the deserialize root element.
+
+
+
+ Gets or sets a flag to indicate whether to write the Json.NET array attribute.
+ This attribute helps preserve arrays when converting the written XML back to JSON.
+
+ true if the array attibute is written to the XML; otherwise, false.
+
+
+
+ Gets or sets a value indicating whether to write the root JSON object.
+
+ true if the JSON root object is omitted; otherwise, false.
+
+
+
+ Writes the JSON representation of the object.
+
+ The to write to.
+ The calling serializer.
+ The value.
+
+
+
+ Reads the JSON representation of the object.
+
+ The to read from.
+ Type of the object.
+ The existing value of object being read.
+ The calling serializer.
+ The object value.
+
+
+
+ Checks if the attributeName is a namespace attribute.
+
+ Attribute name to test.
+ The attribute name prefix if it has one, otherwise an empty string.
+ True if attribute name is for a namespace attribute, otherwise false.
+
+
+
+ Determines whether this instance can convert the specified value type.
+
+ Type of the value.
+
+ true if this instance can convert the specified value type; otherwise, false.
+
+
+
+
+ Specifies how constructors are used when initializing objects during deserialization by the .
+
+
+
+
+ First attempt to use the public default constructor, then fall back to single paramatized constructor, then the non-public default constructor.
+
+
+
+
+ Json.NET will use a non-public default constructor before falling back to a paramatized constructor.
+
+
+
+
+ Specifies how dates are formatted when writing JSON text.
+
+
+
+
+ Dates are written in the ISO 8601 format, e.g. "2012-03-21T05:40Z".
+
+
+
+
+ Dates are written in the Microsoft JSON format, e.g. "\/Date(1198908717056)\/".
+
+
+
+
+ Specifies how date formatted strings, e.g. "\/Date(1198908717056)\/" and "2012-03-21T05:40Z", are parsed when reading JSON text.
+
+
+
+
+ Date formatted strings are not parsed to a date type and are read as strings.
+
+
+
+
+ Date formatted strings, e.g. "\/Date(1198908717056)\/" and "2012-03-21T05:40Z", are parsed to .
+
+
+
+
+ Date formatted strings, e.g. "\/Date(1198908717056)\/" and "2012-03-21T05:40Z", are parsed to .
+
+
+
+
+ Specifies how to treat the time value when converting between string and .
+
+
+
+
+ Treat as local time. If the object represents a Coordinated Universal Time (UTC), it is converted to the local time.
+
+
+
+
+ Treat as a UTC. If the object represents a local time, it is converted to a UTC.
+
+
+
+
+ Treat as a local time if a is being converted to a string.
+ If a string is being converted to , convert to a local time if a time zone is specified.
+
+
+
+
+ Time zone information should be preserved when converting.
+
+
+
+
+ Specifies float format handling options when writing special floating point numbers, e.g. ,
+ and with .
+
+
+
+
+ Write special floating point values as strings in JSON, e.g. "NaN", "Infinity", "-Infinity".
+
+
+
+
+ Write special floating point values as symbols in JSON, e.g. NaN, Infinity, -Infinity.
+ Note that this will produce non-valid JSON.
+
+
+
+
+ Write special floating point values as the property's default value in JSON, e.g. 0.0 for a property, null for a property.
+
+
+
+
+ Specifies how floating point numbers, e.g. 1.0 and 9.9, are parsed when reading JSON text.
+
+
+
+
+ Floating point numbers are parsed to .
+
+
+
+
+ Floating point numbers are parsed to .
+
+
+
+
+ Specifies formatting options for the .
+
+
+
+
+ No special formatting is applied. This is the default.
+
+
+
+
+ Causes child objects to be indented according to the and settings.
+
+
+
+
+ Provides an interface for using pooled arrays.
+
+ The array type content.
+
+
+
+ Rent a array from the pool. This array must be returned when it is no longer needed.
+
+ The minimum required length of the array. The returned array may be longer.
+ The rented array from the pool. This array must be returned when it is no longer needed.
+
+
+
+ Return an array to the pool.
+
+ The array that is being returned.
+
+
+
+ Instructs the to use the specified constructor when deserializing that object.
+
+
+
+
+ Instructs the how to serialize the collection.
+
+
+
+
+ Initializes a new instance of the class.
+
+
+
+
+ Initializes a new instance of the class with the specified container Id.
+
+ The container Id.
+
+
+
+ The exception thrown when an error occurs during JSON serialization or deserialization.
+
+
+
+
+ Initializes a new instance of the class.
+
+
+
+
+ Initializes a new instance of the class
+ with a specified error message.
+
+ The error message that explains the reason for the exception.
+
+
+
+ Initializes a new instance of the class
+ with a specified error message and a reference to the inner exception that is the cause of this exception.
+
+ The error message that explains the reason for the exception.
+ The exception that is the cause of the current exception, or a null reference (Nothing in Visual Basic) if no inner exception is specified.
+
+
+
+ Initializes a new instance of the class.
+
+ The that holds the serialized object data about the exception being thrown.
+ The that contains contextual information about the source or destination.
+ The parameter is null.
+ The class name is null or is zero (0).
+
+
+
+ Instructs the to deserialize properties with no matching class member into the specified collection
+ and write values during serialization.
+
+
+
+
+ Gets or sets a value that indicates whether to write extension data when serializing the object.
+
+
+ true to write extension data when serializing the object; otherwise, false. The default is true.
+
+
+
+
+ Gets or sets a value that indicates whether to read extension data when deserializing the object.
+
+
+ true to read extension data when deserializing the object; otherwise, false. The default is true.
+
+
+
+
+ Initializes a new instance of the class.
+
+
+
+
+ Instructs the to always serialize the member, and require the member has a value.
+
+
+
+
+ Specifies how JSON comments are handled when loading JSON.
+
+
+
+
+ Ignore comments.
+
+
+
+
+ Load comments as a with type .
+
+
+
+
+ Specifies how line information is handled when loading JSON.
+
+
+
+
+ Ignore line information.
+
+
+
+
+ Load line information.
+
+
+
+
+ Represents a view of a .
+
+
+
+
+ Initializes a new instance of the class.
+
+ The name.
+
+
+
+ When overridden in a derived class, returns whether resetting an object changes its value.
+
+
+ true if resetting the component changes its value; otherwise, false.
+
+ The component to test for reset capability.
+
+
+
+
+ When overridden in a derived class, gets the current value of the property on a component.
+
+
+ The value of a property for a given component.
+
+ The component with the property for which to retrieve the value.
+
+
+
+
+ When overridden in a derived class, resets the value for this property of the component to the default value.
+
+ The component with the property value that is to be reset to the default value.
+
+
+
+
+ When overridden in a derived class, sets the value of the component to a different value.
+
+ The component with the property value that is to be set.
+ The new value.
+
+
+
+
+ When overridden in a derived class, determines a value indicating whether the value of this property needs to be persisted.
+
+
+ true if the property should be persisted; otherwise, false.
+
+ The component with the property to be examined for persistence.
+
+
+
+
+ When overridden in a derived class, gets the type of the component this property is bound to.
+
+
+ A that represents the type of component this property is bound to. When the or methods are invoked, the object specified might be an instance of this type.
+
+
+
+
+ When overridden in a derived class, gets a value indicating whether this property is read-only.
+
+
+ true if the property is read-only; otherwise, false.
+
+
+
+
+ When overridden in a derived class, gets the type of the property.
+
+
+ A that represents the type of the property.
+
+
+
+
+ Gets the hash code for the name of the member.
+
+
+
+ The hash code for the name of the member.
+
+
+
+
+ Specifies the settings used when loading JSON.
+
+
+
+
+ Gets or sets how JSON comments are handled when loading JSON.
+
+ The JSON comment handling.
+
+
+
+ Gets or sets how JSON line info is handled when loading JSON.
+
+ The JSON line info handling.
+
+
+
+ Specifies the settings used when merging JSON.
+
+
+
+
+ Gets or sets the method used when merging JSON arrays.
+
+ The method used when merging JSON arrays.
+
+
+
+ Gets or sets how how null value properties are merged.
+
+ How null value properties are merged.
+
+
+
+ Specifies how JSON arrays are merged together.
+
+
+
+ Concatenate arrays.
+
+
+ Union arrays, skipping items that already exist.
+
+
+ Replace all array items.
+
+
+ Merge array items together, matched by index.
+
+
+
+ Specifies how null value properties are merged.
+
+
+
+
+ The content's null value properties will be ignored during merging.
+
+
+
+
+ The content's null value properties will be merged.
+
+
+
+
+ Represents a raw JSON string.
+
+
+
+
+ Initializes a new instance of the class from another object.
+
+ A object to copy from.
+
+
+
+ Initializes a new instance of the class.
+
+ The raw json.
+
+
+
+ Creates an instance of with the content of the reader's current token.
+
+ The reader.
+ An instance of with the content of the reader's current token.
+
+
+
+ Represents a collection of objects.
+
+ The type of token
+
+
+
+ Gets the with the specified key.
+
+
+
+
+
+ Compares tokens to determine whether they are equal.
+
+
+
+
+ Determines whether the specified objects are equal.
+
+ The first object of type to compare.
+ The second object of type to compare.
+
+ true if the specified objects are equal; otherwise, false.
+
+
+
+
+ Returns a hash code for the specified object.
+
+ The for which a hash code is to be returned.
+ A hash code for the specified object.
+ The type of is a reference type and is null.
+
+
+
+ Contains the LINQ to JSON extension methods.
+
+
+
+
+ Returns a collection of tokens that contains the ancestors of every token in the source collection.
+
+ The type of the objects in source, constrained to .
+ An of that contains the source collection.
+ An of that contains the ancestors of every token in the source collection.
+
+
+
+ Returns a collection of tokens that contains every token in the source collection, and the ancestors of every token in the source collection.
+
+ The type of the objects in source, constrained to .
+ An of that contains the source collection.
+ An of that contains every token in the source collection, the ancestors of every token in the source collection.
+
+
+
+ Returns a collection of tokens that contains the descendants of every token in the source collection.
+
+ The type of the objects in source, constrained to .
+ An of that contains the source collection.
+ An of that contains the descendants of every token in the source collection.
+
+
+
+ Returns a collection of tokens that contains every token in the source collection, and the descendants of every token in the source collection.
+
+ The type of the objects in source, constrained to .
+ An of that contains the source collection.
+ An of that contains every token in the source collection, and the descendants of every token in the source collection.
+
+
+
+ Returns a collection of child properties of every object in the source collection.
+
+ An of that contains the source collection.
+ An of that contains the properties of every object in the source collection.
+
+
+
+ Returns a collection of child values of every object in the source collection with the given key.
+
+ An of that contains the source collection.
+ The token key.
+ An of that contains the values of every token in the source collection with the given key.
+
+
+
+ Returns a collection of child values of every object in the source collection.
+
+ An of that contains the source collection.
+ An of that contains the values of every token in the source collection.
+
+
+
+ Returns a collection of converted child values of every object in the source collection with the given key.
+
+ The type to convert the values to.
+ An of that contains the source collection.
+ The token key.
+ An that contains the converted values of every token in the source collection with the given key.
+
+
+
+ Returns a collection of converted child values of every object in the source collection.
+
+ The type to convert the values to.
+ An of that contains the source collection.
+ An that contains the converted values of every token in the source collection.
+
+
+
+ Converts the value.
+
+ The type to convert the value to.
+ A cast as a of .
+ A converted value.
+
+
+
+ Converts the value.
+
+ The source collection type.
+ The type to convert the value to.
+ A cast as a of .
+ A converted value.
+
+
+
+ Returns a collection of child tokens of every array in the source collection.
+
+ The source collection type.
+ An of that contains the source collection.
+ An of that contains the values of every token in the source collection.
+
+
+
+ Returns a collection of converted child tokens of every array in the source collection.
+
+ An of that contains the source collection.
+ The type to convert the values to.
+ The source collection type.
+ An that contains the converted values of every token in the source collection.
+
+
+
+ Returns the input typed as .
+
+ An of that contains the source collection.
+ The input typed as .
+
+
+
+ Returns the input typed as .
+
+ The source collection type.
+ An of that contains the source collection.
+ The input typed as .
+
+
+
+ Represents a JSON constructor.
+
+
+
+
+ Gets the container's children tokens.
+
+ The container's children tokens.
+
+
+
+ Gets or sets the name of this constructor.
+
+ The constructor name.
+
+
+
+ Gets the node type for this .
+
+ The type.
+
+
+
+ Initializes a new instance of the class.
+
+
+
+
+ Initializes a new instance of the class from another object.
+
+ A object to copy from.
+
+
+
+ Initializes a new instance of the class with the specified name and content.
+
+ The constructor name.
+ The contents of the constructor.
+
+
+
+ Initializes a new instance of the class with the specified name and content.
+
+ The constructor name.
+ The contents of the constructor.
+
+
+
+ Initializes a new instance of the class with the specified name.
+
+ The constructor name.
+
+
+
+ Writes this token to a .
+
+ A into which this method will write.
+ A collection of which will be used when writing the token.
+
+
+
+ Gets the with the specified key.
+
+ The with the specified key.
+
+
+
+ Loads an from a .
+
+ A that will be read for the content of the .
+ A that contains the JSON that was read from the specified .
+
+
+
+ Loads an from a .
+
+ A that will be read for the content of the .
+ The used to load the JSON.
+ If this is null, default load settings will be used.
+ A that contains the JSON that was read from the specified .
+
+
+
+ Represents a token that can contain other tokens.
+
+
+
+
+ Occurs when the list changes or an item in the list changes.
+
+
+
+
+ Occurs before an item is added to the collection.
+
+
+
+
+ Gets the container's children tokens.
+
+ The container's children tokens.
+
+
+
+ Raises the event.
+
+ The instance containing the event data.
+
+
+
+ Raises the event.
+
+ The instance containing the event data.
+
+
+
+ Gets a value indicating whether this token has child tokens.
+
+
+ true if this token has child values; otherwise, false.
+
+
+
+
+ Get the first child token of this token.
+
+
+ A containing the first child token of the .
+
+
+
+
+ Get the last child token of this token.
+
+
+ A containing the last child token of the .
+
+
+
+
+ Returns a collection of the child tokens of this token, in document order.
+
+
+ An of containing the child tokens of this , in document order.
+
+
+
+
+ Returns a collection of the child values of this token, in document order.
+
+ The type to convert the values to.
+
+ A containing the child values of this , in document order.
+
+
+
+
+ Returns a collection of the descendant tokens for this token in document order.
+
+ An containing the descendant tokens of the .
+
+
+
+ Returns a collection of the tokens that contain this token, and all descendant tokens of this token, in document order.
+
+ An containing this token, and all the descendant tokens of the .
+
+
+
+ Adds the specified content as children of this .
+
+ The content to be added.
+
+
+
+ Adds the specified content as the first children of this .
+
+ The content to be added.
+
+
+
+ Creates an that can be used to add tokens to the .
+
+ An that is ready to have content written to it.
+
+
+
+ Replaces the children nodes of this token with the specified content.
+
+ The content.
+
+
+
+ Removes the child nodes from this token.
+
+
+
+
+ Merge the specified content into this .
+
+ The content to be merged.
+
+
+
+ Merge the specified content into this using .
+
+ The content to be merged.
+ The used to merge the content.
+
+
+
+ Gets the count of child JSON tokens.
+
+ The count of child JSON tokens
+
+
+
+ Represents a collection of objects.
+
+ The type of token
+
+
+
+ An empty collection of objects.
+
+
+
+
+ Initializes a new instance of the struct.
+
+ The enumerable.
+
+
+
+ Returns an enumerator that iterates through the collection.
+
+
+ A that can be used to iterate through the collection.
+
+
+
+
+ Returns an enumerator that iterates through a collection.
+
+
+ An object that can be used to iterate through the collection.
+
+
+
+
+ Gets the with the specified key.
+
+
+
+
+
+ Determines whether the specified is equal to this instance.
+
+ The to compare with this instance.
+
+ true if the specified is equal to this instance; otherwise, false.
+
+
+
+
+ Determines whether the specified is equal to this instance.
+
+ The to compare with this instance.
+
+ true if the specified is equal to this instance; otherwise, false.
+
+
+
+
+ Returns a hash code for this instance.
+
+
+ A hash code for this instance, suitable for use in hashing algorithms and data structures like a hash table.
+
+
+
+
+ Represents a JSON object.
+
+
+ /*...*/ containing the specified text.
+
+ Text to place inside the comment.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a [] value.
+
+ The [] value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Represents an abstract JSON token.
+
+
+
+
+ Gets a comparer that can compare two tokens for value equality.
+
+ A that can compare two nodes for value equality.
+
+
+
+ Gets or sets the parent.
+
+ The parent.
+
+
+
+ Gets the root of this .
+
+ The root of this .
+
+
+
+ Gets the node type for this .
+
+ The type.
+
+
+
+ Gets a value indicating whether this token has child tokens.
+
+
+ true if this token has child values; otherwise, false.
+
+
+
+
+ Compares the values of two tokens, including the values of all descendant tokens.
+
+ The first to compare.
+ The second to compare.
+ true if the tokens are equal; otherwise false.
+
+
+
+ Gets the next sibling token of this node.
+
+ The that contains the next sibling token.
+
+
+
+ Gets the previous sibling token of this node.
+
+ The that contains the previous sibling token.
+
+
+
+ Gets the path of the JSON token.
+
+
+
+
+ Adds the specified content immediately after this token.
+
+ A content object that contains simple content or a collection of content objects to be added after this token.
+
+
+
+ Adds the specified content immediately before this token.
+
+ A content object that contains simple content or a collection of content objects to be added before this token.
+
+
+
+ Returns a collection of the ancestor tokens of this token.
+
+ A collection of the ancestor tokens of this token.
+
+
+
+ Returns a collection of tokens that contain this token, and the ancestors of this token.
+
+ A collection of tokens that contain this token, and the ancestors of this token.
+
+
+
+ Returns a collection of the sibling tokens after this token, in document order.
+
+ A collection of the sibling tokens after this tokens, in document order.
+
+
+
+ Returns a collection of the sibling tokens before this token, in document order.
+
+ A collection of the sibling tokens before this token, in document order.
+
+
+
+ Gets the with the specified key.
+
+ The with the specified key.
+
+
+
+ Gets the with the specified key converted to the specified type.
+
+ The type to convert the token to.
+ The token key.
+ The converted token value.
+
+
+
+ Get the first child token of this token.
+
+ A containing the first child token of the .
+
+
+
+ Get the last child token of this token.
+
+ A containing the last child token of the .
+
+
+
+ Returns a collection of the child tokens of this token, in document order.
+
+ An of containing the child tokens of this , in document order.
+
+
+
+ Returns a collection of the child tokens of this token, in document order, filtered by the specified type.
+
+ The type to filter the child tokens on.
+ A containing the child tokens of this , in document order.
+
+
+
+ Returns a collection of the child values of this token, in document order.
+
+ The type to convert the values to.
+ A containing the child values of this , in document order.
+
+
+
+ Removes this token from its parent.
+
+
+
+
+ Replaces this token with the specified token.
+
+ The value.
+
+
+
+ Writes this token to a .
+
+ A into which this method will write.
+ A collection of which will be used when writing the token.
+
+
+
+ Returns the indented JSON for this token.
+
+
+ The indented JSON for this token.
+
+
+
+
+ Returns the JSON for this token using the given formatting and converters.
+
+ Indicates how the output is formatted.
+ A collection of which will be used when writing the token.
+ The JSON for this token using the given formatting and converters.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to [].
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an explicit conversion from to .
+
+ The value.
+ The result of the conversion.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from [] to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Performs an implicit conversion from to .
+
+ The value to create a from.
+ The initialized with the specified value.
+
+
+
+ Creates an for this token.
+
+ An that can be used to read this token and its descendants.
+
+
+
+ Creates a from an object.
+
+ The object that will be used to create .
+ A with the value of the specified object
+
+
+
+ Creates a from an object using the specified .
+
+ The object that will be used to create .
+ The that will be used when reading the object.
+ A with the value of the specified object
+
+
+
+ Creates the specified .NET type from the .
+
+ The object type that the token will be deserialized to.
+ The new object created from the JSON value.
+
+
+
+ Creates the specified .NET type from the .
+
+ The object type that the token will be deserialized to.
+ The new object created from the JSON value.
+
+
+
+ Creates the specified .NET type from the using the specified .
+
+ The object type that the token will be deserialized to.
+ The that will be used when creating the object.
+ The new object created from the JSON value.
+
+
+
+ Creates the specified .NET type from the using the specified .
+
+ The object type that the token will be deserialized to.
+ The that will be used when creating the object.
+ The new object created from the JSON value.
+
+
+
+ Creates a from a .
+
+ An positioned at the token to read into this .
+
+ An that contains the token and its descendant tokens
+ that were read from the reader. The runtime type of the token is determined
+ by the token type of the first token encountered in the reader.
+
+
+
+
+ Creates a from a .
+
+ An positioned at the token to read into this .
+ The used to load the JSON.
+ If this is null, default load settings will be used.
+
+ An that contains the token and its descendant tokens
+ that were read from the reader. The runtime type of the token is determined
+ by the token type of the first token encountered in the reader.
+
+
+
+
+ Load a from a string that contains JSON.
+
+ A that contains JSON.
+ A populated from the string that contains JSON.
+
+
+
+ Load a from a string that contains JSON.
+
+ A that contains JSON.
+ The used to load the JSON.
+ If this is null, default load settings will be used.
+ A populated from the string that contains JSON.
+
+
+
+ Creates a from a .
+
+ An positioned at the token to read into this .
+ The used to load the JSON.
+ If this is null, default load settings will be used.
+
+ An that contains the token and its descendant tokens
+ that were read from the reader. The runtime type of the token is determined
+ by the token type of the first token encountered in the reader.
+
+
+
+
+ Creates a from a .
+
+ An positioned at the token to read into this .
+
+ An that contains the token and its descendant tokens
+ that were read from the reader. The runtime type of the token is determined
+ by the token type of the first token encountered in the reader.
+
+
+
+
+ Selects a using a JPath expression. Selects the token that matches the object path.
+
+
+ A that contains a JPath expression.
+
+ A , or null.
+
+
+
+ Selects a using a JPath expression. Selects the token that matches the object path.
+
+
+ A that contains a JPath expression.
+
+ A flag to indicate whether an error should be thrown if no tokens are found when evaluating part of the expression.
+ A .
+
+
+
+ Selects a collection of elements using a JPath expression.
+
+
+ A that contains a JPath expression.
+
+ An that contains the selected elements.
+
+
+
+ Selects a collection of elements using a JPath expression.
+
+
+ A that contains a JPath expression.
+
+ A flag to indicate whether an error should be thrown if no tokens are found when evaluating part of the expression.
+ An that contains the selected elements.
+
+
+
+ Creates a new instance of the . All child tokens are recursively cloned.
+
+ A new instance of the .
+
+
+
+ Adds an object to the annotation list of this .
+
+ The annotation to add.
+
+
+
+ Get the first annotation object of the specified type from this .
+
+ The type of the annotation to retrieve.
+ The first annotation object that matches the specified type, or null if no annotation is of the specified type.
+
+
+
+ Gets the first annotation object of the specified type from this .
+
+ The of the annotation to retrieve.
+ The first annotation object that matches the specified type, or null if no annotation is of the specified type.
+
+
+
+ Gets a collection of annotations of the specified type for this .
+
+ The type of the annotations to retrieve.
+ An that contains the annotations for this .
+
+
+
+ Gets a collection of annotations of the specified type for this .
+
+ The of the annotations to retrieve.
+ An of that contains the annotations that match the specified type for this .
+
+
+
+ Removes the annotations of the specified type from this .
+
+ The type of annotations to remove.
+
+
+
+ Removes the annotations of the specified type from this .
+
+ The of annotations to remove.
+
+
+
+ Represents a JSON property.
+
+
+
+
+ Gets the container's children tokens.
+
+ The container's children tokens.
+
+
+
+ Gets the property name.
+
+ The property name.
+
+
+
+ Gets or sets the property value.
+
+ The property value.
+
+
+
+ Initializes a new instance of the class from another object.
+
+ A object to copy from.
+
+
+
+ Gets the node type for this .
+
+ The type.
+
+
+
+ Initializes a new instance of the class.
+
+ The property name.
+ The property content.
+
+
+
+ Initializes a new instance of the class.
+
+ The property name.
+ The property content.
+
+
+
+ Writes this token to a .
+
+ A into which this method will write.
+ A collection of which will be used when writing the token.
+
+
+
+ Loads an from a .
+
+ A that will be read for the content of the .
+ A that contains the JSON that was read from the specified .
+
+
+
+ Loads an from a .
+
+ A that will be read for the content of the .
+ The used to load the JSON.
+ If this is null, default load settings will be used.
+ A that contains the JSON that was read from the specified .
+
+
+
+ Specifies the type of token.
+
+
+
+
+ No token type has been set.
+
+
+
+
+ A JSON object.
+
+
+
+
+ A JSON array.
+
+
+
+
+ A JSON constructor.
+
+
+
+
+ A JSON object property.
+
+
+
+
+ A comment.
+
+
+
+
+ An integer value.
+
+
+
+
+ A float value.
+
+
+
+
+ A string value.
+
+
+
+
+ A boolean value.
+
+
+
+
+ A null value.
+
+
+
+
+ An undefined value.
+
+
+
+
+ A date value.
+
+
+
+
+ A raw JSON value.
+
+
+
+
+ A collection of bytes value.
+
+
+
+
+ A Guid value.
+
+
+
+
+ A Uri value.
+
+
+
+
+ A TimeSpan value.
+
+
+
+
+ Represents a value in JSON (string, integer, date, etc).
+
+
+
+
+ Initializes a new instance of the class from another object.
+
+ A object to copy from.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Gets a value indicating whether this token has child tokens.
+
+
+ true if this token has child values; otherwise, false.
+
+
+
+
+ Creates a comment with the given value.
+
+ The value.
+ A comment with the given value.
+
+
+
+ Creates a string with the given value.
+
+ The value.
+ A string with the given value.
+
+
+
+ Creates a null value.
+
+ A null value.
+
+
+
+ Creates a undefined value.
+
+ A undefined value.
+
+
+
+ Gets the node type for this .
+
+ The type.
+
+
+
+ Gets or sets the underlying token value.
+
+ The underlying token value.
+
+
+
+ Writes this token to a .
+
+ A into which this method will write.
+ A collection of which will be used when writing the token.
+
+
+
+ Indicates whether the current object is equal to another object of the same type.
+
+
+ true if the current object is equal to the parameter; otherwise, false.
+
+ An object to compare with this object.
+
+
+
+ Determines whether the specified is equal to the current .
+
+ The to compare with the current .
+
+ true if the specified is equal to the current ; otherwise, false.
+
+
+ The parameter is null.
+
+
+
+
+ Serves as a hash function for a particular type.
+
+
+ A hash code for the current .
+
+
+
+
+ Returns a that represents this instance.
+
+
+ A that represents this instance.
+
+
+
+
+ Returns a that represents this instance.
+
+ The format.
+
+ A that represents this instance.
+
+
+
+
+ Returns a that represents this instance.
+
+ The format provider.
+
+ A that represents this instance.
+
+
+
+
+ Returns a that represents this instance.
+
+ The format.
+ The format provider.
+
+ A that represents this instance.
+
+
+
+
+ Compares the current instance with another object of the same type and returns an integer that indicates whether the current instance precedes, follows, or occurs in the same position in the sort order as the other object.
+
+ An object to compare with this instance.
+
+ A 32-bit signed integer that indicates the relative order of the objects being compared. The return value has these meanings:
+ Value
+ Meaning
+ Less than zero
+ This instance is less than .
+ Zero
+ This instance is equal to .
+ Greater than zero
+ This instance is greater than .
+
+
+ is not the same type as this instance.
+
+
+
+
+ Specifies metadata property handling options for the .
+
+
+
+
+ Read metadata properties located at the start of a JSON object.
+
+
+
+
+ Read metadata properties located anywhere in a JSON object. Note that this setting will impact performance.
+
+
+
+
+ Do not try to read metadata properties.
+
+
+
+
+ Represents a trace writer that writes to the application's instances.
+
+
+
+
+ Gets the that will be used to filter the trace messages passed to the writer.
+ For example a filter level of Info will exclude Verbose messages and include Info,
+ Warning and Error messages.
+
+
+ The that will be used to filter the trace messages passed to the writer.
+
+
+
+
+ Writes the specified trace level, message and optional exception.
+
+ The at which to write this trace.
+ The trace message.
+ The trace exception. This parameter is optional.
+
+
+
+ Provides methods to get attributes.
+
+
+
+
+ Returns a collection of all of the attributes, or an empty collection if there are no attributes.
+
+ When true, look up the hierarchy chain for the inherited custom attribute.
+ A collection of s, or an empty collection.
+
+
+
+ Returns a collection of attributes, identified by type, or an empty collection if there are no attributes.
+
+ The type of the attributes.
+ When true, look up the hierarchy chain for the inherited custom attribute.
+ A collection of s, or an empty collection.
+
+
+
+ Represents a trace writer.
+
+
+
+
+ Gets the that will be used to filter the trace messages passed to the writer.
+ For example a filter level of Info will exclude Verbose messages and include Info,
+ Warning and Error messages.
+
+ The that will be used to filter the trace messages passed to the writer.
+
+
+
+ Writes the specified trace level, message and optional exception.
+
+ The at which to write this trace.
+ The trace message.
+ The trace exception. This parameter is optional.
+
+
+
+ Contract details for a used by the .
+
+
+
+
+ Gets or sets the default collection items .
+
+ The converter.
+
+
+
+ Gets or sets a value indicating whether the collection items preserve object references.
+
+ true if collection items preserve object references; otherwise, false.
+
+
+
+ Gets or sets the collection item reference loop handling.
+
+ The reference loop handling.
+
+
+
+ Gets or sets the collection item type name handling.
+
+ The type name handling.
+
+
+
+ Initializes a new instance of the class.
+
+ The underlying type for the contract.
+
+
+
+ Represents a trace writer that writes to memory. When the trace message limit is
+ reached then old trace messages will be removed as new messages are added.
+
+
+
+
+ Gets the that will be used to filter the trace messages passed to the writer.
+ For example a filter level of Info will exclude Verbose messages and include Info,
+ Warning and Error messages.
+
+
+ The that will be used to filter the trace messages passed to the writer.
+
+
+
+
+ Initializes a new instance of the class.
+
+
+
+
+ Writes the specified trace level, message and optional exception.
+
+ The at which to write this trace.
+ The trace message.
+ The trace exception. This parameter is optional.
+
+
+
+ Returns an enumeration of the most recent trace messages.
+
+ An enumeration of the most recent trace messages.
+
+
+
+ Returns a of the most recent trace messages.
+
+
+ A of the most recent trace messages.
+
+
+
+
+ Provides methods to get attributes from a , , or .
+
+
+
+
+ Initializes a new instance of the class.
+
+ The instance to get attributes for. This parameter should be a , , or .
+
+
+
+ Returns a collection of all of the attributes, or an empty collection if there are no attributes.
+
+ When true, look up the hierarchy chain for the inherited custom attribute.
+ A collection of s, or an empty collection.
+
+
+
+ Returns a collection of attributes, identified by type, or an empty collection if there are no attributes.
+
+ The type of the attributes.
+ When true, look up the hierarchy chain for the inherited custom attribute.
+ A collection of s, or an empty collection.
+
+
+
+ Contract details for a used by the .
+
+
+
+
+ Gets or sets the ISerializable object constructor.
+
+ The ISerializable object constructor.
+
+
+
+ Initializes a new instance of the class.
+
+ The underlying type for the contract.
+
+
+
+ Contract details for a used by the .
+
+
+
+
+ Initializes a new instance of the class.
+
+ The underlying type for the contract.
+
+
+
+ Contract details for a used by the .
+
+
+
+
+ Initializes a new instance of the class.
+
+ The underlying type for the contract.
+
+
+
+ Get and set values for a using dynamic methods.
+
+
+
+
+ Initializes a new instance of the class.
+
+ The member info.
+
+
+
+ Sets the value.
+
+ The target to set the value on.
+ The value to set on the target.
+
+
+
+ Gets the value.
+
+ The target to get the value from.
+ The value.
+
+
+
+ Provides data for the Error event.
+
+
+
+
+ Gets the current object the error event is being raised against.
+
+ The current object the error event is being raised against.
+
+
+
+ Gets the error context.
+
+ The error context.
+
+
+
+ Initializes a new instance of the class.
+
+ The current object.
+ The error context.
+
+
+
+ Resolves member mappings for a type, camel casing property names.
+
+
+
+
+ Initializes a new instance of the class.
+
+
+
+
+ Resolves the name of the property.
+
+ Name of the property.
+ The property name camel cased.
+
+
+
+ Used by to resolves a for a given .
+
+
+
+
+ Gets a value indicating whether members are being get and set using dynamic code generation.
+ This value is determined by the runtime permissions available.
+
+
+ true if using dynamic code generation; otherwise, false.
+
+
+
+
+ Gets or sets the default members search flags.
+
+ The default members search flags.
+
+
+
+ Gets or sets a value indicating whether compiler generated members should be serialized.
+
+
+ true if serialized compiler generated members; otherwise, false.
+
+
+
+
+ Gets or sets a value indicating whether to ignore the interface when serializing and deserializing types.
+
+
+ true if the interface will be ignored when serializing and deserializing types; otherwise, false.
+
+
+
+
+ Gets or sets a value indicating whether to ignore the attribute when serializing and deserializing types.
+
+
+ true if the attribute will be ignored when serializing and deserializing types; otherwise, false.
+
+
+
+
+ Initializes a new instance of the class.
+
+
+
+
+ Initializes a new instance of the class.
+
+
+ If set to true the will use a cached shared with other resolvers of the same type.
+ Sharing the cache will significantly improve performance with multiple resolver instances because expensive reflection will only
+ happen once. This setting can cause unexpected behavior if different instances of the resolver are suppose to produce different
+ results. When set to false it is highly recommended to reuse instances with the .
+
+
+
+
+ Resolves the contract for a given type.
+
+ The type to resolve a contract for.
+ The contract for a given type.
+
+
+
+ Gets the serializable members for the type.
+
+ The type to get serializable members for.
+ The serializable members for the type.
+
+
+
+ Creates a for the given type.
+
+ Type of the object.
+ A for the given type.
+
+
+
+ Creates the constructor parameters.
+
+ The constructor to create properties for.
+ The type's member properties.
+ Properties for the given .
+
+
+
+ Creates a for the given .
+
+ The matching member property.
+ The constructor parameter.
+ A created for the given .
+
+
+
+ Resolves the default for the contract.
+
+ Type of the object.
+ The contract's default .
+
+
+
+ Creates a for the given type.
+
+ Type of the object.
+ A for the given type.
+
+
+
+ Creates a for the given type.
+
+ Type of the object.
+ A for the given type.
+
+
+
+ Creates a for the given type.
+
+ Type of the object.
+ A for the given type.
+
+
+
+ Creates a for the given type.
+
+ Type of the object.
+ A for the given type.
+
+
+
+ Creates a for the given type.
+
+ Type of the object.
+ A for the given type.
+
+
+
+ Creates a for the given type.
+
+ Type of the object.
+ A for the given type.
+
+
+
+ Determines which contract type is created for the given type.
+
+ Type of the object.
+ A for the given type.
+
+
+
+ Creates properties for the given .
+
+ The type to create properties for.
+ /// The member serialization mode for the type.
+ Properties for the given .
+
+
+
+ Creates the used by the serializer to get and set values from a member.
+
+ The member.
+ The used by the serializer to get and set values from a member.
+
+
+
+ Creates a for the given .
+
+ The member's parent .
+ The member to create a for.
+ A created for the given .
+
+
+
+ Resolves the name of the property.
+
+ Name of the property.
+ Resolved name of the property.
+
+
+
+ Resolves the key of the dictionary. By default is used to resolve dictionary keys.
+
+ Key of the dictionary.
+ Resolved key of the dictionary.
+
+
+
+ Gets the resolved name of the property.
+
+ Name of the property.
+ Name of the property.
+
+
+
+ The default serialization binder used when resolving and loading classes from type names.
+
+
+
+
+ When overridden in a derived class, controls the binding of a serialized object to a type.
+
+ Specifies the name of the serialized object.
+ Specifies the name of the serialized object.
+
+ The type of the object the formatter creates a new instance of.
+
+
+
+
+ Provides information surrounding an error.
+
+
+
+
+ Gets the error.
+
+ The error.
+
+
+
+ Gets the original object that caused the error.
+
+ The original object that caused the error.
+
+
+
+ Gets the member that caused the error.
+
+ The member that caused the error.
+
+
+
+ Gets the path of the JSON location where the error occurred.
+
+ The path of the JSON location where the error occurred.
+
+
+
+ Gets or sets a value indicating whether this is handled.
+
+ true if handled; otherwise, false.
+
+
+
+ Used by to resolves a for a given .
+
+
+ /*...*/ containing the specified text.
+
+ Text to place inside the comment.
+
+
+
+ Writes out the given white space.
+
+ The string of white space characters.
+
+
+
+ The exception thrown when an error occurs while reading JSON text.
+
+
+
+
+ Gets the path to the JSON where the error occurred.
+
+ The path to the JSON where the error occurred.
+
+
+
+ Initializes a new instance of the class.
+
+
+
+
+ Initializes a new instance of the class
+ with a specified error message.
+
+ The error message that explains the reason for the exception.
+
+
+
+ Initializes a new instance of the class
+ with a specified error message and a reference to the inner exception that is the cause of this exception.
+
+ The error message that explains the reason for the exception.
+ The exception that is the cause of the current exception, or a null reference (Nothing in Visual Basic) if no inner exception is specified.
+
+
+
+ Initializes a new instance of the class.
+
+ The that holds the serialized object data about the exception being thrown.
+ The that contains contextual information about the source or destination.
+ The parameter is null.
+ The class name is null or is zero (0).
+
+
+
+ The exception thrown when an error occurs while reading JSON text.
+
+
+
+
+ Gets the line number indicating where the error occurred.
+
+ The line number indicating where the error occurred.
+
+
+
+ Gets the line position indicating where the error occurred.
+
+ The line position indicating where the error occurred.
+
+
+
+ Gets the path to the JSON where the error occurred.
+
+ The path to the JSON where the error occurred.
+
+
+
+ Initializes a new instance of the class.
+
+
+
+
+ Initializes a new instance of the class
+ with a specified error message.
+
+ The error message that explains the reason for the exception.
+
+
+
+ Initializes a new instance of the class
+ with a specified error message and a reference to the inner exception that is the cause of this exception.
+
+ The error message that explains the reason for the exception.
+ The exception that is the cause of the current exception, or a null reference (Nothing in Visual Basic) if no inner exception is specified.
+
+
+
+ Initializes a new instance of the class.
+
+ The that holds the serialized object data about the exception being thrown.
+ The that contains contextual information about the source or destination.
+ The parameter is null.
+ The class name is null or is zero (0).
+
+
+
+ Converts an object to and from JSON.
+
+
+
+
+ Writes the JSON representation of the object.
+
+ The to write to.
+ The value.
+ The calling serializer.
+
+
+
+ Reads the JSON representation of the object.
+
+ The to read from.
+ Type of the object.
+ The existing value of object being read.
+ The calling serializer.
+ The object value.
+
+
+
+ Determines whether this instance can convert the specified object type.
+
+ Type of the object.
+
+ true if this instance can convert the specified object type; otherwise, false.
+
+
+
+
+ Gets a value indicating whether this can read JSON.
+
+ true if this can read JSON; otherwise, false.
+
+
+
+ Gets a value indicating whether this can write JSON.
+
+ true if this can write JSON; otherwise, false.
+
+
+
+ Represents a collection of .
+
+
+
+
+ Represents a reader that provides fast, non-cached, forward-only access to serialized JSON data.
+
+
+
+
+ Specifies the state of the reader.
+
+
+
+
+ The Read method has not been called.
+
+
+
+
+ The end of the file has been reached successfully.
+
+
+
+
+ Reader is at a property.
+
+
+
+
+ Reader is at the start of an object.
+
+
+
+
+ Reader is in an object.
+
+
+
+
+ Reader is at the start of an array.
+
+
+
+
+ Reader is in an array.
+
+
+
+
+ The Close method has been called.
+
+
+
+
+ Reader has just read a value.
+
+
+
+
+ Reader is at the start of a constructor.
+
+
+
+
+ Reader in a constructor.
+
+
+
+
+ An error occurred that prevents the read operation from continuing.
+
+
+
+
+ The end of the file has been reached successfully.
+
+
+
+
+ Gets the current reader state.
+
+ The current reader state.
+
+
+
+ Gets or sets a value indicating whether the underlying stream or
+ should be closed when the reader is closed.
+
+
+ true to close the underlying stream or when
+ the reader is closed; otherwise false. The default is true.
+
+
+
+
+ Gets or sets a value indicating whether multiple pieces of JSON content can
+ be read from a continuous stream without erroring.
+
+
+ true to support reading multiple pieces of JSON content; otherwise false. The default is false.
+
+
+
+
+ Gets the quotation mark character used to enclose the value of a string.
+
+
+
+
+ Get or set how time zones are handling when reading JSON.
+
+
+
+
+ Get or set how date formatted strings, e.g. "\/Date(1198908717056)\/" and "2012-03-21T05:40Z", are parsed when reading JSON.
+
+
+
+
+ Get or set how floating point numbers, e.g. 1.0 and 9.9, are parsed when reading JSON text.
+
+
+
+
+ Get or set how custom date formatted strings are parsed when reading JSON.
+
+
+
+
+ Gets or sets the maximum depth allowed when reading JSON. Reading past this depth will throw a .
+
+
+
+
+ Gets the type of the current JSON token.
+
+
+
+
+ Gets the text value of the current JSON token.
+
+
+
+
+ Gets The Common Language Runtime (CLR) type for the current JSON token.
+
+
+
+
+ Gets the depth of the current token in the JSON document.
+
+ The depth of the current token in the JSON document.
+
+
+
+ Gets the path of the current JSON token.
+
+
+
+
+ Gets or sets the culture used when reading JSON. Defaults to .
+
+
+
+
+ Initializes a new instance of the class with the specified .
+
+
+
+
+ Reads the next JSON token from the stream.
+
+ true if the next token was read successfully; false if there are no more tokens to read.
+
+
+
+ Reads the next JSON token from the stream as a .
+
+ A . This method will return null at the end of an array.
+
+
+
+ Reads the next JSON token from the stream as a .
+
+ A . This method will return null at the end of an array.
+
+
+
+ Reads the next JSON token from the stream as a [].
+
+ A [] or a null reference if the next JSON token is null. This method will return null at the end of an array.
+
+
+
+ Reads the next JSON token from the stream as a .
+
+ A . This method will return null at the end of an array.
+
+
+
+ Reads the next JSON token from the stream as a .
+
+ A . This method will return null at the end of an array.
+
+
+
+ Reads the next JSON token from the stream as a .
+
+ A . This method will return null at the end of an array.
+
+
+
+ Reads the next JSON token from the stream as a .
+
+ A . This method will return null at the end of an array.
+
+
+
+ Reads the next JSON token from the stream as a .
+
+ A . This method will return null at the end of an array.
+
+
+
+ Skips the children of the current token.
+
+
+
+
+ Sets the current token.
+
+ The new token.
+
+
+
+ Sets the current token and value.
+
+ The new token.
+ The value.
+
+
+
+ Sets the state based on current token type.
+
+
+
+
+ Performs application-defined tasks associated with freeing, releasing, or resetting unmanaged resources.
+
+
+
+
+ Releases unmanaged and - optionally - managed resources
+
+ true to release both managed and unmanaged resources; false to release only unmanaged resources.
+
+
+
+ Changes the to Closed.
+
+
+
+
+ Provides methods for converting between common language runtime types and JSON types.
+
+
+ /*...*/ containing the specified text.
+
+ Text to place inside the comment.
+
+
+
+ Writes out the given white space.
+
+ The string of white space characters.
+
+
+
+ Releases unmanaged and - optionally - managed resources
+
+ true to release both managed and unmanaged resources; false to release only unmanaged resources.
+
+
+
+ Sets the state of the JsonWriter,
+
+ The JsonToken being written.
+ The value being written.
+
+
+
+ Specifies the state of the .
+
+
+
+
+ An exception has been thrown, which has left the in an invalid state.
+ You may call the method to put the in the Closed state.
+ Any other method calls results in an being thrown.
+
+
+
+
+ The method has been called.
+
+
+
+
+ An object is being written.
+
+
+
+
+ A array is being written.
+
+
+
+
+ A constructor is being written.
+
+
+
+
+ A property is being written.
+
+
+
+
+ A write method has not been called.
+
+
+
+
diff --git a/Assets/JsonDotNet/Assemblies/Standalone/Newtonsoft.Json.XML.meta b/Assets/JsonDotNet/Assemblies/Standalone/Newtonsoft.Json.XML.meta
new file mode 100644
index 0000000..7623f10
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+licenseType: Store
+TextScriptImporter:
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diff --git a/Assets/JsonDotNet/Assemblies/Standalone/Newtonsoft.Json.dll b/Assets/JsonDotNet/Assemblies/Standalone/Newtonsoft.Json.dll
new file mode 100644
index 0000000..cea08b2
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new file mode 100644
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+ iconMap: {}
+ executionOrder: {}
+ defineConstraints: []
+ isPreloaded: 0
+ isOverridable: 0
+ isExplicitlyReferenced: 0
+ validateReferences: 1
+ platformData:
+ - first:
+ '': Linux
+ second:
+ enabled: 1
+ settings:
+ CPU: x86
+ - first:
+ '': LinuxUniversal
+ second:
+ enabled: 1
+ settings:
+ CPU: AnyCPU
+ - first:
+ '': OSXIntel
+ second:
+ enabled: 1
+ settings:
+ CPU: AnyCPU
+ - first:
+ '': OSXIntel64
+ second:
+ enabled: 1
+ settings:
+ CPU: AnyCPU
+ - first:
+ '': SamsungTV
+ second:
+ enabled: 0
+ settings:
+ STV_MODEL: STANDARD_13
+ - first:
+ Android: Android
+ second:
+ enabled: 0
+ settings:
+ CPU: AnyCPU
+ - first:
+ Any:
+ second:
+ enabled: 0
+ settings: {}
+ - first:
+ Editor: Editor
+ second:
+ enabled: 1
+ settings:
+ CPU: AnyCPU
+ DefaultValueInitialized: true
+ OS: AnyOS
+ - first:
+ Facebook: Win
+ second:
+ enabled: 1
+ settings:
+ CPU: AnyCPU
+ - first:
+ Facebook: Win64
+ second:
+ enabled: 1
+ settings:
+ CPU: AnyCPU
+ - first:
+ Standalone: Linux64
+ second:
+ enabled: 1
+ settings:
+ CPU: AnyCPU
+ - first:
+ Standalone: OSXUniversal
+ second:
+ enabled: 1
+ settings:
+ CPU: AnyCPU
+ - first:
+ Standalone: Win
+ second:
+ enabled: 1
+ settings:
+ CPU: AnyCPU
+ - first:
+ Standalone: Win64
+ second:
+ enabled: 1
+ settings:
+ CPU: AnyCPU
+ - first:
+ Windows Store Apps: WindowsStoreApps
+ second:
+ enabled: 0
+ settings:
+ CPU: AnyCPU
+ DontProcess: False
+ PlaceholderPath:
+ SDK: AnySDK
+ ScriptingBackend: Il2Cpp
+ - first:
+ iPhone: iOS
+ second:
+ enabled: 0
+ settings:
+ CompileFlags:
+ FrameworkDependencies:
+ userData:
+ assetBundleName:
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diff --git a/Assets/JsonDotNet/Assemblies/Windows.meta b/Assets/JsonDotNet/Assemblies/Windows.meta
new file mode 100644
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diff --git a/Assets/JsonDotNet/Assemblies/Windows/Newtonsoft.Json.XML b/Assets/JsonDotNet/Assemblies/Windows/Newtonsoft.Json.XML
new file mode 100644
index 0000000..ed0eec5
--- /dev/null
+++ b/Assets/JsonDotNet/Assemblies/Windows/Newtonsoft.Json.XML
@@ -0,0 +1,7977 @@
+
+
+
+ Newtonsoft.Json
+
+
+
+
+ Represents a BSON Oid (object id).
+
+
+
+
+ Gets or sets the value of the Oid.
+
+ The value of the Oid.
+
+
+
+ Initializes a new instance of the class.
+
+ The Oid value.
+
+
+
+ Represents a reader that provides fast, non-cached, forward-only access to serialized JSON data.
+
+
+
+
+ Gets or sets a value indicating whether binary data reading should compatible with incorrect Json.NET 3.5 written binary.
+
+
+ true if binary data reading will be compatible with incorrect Json.NET 3.5 written binary; otherwise, false.
+
+
+
+
+ Gets or sets a value indicating whether the root object will be read as a JSON array.
+
+
+ true if the root object will be read as a JSON array; otherwise, false.
+
+
+
+
+ Gets or sets the used when reading values from BSON.
+
+ The used when reading values from BSON.
+
+
+
+ Initializes a new instance of the class.
+
+ The stream.
+
+
+
+ Initializes a new instance of the class.
+
+ The reader.
+
+
+
+ Initializes a new instance of the class.
+
+ The stream.
+ if set to true the root object will be read as a JSON array.
+ The used when reading values from BSON.
+
+
+
+ Initializes a new instance of the class.
+
+ The reader.
+ if set to true the root object will be read as a JSON array.
+ The used when reading values from BSON.
+
+
+
+ Reads the next JSON token from the stream.
+
+
+ true if the next token was read successfully; false if there are no more tokens to read.
+
+
+
+
+ Changes the to Closed.
+
+
+
+
+ Represents a writer that provides a fast, non-cached, forward-only way of generating JSON data.
+
+
+
+
+ Gets or sets the used when writing values to BSON.
+ When set to no conversion will occur.
+
+ The used when writing values to BSON.
+
+
+
+ Initializes a new instance of the class.
+
+ The stream.
+
+
+
+ Initializes a new instance of the class.
+
+ The writer.
+
+
+
+ Flushes whatever is in the buffer to the underlying streams and also flushes the underlying stream.
+
+
+
+
+ Writes the end.
+
+ The token.
+
+
+
+ Writes out a comment /*...*/ containing the specified text.
+
+ Text to place inside the comment.
+
+
+
+ Writes the start of a constructor with the given name.
+
+ The name of the constructor.
+
+
+
+ Writes raw JSON.
+
+ The raw JSON to write.
+
+
+
+ Writes raw JSON where a value is expected and updates the writer's state.
+
+ The raw JSON to write.
+
+
+
+ Writes the beginning of a JSON array.
+
+
+
+
+ Writes the beginning of a JSON object.
+
+
+
+
+ Writes the property name of a name/value pair on a JSON object.
+
+ The name of the property.
+
+
+
+ Closes this stream and the underlying stream.
+
+
+
+
+ Writes a value.
+ An error will raised if the value cannot be written as a single JSON token.
+
+ The value to write.
+
+
+
+ Writes a null value.
+
+
+
+
+ Writes an undefined value.
+
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a [] value.
+
+ The [] value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a [] value that represents a BSON object id.
+
+ The Object ID value to write.
+
+
+
+ Writes a BSON regex.
+
+ The regex pattern.
+ The regex options.
+
+
+
+ Specifies how constructors are used when initializing objects during deserialization by the .
+
+
+
+
+ First attempt to use the public default constructor, then fall back to single paramatized constructor, then the non-public default constructor.
+
+
+
+
+ Json.NET will use a non-public default constructor before falling back to a paramatized constructor.
+
+
+
+
+ Converts a to and from JSON and BSON.
+
+
+
+
+ Writes the JSON representation of the object.
+
+ The to write to.
+ The value.
+ The calling serializer.
+
+
+
+ Reads the JSON representation of the object.
+
+ The to read from.
+ Type of the object.
+ The existing value of object being read.
+ The calling serializer.
+ The object value.
+
+
+
+ Determines whether this instance can convert the specified object type.
+
+ Type of the object.
+
+ true if this instance can convert the specified object type; otherwise, false.
+
+
+
+
+ Create a custom object
+
+ The object type to convert.
+
+
+
+ Writes the JSON representation of the object.
+
+ The to write to.
+ The value.
+ The calling serializer.
+
+
+
+ Reads the JSON representation of the object.
+
+ The to read from.
+ Type of the object.
+ The existing value of object being read.
+ The calling serializer.
+ The object value.
+
+
+
+ Creates an object which will then be populated by the serializer.
+
+ Type of the object.
+ The created object.
+
+
+
+ Determines whether this instance can convert the specified object type.
+
+ Type of the object.
+
+ true if this instance can convert the specified object type; otherwise, false.
+
+
+
+
+ Gets a value indicating whether this can write JSON.
+
+
+ true if this can write JSON; otherwise, false.
+
+
+
+
+ Provides a base class for converting a to and from JSON.
+
+
+
+
+ Determines whether this instance can convert the specified object type.
+
+ Type of the object.
+
+ true if this instance can convert the specified object type; otherwise, false.
+
+
+
+
+ Converts a F# discriminated union type to and from JSON.
+
+
+
+
+ Writes the JSON representation of the object.
+
+ The to write to.
+ The value.
+ The calling serializer.
+
+
+
+ Reads the JSON representation of the object.
+
+ The to read from.
+ Type of the object.
+ The existing value of object being read.
+ The calling serializer.
+ The object value.
+
+
+
+ Determines whether this instance can convert the specified object type.
+
+ Type of the object.
+
+ true if this instance can convert the specified object type; otherwise, false.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Converts an ExpandoObject to and from JSON.
+
+
+
+
+ Writes the JSON representation of the object.
+
+ The to write to.
+ The value.
+ The calling serializer.
+
+
+
+ Reads the JSON representation of the object.
+
+ The to read from.
+ Type of the object.
+ The existing value of object being read.
+ The calling serializer.
+ The object value.
+
+
+
+ Determines whether this instance can convert the specified object type.
+
+ Type of the object.
+
+ true if this instance can convert the specified object type; otherwise, false.
+
+
+
+
+ Gets a value indicating whether this can write JSON.
+
+
+ true if this can write JSON; otherwise, false.
+
+
+
+
+
+
+
+
+
+
+
+ Converts a to and from the ISO 8601 date format (e.g. 2008-04-12T12:53Z).
+
+
+
+
+ Gets or sets the date time styles used when converting a date to and from JSON.
+
+ The date time styles used when converting a date to and from JSON.
+
+
+
+ Gets or sets the date time format used when converting a date to and from JSON.
+
+ The date time format used when converting a date to and from JSON.
+
+
+
+ Gets or sets the culture used when converting a date to and from JSON.
+
+ The culture used when converting a date to and from JSON.
+
+
+
+ Writes the JSON representation of the object.
+
+ The to write to.
+ The value.
+ The calling serializer.
+
+
+
+ Reads the JSON representation of the object.
+
+ The to read from.
+ Type of the object.
+ The existing value of object being read.
+ The calling serializer.
+ The object value.
+
+
+
+ Converts a to and from a JavaScript date constructor (e.g. new Date(52231943)).
+
+
+
+
+ Writes the JSON representation of the object.
+
+ The to write to.
+ The value.
+ The calling serializer.
+
+
+
+ Reads the JSON representation of the object.
+
+ The to read from.
+ Type of the object.
+ The existing property value of the JSON that is being converted.
+ The calling serializer.
+ The object value.
+
+
+
+ Converts a to and from JSON.
+
+
+
+
+ Writes the JSON representation of the object.
+
+ The to write to.
+ The value.
+ The calling serializer.
+
+
+
+ Reads the JSON representation of the object.
+
+ The to read from.
+ Type of the object.
+ The existing value of object being read.
+ The calling serializer.
+ The object value.
+
+
+
+ Determines whether this instance can convert the specified object type.
+
+ Type of the object.
+
+ true if this instance can convert the specified object type; otherwise, false.
+
+
+
+
+ Converts a to and from JSON and BSON.
+
+
+
+
+ Writes the JSON representation of the object.
+
+ The to write to.
+ The value.
+ The calling serializer.
+
+
+
+ Reads the JSON representation of the object.
+
+ The to read from.
+ Type of the object.
+ The existing value of object being read.
+ The calling serializer.
+ The object value.
+
+
+
+ Determines whether this instance can convert the specified object type.
+
+ Type of the object.
+
+ true if this instance can convert the specified object type; otherwise, false.
+
+
+
+
+ Converts an to and from its name string value.
+
+
+
+
+ Gets or sets a value indicating whether the written enum text should be camel case.
+
+ true if the written enum text will be camel case; otherwise, false.
+
+
+
+ Gets or sets a value indicating whether integer values are allowed.
+
+ true if integers are allowed; otherwise, false.
+
+
+
+ Initializes a new instance of the class.
+
+
+
+
+ Initializes a new instance of the class.
+
+ true if the written enum text will be camel case; otherwise, false.
+
+
+
+ Writes the JSON representation of the object.
+
+ The to write to.
+ The value.
+ The calling serializer.
+
+
+
+ Reads the JSON representation of the object.
+
+ The to read from.
+ Type of the object.
+ The existing value of object being read.
+ The calling serializer.
+ The object value.
+
+
+
+ Determines whether this instance can convert the specified object type.
+
+ Type of the object.
+
+ true if this instance can convert the specified object type; otherwise, false.
+
+
+
+
+ Json Converter for Vector2, Vector3 and Vector4. Only serializes x, y, (z) and (w) properties.
+
+
+
+
+ Default Constructor - All Vector types enabled by default
+
+
+
+
+ Selectively enable Vector types
+
+ Use for Vector2 objects
+ Use for Vector3 objects
+ Use for Vector4 objects
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Converts a to and from a string (e.g. "1.2.3.4").
+
+
+
+
+ Writes the JSON representation of the object.
+
+ The to write to.
+ The value.
+ The calling serializer.
+
+
+
+ Reads the JSON representation of the object.
+
+ The to read from.
+ Type of the object.
+ The existing property value of the JSON that is being converted.
+ The calling serializer.
+ The object value.
+
+
+
+ Determines whether this instance can convert the specified object type.
+
+ Type of the object.
+
+ true if this instance can convert the specified object type; otherwise, false.
+
+
+
+
+ Converts XML to and from JSON.
+
+
+
+
+ Gets or sets the name of the root element to insert when deserializing to XML if the JSON structure has produces multiple root elements.
+
+ The name of the deserialize root element.
+
+
+
+ Gets or sets a flag to indicate whether to write the Json.NET array attribute.
+ This attribute helps preserve arrays when converting the written XML back to JSON.
+
+ true if the array attibute is written to the XML; otherwise, false.
+
+
+
+ Gets or sets a value indicating whether to write the root JSON object.
+
+ true if the JSON root object is omitted; otherwise, false.
+
+
+
+ Writes the JSON representation of the object.
+
+ The to write to.
+ The calling serializer.
+ The value.
+
+
+
+ Reads the JSON representation of the object.
+
+ The to read from.
+ Type of the object.
+ The existing value of object being read.
+ The calling serializer.
+ The object value.
+
+
+
+ Checks if the attributeName is a namespace attribute.
+
+ Attribute name to test.
+ The attribute name prefix if it has one, otherwise an empty string.
+ True if attribute name is for a namespace attribute, otherwise false.
+
+
+
+ Determines whether this instance can convert the specified value type.
+
+ Type of the value.
+
+ true if this instance can convert the specified value type; otherwise, false.
+
+
+
+
+ Specifies how dates are formatted when writing JSON text.
+
+
+
+
+ Dates are written in the ISO 8601 format, e.g. "2012-03-21T05:40Z".
+
+
+
+
+ Dates are written in the Microsoft JSON format, e.g. "\/Date(1198908717056)\/".
+
+
+
+
+ Specifies how date formatted strings, e.g. "\/Date(1198908717056)\/" and "2012-03-21T05:40Z", are parsed when reading JSON text.
+
+
+
+
+ Date formatted strings are not parsed to a date type and are read as strings.
+
+
+
+
+ Date formatted strings, e.g. "\/Date(1198908717056)\/" and "2012-03-21T05:40Z", are parsed to .
+
+
+
+
+ Date formatted strings, e.g. "\/Date(1198908717056)\/" and "2012-03-21T05:40Z", are parsed to .
+
+
+
+
+ Specifies how to treat the time value when converting between string and .
+
+
+
+
+ Treat as local time. If the object represents a Coordinated Universal Time (UTC), it is converted to the local time.
+
+
+
+
+ Treat as a UTC. If the object represents a local time, it is converted to a UTC.
+
+
+
+
+ Treat as a local time if a is being converted to a string.
+ If a string is being converted to , convert to a local time if a time zone is specified.
+
+
+
+
+ Time zone information should be preserved when converting.
+
+
+
+
+ Specifies default value handling options for the .
+
+
+ /*...*/ containing the specified text.
+
+ Text to place inside the comment.
+
+
+
+ Writes out the given white space.
+
+ The string of white space characters.
+
+
+
+ Specifies the type of JSON token.
+
+
+
+
+ This is returned by the if a method has not been called.
+
+
+
+
+ An object start token.
+
+
+
+
+ An array start token.
+
+
+
+
+ A constructor start token.
+
+
+
+
+ An object property name.
+
+
+
+
+ A comment.
+
+
+
+
+ Raw JSON.
+
+
+
+
+ An integer.
+
+
+
+
+ A float.
+
+
+
+
+ A string.
+
+
+
+
+ A boolean.
+
+
+
+
+ A null token.
+
+
+
+
+ An undefined token.
+
+
+
+
+ An object end token.
+
+
+
+
+ An array end token.
+
+
+
+
+ A constructor end token.
+
+
+
+
+ A Date.
+
+
+
+
+ Byte data.
+
+
+
+
+ Represents a writer that provides a fast, non-cached, forward-only way of generating JSON data.
+
+
+
+
+ Gets or sets a value indicating whether the underlying stream or
+ should be closed when the writer is closed.
+
+
+ true to close the underlying stream or when
+ the writer is closed; otherwise false. The default is true.
+
+
+
+
+ Gets the top.
+
+ The top.
+
+
+
+ Gets the state of the writer.
+
+
+
+
+ Gets the path of the writer.
+
+
+
+
+ Indicates how JSON text output is formatted.
+
+
+
+
+ Get or set how dates are written to JSON text.
+
+
+
+
+ Get or set how time zones are handling when writing JSON text.
+
+
+
+
+ Get or set how strings are escaped when writing JSON text.
+
+
+
+
+ Get or set how special floating point numbers, e.g. ,
+ and ,
+ are written to JSON text.
+
+
+
+
+ Get or set how and values are formatting when writing JSON text.
+
+
+
+
+ Gets or sets the culture used when writing JSON. Defaults to .
+
+
+
+
+ Creates an instance of the JsonWriter class.
+
+
+
+
+ Flushes whatever is in the buffer to the underlying streams and also flushes the underlying stream.
+
+
+
+
+ Closes this stream and the underlying stream.
+
+
+
+
+ Writes the beginning of a JSON object.
+
+
+
+
+ Writes the end of a JSON object.
+
+
+
+
+ Writes the beginning of a JSON array.
+
+
+
+
+ Writes the end of an array.
+
+
+
+
+ Writes the start of a constructor with the given name.
+
+ The name of the constructor.
+
+
+
+ Writes the end constructor.
+
+
+
+
+ Writes the property name of a name/value pair on a JSON object.
+
+ The name of the property.
+
+
+
+ Writes the property name of a name/value pair on a JSON object.
+
+ The name of the property.
+ A flag to indicate whether the text should be escaped when it is written as a JSON property name.
+
+
+
+ Writes the end of the current JSON object or array.
+
+
+
+
+ Writes the current token and its children.
+
+ The to read the token from.
+
+
+
+ Writes the current token.
+
+ The to read the token from.
+ A flag indicating whether the current token's children should be written.
+
+
+
+ Writes the token and its value.
+
+ The to write.
+
+ The value to write.
+ A value is only required for tokens that have an associated value, e.g. the property name for .
+ A null value can be passed to the method for token's that don't have a value, e.g. .
+
+
+
+ Writes the token.
+
+ The to write.
+
+
+
+ Writes the specified end token.
+
+ The end token to write.
+
+
+
+ Writes indent characters.
+
+
+
+
+ Writes the JSON value delimiter.
+
+
+
+
+ Writes an indent space.
+
+
+
+
+ Writes a null value.
+
+
+
+
+ Writes an undefined value.
+
+
+
+
+ Writes raw JSON without changing the writer's state.
+
+ The raw JSON to write.
+
+
+
+ Writes raw JSON where a value is expected and updates the writer's state.
+
+ The raw JSON to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a [] value.
+
+ The [] value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+ An error will raised if the value cannot be written as a single JSON token.
+
+ The value to write.
+
+
+
+ Writes out a comment /*...*/ containing the specified text.
+
+ Text to place inside the comment.
+
+
+
+ Writes out the given white space.
+
+ The string of white space characters.
+
+
+
+ Releases unmanaged and - optionally - managed resources
+
+ true to release both managed and unmanaged resources; false to release only unmanaged resources.
+
+
+
+ Sets the state of the JsonWriter,
+
+ The JsonToken being written.
+ The value being written.
+
+
+
+ The exception thrown when an error occurs while reading JSON text.
+
+
+
+
+ Gets the path to the JSON where the error occurred.
+
+ The path to the JSON where the error occurred.
+
+
+
+ Initializes a new instance of the class.
+
+
+
+
+ Initializes a new instance of the class
+ with a specified error message.
+
+ The error message that explains the reason for the exception.
+
+
+
+ Initializes a new instance of the class
+ with a specified error message and a reference to the inner exception that is the cause of this exception.
+
+ The error message that explains the reason for the exception.
+ The exception that is the cause of the current exception, or a null reference (Nothing in Visual Basic) if no inner exception is specified.
+
+
+
+ Specifies how JSON comments are handled when loading JSON.
+
+
+
+
+ Ignore comments.
+
+
+
+
+ Load comments as a with type .
+
+
+
+
+ Specifies how line information is handled when loading JSON.
+
+
+
+
+ Ignore line information.
+
+
+
+
+ Load line information.
+
+
+
+
+ Contains the LINQ to JSON extension methods.
+
+
+
+
+ Returns a collection of tokens that contains the ancestors of every token in the source collection.
+
+ The type of the objects in source, constrained to .
+ An of that contains the source collection.
+ An of that contains the ancestors of every token in the source collection.
+
+
+
+ Returns a collection of tokens that contains every token in the source collection, and the ancestors of every token in the source collection.
+
+ The type of the objects in source, constrained to .
+ An of that contains the source collection.
+ An of that contains every token in the source collection, the ancestors of every token in the source collection.
+
+
+
+ Returns a collection of tokens that contains the descendants of every token in the source collection.
+
+ The type of the objects in source, constrained to .
+ An of that contains the source collection.
+ An of that contains the descendants of every token in the source collection.
+
+
+
+ Returns a collection of tokens that contains every token in the source collection, and the descendants of every token in the source collection.
+
+ The type of the objects in source, constrained to .
+ An of that contains the source collection.
+ An of that contains every token in the source collection, and the descendants of every token in the source collection.
+
+
+
+ Returns a collection of child properties of every object in the source collection.
+
+ An of that contains the source collection.
+ An of that contains the properties of every object in the source collection.
+
+
+
+ Returns a collection of child values of every object in the source collection with the given key.
+
+ An of that contains the source collection.
+ The token key.
+ An of that contains the values of every token in the source collection with the given key.
+
+
+
+ Returns a collection of child values of every object in the source collection.
+
+ An of that contains the source collection.
+ An of that contains the values of every token in the source collection.
+
+
+
+ Returns a collection of converted child values of every object in the source collection with the given key.
+
+ The type to convert the values to.
+ An of that contains the source collection.
+ The token key.
+ An that contains the converted values of every token in the source collection with the given key.
+
+
+
+ Returns a collection of converted child values of every object in the source collection.
+
+ The type to convert the values to.
+ An of that contains the source collection.
+ An that contains the converted values of every token in the source collection.
+
+
+
+ Converts the value.
+
+ The type to convert the value to.
+ A cast as a of .
+ A converted value.
+
+
+
+ Converts the value.
+
+ The source collection type.
+ The type to convert the value to.
+ A cast as a of .
+ A converted value.
+
+
+
+ Returns a collection of child tokens of every array in the source collection.
+
+ The source collection type.
+ An of that contains the source collection.
+ An of that contains the values of every token in the source collection.
+
+
+
+ Returns a collection of converted child tokens of every array in the source collection.
+
+ An of that contains the source collection.
+ The type to convert the values to.
+ The source collection type.
+ An that contains the converted values of every token in the source collection.
+
+
+
+ Returns the input typed as .
+
+ An of that contains the source collection.
+ The input typed as .
+
+
+
+ Returns the input typed as .
+
+ The source collection type.
+ An of that contains the source collection.
+ The input typed as .
+
+
+
+ Represents a collection of objects.
+
+ The type of token
+
+
+
+ Gets the with the specified key.
+
+
+
+
+
+ Represents a JSON array.
+
+
+ /*...*/ containing the specified text.
+
+ Text to place inside the comment.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a [] value.
+
+ The [] value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Writes a value.
+
+ The value to write.
+
+
+
+ Represents a value in JSON (string, integer, date, etc).
+
+
+
+
+ Initializes a new instance of the class from another object.
+
+ A object to copy from.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Initializes a new instance of the class with the given value.
+
+ The value.
+
+
+
+ Gets a value indicating whether this token has child tokens.
+
+
+ true if this token has child values; otherwise, false.
+
+
+
+
+ Creates a comment with the given value.
+
+ The value.
+ A comment with the given value.
+
+
+
+ Creates a string with the given value.
+
+ The value.
+ A string with the given value.
+
+
+
+ Creates a null value.
+
+ A null value.
+
+
+
+ Creates a undefined value.
+
+ A undefined value.
+
+
+
+ Gets the node type for this .
+
+ The type.
+
+
+
+ Gets or sets the underlying token value.
+
+ The underlying token value.
+
+
+
+ Writes this token to a .
+
+ A into which this method will write.
+ A collection of which will be used when writing the token.
+
+
+
+ Indicates whether the current object is equal to another object of the same type.
+
+
+ true if the current object is equal to the parameter; otherwise, false.
+
+ An object to compare with this object.
+
+
+
+ Determines whether the specified is equal to the current .
+
+ The to compare with the current .
+
+ true if the specified is equal to the current ; otherwise, false.
+
+
+ The parameter is null.
+
+
+
+
+ Serves as a hash function for a particular type.
+
+
+ A hash code for the current .
+
+
+
+
+ Returns a that represents this instance.
+
+
+ A that represents this instance.
+
+
+
+
+ Returns a that represents this instance.
+
+ The format.
+
+ A that represents this instance.
+
+
+
+
+ Returns a that represents this instance.
+
+ The format provider.
+
+ A that represents this instance.
+
+
+
+
+ Returns a that represents this instance.
+
+ The format.
+ The format provider.
+
+ A that represents this instance.
+
+
+
+
+ Returns the responsible for binding operations performed on this object.
+
+ The expression tree representation of the runtime value.
+
+ The to bind this object.
+
+
+
+
+ Compares the current instance with another object of the same type and returns an integer that indicates whether the current instance precedes, follows, or occurs in the same position in the sort order as the other object.
+
+ An object to compare with this instance.
+
+ A 32-bit signed integer that indicates the relative order of the objects being compared. The return value has these meanings:
+ Value
+ Meaning
+ Less than zero
+ This instance is less than .
+ Zero
+ This instance is equal to .
+ Greater than zero
+ This instance is greater than .
+
+
+ is not the same type as this instance.
+
+
+
+
+ Specifies how JSON arrays are merged together.
+
+
+
+ Concatenate arrays.
+
+
+ Union arrays, skipping items that already exist.
+
+
+ Replace all array items.
+
+
+ Merge array items together, matched by index.
+
+
+
+ Specifies how null value properties are merged.
+
+
+
+
+ The content's null value properties will be ignored during merging.
+
+
+
+
+ The content's null value properties will be merged.
+
+
+
+
+ Specifies the member serialization options for the .
+
+
+
+
+ All public members are serialized by default. Members can be excluded using or .
+ This is the default member serialization mode.
+
+
+
+
+ Only members marked with or are serialized.
+ This member serialization mode can also be set by marking the class with .
+
+
+
+
+ All public and private fields are serialized. Members can be excluded using or .
+ This member serialization mode can also be set by marking the class with
+ and setting IgnoreSerializableAttribute on to false.
+
+
+
+
+ Specifies metadata property handling options for the .
+
+
+
+
+ Read metadata properties located at the start of a JSON object.
+
+
+
+
+ Read metadata properties located anywhere in a JSON object. Note that this setting will impact performance.
+
+
+
+
+ Do not try to read metadata properties.
+
+
+
+
+ Specifies missing member handling options for the .
+
+
+
+
+ Ignore a missing member and do not attempt to deserialize it.
+
+
+
+
+ Throw a when a missing member is encountered during deserialization.
+
+
+
+
+ Specifies null value handling options for the .
+
+
+ Info will exclude Verbose messages and include Info,
+ Warning and Error messages.
+
+ The that will be used to filter the trace messages passed to the writer.
+
+
+
+ Writes the specified trace level, message and optional exception.
+
+ The at which to write this trace.
+ The trace message.
+ The trace exception. This parameter is optional.
+
+
+
+ Provides methods to get and set values.
+
+
+
+
+ Sets the value.
+
+ The target to set the value on.
+ The value to set on the target.
+
+
+
+ Gets the value.
+
+ The target to get the value from.
+ The value.
+
+
+
+ Contract details for a used by the .
+
+
+
+
+ Gets the of the collection items.
+
+ The of the collection items.
+
+
+
+ Gets a value indicating whether the collection type is a multidimensional array.
+
+ true if the collection type is a multidimensional array; otherwise, false.
+
+
+
+ Gets or sets the function used to create the object. When set this function will override .
+
+ The function used to create the object.
+
+
+
+ Gets a value indicating whether the creator has a parameter with the collection values.
+
+ true if the creator has a parameter with the collection values; otherwise, false.
+
+
+
+ Initializes a new instance of the class.
+
+ The underlying type for the contract.
+
+
+
+ Contract details for a used by the .
+
+
+
+
+ Gets or sets the default collection items .
+
+ The converter.
+
+
+
+ Gets or sets a value indicating whether the collection items preserve object references.
+
+ true if collection items preserve object references; otherwise, false.
+
+
+
+ Gets or sets the collection item reference loop handling.
+
+ The reference loop handling.
+
+
+
+ Gets or sets the collection item type name handling.
+
+ The type name handling.
+
+
+
+ Initializes a new instance of the class.
+
+ The underlying type for the contract.
+
+
+
+ Handles serialization callback events.
+
+ The object that raised the callback event.
+ The streaming context.
+
+
+
+ Handles serialization error callback events.
+
+ The object that raised the callback event.
+ The streaming context.
+ The error context.
+
+
+
+ Sets extension data for an object during deserialization.
+
+ The object to set extension data on.
+ The extension data key.
+ The extension data value.
+
+
+
+ Gets extension data for an object during serialization.
+
+ The object to set extension data on.
+
+
+
+ Contract details for a used by the .
+
+
+
+
+ Gets the underlying type for the contract.
+
+ The underlying type for the contract.
+
+
+
+ Gets or sets the type created during deserialization.
+
+ The type created during deserialization.
+
+
+
+ Gets or sets whether this type contract is serialized as a reference.
+
+ Whether this type contract is serialized as a reference.
+
+
+
+ Gets or sets the default for this contract.
+
+ The converter.
+
+
+
+ Gets or sets all methods called immediately after deserialization of the object.
+
+ The methods called immediately after deserialization of the object.
+
+
+
+ Gets or sets all methods called during deserialization of the object.
+
+ The methods called during deserialization of the object.
+
+
+
+ Gets or sets all methods called after serialization of the object graph.
+
+ The methods called after serialization of the object graph.
+
+
+
+ Gets or sets all methods called before serialization of the object.
+
+ The methods called before serialization of the object.
+
+
+
+ Gets or sets all method called when an error is thrown during the serialization of the object.
+
+ The methods called when an error is thrown during the serialization of the object.
+
+
+
+ Gets or sets the method called immediately after deserialization of the object.
+
+ The method called immediately after deserialization of the object.
+
+
+
+ Gets or sets the method called during deserialization of the object.
+
+ The method called during deserialization of the object.
+
+
+
+ Gets or sets the method called after serialization of the object graph.
+
+ The method called after serialization of the object graph.
+
+
+
+ Gets or sets the method called before serialization of the object.
+
+ The method called before serialization of the object.
+
+
+
+ Gets or sets the method called when an error is thrown during the serialization of the object.
+
+ The method called when an error is thrown during the serialization of the object.
+
+
+
+ Gets or sets the default creator method used to create the object.
+
+ The default creator method used to create the object.
+
+
+
+ Gets or sets a value indicating whether the default creator is non public.
+
+ true if the default object creator is non-public; otherwise, false.
+
+
+
+ Contract details for a used by the .
+
+
+
+
+ Gets or sets the property name resolver.
+
+ The property name resolver.
+
+
+
+ Gets or sets the dictionary key resolver.
+
+ The dictionary key resolver.
+
+
+
+ Gets the of the dictionary keys.
+
+ The of the dictionary keys.
+
+
+
+ Gets the of the dictionary values.
+
+ The of the dictionary values.
+
+
+
+ Gets or sets the function used to create the object. When set this function will override .
+
+ The function used to create the object.
+
+
+
+ Gets a value indicating whether the creator has a parameter with the dictionary values.
+
+ true if the creator has a parameter with the dictionary values; otherwise, false.
+
+
+
+ Initializes a new instance of the class.
+
+ The underlying type for the contract.
+
+
+
+ Contract details for a used by the .
+
+
+
+
+ Gets the object's properties.
+
+ The object's properties.
+
+
+
+ Gets or sets the property name resolver.
+
+ The property name resolver.
+
+
+
+ Initializes a new instance of the class.
+
+ The underlying type for the contract.
+
+
+
+ Contract details for a used by the .
+
+
+
+
+ Initializes a new instance of the class.
+
+ The underlying type for the contract.
+
+
+
+ Contract details for a used by the .
+
+
+
+
+ Gets or sets the object member serialization.
+
+ The member object serialization.
+
+
+
+ Gets or sets a value that indicates whether the object's properties are required.
+
+
+ A value indicating whether the object's properties are required.
+
+
+
+
+ Gets the object's properties.
+
+ The object's properties.
+
+
+
+ Gets the constructor parameters required for any non-default constructor
+
+
+
+
+ Gets a collection of instances that define the parameters used with .
+
+
+
+
+ Gets or sets the override constructor used to create the object.
+ This is set when a constructor is marked up using the
+ JsonConstructor attribute.
+
+ The override constructor.
+
+
+
+ Gets or sets the parametrized constructor used to create the object.
+
+ The parametrized constructor.
+
+
+
+ Gets or sets the function used to create the object. When set this function will override .
+ This function is called with a collection of arguments which are defined by the collection.
+
+ The function used to create the object.
+
+
+
+ Gets or sets the extension data setter.
+
+
+
+
+ Gets or sets the extension data getter.
+
+
+
+
+ Gets or sets the extension data value type.
+
+
+
+
+ Initializes a new instance of the class.
+
+ The underlying type for the contract.
+
+
+
+ Contract details for a used by the .
+
+
+
+
+ Initializes a new instance of the class.
+
+ The underlying type for the contract.
+
+
+
+ Maps a JSON property to a .NET member or constructor parameter.
+
+
+
+
+ Gets or sets the name of the property.
+
+ The name of the property.
+
+
+
+ Gets or sets the type that declared this property.
+
+ The type that declared this property.
+
+
+
+ Gets or sets the order of serialization of a member.
+
+ The numeric order of serialization.
+
+
+
+ Gets or sets the name of the underlying member or parameter.
+
+ The name of the underlying member or parameter.
+
+
+
+ Gets the that will get and set the during serialization.
+
+ The that will get and set the during serialization.
+
+
+
+ Gets or sets the for this property.
+
+ The for this property.
+
+
+
+ Gets or sets the type of the property.
+
+ The type of the property.
+
+
+
+ Gets or sets the for the property.
+ If set this converter takes presidence over the contract converter for the property type.
+
+ The converter.
+
+
+
+ Gets or sets the member converter.
+
+ The member converter.
+
+
+
+ Gets or sets a value indicating whether this is ignored.
+
+ true if ignored; otherwise, false.
+
+
+
+ Gets or sets a value indicating whether this is readable.
+
+ true if readable; otherwise, false.
+
+
+
+ Gets or sets a value indicating whether this is writable.
+
+ true if writable; otherwise, false.
+
+
+
+ Gets or sets a value indicating whether this has a member attribute.
+
+ true if has a member attribute; otherwise, false.
+
+
+
+ Gets the default value.
+
+ The default value.
+
+
+
+ Gets or sets a value indicating whether this is required.
+
+ A value indicating whether this is required.
+
+
+
+ Gets or sets a value indicating whether this property preserves object references.
+
+
+ true if this instance is reference; otherwise, false.
+
+
+
+
+ Gets or sets the property null value handling.
+
+ The null value handling.
+
+
+
+ Gets or sets the property default value handling.
+
+ The default value handling.
+
+
+
+ Gets or sets the property reference loop handling.
+
+ The reference loop handling.
+
+
+
+ Gets or sets the property object creation handling.
+
+ The object creation handling.
+
+
+
+ Gets or sets or sets the type name handling.
+
+ The type name handling.
+
+
+
+ Gets or sets a predicate used to determine whether the property should be serialize.
+
+ A predicate used to determine whether the property should be serialize.
+
+
+
+ Gets or sets a predicate used to determine whether the property should be deserialized.
+
+ A predicate used to determine whether the property should be deserialized.
+
+
+
+ Gets or sets a predicate used to determine whether the property should be serialized.
+
+ A predicate used to determine whether the property should be serialized.
+
+
+
+ Gets or sets an action used to set whether the property has been deserialized.
+
+ An action used to set whether the property has been deserialized.
+
+
+
+ Returns a that represents this instance.
+
+
+ A that represents this instance.
+
+
+
+
+ Gets or sets the converter used when serializing the property's collection items.
+
+ The collection's items converter.
+
+
+
+ Gets or sets whether this property's collection items are serialized as a reference.
+
+ Whether this property's collection items are serialized as a reference.
+
+
+
+ Gets or sets the the type name handling used when serializing the property's collection items.
+
+ The collection's items type name handling.
+
+
+
+ Gets or sets the the reference loop handling used when serializing the property's collection items.
+
+ The collection's items reference loop handling.
+
+
+
+ A collection of objects.
+
+
+
+
+ Initializes a new instance of the class.
+
+ The type.
+
+
+
+ When implemented in a derived class, extracts the key from the specified element.
+
+ The element from which to extract the key.
+ The key for the specified element.
+
+
+
+ Adds a object.
+
+ The property to add to the collection.
+
+
+
+ Gets the closest matching object.
+ First attempts to get an exact case match of propertyName and then
+ a case insensitive match.
+
+ Name of the property.
+ A matching property if found.
+
+
+
+ Gets a property by property name.
+
+ The name of the property to get.
+ Type property name string comparison.
+ A matching property if found.
+
+
+
+ Contract details for a used by the .
+
+
+
+
+ Initializes a new instance of the class.
+
+ The underlying type for the contract.
+
+
+
+ Lookup and create an instance of the JsonConverter type described by the argument.
+
+ The JsonConverter type to create.
+ Optional arguments to pass to an initializing constructor of the JsonConverter.
+ If null, the default constructor is used.
+
+
+
+ Create a factory function that can be used to create instances of a JsonConverter described by the
+ argument type. The returned function can then be used to either invoke the converter's default ctor, or any
+ parameterized constructors by way of an object array.
+
+
+
+
+ Represents a trace writer that writes to memory. When the trace message limit is
+ reached then old trace messages will be removed as new messages are added.
+
+
+
+
+ Gets the that will be used to filter the trace messages passed to the writer.
+ For example a filter level of Info will exclude Verbose messages and include Info,
+ Warning and Error messages.
+
+
+ The that will be used to filter the trace messages passed to the writer.
+
+
+
+
+ Initializes a new instance of the class.
+
+
+
+
+ Writes the specified trace level, message and optional exception.
+
+ The at which to write this trace.
+ The trace message.
+ The trace exception. This parameter is optional.
+
+
+
+ Returns an enumeration of the most recent trace messages.
+
+ An enumeration of the most recent trace messages.
+
+
+
+ Returns a of the most recent trace messages.
+
+
+ A of the most recent trace messages.
+
+
+
+
+ Represents a method that constructs an object.
+
+ The object type to create.
+
+
+
+ When applied to a method, specifies that the method is called when an error occurs serializing an object.
+
+
+
+
+ Provides methods to get attributes from a , , or .
+
+
+
+
+ Initializes a new instance of the class.
+
+ The instance to get attributes for. This parameter should be a , , or .
+
+
+
+ Returns a collection of all of the attributes, or an empty collection if there are no attributes.
+
+ When true, look up the hierarchy chain for the inherited custom attribute.
+ A collection of s, or an empty collection.
+
+
+
+ Returns a collection of attributes, identified by type, or an empty collection if there are no attributes.
+
+ The type of the attributes.
+ When true, look up the hierarchy chain for the inherited custom attribute.
+ A collection of s, or an empty collection.
+
+
+
+ Get and set values for a using reflection.
+
+
+
+
+ Initializes a new instance of the class.
+
+ The member info.
+
+
+
+ Sets the value.
+
+ The target to set the value on.
+ The value to set on the target.
+
+
+
+ Gets the value.
+
+ The target to get the value from.
+ The value.
+
+
+
+ Specifies how strings are escaped when writing JSON text.
+
+
+
+
+ Only control characters (e.g. newline) are escaped.
+
+
+
+
+ All non-ASCII and control characters (e.g. newline) are escaped.
+
+
+
+
+ HTML (<, >, &, ', ") and control characters (e.g. newline) are escaped.
+
+
+
+
+ Specifies what messages to output for the class.
+
+
+
+
+ Output no tracing and debugging messages.
+
+
+
+
+ Output error-handling messages.
+
+
+
+
+ Output warnings and error-handling messages.
+
+
+
+
+ Output informational messages, warnings, and error-handling messages.
+
+
+
+
+ Output all debugging and tracing messages.
+
+
+
+
+ Specifies type name handling options for the .
+
+
+ should be used with caution when your application deserializes JSON from an external source.
+ Incoming types should be validated with a custom
+ when deserializing with a value other than TypeNameHandling.None.
+
+
+
+
+ Do not include the .NET type name when serializing types.
+
+
+
+
+ Include the .NET type name when serializing into a JSON object structure.
+
+
+
+
+ Include the .NET type name when serializing into a JSON array structure.
+
+
+
+
+ Always include the .NET type name when serializing.
+
+
+
+
+ Include the .NET type name when the type of the object being serialized is not the same as its declared type.
+
+
+
+
+ Determines whether the collection is null or empty.
+
+ The collection.
+
+ true if the collection is null or empty; otherwise, false.
+
+
+
+
+ Adds the elements of the specified collection to the specified generic IList.
+
+ The list to add to.
+ The collection of elements to add.
+
+
+
+ Converts the value to the specified type. If the value is unable to be converted, the
+ value is checked whether it assignable to the specified type.
+
+ The value to convert.
+ The culture to use when converting.
+ The type to convert or cast the value to.
+
+ The converted type. If conversion was unsuccessful, the initial value
+ is returned if assignable to the target type.
+
+
+
+
+ Helper method for generating a MetaObject which calls a
+ specific method on Dynamic that returns a result
+
+
+
+
+ Helper method for generating a MetaObject which calls a
+ specific method on Dynamic, but uses one of the arguments for
+ the result.
+
+
+
+
+ Helper method for generating a MetaObject which calls a
+ specific method on Dynamic, but uses one of the arguments for
+ the result.
+
+
+
+
+ Returns a Restrictions object which includes our current restrictions merged
+ with a restriction limiting our type
+
+
+
+
+ Gets a dictionary of the names and values of an Enum type.
+
+
+
+
+
+ Gets a dictionary of the names and values of an Enum type.
+
+ The enum type to get names and values for.
+
+
+
+
+ Gets the type of the typed collection's items.
+
+ The type.
+ The type of the typed collection's items.
+
+
+
+ Gets the member's underlying type.
+
+ The member.
+ The underlying type of the member.
+
+
+
+ Determines whether the member is an indexed property.
+
+ The member.
+
+ true if the member is an indexed property; otherwise, false.
+
+
+
+
+ Determines whether the property is an indexed property.
+
+ The property.
+
+ true if the property is an indexed property; otherwise, false.
+
+
+
+
+ Gets the member's value on the object.
+
+ The member.
+ The target object.
+ The member's value on the object.
+
+
+
+ Sets the member's value on the target object.
+
+ The member.
+ The target.
+ The value.
+
+
+
+ Determines whether the specified MemberInfo can be read.
+
+ The MemberInfo to determine whether can be read.
+ /// if set to true then allow the member to be gotten non-publicly.
+
+ true if the specified MemberInfo can be read; otherwise, false.
+
+
+
+
+ Determines whether the specified MemberInfo can be set.
+
+ The MemberInfo to determine whether can be set.
+ if set to true then allow the member to be set non-publicly.
+ if set to true then allow the member to be set if read-only.
+
+ true if the specified MemberInfo can be set; otherwise, false.
+
+
+
+
+ Builds a string. Unlike StringBuilder this class lets you reuse it's internal buffer.
+
+
+
+
+ Determines whether the string is all white space. Empty string will return false.
+
+ The string to test whether it is all white space.
+
+ true if the string is all white space; otherwise, false.
+
+
+
+
+ Nulls an empty string.
+
+ The string.
+ Null if the string was null, otherwise the string unchanged.
+
+
+
+ Specifies the state of the .
+
+
+
+
+ An exception has been thrown, which has left the in an invalid state.
+ You may call the method to put the in the Closed state.
+ Any other method calls results in an being thrown.
+
+
+
+
+ The method has been called.
+
+
+
+
+ An object is being written.
+
+
+
+
+ A array is being written.
+
+
+
+
+ A constructor is being written.
+
+
+
+
+ A property is being written.
+
+
+
+
+ A write method has not been called.
+
+
+
+
+ Indicates the method that will be used during deserialization for locating and loading assemblies.
+
+
+
+
+ In simple mode, the assembly used during deserialization need not match exactly the assembly used during serialization. Specifically, the version numbers need not match as the LoadWithPartialName method is used to load the assembly.
+
+
+
+
+ In full mode, the assembly used during deserialization must match exactly the assembly used during serialization. The Load method of the Assembly class is used to load the assembly.
+
+
+
+
diff --git a/Assets/JsonDotNet/Assemblies/Windows/Newtonsoft.Json.XML.meta b/Assets/JsonDotNet/Assemblies/Windows/Newtonsoft.Json.XML.meta
new file mode 100644
index 0000000..c4619d0
--- /dev/null
+++ b/Assets/JsonDotNet/Assemblies/Windows/Newtonsoft.Json.XML.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: 36f7323c55864364d8bb88c736e4bca6
+timeCreated: 1466788355
+licenseType: Store
+TextScriptImporter:
+ userData:
+ assetBundleName:
+ assetBundleVariant:
diff --git a/Assets/JsonDotNet/Assemblies/Windows/Newtonsoft.Json.dll b/Assets/JsonDotNet/Assemblies/Windows/Newtonsoft.Json.dll
new file mode 100644
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+
+
+
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new file mode 100644
index 0000000..2286255
--- /dev/null
+++ b/Assets/TensorFlowSharp/TensorFlowSharp.xml
@@ -0,0 +1,35329 @@
+
+
+
+ TensorFlowSharp
+
+
+
+
+ This attribute can be applied to callback functions that will be invoked
+ from unmanaged code to managed code.
+
+
+
+ [TensorFlow.MonoPInvokeCallback (typeof (BufferReleaseFunc))]
+ internal static void MyFreeFunc (IntPtr data, IntPtr length){..}
+
+
+
+
+
+ Use this constructor to annotate the type of the callback function that
+ will be invoked from unmanaged code.
+
+ T.
+
+
+
+ Holds a block of data, suitable to pass, or retrieve from TensorFlow.
+
+
+
+ Use the TFBuffer to blobs of data into TensorFlow, or to retrieve blocks
+ of data out of TensorFlow.
+
+
+ There are two constructors to wrap existing data, one to wrap blocks that are
+ pointed to by an IntPtr and one that takes a byte array that we want to wrap.
+
+
+ The empty constructor can be used to create a new TFBuffer that can be populated
+ by the TensorFlow library and returned to user code.
+
+
+ Typically, the data consists of a serialized protocol buffer, but other data
+ may also be held in a buffer.
+
+
+
+
+
+ Initializes a new instance of the class.
+
+
+
+
+ Signature of the method that is invoked to release the data.
+
+
+ Methods of this signature are invoked with the data pointer and the
+ lenght pointer when then TFBuffer no longer needs to hold on to the
+ data. If you are using this on platforms with static compilation
+ like iOS, you need to annotate your callback with the MonoPInvokeCallbackAttribute,
+ like this:
+
+
+ [TensorFlow.MonoPInvokeCallback (typeof (BufferReleaseFunc))]
+ internal static void MyFreeFunc (IntPtr data, IntPtr length){..}
+
+
+
+
+
+ Initializes a new instance of the by wrapping the unmanaged resource pointed by the buffer.
+
+ Pointer to the data that will be wrapped.
+ The size of the buffer to wrap.
+ Optional, if not null, this method will be invoked to release the block.
+
+ This constructor wraps the buffer as a the data to be held by the ,
+ if the release parameter is null, then you must ensure that the data is not released before the TFBuffer
+ is no longer in use. If the value is not null, the provided method will be invoked to release
+ the data when the TFBuffer is disposed, or the contents of the buffer replaced.
+
+
+
+
+ Initializes a new instance of the by making a copy of the provided byte array.
+
+ Buffer of data that will be wrapped.
+
+ This constructor makes a copy of the data into an unmanaged buffer,
+ so the byte array is not pinned.
+
+
+
+
+ Initializes a new instance of the by making a copy of the provided byte array.
+
+ Buffer of data that will be wrapped.
+ Starting offset into the buffer to wrap.
+ Number of bytes from the buffer to keep.
+
+ This constructor makes a copy of the data into an unmanaged buffer,
+ so the byte array is not pinned.
+
+
+
+
+ Returns a byte array representing the data wrapped by this buffer.
+
+ The array.
+
+
+
+ Represents a computation graph. Graphs may be shared between sessions and are thread safe.
+
+
+
+ Graphs consist of operations (represented by TFOperation objects), these can be named, or
+ the runtime will automatically assign a name.
+
+
+ For debugging purposes, you might want to group operations together, for this, call the
+ WithScope method with your new scope, which will create a new namespace for your object names.
+
+
+ For example, if you call WithScope ("demo"), and add an operation named "add" inside the
+ scope, the full name of the operation will be "demo/add", if you create a new scope inside, say
+ "hot", and add a "sub" operation there the result will be "demo/hot/sub".
+
+
+
+
+
+ Raise a exception to abort the process when called.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Abort'.
+
+
+ Optional argument
+ A string which is the message associated with the exception.
+
+
+ Optional argument
+
+
+ Returns the description of the operation
+
+
+ If exit_without_error is true, the process will exit normally,
+ otherwise it will exit with a SIGABORT signal.
+
+ Returns nothing but an exception.
+
+
+
+
+ Computes the absolute value of a tensor.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Abs'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Given a tensor x, this operation returns a tensor containing the absolute
+ value of each element in x. For example, if x is an input element and y is
+ an output element, this operation computes \\(y = |x|\\).
+
+
+
+
+ Returns the element-wise sum of a list of tensors.
+
+
+ A list of Tensor objects, each with same shape and type.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'AccumulateNV2'.
+
+
+ Shape of elements of inputs.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ tf.accumulate_n_v2 performs the same operation as tf.add_n, but does not
+ wait for all of its inputs to be ready before beginning to sum. This can
+ save memory if inputs are ready at different times, since minimum temporary
+ storage is proportional to the output size rather than the inputs size.
+
+ Unlike the original accumulate_n, accumulate_n_v2 is differentiable.
+
+ Returns a Tensor of same shape and type as the elements of inputs.
+
+
+
+
+ Applies a gradient to a given accumulator.
+
+
+ The handle to a accumulator.
+
+
+ The local_step value at which the gradient was computed.
+
+
+ A tensor of the gradient to be accumulated.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'AccumulatorApplyGradient'.
+
+
+ Returns the description of the operation
+
+
+ Does not add if local_step is lesser than the accumulator's global_step.
+
+
+
+
+ Returns the number of gradients aggregated in the given accumulators.
+
+
+ The handle to an accumulator.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'AccumulatorNumAccumulated'.
+
+
+ The number of gradients aggregated in the given accumulator.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Updates the accumulator with a new value for global_step.
+
+
+ The handle to an accumulator.
+
+
+ The new global_step value to set.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'AccumulatorSetGlobalStep'.
+
+
+ Returns the description of the operation
+
+
+ Logs warning if the accumulator's value is already higher than
+ new_global_step.
+
+
+
+
+ Extracts the average gradient in the given ConditionalAccumulator.
+
+
+ The handle to an accumulator.
+
+
+ Number of gradients required before we return an aggregate.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'AccumulatorTakeGradient'.
+
+
+ The data type of accumulated gradients. Needs to correspond to the type
+ of the accumulator.
+
+
+ The average of the accumulated gradients.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The op blocks until sufficient (i.e., more than num_required)
+ gradients have been accumulated. If the accumulator has already
+ aggregated more than num_required gradients, it returns the average of
+ the accumulated gradients. Also automatically increments the recorded
+ global_step in the accumulator by 1, and resets the aggregate to 0.
+
+
+
+
+ Computes acos of x element-wise.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Acos'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes inverse hyperbolic cosine of x element-wise.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Acosh'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Returns x + y element-wise.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Add'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ *NOTE*: Add supports broadcasting. AddN does not. More about broadcasting
+ [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+
+
+
+
+ Add an N-minibatch SparseTensor to a SparseTensorsMap, return N handles.
+
+
+ 2-D. The indices of the minibatch SparseTensor.
+ sparse_indices[:, 0] must be ordered values in [0, N).
+
+
+ 1-D. The values of the minibatch SparseTensor.
+
+
+ 1-D. The shape of the minibatch SparseTensor.
+ The minibatch size N == sparse_shape[0].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'AddManySparseToTensorsMap'.
+
+
+ Optional argument
+ The container name for the SparseTensorsMap created by this op.
+
+
+ Optional argument
+ The shared name for the SparseTensorsMap created by this op.
+ If blank, the new Operation's unique name is used.
+
+
+ 1-D. The handles of the SparseTensor now stored in the
+ SparseTensorsMap. Shape: [N].
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ A SparseTensor of rank R is represented by three tensors: sparse_indices,
+ sparse_values, and sparse_shape, where
+
+
+ sparse_indices.shape[1] == sparse_shape.shape[0] == R
+
+
+ An N-minibatch of SparseTensor objects is represented as a SparseTensor
+ having a first sparse_indices column taking values between [0, N), where
+ the minibatch size N == sparse_shape[0].
+
+ The input SparseTensor must have rank R greater than 1, and the first
+ dimension is treated as the minibatch dimension. Elements of the SparseTensor
+ must be sorted in increasing order of this first dimension. The stored
+ SparseTensor objects pointed to by each row of the output sparse_handles
+ will have rank R-1.
+
+ The SparseTensor values can then be read out as part of a minibatch by passing
+ the given keys as vector elements to TakeManySparseFromTensorsMap. To ensure
+ the correct SparseTensorsMap is accessed, ensure that the same
+ container and shared_name are passed to that Op. If no shared_name
+ is provided here, instead use the *name* of the Operation created by calling
+ AddManySparseToTensorsMap as the shared_name passed to
+ TakeManySparseFromTensorsMap. Ensure the Operations are colocated.
+
+
+
+
+ Add all input tensors element wise.
+
+
+ Must all be the same size and shape.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'AddN'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Add a SparseTensor to a SparseTensorsMap return its handle.
+
+
+ 2-D. The indices of the SparseTensor.
+
+
+ 1-D. The values of the SparseTensor.
+
+
+ 1-D. The shape of the SparseTensor.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'AddSparseToTensorsMap'.
+
+
+ Optional argument
+ The container name for the SparseTensorsMap created by this op.
+
+
+ Optional argument
+ The shared name for the SparseTensorsMap created by this op.
+ If blank, the new Operation's unique name is used.
+
+
+ 0-D. The handle of the SparseTensor now stored in the
+ SparseTensorsMap.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ A SparseTensor is represented by three tensors: sparse_indices,
+ sparse_values, and sparse_shape.
+
+ This operator takes the given SparseTensor and adds it to a container
+ object (a SparseTensorsMap). A unique key within this container is generated
+ in the form of an int64, and this is the value that is returned.
+
+ The SparseTensor can then be read out as part of a minibatch by passing
+ the key as a vector element to TakeManySparseFromTensorsMap. To ensure
+ the correct SparseTensorsMap is accessed, ensure that the same
+ container and shared_name are passed to that Op. If no shared_name
+ is provided here, instead use the *name* of the Operation created by calling
+ AddSparseToTensorsMap as the shared_name passed to
+ TakeManySparseFromTensorsMap. Ensure the Operations are colocated.
+
+
+
+
+ Returns x + y element-wise.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'AddV2'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ *NOTE*: Add supports broadcasting. AddN does not. More about broadcasting
+ [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+
+
+
+
+ Deprecated. Disallowed in GraphDef version >= 2.
+
+
+
+
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'AdjustContrast'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Adjust the contrast of one or more images.
+
+
+ Images to adjust. At least 3-D.
+
+
+ A float multiplier for adjusting contrast.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'AdjustContrastv2'.
+
+
+ The contrast-adjusted image or images.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ images is a tensor of at least 3 dimensions. The last 3 dimensions are
+ interpreted as [height, width, channels]. The other dimensions only
+ represent a collection of images, such as [batch, height, width, channels].
+
+ Contrast is adjusted independently for each channel of each image.
+
+ For each channel, the Op first computes the mean of the image pixels in the
+ channel and then adjusts each component of each pixel to
+ (x - mean) * contrast_factor + mean.
+
+
+
+
+ Adjust the hue of one or more images.
+
+
+ Images to adjust. At least 3-D.
+
+
+ A float delta to add to the hue.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'AdjustHue'.
+
+
+ The hue-adjusted image or images.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ images is a tensor of at least 3 dimensions. The last dimension is
+ interpretted as channels, and must be three.
+
+ The input image is considered in the RGB colorspace. Conceptually, the RGB
+ colors are first mapped into HSV. A delta is then applied all the hue values,
+ and then remapped back to RGB colorspace.
+
+
+
+
+ Adjust the saturation of one or more images.
+
+
+ Images to adjust. At least 3-D.
+
+
+ A float scale to add to the saturation.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'AdjustSaturation'.
+
+
+ The hue-adjusted image or images.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ images is a tensor of at least 3 dimensions. The last dimension is
+ interpretted as channels, and must be three.
+
+ The input image is considered in the RGB colorspace. Conceptually, the RGB
+ colors are first mapped into HSV. A scale is then applied all the saturation
+ values, and then remapped back to RGB colorspace.
+
+
+
+
+ Computes the "logical and" of elements across dimensions of a tensor.
+
+
+ The tensor to reduce.
+
+
+ The dimensions to reduce. Must be in the range
+ [-rank(input), rank(input)).
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'All'.
+
+
+ Optional argument
+ If true, retain reduced dimensions with length 1.
+
+
+ The reduced tensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Reduces input along the dimensions given in axis. Unless
+ keep_dims is true, the rank of the tensor is reduced by 1 for each entry in
+ axis. If keep_dims is true, the reduced dimensions are
+ retained with length 1.
+
+
+
+
+ Generates labels for candidate sampling with a learned unigram distribution.
+
+
+ A batch_size * num_true matrix, in which each row contains the
+ IDs of the num_true target_classes in the corresponding original label.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'AllCandidateSampler'.
+
+
+ Optional argument
+ If either seed or seed2 are set to be non-zero, the random number
+ generator is seeded by the given seed. Otherwise, it is seeded by a
+ random seed.
+
+
+ Optional argument
+ An second seed to avoid seed collision.
+
+
+ Number of true labels per context.
+
+
+ Number of candidates to produce.
+
+
+ If unique is true, we sample with rejection, so that all sampled
+ candidates in a batch are unique. This requires some approximation to
+ estimate the post-rejection sampling probabilities.
+
+
+ Returns a tuple with multiple values, as follows:
+ sampled_candidates: A vector of length num_sampled, in which each element is
+ the ID of a sampled candidate.
+ true_expected_count: A batch_size * num_true matrix, representing
+ the number of times each candidate is expected to occur in a batch
+ of sampled candidates. If unique=true, then this is a probability.
+ sampled_expected_count: A vector of length num_sampled, for each sampled
+ candidate representing the number of times the candidate is expected
+ to occur in a batch of sampled candidates. If unique=true, then this is a
+ probability.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ See explanations of candidate sampling and the data formats at
+ go/candidate-sampling.
+
+ For each batch, this op picks a single set of sampled candidate labels.
+
+ The advantages of sampling candidates per-batch are simplicity and the
+ possibility of efficient dense matrix multiplication. The disadvantage is that
+ the sampled candidates must be chosen independently of the context and of the
+ true labels.
+
+
+
+
+ An Op to exchange data across TPU replicas. On each replica, the input is
+
+
+ The local input to the sum.
+
+
+ An int32 tensor with shape
+ [num_groups, num_replicas_per_group]. group_assignment[i] represents the
+ replica ids in the ith subgroup.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'AllToAll'.
+
+
+ The dimension number to concatenate.
+
+
+ The dimension number to split.
+
+
+ The number of splits, this number must equal to the sub-group
+ size(group_assignment.get_shape()[1])
+
+
+ The exchanged result.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ split into split_count blocks along split_dimension and send to the other
+ replicas given group_assignment. After receiving split_count - 1 blocks from
+ other replicas, we concatenate the blocks along concat_dimension as the
+ output.
+
+ For example, suppose there are 2 TPU replicas:
+ replica 0 receives input: [[A, B]]
+ replica 1 receives input: [[C, D]]
+
+ group_assignment=[[0, 1]]
+ concat_dimension=0
+ split_dimension=1
+ split_count=2
+
+ replica 0's output: [[A], [C]]
+ replica 1's output: [[B], [D]]
+
+
+
+
+ Returns the argument of a complex number.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Angle'.
+
+
+ Optional argument
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Given a tensor input of complex numbers, this operation returns a tensor of
+ type float that is the argument of each element in input. All elements in
+ input must be complex numbers of the form \\(a + bj\\), where *a*
+ is the real part and *b* is the imaginary part.
+
+ The argument returned by this operation is of the form \\(atan2(b, a)\\).
+
+ For example:
+
+
+ # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]
+ tf.angle(input) ==> [2.0132, 1.056]
+
+
+ @compatibility(numpy)
+ Equivalent to np.angle.
+ @end_compatibility
+
+
+
+
+ A container for an iterator resource.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'AnonymousIterator'.
+
+
+
+
+
+
+ A handle to the iterator that can be passed to a "MakeIterator" or
+ "IteratorGetNext" op. In contrast to Iterator, AnonymousIterator prevents
+ resource sharing by name, and does not keep a reference to the resource
+ container.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes the "logical or" of elements across dimensions of a tensor.
+
+
+ The tensor to reduce.
+
+
+ The dimensions to reduce. Must be in the range
+ [-rank(input), rank(input)).
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Any'.
+
+
+ Optional argument
+ If true, retain reduced dimensions with length 1.
+
+
+ The reduced tensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Reduces input along the dimensions given in axis. Unless
+ keep_dims is true, the rank of the tensor is reduced by 1 for each entry in
+ axis. If keep_dims is true, the reduced dimensions are
+ retained with length 1.
+
+
+
+
+ Update '*var' according to the adadelta scheme.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Scaling factor. Must be a scalar.
+
+
+ Decay factor. Must be a scalar.
+
+
+ Constant factor. Must be a scalar.
+
+
+ The gradient.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ApplyAdadelta'.
+
+
+ Optional argument
+ If True, updating of the var, accum and update_accum tensors will be protected by
+ a lock; otherwise the behavior is undefined, but may exhibit less contention.
+
+
+ Same as "var".
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ accum = rho() * accum + (1 - rho()) * grad.square();
+ update = (update_accum + epsilon).sqrt() * (accum + epsilon()).rsqrt() * grad;
+ update_accum = rho() * update_accum + (1 - rho()) * update.square();
+ var -= update;
+
+
+
+
+ Update '*var' according to the adagrad scheme.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Scaling factor. Must be a scalar.
+
+
+ The gradient.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ApplyAdagrad'.
+
+
+ Optional argument
+ If True, updating of the var and accum tensors will be protected
+ by a lock; otherwise the behavior is undefined, but may exhibit less
+ contention.
+
+
+ Optional argument
+
+
+ Same as "var".
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ accum += grad * grad
+ var -= lr * grad * (1 / sqrt(accum))
+
+
+
+
+ Update '*var' according to the proximal adagrad scheme.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ The gradient.
+
+
+ Scaling factor. Must be a scalar.
+
+
+ L1 regularization. Must be a scalar.
+
+
+ L2 regularization. Must be a scalar.
+
+
+ Training step number. Must be a scalar.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ApplyAdagradDA'.
+
+
+ Optional argument
+ If True, updating of the var and accum tensors will be protected by
+ a lock; otherwise the behavior is undefined, but may exhibit less contention.
+
+
+ Same as "var".
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Update '*var' according to the Adam algorithm.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Must be a scalar.
+
+
+ Must be a scalar.
+
+
+ Scaling factor. Must be a scalar.
+
+
+ Momentum factor. Must be a scalar.
+
+
+ Momentum factor. Must be a scalar.
+
+
+ Ridge term. Must be a scalar.
+
+
+ The gradient.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ApplyAdam'.
+
+
+ Optional argument
+ If True, updating of the var, m, and v tensors will be protected
+ by a lock; otherwise the behavior is undefined, but may exhibit less
+ contention.
+
+
+ Optional argument
+ If True, uses the nesterov update.
+
+
+ Same as "var".
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ $$lr_t := \text{learning\_rate} * \sqrt{1 - beta_2^t} / (1 - beta_1^t)$$
+ $$m_t := beta_1 * m_{t-1} + (1 - beta_1) * g$$
+ $$v_t := beta_2 * v_{t-1} + (1 - beta_2) * g * g$$
+ $$variable := variable - lr_t * m_t / (\sqrt{v_t} + \epsilon)$$
+
+
+
+
+ Update '*var' according to the AdaMax algorithm.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Must be a scalar.
+
+
+ Scaling factor. Must be a scalar.
+
+
+ Momentum factor. Must be a scalar.
+
+
+ Momentum factor. Must be a scalar.
+
+
+ Ridge term. Must be a scalar.
+
+
+ The gradient.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ApplyAdaMax'.
+
+
+ Optional argument
+ If True, updating of the var, m, and v tensors will be protected
+ by a lock; otherwise the behavior is undefined, but may exhibit less
+ contention.
+
+
+ Same as "var".
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ m_t <- beta1 * m_{t-1} + (1 - beta1) * g
+ v_t <- max(beta2 * v_{t-1}, abs(g))
+ variable <- variable - learning_rate / (1 - beta1^t) * m_t / (v_t + epsilon)
+
+
+
+
+ Update '*var' according to the AddSign update.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Scaling factor. Must be a scalar.
+
+
+ Must be a scalar.
+
+
+ Must be a scalar.
+
+
+ Must be a scalar.
+
+
+ The gradient.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ApplyAddSign'.
+
+
+ Optional argument
+ If True, updating of the var and m tensors is
+ protected by a lock; otherwise the behavior is undefined, but may exhibit less
+ contention.
+
+
+ Same as "var".
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ m_t <- beta1 * m_{t-1} + (1 - beta1) * g
+ update <- (alpha + sign_decay * sign(g) *sign(m)) * g
+ variable <- variable - lr_t * update
+
+
+
+
+ Update '*var' according to the centered RMSProp algorithm.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Scaling factor. Must be a scalar.
+
+
+ Decay rate. Must be a scalar.
+
+
+
+
+ Ridge term. Must be a scalar.
+
+
+ The gradient.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ApplyCenteredRMSProp'.
+
+
+ Optional argument
+ If True, updating of the var, mg, ms, and mom tensors is
+ protected by a lock; otherwise the behavior is undefined, but may exhibit less
+ contention.
+
+
+ Same as "var".
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The centered RMSProp algorithm uses an estimate of the centered second moment
+ (i.e., the variance) for normalization, as opposed to regular RMSProp, which
+ uses the (uncentered) second moment. This often helps with training, but is
+ slightly more expensive in terms of computation and memory.
+
+ Note that in dense implementation of this algorithm, mg, ms, and mom will
+ update even if the grad is zero, but in this sparse implementation, mg, ms,
+ and mom will not update in iterations during which the grad is zero.
+
+ mean_square = decay * mean_square + (1-decay) * gradient ** 2
+ mean_grad = decay * mean_grad + (1-decay) * gradient
+
+ Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2)
+
+ mg <- rho * mg_{t-1} + (1-rho) * grad
+ ms <- rho * ms_{t-1} + (1-rho) * grad * grad
+ mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms - mg * mg + epsilon)
+ var <- var - mom
+
+
+
+
+ Update '*var' according to the Ftrl-proximal scheme.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ The gradient.
+
+
+ Scaling factor. Must be a scalar.
+
+
+ L1 regulariation. Must be a scalar.
+
+
+ L2 regulariation. Must be a scalar.
+
+
+ Scaling factor. Must be a scalar.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ApplyFtrl'.
+
+
+ Optional argument
+ If True, updating of the var and accum tensors will be protected
+ by a lock; otherwise the behavior is undefined, but may exhibit less
+ contention.
+
+
+ Same as "var".
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ accum_new = accum + grad * grad
+ linear += grad + (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var
+ quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2
+ var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0
+ accum = accum_new
+
+
+
+
+ Update '*var' according to the Ftrl-proximal scheme.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ The gradient.
+
+
+ Scaling factor. Must be a scalar.
+
+
+ L1 regulariation. Must be a scalar.
+
+
+ L2 shrinkage regulariation. Must be a scalar.
+
+
+
+
+ Scaling factor. Must be a scalar.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ApplyFtrlV2'.
+
+
+ Optional argument
+ If True, updating of the var and accum tensors will be protected
+ by a lock; otherwise the behavior is undefined, but may exhibit less
+ contention.
+
+
+ Same as "var".
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ grad_with_shrinkage = grad + 2 * l2_shrinkage * var
+ accum_new = accum + grad_with_shrinkage * grad_with_shrinkage
+ linear += grad_with_shrinkage +
+ (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var
+ quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2
+ var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0
+ accum = accum_new
+
+
+
+
+ Update '*var' by subtracting 'alpha' * 'delta' from it.
+
+
+ Should be from a Variable().
+
+
+ Scaling factor. Must be a scalar.
+
+
+ The change.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ApplyGradientDescent'.
+
+
+ Optional argument
+ If True, the subtraction will be protected by a lock;
+ otherwise the behavior is undefined, but may exhibit less contention.
+
+
+ Same as "var".
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Update '*var' according to the momentum scheme. Set use_nesterov = True if you
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Scaling factor. Must be a scalar.
+
+
+ The gradient.
+
+
+ Momentum. Must be a scalar.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ApplyMomentum'.
+
+
+ Optional argument
+ If True, updating of the var and accum tensors will be protected
+ by a lock; otherwise the behavior is undefined, but may exhibit less
+ contention.
+
+
+ Optional argument
+ If True, the tensor passed to compute grad will be
+ var - lr * momentum * accum, so in the end, the var you get is actually
+ var - lr * momentum * accum.
+
+
+ Same as "var".
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ want to use Nesterov momentum.
+
+ accum = accum * momentum + grad
+ var -= lr * accum
+
+
+
+
+ Update '*var' according to the AddSign update.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Scaling factor. Must be a scalar.
+
+
+ Must be a scalar.
+
+
+ Must be a scalar.
+
+
+ Must be a scalar.
+
+
+ The gradient.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ApplyPowerSign'.
+
+
+ Optional argument
+ If True, updating of the var and m tensors is
+ protected by a lock; otherwise the behavior is undefined, but may exhibit less
+ contention.
+
+
+ Same as "var".
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ m_t <- beta1 * m_{t-1} + (1 - beta1) * g
+ update <- exp(logbase * sign_decay * sign(g) * sign(m_t)) * g
+ variable <- variable - lr_t * update
+
+
+
+
+ Update '*var' and '*accum' according to FOBOS with Adagrad learning rate.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Scaling factor. Must be a scalar.
+
+
+ L1 regularization. Must be a scalar.
+
+
+ L2 regularization. Must be a scalar.
+
+
+ The gradient.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ApplyProximalAdagrad'.
+
+
+ Optional argument
+ If True, updating of the var and accum tensors will be protected by
+ a lock; otherwise the behavior is undefined, but may exhibit less contention.
+
+
+ Same as "var".
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ accum += grad * grad
+ prox_v = var - lr * grad * (1 / sqrt(accum))
+ var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0}
+
+
+
+
+ Update '*var' as FOBOS algorithm with fixed learning rate.
+
+
+ Should be from a Variable().
+
+
+ Scaling factor. Must be a scalar.
+
+
+ L1 regularization. Must be a scalar.
+
+
+ L2 regularization. Must be a scalar.
+
+
+ The change.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ApplyProximalGradientDescent'.
+
+
+ Optional argument
+ If True, the subtraction will be protected by a lock;
+ otherwise the behavior is undefined, but may exhibit less contention.
+
+
+ Same as "var".
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ prox_v = var - alpha * delta
+ var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0}
+
+
+
+
+ Update '*var' according to the RMSProp algorithm.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Scaling factor. Must be a scalar.
+
+
+ Decay rate. Must be a scalar.
+
+
+
+
+ Ridge term. Must be a scalar.
+
+
+ The gradient.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ApplyRMSProp'.
+
+
+ Optional argument
+ If True, updating of the var, ms, and mom tensors is protected
+ by a lock; otherwise the behavior is undefined, but may exhibit less
+ contention.
+
+
+ Same as "var".
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Note that in dense implementation of this algorithm, ms and mom will
+ update even if the grad is zero, but in this sparse implementation, ms
+ and mom will not update in iterations during which the grad is zero.
+
+ mean_square = decay * mean_square + (1-decay) * gradient ** 2
+ Delta = learning_rate * gradient / sqrt(mean_square + epsilon)
+
+ ms <- rho * ms_{t-1} + (1-rho) * grad * grad
+ mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon)
+ var <- var - mom
+
+
+
+
+ Returns the truth value of abs(x-y) < tolerance element-wise.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ApproximateEqual'.
+
+
+ Optional argument
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Returns the index with the largest value across dimensions of a tensor.
+
+
+
+
+ int32 or int64, must be in the range [-rank(input), rank(input)).
+ Describes which dimension of the input Tensor to reduce across. For vectors,
+ use dimension = 0.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ArgMax'.
+
+
+ Optional argument
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Note that in case of ties the identity of the return value is not guaranteed.
+
+
+
+
+ Returns the index with the smallest value across dimensions of a tensor.
+
+
+
+
+ int32 or int64, must be in the range [-rank(input), rank(input)).
+ Describes which dimension of the input Tensor to reduce across. For vectors,
+ use dimension = 0.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ArgMin'.
+
+
+ Optional argument
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Note that in case of ties the identity of the return value is not guaranteed.
+
+
+
+
+ Computes asin of x element-wise.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Asin'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes inverse hyperbolic sine of x element-wise.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Asinh'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Asserts that the given condition is true.
+
+
+ The condition to evaluate.
+
+
+ The tensors to print out when condition is false.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Assert'.
+
+
+ Optional argument
+ Print this many entries of each tensor.
+
+
+ Returns the description of the operation
+
+
+ If condition evaluates to false, print the list of tensors in data.
+ summarize determines how many entries of the tensors to print.
+
+
+
+
+ Update 'ref' by assigning 'value' to it.
+
+
+ Should be from a Variable node. May be uninitialized.
+
+
+ The value to be assigned to the variable.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Assign'.
+
+
+ Optional argument
+ If true, the operation will validate that the shape
+ of 'value' matches the shape of the Tensor being assigned to. If false,
+ 'ref' will take on the shape of 'value'.
+
+
+ Optional argument
+ If True, the assignment will be protected by a lock;
+ otherwise the behavior is undefined, but may exhibit less contention.
+
+
+ = Same as "ref". Returned as a convenience for operations that want
+ to use the new value after the variable has been reset.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation outputs "ref" after the assignment is done.
+ This makes it easier to chain operations that need to use the reset value.
+
+
+
+
+ Update 'ref' by adding 'value' to it.
+
+
+ Should be from a Variable node.
+
+
+ The value to be added to the variable.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'AssignAdd'.
+
+
+ Optional argument
+ If True, the addition will be protected by a lock;
+ otherwise the behavior is undefined, but may exhibit less contention.
+
+
+ = Same as "ref". Returned as a convenience for operations that want
+ to use the new value after the variable has been updated.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation outputs "ref" after the update is done.
+ This makes it easier to chain operations that need to use the reset value.
+
+
+
+
+ Adds a value to the current value of a variable.
+
+
+ handle to the resource in which to store the variable.
+
+
+ the value by which the variable will be incremented.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'AssignAddVariableOp'.
+
+
+ Returns the description of the operation
+
+
+ Any ReadVariableOp with a control dependency on this op is guaranteed to
+ see the incremented value or a subsequent newer one.
+
+
+
+
+ Update 'ref' by subtracting 'value' from it.
+
+
+ Should be from a Variable node.
+
+
+ The value to be subtracted to the variable.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'AssignSub'.
+
+
+ Optional argument
+ If True, the subtraction will be protected by a lock;
+ otherwise the behavior is undefined, but may exhibit less contention.
+
+
+ = Same as "ref". Returned as a convenience for operations that want
+ to use the new value after the variable has been updated.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation outputs "ref" after the update is done.
+ This makes it easier to chain operations that need to use the reset value.
+
+
+
+
+ Subtracts a value from the current value of a variable.
+
+
+ handle to the resource in which to store the variable.
+
+
+ the value by which the variable will be incremented.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'AssignSubVariableOp'.
+
+
+ Returns the description of the operation
+
+
+ Any ReadVariableOp with a control dependency on this op is guaranteed to
+ see the decremented value or a subsequent newer one.
+
+
+
+
+ Assigns a new value to a variable.
+
+
+ handle to the resource in which to store the variable.
+
+
+ the value to set the new tensor to use.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'AssignVariableOp'.
+
+
+ Returns the description of the operation
+
+
+ Any ReadVariableOp with a control dependency on this op is guaranteed to return
+ this value or a subsequent newer value of the variable.
+
+
+
+
+ Converts each entry in the given tensor to strings. Supports many numeric
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'AsString'.
+
+
+ Optional argument
+ The post-decimal precision to use for floating point numbers.
+ Only used if precision > -1.
+
+
+ Optional argument
+ Use scientific notation for floating point numbers.
+
+
+ Optional argument
+ Use shortest representation (either scientific or standard) for
+ floating point numbers.
+
+
+ Optional argument
+ Pad pre-decimal numbers to this width.
+ Applies to both floating point and integer numbers.
+ Only used if width > -1.
+
+
+ Optional argument
+ The value to pad if width > -1. If empty, pads with spaces.
+ Another typical value is '0'. String cannot be longer than 1 character.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ types and boolean.
+
+
+
+
+ Computes atan of x element-wise.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Atan'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes arctangent of y/x element-wise, respecting signs of the arguments.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Atan2'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This is the angle \( \theta \in [-\pi, \pi] \) such that
+ \[ x = r \cos(\theta) \]
+ and
+ \[ y = r \sin(\theta) \]
+ where \(r = \sqrt(x^2 + y^2) \).
+
+
+
+
+ Computes inverse hyperbolic tangent of x element-wise.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Atanh'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Produces a visualization of audio data over time.
+
+
+ Float representation of audio data.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'AudioSpectrogram'.
+
+
+ Optional argument
+ Whether to return the squared magnitude or just the
+ magnitude. Using squared magnitude can avoid extra calculations.
+
+
+ How wide the input window is in samples. For the highest efficiency
+ this should be a power of two, but other values are accepted.
+
+
+ How widely apart the center of adjacent sample windows should be.
+
+
+ 3D representation of the audio frequencies as an image.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Spectrograms are a standard way of representing audio information as a series of
+ slices of frequency information, one slice for each window of time. By joining
+ these together into a sequence, they form a distinctive fingerprint of the sound
+ over time.
+
+ This op expects to receive audio data as an input, stored as floats in the range
+ -1 to 1, together with a window width in samples, and a stride specifying how
+ far to move the window between slices. From this it generates a three
+ dimensional output. The lowest dimension has an amplitude value for each
+ frequency during that time slice. The next dimension is time, with successive
+ frequency slices. The final dimension is for the channels in the input, so a
+ stereo audio input would have two here for example.
+
+ This means the layout when converted and saved as an image is rotated 90 degrees
+ clockwise from a typical spectrogram. Time is descending down the Y axis, and
+ the frequency decreases from left to right.
+
+ Each value in the result represents the square root of the sum of the real and
+ imaginary parts of an FFT on the current window of samples. In this way, the
+ lowest dimension represents the power of each frequency in the current window,
+ and adjacent windows are concatenated in the next dimension.
+
+ To get a more intuitive and visual look at what this operation does, you can run
+ tensorflow/examples/wav_to_spectrogram to read in an audio file and save out the
+ resulting spectrogram as a PNG image.
+
+
+
+
+ Outputs a Summary protocol buffer with audio.
+
+
+ Scalar. Used to build the tag attribute of the summary values.
+
+
+ 2-D of shape [batch_size, frames].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'AudioSummary'.
+
+
+ Optional argument
+ Max number of batch elements to generate audio for.
+
+
+ The sample rate of the signal in hertz.
+
+
+ Scalar. Serialized Summary protocol buffer.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The summary has up to max_outputs summary values containing audio. The
+ audio is built from tensor which must be 3-D with shape [batch_size,
+ frames, channels] or 2-D with shape [batch_size, frames]. The values are
+ assumed to be in the range of [-1.0, 1.0] with a sample rate of sample_rate.
+
+ The tag argument is a scalar Tensor of type string. It is used to
+ build the tag of the summary values:
+
+ * If max_outputs is 1, the summary value tag is '*tag*/audio'.
+ * If max_outputs is greater than 1, the summary value tags are
+ generated sequentially as '*tag*/audio/0', '*tag*/audio/1', etc.
+
+
+
+
+ Outputs a Summary protocol buffer with audio.
+
+
+ Scalar. Used to build the tag attribute of the summary values.
+
+
+ 2-D of shape [batch_size, frames].
+
+
+ The sample rate of the signal in hertz.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'AudioSummaryV2'.
+
+
+ Optional argument
+ Max number of batch elements to generate audio for.
+
+
+ Scalar. Serialized Summary protocol buffer.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The summary has up to max_outputs summary values containing audio. The
+ audio is built from tensor which must be 3-D with shape [batch_size,
+ frames, channels] or 2-D with shape [batch_size, frames]. The values are
+ assumed to be in the range of [-1.0, 1.0] with a sample rate of sample_rate.
+
+ The tag argument is a scalar Tensor of type string. It is used to
+ build the tag of the summary values:
+
+ * If max_outputs is 1, the summary value tag is '*tag*/audio'.
+ * If max_outputs is greater than 1, the summary value tags are
+ generated sequentially as '*tag*/audio/0', '*tag*/audio/1', etc.
+
+
+
+
+ Performs average pooling on the input.
+
+
+ 4-D with shape [batch, height, width, channels].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'AvgPool'.
+
+
+ Optional argument
+ Specify the data format of the input and output data. With the
+ default format "NHWC", the data is stored in the order of:
+ [batch, in_height, in_width, in_channels].
+ Alternatively, the format could be "NCHW", the data storage order of:
+ [batch, in_channels, in_height, in_width].
+
+
+ The size of the sliding window for each dimension of value.
+
+
+ The stride of the sliding window for each dimension of value.
+
+
+ The type of padding algorithm to use.
+
+
+ The average pooled output tensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Each entry in output is the mean of the corresponding size ksize
+ window in value.
+
+
+
+
+ Performs 3D average pooling on the input.
+
+
+ Shape [batch, depth, rows, cols, channels] tensor to pool over.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'AvgPool3D'.
+
+
+ Optional argument
+ The data format of the input and output data. With the
+ default format "NDHWC", the data is stored in the order of:
+ [batch, in_depth, in_height, in_width, in_channels].
+ Alternatively, the format could be "NCDHW", the data storage order is:
+ [batch, in_channels, in_depth, in_height, in_width].
+
+
+ 1-D tensor of length 5. The size of the window for each dimension of
+ the input tensor. Must have ksize[0] = ksize[4] = 1.
+
+
+ 1-D tensor of length 5. The stride of the sliding window for each
+ dimension of input. Must have strides[0] = strides[4] = 1.
+
+
+ The type of padding algorithm to use.
+
+
+ The average pooled output tensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes gradients of average pooling function.
+
+
+ The original input dimensions.
+
+
+ Output backprop of shape [batch, depth, rows, cols, channels].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'AvgPool3DGrad'.
+
+
+ Optional argument
+ The data format of the input and output data. With the
+ default format "NDHWC", the data is stored in the order of:
+ [batch, in_depth, in_height, in_width, in_channels].
+ Alternatively, the format could be "NCDHW", the data storage order is:
+ [batch, in_channels, in_depth, in_height, in_width].
+
+
+ 1-D tensor of length 5. The size of the window for each dimension of
+ the input tensor. Must have ksize[0] = ksize[4] = 1.
+
+
+ 1-D tensor of length 5. The stride of the sliding window for each
+ dimension of input. Must have strides[0] = strides[4] = 1.
+
+
+ The type of padding algorithm to use.
+
+
+ The backprop for input.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes gradients of the average pooling function.
+
+
+ 1-D. Shape of the original input to avg_pool.
+
+
+ 4-D with shape [batch, height, width, channels]. Gradients w.r.t.
+ the output of avg_pool.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'AvgPoolGrad'.
+
+
+ Optional argument
+ Specify the data format of the input and output data. With the
+ default format "NHWC", the data is stored in the order of:
+ [batch, in_height, in_width, in_channels].
+ Alternatively, the format could be "NCHW", the data storage order of:
+ [batch, in_channels, in_height, in_width].
+
+
+ The size of the sliding window for each dimension of the input.
+
+
+ The stride of the sliding window for each dimension of the input.
+
+
+ The type of padding algorithm to use.
+
+
+ 4-D. Gradients w.r.t. the input of avg_pool.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Defines a barrier that persists across different graph executions.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Barrier'.
+
+
+ Optional argument
+ The shape of each component in a value. Each shape must be 1 in the
+ first dimension. The length of this attr must be the same as the length of
+ component_types.
+
+
+ Optional argument
+ The capacity of the barrier. The default capacity is MAX_INT32,
+ which is the largest capacity of the underlying queue.
+
+
+ Optional argument
+ If non-empty, this barrier is placed in the given container.
+ Otherwise, a default container is used.
+
+
+ Optional argument
+ If non-empty, this barrier will be shared under the given name
+ across multiple sessions.
+
+
+ The type of each component in a value.
+
+
+ The handle to the barrier.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ A barrier represents a key-value map, where each key is a string, and
+ each value is a tuple of tensors.
+
+ At runtime, the barrier contains 'complete' and 'incomplete'
+ elements. A complete element has defined tensors for all components of
+ its value tuple, and may be accessed using BarrierTakeMany. An
+ incomplete element has some undefined components in its value tuple,
+ and may be updated using BarrierInsertMany.
+
+
+
+
+ Closes the given barrier.
+
+
+ The handle to a barrier.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BarrierClose'.
+
+
+ Optional argument
+ If true, all pending enqueue requests that are
+ blocked on the barrier's queue will be canceled. InsertMany will fail, even
+ if no new key is introduced.
+
+
+ Returns the description of the operation
+
+
+ This operation signals that no more new elements will be inserted in the
+ given barrier. Subsequent InsertMany that try to introduce a new key will fail.
+ Subsequent InsertMany operations that just add missing components to already
+ existing elements will continue to succeed. Subsequent TakeMany operations will
+ continue to succeed if sufficient completed elements remain in the barrier.
+ Subsequent TakeMany operations that would block will fail immediately.
+
+
+
+
+ Computes the number of incomplete elements in the given barrier.
+
+
+ The handle to a barrier.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BarrierIncompleteSize'.
+
+
+ The number of incomplete elements (i.e. those with some of their value
+ components not set) in the barrier.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ For each key, assigns the respective value to the specified component.
+
+
+ The handle to a barrier.
+
+
+ A one-dimensional tensor of keys, with length n.
+
+
+ An any-dimensional tensor of values, which are associated with the
+ respective keys. The 0th dimension must have length n.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BarrierInsertMany'.
+
+
+ The component of the barrier elements that is being assigned.
+
+
+ Returns the description of the operation
+
+
+ If a key is not found in the barrier, this operation will create a new
+ incomplete element. If a key is found in the barrier, and the element
+ already has a value at component_index, this operation will fail with
+ INVALID_ARGUMENT, and leave the barrier in an undefined state.
+
+
+
+
+ Computes the number of complete elements in the given barrier.
+
+
+ The handle to a barrier.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BarrierReadySize'.
+
+
+ The number of complete elements (i.e. those with all of their value
+ components set) in the barrier.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Takes the given number of completed elements from a barrier.
+
+
+ The handle to a barrier.
+
+
+ A single-element tensor containing the number of elements to
+ take.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BarrierTakeMany'.
+
+
+ Optional argument
+ Allow to return less than num_elements items if barrier is
+ already closed.
+
+
+ Optional argument
+
+
+ Optional argument
+ If the queue is empty, this operation will block for up to
+ timeout_ms milliseconds.
+ Note: This option is not supported yet.
+
+
+ The type of each component in a value.
+
+
+ Returns a tuple with multiple values, as follows:
+ indices: A one-dimensional tensor of indices, with length num_elems.
+ These indices refer to the batch in which the values were placed into the
+ barrier (starting with MIN_LONG and increasing with each BarrierInsertMany).
+ keys: A one-dimensional tensor of keys, with length num_elements.
+ values: One any-dimensional tensor per component in a barrier element. All
+ values have length num_elements in the 0th dimension.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ This operation concatenates completed-element component tensors along
+ the 0th dimension to make a single component tensor.
+
+ Elements come out of the barrier when they are complete, and in the order
+ in which they were placed into the barrier. The indices output provides
+ information about the batch in which each element was originally inserted
+ into the barrier.
+
+
+
+
+ Batches all input tensors nondeterministically.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Batch'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+
+
+
+
+ Returns a tuple with multiple values, as follows:
+ batched_tensors:
+ batch_index:
+ id:
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ When many instances of this Op are being run concurrently with the same
+ container/shared_name in the same device, some will output zero-shaped Tensors
+ and others will output Tensors of size up to max_batch_size.
+
+ All Tensors in in_tensors are batched together (so, for example, labels and
+ features should be batched with a single instance of this operation.
+
+ Each invocation of batch emits an id scalar which will be used to identify
+ this particular invocation when doing unbatch or its gradient.
+
+ Each op which emits a non-empty batch will also emit a non-empty batch_index
+ Tensor, which, is a [K, 3] matrix where each row contains the invocation's id,
+ start, and length of elements of each set of Tensors present in batched_tensors.
+
+ Batched tensors are concatenated along the first dimension, and all tensors in
+ in_tensors must have the first dimension of the same size.
+
+ in_tensors: The tensors to be batched.
+ num_batch_threads: Number of scheduling threads for processing batches of work.
+ Determines the number of batches processed in parallel.
+ max_batch_size: Batch sizes will never be bigger than this.
+ batch_timeout_micros: Maximum number of microseconds to wait before outputting
+ an incomplete batch.
+ allowed_batch_sizes: Optional list of allowed batch sizes. If left empty, does
+ nothing. Otherwise, supplies a list of batch sizes, causing the op to pad
+ batches up to one of those sizes. The entries must increase monotonically, and
+ the final entry must equal max_batch_size.
+ grad_timeout_micros: The timeout to use for the gradient. See Unbatch.
+ batched_tensors: Either empty tensors or a batch of concatenated Tensors.
+ batch_index: If out_tensors is non-empty, has information to invert it.
+ container: Controls the scope of sharing of this batch.
+ id: always contains a scalar with a unique ID for this invocation of Batch.
+ shared_name: Concurrently running instances of batch in the same device with the
+ same container and shared_name will batch their elements together. If left
+ empty, the op name will be used as the shared name.
+ T: the types of tensors to be batched.
+
+
+
+
+ Creates a dataset that batches batch_size elements from input_dataset.
+
+
+
+
+ A scalar representing the number of elements to accumulate in a
+ batch.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BatchDataset'.
+
+
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Creates a dataset that batches batch_size elements from input_dataset.
+
+
+
+
+ A scalar representing the number of elements to accumulate in a batch.
+
+
+ A scalar representing whether the last batch should be dropped in case its size
+ is smaller than desired.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BatchDatasetV2'.
+
+
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Multiplies slices of two tensors in batches.
+
+
+ 2-D or higher with shape [..., r_x, c_x].
+
+
+ 2-D or higher with shape [..., r_y, c_y].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BatchMatMul'.
+
+
+ Optional argument
+ If True, adjoint the slices of x. Defaults to False.
+
+
+ Optional argument
+ If True, adjoint the slices of y. Defaults to False.
+
+
+ 3-D or higher with shape [..., r_o, c_o]
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Multiplies all slices of Tensor x and y (each slice can be
+ viewed as an element of a batch), and arranges the individual results
+ in a single output tensor of the same batch size. Each of the
+ individual slices can optionally be adjointed (to adjoint a matrix
+ means to transpose and conjugate it) before multiplication by setting
+ the adj_x or adj_y flag to True, which are by default False.
+
+ The input tensors x and y are 2-D or higher with shape [..., r_x, c_x]
+ and [..., r_y, c_y].
+
+ The output tensor is 2-D or higher with shape [..., r_o, c_o], where:
+
+ r_o = c_x if adj_x else r_x
+ c_o = r_y if adj_y else c_y
+
+ It is computed as:
+
+ output[..., :, :] = matrix(x[..., :, :]) * matrix(y[..., :, :])
+
+
+
+
+ Batch normalization.
+
+
+ A 4D input Tensor.
+
+
+ A 1D mean Tensor with size matching the last dimension of t.
+ This is the first output from tf.nn.moments,
+ or a saved moving average thereof.
+
+
+ A 1D variance Tensor with size matching the last dimension of t.
+ This is the second output from tf.nn.moments,
+ or a saved moving average thereof.
+
+
+ A 1D beta Tensor with size matching the last dimension of t.
+ An offset to be added to the normalized tensor.
+
+
+ A 1D gamma Tensor with size matching the last dimension of t.
+ If "scale_after_normalization" is true, this tensor will be multiplied
+ with the normalized tensor.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BatchNormWithGlobalNormalization'.
+
+
+ A small float number to avoid dividing by 0.
+
+
+ A bool indicating whether the resulted tensor
+ needs to be multiplied with gamma.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This op is deprecated. Prefer tf.nn.batch_normalization.
+
+
+
+
+ Gradients for batch normalization.
+
+
+ A 4D input Tensor.
+
+
+ A 1D mean Tensor with size matching the last dimension of t.
+ This is the first output from tf.nn.moments,
+ or a saved moving average thereof.
+
+
+ A 1D variance Tensor with size matching the last dimension of t.
+ This is the second output from tf.nn.moments,
+ or a saved moving average thereof.
+
+
+ A 1D gamma Tensor with size matching the last dimension of t.
+ If "scale_after_normalization" is true, this Tensor will be multiplied
+ with the normalized Tensor.
+
+
+ 4D backprop Tensor.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BatchNormWithGlobalNormalizationGrad'.
+
+
+ A small float number to avoid dividing by 0.
+
+
+ A bool indicating whether the resulted tensor
+ needs to be multiplied with gamma.
+
+
+ Returns a tuple with multiple values, as follows:
+ dx: 4D backprop tensor for input.
+ dm: 1D backprop tensor for mean.
+ dv: 1D backprop tensor for variance.
+ db: 1D backprop tensor for beta.
+ dg: 1D backprop tensor for gamma.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ This op is deprecated. See tf.nn.batch_normalization.
+
+
+
+
+ BatchToSpace for 4-D tensors of type T.
+
+
+ 4-D tensor with shape
+ [batch*block_size*block_size, height_pad/block_size, width_pad/block_size,
+ depth]. Note that the batch size of the input tensor must be divisible by
+ block_size * block_size.
+
+
+ 2-D tensor of non-negative integers with shape [2, 2]. It specifies
+ how many elements to crop from the intermediate result across the spatial
+ dimensions as follows:
+
+ crops = [[crop_top, crop_bottom], [crop_left, crop_right]]
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BatchToSpace'.
+
+
+
+
+ 4-D with shape [batch, height, width, depth], where:
+
+ height = height_pad - crop_top - crop_bottom
+ width = width_pad - crop_left - crop_right
+
+ The attr block_size must be greater than one. It indicates the block size.
+
+ Some examples:
+
+ (1) For the following input of shape [4, 1, 1, 1] and block_size of 2:
+
+
+ [[[[1]]], [[[2]]], [[[3]]], [[[4]]]]
+
+
+ The output tensor has shape [1, 2, 2, 1] and value:
+
+
+ x = [[[[1], [2]], [[3], [4]]]]
+
+
+ (2) For the following input of shape [4, 1, 1, 3] and block_size of 2:
+
+
+ [[[1, 2, 3]], [[4, 5, 6]], [[7, 8, 9]], [[10, 11, 12]]]
+
+
+ The output tensor has shape [1, 2, 2, 3] and value:
+
+
+ x = [[[[1, 2, 3], [4, 5, 6]],
+ [[7, 8, 9], [10, 11, 12]]]]
+
+
+ (3) For the following input of shape [4, 2, 2, 1] and block_size of 2:
+
+
+ x = [[[[1], [3]], [[9], [11]]],
+ [[[2], [4]], [[10], [12]]],
+ [[[5], [7]], [[13], [15]]],
+ [[[6], [8]], [[14], [16]]]]
+
+
+ The output tensor has shape [1, 4, 4, 1] and value:
+
+
+ x = [[[1], [2], [3], [4]],
+ [[5], [6], [7], [8]],
+ [[9], [10], [11], [12]],
+ [[13], [14], [15], [16]]]
+
+
+ (4) For the following input of shape [8, 1, 2, 1] and block_size of 2:
+
+
+ x = [[[[1], [3]]], [[[9], [11]]], [[[2], [4]]], [[[10], [12]]],
+ [[[5], [7]]], [[[13], [15]]], [[[6], [8]]], [[[14], [16]]]]
+
+
+ The output tensor has shape [2, 2, 4, 1] and value:
+
+
+ x = [[[[1], [3]], [[5], [7]]],
+ [[[2], [4]], [[10], [12]]],
+ [[[5], [7]], [[13], [15]]],
+ [[[6], [8]], [[14], [16]]]]
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This is a legacy version of the more general BatchToSpaceND.
+
+ Rearranges (permutes) data from batch into blocks of spatial data, followed by
+ cropping. This is the reverse transformation of SpaceToBatch. More specifically,
+ this op outputs a copy of the input tensor where values from the batch
+ dimension are moved in spatial blocks to the height and width dimensions,
+ followed by cropping along the height and width dimensions.
+
+
+
+
+ BatchToSpace for N-D tensors of type T.
+
+
+ N-D with shape input_shape = [batch] + spatial_shape + remaining_shape,
+ where spatial_shape has M dimensions.
+
+
+ 1-D with shape [M], all values must be >= 1.
+
+
+ 2-D with shape [M, 2], all values must be >= 0.
+ crops[i] = [crop_start, crop_end] specifies the amount to crop from input
+ dimension i + 1, which corresponds to spatial dimension i. It is
+ required that
+ crop_start[i] + crop_end[i] <= block_shape[i] * input_shape[i + 1].
+
+ This operation is equivalent to the following steps:
+
+ 1. Reshape input to reshaped of shape:
+ [block_shape[0], ..., block_shape[M-1],
+ batch / prod(block_shape),
+ input_shape[1], ..., input_shape[N-1]]
+
+ 2. Permute dimensions of reshaped to produce permuted of shape
+ [batch / prod(block_shape),
+
+ input_shape[1], block_shape[0],
+ ...,
+ input_shape[M], block_shape[M-1],
+
+ input_shape[M+1], ..., input_shape[N-1]]
+
+ 3. Reshape permuted to produce reshaped_permuted of shape
+ [batch / prod(block_shape),
+
+ input_shape[1] * block_shape[0],
+ ...,
+ input_shape[M] * block_shape[M-1],
+
+ input_shape[M+1],
+ ...,
+ input_shape[N-1]]
+
+ 4. Crop the start and end of dimensions [1, ..., M] of
+ reshaped_permuted according to crops to produce the output of shape:
+ [batch / prod(block_shape),
+
+ input_shape[1] * block_shape[0] - crops[0,0] - crops[0,1],
+ ...,
+ input_shape[M] * block_shape[M-1] - crops[M-1,0] - crops[M-1,1],
+
+ input_shape[M+1], ..., input_shape[N-1]]
+
+ Some examples:
+
+ (1) For the following input of shape [4, 1, 1, 1], block_shape = [2, 2], and
+ crops = [[0, 0], [0, 0]]:
+
+
+ [[[[1]]], [[[2]]], [[[3]]], [[[4]]]]
+
+
+ The output tensor has shape [1, 2, 2, 1] and value:
+
+
+ x = [[[[1], [2]], [[3], [4]]]]
+
+
+ (2) For the following input of shape [4, 1, 1, 3], block_shape = [2, 2], and
+ crops = [[0, 0], [0, 0]]:
+
+
+ [[[1, 2, 3]], [[4, 5, 6]], [[7, 8, 9]], [[10, 11, 12]]]
+
+
+ The output tensor has shape [1, 2, 2, 3] and value:
+
+
+ x = [[[[1, 2, 3], [4, 5, 6]],
+ [[7, 8, 9], [10, 11, 12]]]]
+
+
+ (3) For the following input of shape [4, 2, 2, 1], block_shape = [2, 2], and
+ crops = [[0, 0], [0, 0]]:
+
+
+ x = [[[[1], [3]], [[9], [11]]],
+ [[[2], [4]], [[10], [12]]],
+ [[[5], [7]], [[13], [15]]],
+ [[[6], [8]], [[14], [16]]]]
+
+
+ The output tensor has shape [1, 4, 4, 1] and value:
+
+
+ x = [[[1], [2], [3], [4]],
+ [[5], [6], [7], [8]],
+ [[9], [10], [11], [12]],
+ [[13], [14], [15], [16]]]
+
+
+ (4) For the following input of shape [8, 1, 3, 1], block_shape = [2, 2], and
+ crops = [[0, 0], [2, 0]]:
+
+
+ x = [[[[0], [1], [3]]], [[[0], [9], [11]]],
+ [[[0], [2], [4]]], [[[0], [10], [12]]],
+ [[[0], [5], [7]]], [[[0], [13], [15]]],
+ [[[0], [6], [8]]], [[[0], [14], [16]]]]
+
+
+ The output tensor has shape [2, 2, 4, 1] and value:
+
+
+ x = [[[[1], [2], [3], [4]],
+ [[5], [6], [7], [8]]],
+ [[[9], [10], [11], [12]],
+ [[13], [14], [15], [16]]]]
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BatchToSpaceND'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation reshapes the "batch" dimension 0 into M + 1 dimensions of shape
+ block_shape + [batch], interleaves these blocks back into the grid defined by
+ the spatial dimensions [1, ..., M], to obtain a result with the same rank as
+ the input. The spatial dimensions of this intermediate result are then
+ optionally cropped according to crops to produce the output. This is the
+ reverse of SpaceToBatch. See below for a precise description.
+
+
+
+
+ Computes the Bessel i0e function of x element-wise.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BesselI0e'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Exponentially scaled modified Bessel function of order 0 defined as
+ bessel_i0e(x) = exp(-abs(x)) bessel_i0(x).
+
+ This function is faster and numerically stabler than bessel_i0(x).
+
+
+
+
+ Computes the Bessel i1e function of x element-wise.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BesselI1e'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Exponentially scaled modified Bessel function of order 0 defined as
+ bessel_i1e(x) = exp(-abs(x)) bessel_i1(x).
+
+ This function is faster and numerically stabler than bessel_i1(x).
+
+
+
+
+ Compute the regularized incomplete beta integral \\(I_x(a, b)\\).
+
+
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Betainc'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The regularized incomplete beta integral is defined as:
+
+
+ \\(I_x(a, b) = \frac{B(x; a, b)}{B(a, b)}\\)
+
+ where
+
+
+ \\(B(x; a, b) = \int_0^x t^{a-1} (1 - t)^{b-1} dt\\)
+
+
+ is the incomplete beta function and \\(B(a, b)\\) is the *complete*
+ beta function.
+
+
+
+
+ Adds bias to value.
+
+
+ Any number of dimensions.
+
+
+ 1-D with size the last dimension of value.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BiasAdd'.
+
+
+ Optional argument
+ Specify the data format of the input and output data. With the
+ default format "NHWC", the bias tensor will be added to the last dimension
+ of the value tensor.
+ Alternatively, the format could be "NCHW", the data storage order of:
+ [batch, in_channels, in_height, in_width].
+ The tensor will be added to "in_channels", the third-to-the-last
+ dimension.
+
+
+ Broadcasted sum of value and bias.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This is a special case of tf.add where bias is restricted to be 1-D.
+ Broadcasting is supported, so value may have any number of dimensions.
+
+
+
+
+ The backward operation for "BiasAdd" on the "bias" tensor.
+
+
+ Any number of dimensions.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BiasAddGrad'.
+
+
+ Optional argument
+ Specify the data format of the input and output data. With the
+ default format "NHWC", the bias tensor will be added to the last dimension
+ of the value tensor.
+ Alternatively, the format could be "NCHW", the data storage order of:
+ [batch, in_channels, in_height, in_width].
+ The tensor will be added to "in_channels", the third-to-the-last
+ dimension.
+
+
+ 1-D with size the feature dimension of out_backprop.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ It accumulates all the values from out_backprop into the feature dimension.
+ For NHWC data format, the feature dimension is the last. For NCHW data format,
+ the feature dimension is the third-to-last.
+
+
+
+
+ Adds bias to value.
+
+
+ Any number of dimensions.
+
+
+ 1-D with size the last dimension of value.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BiasAddV1'.
+
+
+ Broadcasted sum of value and bias.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This is a deprecated version of BiasAdd and will be soon removed.
+
+ This is a special case of tf.add where bias is restricted to be 1-D.
+ Broadcasting is supported, so value may have any number of dimensions.
+
+
+
+
+ A Reader that outputs rows from a BigQuery table as tensorflow Examples.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BigQueryReader'.
+
+
+ Optional argument
+ If non-empty, this reader is placed in the given container.
+ Otherwise, a default container is used.
+
+
+ Optional argument
+ If non-empty, this reader is named in the given bucket
+ with this shared_name. Otherwise, the node name is used instead.
+
+
+ Optional argument
+ Do not use. For testing purposes only.
+
+
+ GCP project ID.
+
+
+ BigQuery Dataset ID.
+
+
+ Table to read.
+
+
+ List of columns to read. Leave empty to read all columns.
+
+
+ Table snapshot timestamp in millis since epoch. Relative
+ (negative or zero) snapshot times are not allowed. For more details, see
+ 'Table Decorators' in BigQuery docs.
+
+
+ The handle to reference the Reader.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Counts the number of occurrences of each value in an integer array.
+
+
+ int32 Tensor.
+
+
+ non-negative int32 scalar Tensor.
+
+
+ is an int32, int64, float32, or float64 Tensor with the same
+ shape as arr, or a length-0 Tensor, in which case it acts as all weights
+ equal to 1.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Bincount'.
+
+
+ 1D Tensor with length equal to size. The counts or summed weights for
+ each value in the range [0, size).
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Outputs a vector with length size and the same dtype as weights. If
+ weights are empty, then index i stores the number of times the value i is
+ counted in arr. If weights are non-empty, then index i stores the sum of
+ the value in weights at each index where the corresponding value in arr is
+ i.
+
+ Values in arr outside of the range [0, size) are ignored.
+
+
+
+
+ Bitcasts a tensor from one type to another without copying data.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Bitcast'.
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Given a tensor input, this operation returns a tensor that has the same buffer
+ data as input with datatype type.
+
+ If the input datatype T is larger than the output datatype type then the
+ shape changes from [...] to [..., sizeof(T)/sizeof(type)].
+
+ If T is smaller than type, the operator requires that the rightmost
+ dimension be equal to sizeof(type)/sizeof(T). The shape then goes from
+ [..., sizeof(type)/sizeof(T)] to [...].
+
+ *NOTE*: Bitcast is implemented as a low-level cast, so machines with different
+ endian orderings will give different results.
+
+
+
+
+ Elementwise computes the bitwise AND of x and y.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BitwiseAnd'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The result will have those bits set, that are set in both x and y. The
+ computation is performed on the underlying representations of x and y.
+
+
+
+
+ Elementwise computes the bitwise OR of x and y.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BitwiseOr'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The result will have those bits set, that are set in x, y or both. The
+ computation is performed on the underlying representations of x and y.
+
+
+
+
+ Elementwise computes the bitwise XOR of x and y.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BitwiseXor'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The result will have those bits set, that are different in x and y. The
+ computation is performed on the underlying representations of x and y.
+
+
+
+
+ Bucketize each feature based on bucket boundaries.
+
+
+ float; List of Rank 1 Tensor each containing float values for a single feature.
+
+
+ float; List of Rank 1 Tensors each containing the bucket boundaries for a single
+ feature.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BoostedTreesBucketize'.
+
+
+ int; List of Rank 1 Tensors each containing the bucketized values for a single feature.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ An op that returns a list of float tensors, where each tensor represents the
+ bucketized values for a single feature.
+
+
+
+
+ Calculates gains for each feature and returns the best possible split information for the feature.
+
+
+ A Rank 1 tensor (shape=[2]) to specify the range [first, last) of node ids to process within stats_summary_list. The nodes are iterated between the two nodes specified by the tensor, as like for node_id in range(node_id_range[0], node_id_range[1]) (Note that the last index node_id_range[1] is exclusive).
+
+
+ A list of Rank 3 tensor (#shape=[max_splits, bucket, 2]) for accumulated stats summary (gradient/hessian) per node per buckets for each feature. The first dimension of the tensor is the maximum number of splits, and thus not all elements of it will be used, but only the indexes specified by node_ids will be used.
+
+
+ l1 regularization factor on leaf weights, per instance based.
+
+
+ l2 regularization factor on leaf weights, per instance based.
+
+
+ adjustment to the gain, per leaf based.
+
+
+ mininum avg of hessians in a node before required for the node to be considered for splitting.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BoostedTreesCalculateBestGainsPerFeature'.
+
+
+ the number of nodes that can be split in the whole tree. Used as a dimension of output tensors.
+
+
+ Returns a tuple with multiple values, as follows:
+ node_ids_list: An output list of Rank 1 tensors indicating possible split node ids for each feature. The length of the list is num_features, but each tensor has different size as each feature provides different possible nodes. See above for details like shapes and sizes.
+ gains_list: An output list of Rank 1 tensors indicating the best gains for each feature to split for certain nodes. See above for details like shapes and sizes.
+ thresholds_list: An output list of Rank 1 tensors indicating the bucket id to compare with (as a threshold) for split in each node. See above for details like shapes and sizes.
+ left_node_contribs_list: A list of Rank 2 tensors indicating the contribution of the left nodes when branching from parent nodes (given by the tensor element in the output node_ids_list) to the left direction by the given threshold for each feature. This value will be used to make the left node value by adding to the parent node value. Second dimension size is 1 for 1-dimensional logits, but would be larger for multi-class problems. See above for details like shapes and sizes.
+ right_node_contribs_list: A list of Rank 2 tensors, with the same shape/conditions as left_node_contribs_list, but just that the value is for the right node.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ The split information is the best threshold (bucket id), gains and left/right node contributions per node for each feature.
+
+ It is possible that not all nodes can be split on each feature. Hence, the list of possible nodes can differ between the features. Therefore, we return node_ids_list for each feature, containing the list of nodes that this feature can be used to split.
+
+ In this manner, the output is the best split per features and per node, so that it needs to be combined later to produce the best split for each node (among all possible features).
+
+ The length of output lists are all of the same length, num_features.
+ The output shapes are compatible in a way that the first dimension of all tensors of all lists are the same and equal to the number of possible split nodes for each feature.
+
+
+
+
+ Calculates the prior from the training data (the bias) and fills in the first node with the logits' prior. Returns a boolean indicating whether to continue centering.
+
+
+ Handle to the tree ensemble.
+
+
+ A tensor with shape=[logits_dimension] with mean of gradients for a first node.
+
+
+ A tensor with shape=[logits_dimension] mean of hessians for a first node.
+
+
+ l1 regularization factor on leaf weights, per instance based.
+
+
+ l2 regularization factor on leaf weights, per instance based.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BoostedTreesCenterBias'.
+
+
+ Bool, whether to continue bias centering.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Creates a tree ensemble model and returns a handle to it.
+
+
+ Handle to the tree ensemble resource to be created.
+
+
+ Token to use as the initial value of the resource stamp.
+
+
+ Serialized proto of the tree ensemble.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BoostedTreesCreateEnsemble'.
+
+
+ Returns the description of the operation
+
+
+
+
+ Create the Resource for Quantile Streams.
+
+
+ resource; Handle to quantile stream resource.
+
+
+ float; The required approximation error of the stream resource.
+
+
+ int; The number of streams managed by the resource that shares the same epsilon.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BoostedTreesCreateQuantileStreamResource'.
+
+
+ Optional argument
+ int; The maximum number of data points that can be fed to the stream.
+
+
+ Returns the description of the operation
+
+
+
+
+ Deserializes a serialized tree ensemble config and replaces current tree
+
+
+ Handle to the tree ensemble.
+
+
+ Token to use as the new value of the resource stamp.
+
+
+ Serialized proto of the ensemble.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BoostedTreesDeserializeEnsemble'.
+
+
+ Returns the description of the operation
+
+
+ ensemble.
+
+
+
+
+ Creates a handle to a BoostedTreesEnsembleResource
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BoostedTreesEnsembleResourceHandleOp'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Debugging/model interpretability outputs for each example.
+
+
+
+
+ A list of rank 1 Tensors containing bucket id for each
+ feature.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BoostedTreesExampleDebugOutputs'.
+
+
+ scalar, dimension of the logits, to be used for constructing the protos in
+ examples_debug_outputs_serialized.
+
+
+ Output rank 1 Tensor containing a proto serialized as a string for each example.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ It traverses all the trees and computes debug metrics for individual examples,
+ such as getting split feature ids and logits after each split along the decision
+ path used to compute directional feature contributions.
+
+
+
+
+ Retrieves the tree ensemble resource stamp token, number of trees and growing statistics.
+
+
+ Handle to the tree ensemble.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BoostedTreesGetEnsembleStates'.
+
+
+ Returns a tuple with multiple values, as follows:
+ stamp_token: Stamp token of the tree ensemble resource.
+ num_trees: The number of trees in the tree ensemble resource.
+ num_finalized_trees: The number of trees that were finished successfully.
+ num_attempted_layers: The number of layers we attempted to build (but not necessarily succeeded).
+ last_layer_nodes_range: Rank size 2 tensor that contains start and end ids of the nodes in the latest
+ layer.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+
+ Makes the summary of quantiles for the batch.
+
+
+ float; List of Rank 1 Tensors each containing values for a single feature.
+
+
+ float; Rank 1 Tensor with weights per instance.
+
+
+ float; The required maximum approximation error.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BoostedTreesMakeQuantileSummaries'.
+
+
+ float; List of Rank 2 Tensors each containing the quantile summary
+ (value, weight, min_rank, max_rank) of a single feature.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ An op that takes a list of tensors (one tensor per feature) and outputs the
+ quantile summaries for each tensor.
+
+
+
+
+ Makes the summary of accumulated stats for the batch.
+
+
+ int32 Rank 1 Tensor containing node ids, which each example falls into for the requested layer.
+
+
+ float32; Rank 2 Tensor (shape=[#examples, 1]) for gradients.
+
+
+ float32; Rank 2 Tensor (shape=[#examples, 1]) for hessians.
+
+
+ int32 list of Rank 1 Tensors, each containing the bucketized feature (for each feature column).
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BoostedTreesMakeStatsSummary'.
+
+
+ int; the maximum number of splits possible in the whole tree.
+
+
+ int; equals to the maximum possible value of bucketized feature.
+
+
+ output Rank 4 Tensor (shape=[#features, #splits, #buckets, 2]) containing accumulated stats put into the corresponding node and bucket. The first index of 4th dimension refers to gradients, and the second to hessians.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The summary stats contains gradients and hessians accumulated into the corresponding node and bucket for each example.
+
+
+
+
+ Runs multiple additive regression ensemble predictors on input instances and
+
+
+
+
+ A list of rank 1 Tensors containing bucket id for each
+ feature.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BoostedTreesPredict'.
+
+
+ scalar, dimension of the logits, to be used for partial logits
+ shape.
+
+
+ Output rank 2 Tensor containing logits for each example.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ computes the logits. It is designed to be used during prediction.
+ It traverses all the trees and calculates the final score for each instance.
+
+
+
+
+ Add the quantile summaries to each quantile stream resource.
+
+
+ resource handle referring to a QuantileStreamResource.
+
+
+ string; List of Rank 2 Tensor each containing the summaries for a single feature.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BoostedTreesQuantileStreamResourceAddSummaries'.
+
+
+ Returns the description of the operation
+
+
+ An op that adds a list of quantile summaries to a quantile stream resource. Each
+ summary Tensor is rank 2, containing summaries (value, weight, min_rank, max_rank)
+ for a single feature.
+
+
+
+
+ Flush the summaries for a quantile stream resource.
+
+
+ resource handle referring to a QuantileStreamResource.
+
+
+ int; approximate number of buckets unless using generate_quantiles.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BoostedTreesQuantileStreamResourceFlush'.
+
+
+ Optional argument
+ bool; If True, the output will be the num_quantiles for each stream where the ith
+ entry is the ith quantile of the input with an approximation error of epsilon.
+ Duplicate values may be present.
+ If False, the output will be the points in the histogram that we got which roughly
+ translates to 1/epsilon boundaries and without any duplicates.
+ Default to False.
+
+
+ Returns the description of the operation
+
+
+ An op that flushes the summaries for a quantile stream resource.
+
+
+
+
+ Generate the bucket boundaries for each feature based on accumulated summaries.
+
+
+ resource handle referring to a QuantileStreamResource.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BoostedTreesQuantileStreamResourceGetBucketBoundaries'.
+
+
+ inferred int; number of features to get bucket boundaries for.
+
+
+ float; List of Rank 1 Tensors each containing the bucket boundaries for a feature.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ An op that returns a list of float tensors for a quantile stream resource. Each
+ tensor is Rank 1 containing bucket boundaries for a single feature.
+
+
+
+
+ Creates a handle to a BoostedTreesQuantileStreamResource.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BoostedTreesQuantileStreamResourceHandleOp'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Serializes the tree ensemble to a proto.
+
+
+ Handle to the tree ensemble.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BoostedTreesSerializeEnsemble'.
+
+
+ Returns a tuple with multiple values, as follows:
+ stamp_token: Stamp token of the tree ensemble resource.
+ tree_ensemble_serialized: Serialized proto of the ensemble.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+
+ Runs multiple additive regression ensemble predictors on input instances and
+
+
+
+
+ Rank 1 Tensor containing cached tree ids which is the starting
+ tree of prediction.
+
+
+ Rank 1 Tensor containing cached node id which is the starting
+ node of prediction.
+
+
+ A list of rank 1 Tensors containing bucket id for each
+ feature.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BoostedTreesTrainingPredict'.
+
+
+ scalar, dimension of the logits, to be used for partial logits
+ shape.
+
+
+ Returns a tuple with multiple values, as follows:
+ partial_logits: Rank 2 Tensor containing logits update (with respect to cached
+ values stored) for each example.
+ tree_ids: Rank 1 Tensor containing new tree ids for each example.
+ node_ids: Rank 1 Tensor containing new node ids in the new tree_ids.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ computes the update to cached logits. It is designed to be used during training.
+ It traverses the trees starting from cached tree id and cached node id and
+ calculates the updates to be pushed to the cache.
+
+
+
+
+ Updates the tree ensemble by either adding a layer to the last tree being grown
+
+
+ Handle to the ensemble variable.
+
+
+ Rank 1 tensor with ids for each feature. This is the real id of
+ the feature that will be used in the split.
+
+
+ List of rank 1 tensors representing the nodes for which this feature
+ has a split.
+
+
+ List of rank 1 tensors representing the gains for each of the feature's
+ split.
+
+
+ List of rank 1 tensors representing the thesholds for each of the
+ feature's split.
+
+
+ List of rank 2 tensors with left leaf contribs for each of
+ the feature's splits. Will be added to the previous node values to constitute
+ the values of the left nodes.
+
+
+ List of rank 2 tensors with right leaf contribs for each
+ of the feature's splits. Will be added to the previous node values to constitute
+ the values of the right nodes.
+
+
+ Max depth of the tree to build.
+
+
+ shrinkage const for each new tree.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BoostedTreesUpdateEnsemble'.
+
+
+ 0-No pruning, 1-Pre-pruning, 2-Post-pruning.
+
+
+ Returns the description of the operation
+
+
+ or by starting a new tree.
+
+
+
+
+ Return the shape of s0 op s1 with broadcast.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BroadcastArgs'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Given s0 and s1, tensors that represent shapes, compute r0, the
+ broadcasted shape. s0, s1 and r0 are all integer vectors.
+
+
+
+
+ Return the reduction indices for computing gradients of s0 op s1 with broadcast.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BroadcastGradientArgs'.
+
+
+ Returns a tuple with multiple values, as follows:
+ r0:
+ r1:
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ This is typically used by gradient computations for a broadcasting operation.
+
+
+
+
+ Broadcast an array for a compatible shape.
+
+
+ A Tensor to broadcast.
+
+
+ An 1-D int Tensor. The shape of the desired output.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'BroadcastTo'.
+
+
+ A Tensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Broadcasting is the process of making arrays to have compatible shapes
+ for arithmetic operations. Two shapes are compatible if for each
+ dimension pair they are either equal or one of them is one. When trying
+ to broadcast a Tensor to a shape, it starts with the trailing dimensions,
+ and works its way forward.
+
+ For example,
+
+ >>> x = tf.constant([1, 2, 3])
+ >>> y = tf.broadcast_to(x, [3, 3])
+ >>> sess.run(y)
+ array([[1, 2, 3],
+ [1, 2, 3],
+ [1, 2, 3]], dtype=int32)
+
+ In the above example, the input Tensor with the shape of [1, 3]
+ is broadcasted to output Tensor with shape of [3, 3].
+
+
+
+
+ Bucketizes 'input' based on 'boundaries'.
+
+
+ Any shape of Tensor contains with int or float type.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Bucketize'.
+
+
+ A sorted list of floats gives the boundary of the buckets.
+
+
+ Same shape with 'input', each value of input replaced with bucket index.
+
+ @compatibility(numpy)
+ Equivalent to np.digitize.
+ @end_compatibility
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ For example, if the inputs are
+ boundaries = [0, 10, 100]
+ input = [[-5, 10000]
+ [150, 10]
+ [5, 100]]
+
+ then the output will be
+ output = [[0, 3]
+ [3, 2]
+ [1, 3]]
+
+
+
+
+ Creates a dataset that caches elements from input_dataset.
+
+
+
+
+ A path on the filesystem where we should cache the dataset. Note: this
+ will be a directory.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'CacheDataset'.
+
+
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ A CacheDataset will iterate over the input_dataset, and store tensors. If the
+ cache already exists, the cache will be used. If the cache is inappropriate
+ (e.g. cannot be opened, contains tensors of the wrong shape / size), an error
+ will the returned when used.
+
+
+
+
+ Cast x of type SrcT to y of DstT.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Cast'.
+
+
+ Optional argument
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Returns element-wise smallest integer not less than x.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Ceil'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Checks a tensor for NaN and Inf values.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'CheckNumerics'.
+
+
+ Prefix of the error message.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ When run, reports an InvalidArgument error if tensor has any values
+ that are not a number (NaN) or infinity (Inf). Otherwise, passes tensor as-is.
+
+
+
+
+ Computes the Cholesky decomposition of one or more square matrices.
+
+
+ Shape is [..., M, M].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Cholesky'.
+
+
+ Shape is [..., M, M].
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The input is a tensor of shape [..., M, M] whose inner-most 2 dimensions
+ form square matrices.
+
+ The input has to be symmetric and positive definite. Only the lower-triangular
+ part of the input will be used for this operation. The upper-triangular part
+ will not be read.
+
+ The output is a tensor of the same shape as the input
+ containing the Cholesky decompositions for all input submatrices [..., :, :].
+
+ **Note**: The gradient computation on GPU is faster for large matrices but
+ not for large batch dimensions when the submatrices are small. In this
+ case it might be faster to use the CPU.
+
+
+
+
+ Computes the reverse mode backpropagated gradient of the Cholesky algorithm.
+
+
+ Output of batch Cholesky algorithm l = cholesky(A). Shape is [..., M, M].
+ Algorithm depends only on lower triangular part of the innermost matrices of
+ this tensor.
+
+
+ df/dl where f is some scalar function. Shape is [..., M, M].
+ Algorithm depends only on lower triangular part of the innermost matrices of
+ this tensor.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'CholeskyGrad'.
+
+
+ Symmetrized version of df/dA . Shape is [..., M, M]
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ For an explanation see "Differentiation of the Cholesky algorithm" by
+ Iain Murray http://arxiv.org/abs/1602.07527.
+
+
+
+
+ Clips tensor values to a specified min and max.
+
+
+ A Tensor.
+
+
+ A 0-D (scalar) Tensor, or a Tensor with the same shape
+ as t. The minimum value to clip by.
+
+
+ A 0-D (scalar) Tensor, or a Tensor with the same shape
+ as t. The maximum value to clip by.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ClipByValue'.
+
+
+ A clipped Tensor with the same shape as input 't'.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Given a tensor t, this operation returns a tensor of the same type and
+ shape as t with its values clipped to clip_value_min and clip_value_max.
+ Any values less than clip_value_min are set to clip_value_min. Any values
+ greater than clip_value_max are set to clip_value_max.
+
+
+
+
+ Receives a tensor value broadcast from another device.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'CollectiveBcastRecv'.
+
+
+
+
+
+
+
+
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Broadcasts a tensor value to one or more other devices.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'CollectiveBcastSend'.
+
+
+
+
+
+
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ An Op to permute tensors across replicated TPU instances. Each instance
+
+
+ The local input to be permuted. Currently only supports float and
+ bfloat16.
+
+
+ A tensor with shape [num_pairs, 2].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'CollectivePermute'.
+
+
+ The permuted input.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ supplies its own input.
+
+ For example, suppose there are 4 TPU instances: [A, B, C, D]. Passing
+ source_target_pairs=[[0,1],[1,2],[2,3],[3,0]] gets the outputs:
+ [D, A, B, C].
+
+
+
+
+ Mutually reduces multiple tensors of identical type and shape.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'CollectiveReduce'.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Compare values of input to threshold and pack resulting bits into a uint8.
+
+
+ Values to compare against threshold and bitpack.
+
+
+ Threshold to compare against.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'CompareAndBitpack'.
+
+
+ The bitpacked comparisons.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Each comparison returns a boolean true (if input_value > threshold)
+ or and false otherwise.
+
+ This operation is useful for Locality-Sensitive-Hashing (LSH) and other
+ algorithms that use hashing approximations of cosine and L2 distances;
+ codes can be generated from an input via:
+
+
+ codebook_size = 50
+ codebook_bits = codebook_size * 32
+ codebook = tf.get_variable('codebook', [x.shape[-1].value, codebook_bits],
+ dtype=x.dtype,
+ initializer=tf.orthogonal_initializer())
+ codes = compare_and_threshold(tf.matmul(x, codebook), threshold=0.)
+ codes = tf.bitcast(codes, tf.int32) # go from uint8 to int32
+ # now codes has shape x.shape[:-1] + [codebook_size]
+
+
+ **NOTE**: Currently, the innermost dimension of the tensor must be divisible
+ by 8.
+
+ Given an input shaped [s0, s1, ..., s_n], the output is
+ a uint8 tensor shaped [s0, s1, ..., s_n / 8].
+
+
+
+
+ Converts two real numbers to a complex number.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Complex'.
+
+
+ Optional argument
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Given a tensor real representing the real part of a complex number, and a
+ tensor imag representing the imaginary part of a complex number, this
+ operation returns complex numbers elementwise of the form \\(a + bj\\), where
+ *a* represents the real part and *b* represents the imag part.
+
+ The input tensors real and imag must have the same shape.
+
+ For example:
+
+
+ # tensor 'real' is [2.25, 3.25]
+ # tensor imag is [4.75, 5.75]
+ tf.complex(real, imag) ==> [[2.25 + 4.75j], [3.25 + 5.75j]]
+
+
+
+
+
+ Computes the complex absolute value of a tensor.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ComplexAbs'.
+
+
+ Optional argument
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Given a tensor x of complex numbers, this operation returns a tensor of type
+ float or double that is the absolute value of each element in x. All
+ elements in x must be complex numbers of the form \\(a + bj\\). The absolute
+ value is computed as \\( \sqrt{a^2 + b^2}\\).
+
+
+
+
+ Computes the ids of the positions in sampled_candidates that match true_labels.
+
+
+ The true_classes output of UnpackSparseLabels.
+
+
+ The sampled_candidates output of CandidateSampler.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ComputeAccidentalHits'.
+
+
+ Optional argument
+ If either seed or seed2 are set to be non-zero, the random number
+ generator is seeded by the given seed. Otherwise, it is seeded by a
+ random seed.
+
+
+ Optional argument
+ An second seed to avoid seed collision.
+
+
+ Number of true labels per context.
+
+
+ Returns a tuple with multiple values, as follows:
+ indices: A vector of indices corresponding to rows of true_candidates.
+ ids: A vector of IDs of positions in sampled_candidates that match a true_label
+ for the row with the corresponding index in indices.
+ weights: A vector of the same length as indices and ids, in which each element
+ is -FLOAT_MAX.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ When doing log-odds NCE, the result of this op should be passed through a
+ SparseToDense op, then added to the logits of the sampled candidates. This has
+ the effect of 'removing' the sampled labels that match the true labels by
+ making the classifier sure that they are sampled labels.
+
+
+
+
+ Concatenates tensors along one dimension.
+
+
+ 0-D. The dimension along which to concatenate. Must be in the
+ range [0, rank(values)).
+
+
+ The N Tensors to concatenate. Their ranks and types must match,
+ and their sizes must match in all dimensions except concat_dim.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Concat'.
+
+
+ A Tensor with the concatenation of values stacked along the
+ concat_dim dimension. This tensor's shape matches that of values except
+ in concat_dim where it has the sum of the sizes.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Creates a dataset that concatenates input_dataset with another_dataset.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ConcatenateDataset'.
+
+
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes offsets of concat inputs within its output.
+
+
+ The dimension along which to concatenate.
+
+
+ The N int32 vectors representing shape of tensors being concatenated.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ConcatOffset'.
+
+
+ The N int32 vectors representing the starting offset
+ of input tensors within the concatenated output.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ For example:
+
+
+ # 'x' is [2, 2, 7]
+ # 'y' is [2, 3, 7]
+ # 'z' is [2, 5, 7]
+ concat_offset(2, [x, y, z]) => [0, 0, 0], [0, 2, 0], [0, 5, 0]
+
+
+ This is typically used by gradient computations for a concat operation.
+
+
+
+
+ Concatenates tensors along one dimension.
+
+
+ List of N Tensors to concatenate. Their ranks and types must match,
+ and their sizes must match in all dimensions except concat_dim.
+
+
+ 0-D. The dimension along which to concatenate. Must be in the
+ range [-rank(values), rank(values)).
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ConcatV2'.
+
+
+ A Tensor with the concatenation of values stacked along the
+ concat_dim dimension. This tensor's shape matches that of values except
+ in concat_dim where it has the sum of the sizes.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ A conditional accumulator for aggregating gradients.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ConditionalAccumulator'.
+
+
+ Optional argument
+ If non-empty, this accumulator is placed in the given container.
+ Otherwise, a default container is used.
+
+
+ Optional argument
+ If non-empty, this accumulator will be shared under the
+ given name across multiple sessions.
+
+
+ Optional argument
+
+
+ The type of the value being accumulated.
+
+
+ The shape of the values, can be [], in which case shape is unknown.
+
+
+ The handle to the accumulator.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The accumulator accepts gradients marked with local_step greater or
+ equal to the most recent global_step known to the accumulator. The
+ average can be extracted from the accumulator, provided sufficient
+ gradients have been accumulated. Extracting the average automatically
+ resets the aggregate to 0, and increments the global_step recorded by
+ the accumulator.
+
+
+
+
+ An op that sets up the centralized structures for a distributed TPU
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ConfigureDistributedTPU'.
+
+
+ Optional argument
+ Reserved. Do not use.
+
+
+ Optional argument
+ Serialized tensorflow.tpu.TPUEmbeddingConfiguration that
+ describes the embedding lookups of the program.
+
+
+ Optional argument
+ Reserved. Do not use.
+
+
+ A serialized tensorflow.tpu.TopologyProto that describes the TPU
+ topology.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ system.
+
+
+
+
+ Returns the complex conjugate of a complex number.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Conj'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Given a tensor input of complex numbers, this operation returns a tensor of
+ complex numbers that are the complex conjugate of each element in input. The
+ complex numbers in input must be of the form \\(a + bj\\), where *a* is the
+ real part and *b* is the imaginary part.
+
+ The complex conjugate returned by this operation is of the form \\(a - bj\\).
+
+ For example:
+
+
+ # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]
+ tf.conj(input) ==> [-2.25 - 4.75j, 3.25 - 5.75j]
+
+
+
+
+
+ Shuffle dimensions of x according to a permutation and conjugate the result.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ConjugateTranspose'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The output y has the same rank as x. The shapes of x and y satisfy:
+ y.shape[i] == x.shape[perm[i]] for i in [0, 1, ..., rank(x) - 1]
+ y[i,j,k,...,s,t,u] == conj(x[perm[i], perm[j], perm[k],...,perm[s], perm[t], perm[u]])
+
+
+
+
+ Returns a constant tensor.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Const'.
+
+
+ Attr value is the tensor to return.
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ This op consumes a lock created by MutexLock.
+
+
+ A tensor returned by MutexLock.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ConsumeMutexLock'.
+
+
+ Returns the description of the operation
+
+
+ This op exists to consume a tensor created by MutexLock (other than
+ direct control dependencies). It should be the only that consumes the tensor,
+ and will raise an error if it is not. Its only purpose is to keep the
+ mutex lock tensor alive until it is consumed by this op.
+
+ **NOTE**: This operation must run on the same device as its input. This may
+ be enforced via the colocate_with mechanism.
+
+
+
+
+ Does nothing. Serves as a control trigger for scheduling.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ControlTrigger'.
+
+
+ Returns the description of the operation
+
+
+ Only useful as a placeholder for control edges.
+
+
+
+
+ Computes a 2-D convolution given 4-D input and filter tensors.
+
+
+ A 4-D tensor. The dimension order is interpreted according to the value
+ of data_format, see below for details.
+
+
+ A 4-D tensor of shape
+ [filter_height, filter_width, in_channels, out_channels]
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Conv2D'.
+
+
+ Optional argument
+
+
+ Optional argument
+ Specify the data format of the input and output data. With the
+ default format "NHWC", the data is stored in the order of:
+ [batch, height, width, channels].
+ Alternatively, the format could be "NCHW", the data storage order of:
+ [batch, channels, height, width].
+
+
+ Optional argument
+ 1-D tensor of length 4. The dilation factor for each dimension of
+ input. If set to k > 1, there will be k-1 skipped cells between each
+ filter element on that dimension. The dimension order is determined by the
+ value of data_format, see above for details. Dilations in the batch and
+ depth dimensions must be 1.
+
+
+ 1-D tensor of length 4. The stride of the sliding window for each
+ dimension of input. The dimension order is determined by the value of
+ data_format, see below for details.
+
+
+ The type of padding algorithm to use.
+
+
+ A 4-D tensor. The dimension order is determined by the value of
+ data_format, see below for details.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Given an input tensor of shape [batch, in_height, in_width, in_channels]
+ and a filter / kernel tensor of shape
+ [filter_height, filter_width, in_channels, out_channels], this op
+ performs the following:
+
+ 1. Flattens the filter to a 2-D matrix with shape
+ [filter_height * filter_width * in_channels, output_channels].
+ 2. Extracts image patches from the input tensor to form a *virtual*
+ tensor of shape [batch, out_height, out_width,
+ filter_height * filter_width * in_channels].
+ 3. For each patch, right-multiplies the filter matrix and the image patch
+ vector.
+
+ In detail, with the default NHWC format,
+
+ output[b, i, j, k] =
+ sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] *
+ filter[di, dj, q, k]
+
+ Must have strides[0] = strides[3] = 1. For the most common case of the same
+ horizontal and vertices strides, strides = [1, stride, stride, 1].
+
+
+
+
+ Computes the gradients of convolution with respect to the filter.
+
+
+ 4-D with shape [batch, in_height, in_width, in_channels].
+
+
+ An integer vector representing the tensor shape of filter,
+ where filter is a 4-D
+ [filter_height, filter_width, in_channels, out_channels] tensor.
+
+
+ 4-D with shape [batch, out_height, out_width, out_channels].
+ Gradients w.r.t. the output of the convolution.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Conv2DBackpropFilter'.
+
+
+ Optional argument
+
+
+ Optional argument
+ Specify the data format of the input and output data. With the
+ default format "NHWC", the data is stored in the order of:
+ [batch, in_height, in_width, in_channels].
+ Alternatively, the format could be "NCHW", the data storage order of:
+ [batch, in_channels, in_height, in_width].
+
+
+ Optional argument
+ 1-D tensor of length 4. The dilation factor for each dimension of
+ input. If set to k > 1, there will be k-1 skipped cells between each filter
+ element on that dimension. The dimension order is determined by the value of
+ data_format, see above for details. Dilations in the batch and depth
+ dimensions must be 1.
+
+
+ The stride of the sliding window for each dimension of the input
+ of the convolution. Must be in the same order as the dimension specified with
+ format.
+
+
+ The type of padding algorithm to use.
+
+
+ 4-D with shape
+ [filter_height, filter_width, in_channels, out_channels]. Gradient w.r.t.
+ the filter input of the convolution.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes the gradients of convolution with respect to the input.
+
+
+ An integer vector representing the shape of input,
+ where input is a 4-D [batch, height, width, channels] tensor.
+
+
+ 4-D with shape
+ [filter_height, filter_width, in_channels, out_channels].
+
+
+ 4-D with shape [batch, out_height, out_width, out_channels].
+ Gradients w.r.t. the output of the convolution.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Conv2DBackpropInput'.
+
+
+ Optional argument
+
+
+ Optional argument
+ Specify the data format of the input and output data. With the
+ default format "NHWC", the data is stored in the order of:
+ [batch, in_height, in_width, in_channels].
+ Alternatively, the format could be "NCHW", the data storage order of:
+ [batch, in_channels, in_height, in_width].
+
+
+ Optional argument
+ 1-D tensor of length 4. The dilation factor for each dimension of
+ input. If set to k > 1, there will be k-1 skipped cells between each filter
+ element on that dimension. The dimension order is determined by the value of
+ data_format, see above for details. Dilations in the batch and depth
+ dimensions must be 1.
+
+
+ The stride of the sliding window for each dimension of the input
+ of the convolution. Must be in the same order as the dimension specified with
+ format.
+
+
+ The type of padding algorithm to use.
+
+
+ 4-D with shape [batch, in_height, in_width, in_channels]. Gradient
+ w.r.t. the input of the convolution.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes a 3-D convolution given 5-D input and filter tensors.
+
+
+ Shape [batch, in_depth, in_height, in_width, in_channels].
+
+
+ Shape [filter_depth, filter_height, filter_width, in_channels,
+ out_channels]. in_channels must match between input and filter.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Conv3D'.
+
+
+ Optional argument
+ The data format of the input and output data. With the
+ default format "NDHWC", the data is stored in the order of:
+ [batch, in_depth, in_height, in_width, in_channels].
+ Alternatively, the format could be "NCDHW", the data storage order is:
+ [batch, in_channels, in_depth, in_height, in_width].
+
+
+ Optional argument
+ 1-D tensor of length 5. The dilation factor for each dimension of
+ input. If set to k > 1, there will be k-1 skipped cells between each
+ filter element on that dimension. The dimension order is determined by the
+ value of data_format, see above for details. Dilations in the batch and
+ depth dimensions must be 1.
+
+
+ 1-D tensor of length 5. The stride of the sliding window for each
+ dimension of input. Must have strides[0] = strides[4] = 1.
+
+
+ The type of padding algorithm to use.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ In signal processing, cross-correlation is a measure of similarity of
+ two waveforms as a function of a time-lag applied to one of them. This
+ is also known as a sliding dot product or sliding inner-product.
+
+ Our Conv3D implements a form of cross-correlation.
+
+
+
+
+ Computes the gradients of 3-D convolution with respect to the filter.
+
+
+ Shape [batch, depth, rows, cols, in_channels].
+
+
+ Shape [depth, rows, cols, in_channels, out_channels].
+ in_channels must match between input and filter.
+
+
+ Backprop signal of shape [batch, out_depth, out_rows, out_cols,
+ out_channels].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Conv3DBackpropFilter'.
+
+
+ Optional argument
+
+
+ 1-D tensor of length 5. The stride of the sliding window for each
+ dimension of input. Must have strides[0] = strides[4] = 1.
+
+
+ The type of padding algorithm to use.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes the gradients of 3-D convolution with respect to the filter.
+
+
+ Shape [batch, depth, rows, cols, in_channels].
+
+
+ An integer vector representing the tensor shape of filter,
+ where filter is a 5-D
+ [filter_depth, filter_height, filter_width, in_channels, out_channels]
+ tensor.
+
+
+ Backprop signal of shape [batch, out_depth, out_rows, out_cols,
+ out_channels].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Conv3DBackpropFilterV2'.
+
+
+ Optional argument
+ The data format of the input and output data. With the
+ default format "NDHWC", the data is stored in the order of:
+ [batch, in_depth, in_height, in_width, in_channels].
+ Alternatively, the format could be "NCDHW", the data storage order is:
+ [batch, in_channels, in_depth, in_height, in_width].
+
+
+ Optional argument
+ 1-D tensor of length 5. The dilation factor for each dimension of
+ input. If set to k > 1, there will be k-1 skipped cells between each
+ filter element on that dimension. The dimension order is determined by the
+ value of data_format, see above for details. Dilations in the batch and
+ depth dimensions must be 1.
+
+
+ 1-D tensor of length 5. The stride of the sliding window for each
+ dimension of input. Must have strides[0] = strides[4] = 1.
+
+
+ The type of padding algorithm to use.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes the gradients of 3-D convolution with respect to the input.
+
+
+ Shape [batch, depth, rows, cols, in_channels].
+
+
+ Shape [depth, rows, cols, in_channels, out_channels].
+ in_channels must match between input and filter.
+
+
+ Backprop signal of shape [batch, out_depth, out_rows, out_cols,
+ out_channels].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Conv3DBackpropInput'.
+
+
+ Optional argument
+
+
+ 1-D tensor of length 5. The stride of the sliding window for each
+ dimension of input. Must have strides[0] = strides[4] = 1.
+
+
+ The type of padding algorithm to use.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes the gradients of 3-D convolution with respect to the input.
+
+
+ An integer vector representing the tensor shape of input,
+ where input is a 5-D
+ [batch, depth, rows, cols, in_channels] tensor.
+
+
+ Shape [depth, rows, cols, in_channels, out_channels].
+ in_channels must match between input and filter.
+
+
+ Backprop signal of shape [batch, out_depth, out_rows, out_cols,
+ out_channels].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Conv3DBackpropInputV2'.
+
+
+ Optional argument
+ The data format of the input and output data. With the
+ default format "NDHWC", the data is stored in the order of:
+ [batch, in_depth, in_height, in_width, in_channels].
+ Alternatively, the format could be "NCDHW", the data storage order is:
+ [batch, in_channels, in_depth, in_height, in_width].
+
+
+ Optional argument
+ 1-D tensor of length 5. The dilation factor for each dimension of
+ input. If set to k > 1, there will be k-1 skipped cells between each
+ filter element on that dimension. The dimension order is determined by the
+ value of data_format, see above for details. Dilations in the batch and
+ depth dimensions must be 1.
+
+
+ 1-D tensor of length 5. The stride of the sliding window for each
+ dimension of input. Must have strides[0] = strides[4] = 1.
+
+
+ The type of padding algorithm to use.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Copy Op.
+
+
+ Input tensor.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Copy'.
+
+
+ Optional argument
+ The name of the input tensor.
+
+
+ Optional argument
+ A list of debug op spec (op, url, gated_grpc) for attached debug
+ ops. Each element of the list has the format
+ <debug_op>;<grpc_url>;<gated_grpc>, wherein gated_grpc is boolean represented
+ as 0/1. E.g., "DebugIdentity;grpc://foo:3333;1",
+ "DebugIdentity;file:///tmp/tfdbg_1;0".
+
+
+ Output tensor, deep-copied from input.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Performs CPU-to-CPU or GPU-to-GPU deep-copying of tensor, depending on the
+ device on which the tensor is allocated.
+ N.B.: If the all downstream attached debug ops are disabled given the current
+ gRPC gating status, the output will simply forward the input tensor without
+ deep-copying. See the documentation of Debug* ops for more details.
+
+ Unlike the CopyHost Op, this op does not have HostMemory constraint on its
+ input or output.
+
+
+
+
+ Copy Host Op.
+
+
+ Input tensor.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'CopyHost'.
+
+
+ Optional argument
+ The name of the input tensor.
+
+
+ Optional argument
+ A list of debug op spec (op, url, gated_grpc) for attached debug
+ ops. Each element of the list has the format
+ <debug_op>;<grpc_url>;<gated_grpc>, wherein gated_grpc is boolean represented
+ as 0/1. E.g., "DebugIdentity;grpc://foo:3333;1",
+ "DebugIdentity;file:///tmp/tfdbg_1;0".
+
+
+ Output tensor, deep-copied from input.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Performs CPU-to-CPU deep-copying of tensor.
+ N.B.: If the all downstream attached debug ops are disabled given the current
+ gRPC gating status, the output will simply forward the input tensor without
+ deep-copying. See the documentation of Debug* ops for more details.
+
+ Unlike the Copy Op, this op has HostMemory constraint on its input or output.
+
+
+
+
+ Computes cos of x element-wise.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Cos'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes hyperbolic cosine of x element-wise.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Cosh'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Increments 'ref' until it reaches 'limit'.
+
+
+ Should be from a scalar Variable node.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'CountUpTo'.
+
+
+ If incrementing ref would bring it above limit, instead generates an
+ 'OutOfRange' error.
+
+
+ A copy of the input before increment. If nothing else modifies the
+ input, the values produced will all be distinct.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Extracts crops from the input image tensor and resizes them.
+
+
+ A 4-D tensor of shape [batch, image_height, image_width, depth].
+ Both image_height and image_width need to be positive.
+
+
+ A 2-D tensor of shape [num_boxes, 4]. The i-th row of the tensor
+ specifies the coordinates of a box in the box_ind[i] image and is specified
+ in normalized coordinates [y1, x1, y2, x2]. A normalized coordinate value of
+ y is mapped to the image coordinate at y * (image_height - 1), so as the
+ [0, 1] interval of normalized image height is mapped to
+ [0, image_height - 1] in image height coordinates. We do allow y1 > y2, in
+ which case the sampled crop is an up-down flipped version of the original
+ image. The width dimension is treated similarly. Normalized coordinates
+ outside the [0, 1] range are allowed, in which case we use
+ extrapolation_value to extrapolate the input image values.
+
+
+ A 1-D tensor of shape [num_boxes] with int32 values in [0, batch).
+ The value of box_ind[i] specifies the image that the i-th box refers to.
+
+
+ A 1-D tensor of 2 elements, size = [crop_height, crop_width]. All
+ cropped image patches are resized to this size. The aspect ratio of the image
+ content is not preserved. Both crop_height and crop_width need to be
+ positive.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'CropAndResize'.
+
+
+ Optional argument
+ A string specifying the sampling method for resizing. It can be either
+ "bilinear" or "nearest" and default to "bilinear". Currently two sampling
+ methods are supported: Bilinear and Nearest Neighbor.
+
+
+ Optional argument
+ Value used for extrapolation, when applicable.
+
+
+ A 4-D tensor of shape [num_boxes, crop_height, crop_width, depth].
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Extracts crops from the input image tensor and resizes them using bilinear
+ sampling or nearest neighbor sampling (possibly with aspect ratio change) to a
+ common output size specified by crop_size. This is more general than the
+ crop_to_bounding_box op which extracts a fixed size slice from the input image
+ and does not allow resizing or aspect ratio change.
+
+ Returns a tensor with crops from the input image at positions defined at the
+ bounding box locations in boxes. The cropped boxes are all resized (with
+ bilinear or nearest neighbor interpolation) to a fixed
+ size = [crop_height, crop_width]. The result is a 4-D tensor
+ [num_boxes, crop_height, crop_width, depth]. The resizing is corner aligned.
+ In particular, if boxes = [[0, 0, 1, 1]], the method will give identical
+ results to using tf.image.resize_bilinear() or
+ tf.image.resize_nearest_neighbor()(depends on the method argument) with
+ align_corners=True.
+
+
+
+
+
+
+ Compute the pairwise cross product.
+
+
+ A tensor containing 3-element vectors.
+
+
+ Another tensor, of same type and shape as a.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Cross'.
+
+
+ Pairwise cross product of the vectors in a and b.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ a and b must be the same shape; they can either be simple 3-element vectors,
+ or any shape where the innermost dimension is 3. In the latter case, each pair
+ of corresponding 3-element vectors is cross-multiplied independently.
+
+
+
+
+ An Op to sum inputs across replicated TPU instances. Each instance supplies its
+
+
+ The local input to the sum.
+
+
+ An int32 tensor with shape
+ [num_groups, num_replicas_per_group]. group_assignment[i] represents the
+ replica ids in the ith subgroup.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'CrossReplicaSum'.
+
+
+ The sum of all the distributed inputs.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ own input.
+
+ For example, suppose there are 8 TPU instances: [A, B, C, D, E, F, G, H].
+ Passing group_assignment=[[0,2,4,6],[1,3,5,7]] sets A, C, E, G as group 0,
+ and B, D, F, H as group 1. Thus we get the outputs:
+ [A+C+E+G, B+D+F+H, A+C+E+G, B+D+F+H, A+C+E+G, B+D+F+H, A+C+E+G, B+D+F+H].
+
+
+
+
+ Performs beam search decoding on the logits given in input.
+
+
+ 3-D, shape: (max_time x batch_size x num_classes), the logits.
+
+
+ A vector containing sequence lengths, size (batch).
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'CTCBeamSearchDecoder'.
+
+
+ Optional argument
+ If true, merge repeated classes in output.
+
+
+ A scalar >= 0 (beam search beam width).
+
+
+ A scalar >= 0, <= beam_width (controls output size).
+
+
+ Returns a tuple with multiple values, as follows:
+ decoded_indices: A list (length: top_paths) of indices matrices. Matrix j,
+ size (total_decoded_outputs[j] x 2), has indices of a
+ SparseTensor<int64, 2>. The rows store: [batch, time].
+ decoded_values: A list (length: top_paths) of values vectors. Vector j,
+ size (length total_decoded_outputs[j]), has the values of a
+ SparseTensor<int64, 2>. The vector stores the decoded classes for beam j.
+ decoded_shape: A list (length: top_paths) of shape vector. Vector j,
+ size (2), stores the shape of the decoded SparseTensor[j].
+ Its values are: [batch_size, max_decoded_length[j]].
+ log_probability: A matrix, shaped: (batch_size x top_paths). The
+ sequence log-probabilities.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ A note about the attribute merge_repeated: For the beam search decoder,
+ this means that if consecutive entries in a beam are the same, only
+ the first of these is emitted. That is, when the top path is "A B B B B",
+ "A B" is returned if merge_repeated = True but "A B B B B" is
+ returned if merge_repeated = False.
+
+
+
+
+ Performs greedy decoding on the logits given in inputs.
+
+
+ 3-D, shape: (max_time x batch_size x num_classes), the logits.
+
+
+ A vector containing sequence lengths, size (batch_size).
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'CTCGreedyDecoder'.
+
+
+ Optional argument
+ If True, merge repeated classes in output.
+
+
+ Returns a tuple with multiple values, as follows:
+ decoded_indices: Indices matrix, size (total_decoded_outputs x 2),
+ of a SparseTensor<int64, 2>. The rows store: [batch, time].
+ decoded_values: Values vector, size: (total_decoded_outputs),
+ of a SparseTensor<int64, 2>. The vector stores the decoded classes.
+ decoded_shape: Shape vector, size (2), of the decoded SparseTensor.
+ Values are: [batch_size, max_decoded_length].
+ log_probability: Matrix, size (batch_size x 1), containing sequence
+ log-probabilities.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ A note about the attribute merge_repeated: if enabled, when
+ consecutive logits' maximum indices are the same, only the first of
+ these is emitted. Labeling the blank '*', the sequence "A B B * B B"
+ becomes "A B B" if merge_repeated = True and "A B B B B" if
+ merge_repeated = False.
+
+ Regardless of the value of merge_repeated, if the maximum index of a given
+ time and batch corresponds to the blank, index (num_classes - 1), no new
+ element is emitted.
+
+
+
+
+ Calculates the CTC Loss (log probability) for each batch entry. Also calculates
+
+
+ 3-D, shape: (max_time x batch_size x num_classes), the logits.
+
+
+ The indices of a SparseTensor<int32, 2>.
+ labels_indices(i, :) == [b, t] means labels_values(i) stores the id for
+ (batch b, time t).
+
+
+ The values (labels) associated with the given batch and time.
+
+
+ A vector containing sequence lengths (batch).
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'CTCLoss'.
+
+
+ Optional argument
+ Scalar, if true then repeated labels are
+ collapsed prior to the CTC calculation.
+
+
+ Optional argument
+ Scalar. If set to false, *during* CTC calculation
+ repeated non-blank labels will not be merged and are interpreted as
+ individual labels. This is a simplified version of CTC.
+
+
+ Optional argument
+ Scalar. If set to true, during CTC
+ calculation, items that have longer output sequences than input sequences
+ are skipped: they don't contribute to the loss term and have zero-gradient.
+
+
+ Returns a tuple with multiple values, as follows:
+ loss: A vector (batch) containing log-probabilities.
+ gradient: The gradient of loss. 3-D, shape:
+ (max_time x batch_size x num_classes).
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ the gradient. This class performs the softmax operation for you, so inputs
+ should be e.g. linear projections of outputs by an LSTM.
+
+
+
+
+ A RNN backed by cuDNN.
+
+
+
+
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'CudnnRNN'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Returns a tuple with multiple values, as follows:
+ output:
+ output_h:
+ output_c:
+ reserve_space:
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Computes the RNN from the input and initial states, with respect to the params
+ buffer.
+
+ rnn_mode: Indicates the type of the RNN model.
+ input_mode: Indicate whether there is a linear projection between the input and
+ the actual computation before the first layer. 'skip_input' is only allowed
+ when input_size == num_units; 'auto_select' implies 'skip_input' when
+ input_size == num_units; otherwise, it implies 'linear_input'.
+ direction: Indicates whether a bidirectional model will be used. Should be
+ "unidirectional" or "bidirectional".
+ dropout: Dropout probability. When set to 0., dropout is disabled.
+ seed: The 1st part of a seed to initialize dropout.
+ seed2: The 2nd part of a seed to initialize dropout.
+ input: A 3-D tensor with the shape of [seq_length, batch_size, input_size].
+ input_h: A 3-D tensor with the shape of [num_layer * dir, batch_size,
+ num_units].
+ input_c: For LSTM, a 3-D tensor with the shape of
+ [num_layer * dir, batch, num_units]. For other models, it is ignored.
+ params: A 1-D tensor that contains the weights and biases in an opaque layout.
+ The size must be created through CudnnRNNParamsSize, and initialized
+ separately. Note that they might not be compatible across different
+ generations. So it is a good idea to save and restore
+ output: A 3-D tensor with the shape of [seq_length, batch_size,
+ dir * num_units].
+ output_h: The same shape has input_h.
+ output_c: The same shape as input_c for LSTM. An empty tensor for other models.
+ is_training: Indicates whether this operation is used for inferenece or
+ training.
+ reserve_space: An opaque tensor that can be used in backprop calculation. It
+ is only produced if is_training is false.
+
+
+
+
+ Backprop step of CudnnRNN.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'CudnnRNNBackprop'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Returns a tuple with multiple values, as follows:
+ input_backprop:
+ input_h_backprop:
+ input_c_backprop:
+ params_backprop:
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Compute the backprop of both data and weights in a RNN.
+
+ rnn_mode: Indicates the type of the RNN model.
+ input_mode: Indicate whether there is a linear projection between the input and
+ the actual computation before the first layer. 'skip_input' is only allowed
+ when input_size == num_units; 'auto_select' implies 'skip_input' when
+ input_size == num_units; otherwise, it implies 'linear_input'.
+ direction: Indicates whether a bidirectional model will be used. Should be
+ "unidirectional" or "bidirectional".
+ dropout: Dropout probability. When set to 0., dropout is disabled.
+ seed: The 1st part of a seed to initialize dropout.
+ seed2: The 2nd part of a seed to initialize dropout.
+ input: A 3-D tensor with the shape of [seq_length, batch_size, input_size].
+ input_h: A 3-D tensor with the shape of [num_layer * dir, batch_size,
+ num_units].
+ input_c: For LSTM, a 3-D tensor with the shape of
+ [num_layer * dir, batch, num_units]. For other models, it is ignored.
+ params: A 1-D tensor that contains the weights and biases in an opaque layout.
+ The size must be created through CudnnRNNParamsSize, and initialized
+ separately. Note that they might not be compatible across different
+ generations. So it is a good idea to save and restore
+ output: A 3-D tensor with the shape of [seq_length, batch_size,
+ dir * num_units].
+ output_h: The same shape has input_h.
+ output_c: The same shape as input_c for LSTM. An empty tensor for other models.
+ output_backprop: A 3-D tensor with the same shape as output in the forward pass.
+ output_h_backprop: A 3-D tensor with the same shape as output_h in the forward
+ pass.
+ output_c_backprop: A 3-D tensor with the same shape as output_c in the forward
+ pass.
+ reserve_space: The same reserve_space produced in for forward operation.
+ input_backprop: The backprop to input in the forward pass. Has the same shape
+ as input.
+ input_h_backprop: The backprop to input_h in the forward pass. Has the same
+ shape as input_h.
+ input_c_backprop: The backprop to input_c in the forward pass. Has the same
+ shape as input_c.
+ params_backprop: The backprop to the params buffer in the forward pass. Has the
+ same shape as params.
+
+
+
+
+ Backprop step of CudnnRNN.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'CudnnRNNBackpropV2'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Returns a tuple with multiple values, as follows:
+ input_backprop:
+ input_h_backprop:
+ input_c_backprop:
+ params_backprop:
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Compute the backprop of both data and weights in a RNN. Takes an extra
+ "host_reserved" inupt than CudnnRNNBackprop, which is used to determine RNN
+ cudnnRNNAlgo_t and cudnnMathType_t.
+
+ rnn_mode: Indicates the type of the RNN model.
+ input_mode: Indicates whether there is a linear projection between the input and
+ the actual computation before the first layer. 'skip_input' is only allowed
+ when input_size == num_units; 'auto_select' implies 'skip_input' when
+ input_size == num_units; otherwise, it implies 'linear_input'.
+ direction: Indicates whether a bidirectional model will be used. Should be
+ "unidirectional" or "bidirectional".
+ dropout: Dropout probability. When set to 0., dropout is disabled.
+ seed: The 1st part of a seed to initialize dropout.
+ seed2: The 2nd part of a seed to initialize dropout.
+ input: A 3-D tensor with the shape of [seq_length, batch_size, input_size].
+ input_h: A 3-D tensor with the shape of [num_layer * dir, batch_size,
+ num_units].
+ input_c: For LSTM, a 3-D tensor with the shape of
+ [num_layer * dir, batch, num_units]. For other models, it is ignored.
+ params: A 1-D tensor that contains the weights and biases in an opaque layout.
+ The size must be created through CudnnRNNParamsSize, and initialized
+ separately. Note that they might not be compatible across different
+ generations. So it is a good idea to save and restore
+ output: A 3-D tensor with the shape of [seq_length, batch_size,
+ dir * num_units].
+ output_h: The same shape has input_h.
+ output_c: The same shape as input_c for LSTM. An empty tensor for other models.
+ output_backprop: A 3-D tensor with the same shape as output in the forward pass.
+ output_h_backprop: A 3-D tensor with the same shape as output_h in the forward
+ pass.
+ output_c_backprop: A 3-D tensor with the same shape as output_c in the forward
+ pass.
+ reserve_space: The same reserve_space produced in the forward operation.
+ host_reserved: The same host_reserved produced in the forward operation.
+ input_backprop: The backprop to input in the forward pass. Has the same shape
+ as input.
+ input_h_backprop: The backprop to input_h in the forward pass. Has the same
+ shape as input_h.
+ input_c_backprop: The backprop to input_c in the forward pass. Has the same
+ shape as input_c.
+ params_backprop: The backprop to the params buffer in the forward pass. Has the
+ same shape as params.
+
+
+
+
+ Converts CudnnRNN params from canonical form to usable form.
+
+
+
+
+
+
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'CudnnRNNCanonicalToParams'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Writes a set of weights into the opaque params buffer so they can be used in
+ upcoming training or inferences.
+
+ Note that the params buffer may not be compatible across different GPUs. So any
+ save and restoration should be converted to and from the canonical weights and
+ biases.
+
+ num_layers: Specifies the number of layers in the RNN model.
+ num_units: Specifies the size of the hidden state.
+ input_size: Specifies the size of the input state.
+ weights: the canonical form of weights that can be used for saving
+ and restoration. They are more likely to be compatible across different
+ generations.
+ biases: the canonical form of biases that can be used for saving
+ and restoration. They are more likely to be compatible across different
+ generations.
+ num_params: number of parameter sets for all layers.
+ Each layer may contain multiple parameter sets, with each set consisting of
+ a weight matrix and a bias vector.
+ rnn_mode: Indicates the type of the RNN model.
+ input_mode: Indicate whether there is a linear projection between the input and
+ The actual computation before the first layer. 'skip_input' is only allowed
+ when input_size == num_units; 'auto_select' implies 'skip_input' when
+ input_size == num_units; otherwise, it implies 'linear_input'.
+ direction: Indicates whether a bidirectional model will be used.
+ dir = (direction == bidirectional) ? 2 : 1
+ dropout: dropout probability. When set to 0., dropout is disabled.
+ seed: the 1st part of a seed to initialize dropout.
+ seed2: the 2nd part of a seed to initialize dropout.
+
+
+
+
+ Computes size of weights that can be used by a Cudnn RNN model.
+
+
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'CudnnRNNParamsSize'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Return the params size that can be used by the Cudnn RNN model. Subsequent
+ weight allocation and initialization should use this size.
+
+ num_layers: Specifies the number of layers in the RNN model.
+ num_units: Specifies the size of the hidden state.
+ input_size: Specifies the size of the input state.
+ rnn_mode: Indicates the type of the RNN model.
+ input_mode: Indicate whether there is a linear projection between the input and
+ The actual computation before the first layer. 'skip_input' is only allowed
+ when input_size == num_units; 'auto_select' implies 'skip_input' when
+ input_size == num_units; otherwise, it implies 'linear_input'.
+ direction: Indicates whether a bidirectional model will be used.
+ dir = (direction == bidirectional) ? 2 : 1
+ dropout: dropout probability. When set to 0., dropout is disabled.
+ seed: the 1st part of a seed to initialize dropout.
+ seed2: the 2nd part of a seed to initialize dropout.
+ params_size: The size of the params buffer that should be allocated and
+ initialized for this RNN model. Note that this params buffer may not be
+ compatible across GPUs. Please use CudnnRNNParamsWeights and
+ CudnnRNNParamsBiases to save and restore them in a way that is compatible
+ across different runs.
+
+
+
+
+ Retrieves CudnnRNN params in canonical form.
+
+
+
+
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'CudnnRNNParamsToCanonical'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+ Returns a tuple with multiple values, as follows:
+ weights:
+ biases:
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Retrieves a set of weights from the opaque params buffer that can be saved and
+ restored in a way compatible with future runs.
+
+ Note that the params buffer may not be compatible across different GPUs. So any
+ save and restoration should be converted to and from the canonical weights and
+ biases.
+
+ num_layers: Specifies the number of layers in the RNN model.
+ num_units: Specifies the size of the hidden state.
+ input_size: Specifies the size of the input state.
+ num_params: number of parameter sets for all layers.
+ Each layer may contain multiple parameter sets, with each set consisting of
+ a weight matrix and a bias vector.
+ weights: the canonical form of weights that can be used for saving
+ and restoration. They are more likely to be compatible across different
+ generations.
+ biases: the canonical form of biases that can be used for saving
+ and restoration. They are more likely to be compatible across different
+ generations.
+ rnn_mode: Indicates the type of the RNN model.
+ input_mode: Indicate whether there is a linear projection between the input and
+ The actual computation before the first layer. 'skip_input' is only allowed
+ when input_size == num_units; 'auto_select' implies 'skip_input' when
+ input_size == num_units; otherwise, it implies 'linear_input'.
+ direction: Indicates whether a bidirectional model will be used.
+ dir = (direction == bidirectional) ? 2 : 1
+ dropout: dropout probability. When set to 0., dropout is disabled.
+ seed: the 1st part of a seed to initialize dropout.
+ seed2: the 2nd part of a seed to initialize dropout.
+
+
+
+
+ A RNN backed by cuDNN.
+
+
+
+
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'CudnnRNNV2'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Returns a tuple with multiple values, as follows:
+ output:
+ output_h:
+ output_c:
+ reserve_space:
+ host_reserved:
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Computes the RNN from the input and initial states, with respect to the params
+ buffer. Produces one extra output "host_reserved" than CudnnRNN.
+
+ rnn_mode: Indicates the type of the RNN model.
+ input_mode: Indicates whether there is a linear projection between the input and
+ the actual computation before the first layer. 'skip_input' is only allowed
+ when input_size == num_units; 'auto_select' implies 'skip_input' when
+ input_size == num_units; otherwise, it implies 'linear_input'.
+ direction: Indicates whether a bidirectional model will be used. Should be
+ "unidirectional" or "bidirectional".
+ dropout: Dropout probability. When set to 0., dropout is disabled.
+ seed: The 1st part of a seed to initialize dropout.
+ seed2: The 2nd part of a seed to initialize dropout.
+ input: A 3-D tensor with the shape of [seq_length, batch_size, input_size].
+ input_h: A 3-D tensor with the shape of [num_layer * dir, batch_size,
+ num_units].
+ input_c: For LSTM, a 3-D tensor with the shape of
+ [num_layer * dir, batch, num_units]. For other models, it is ignored.
+ params: A 1-D tensor that contains the weights and biases in an opaque layout.
+ The size must be created through CudnnRNNParamsSize, and initialized
+ separately. Note that they might not be compatible across different
+ generations. So it is a good idea to save and restore
+ output: A 3-D tensor with the shape of [seq_length, batch_size,
+ dir * num_units].
+ output_h: The same shape has input_h.
+ output_c: The same shape as input_c for LSTM. An empty tensor for other models.
+ is_training: Indicates whether this operation is used for inferenece or
+ training.
+ reserve_space: An opaque tensor that can be used in backprop calculation. It
+ is only produced if is_training is true.
+ host_reserved: An opaque tensor that can be used in backprop calculation. It is
+ only produced if is_training is true. It is output on host memory rather than
+ device memory.
+
+
+
+
+ Compute the cumulative product of the tensor x along axis.
+
+
+ A Tensor. Must be one of the following types: float32, float64,
+ int64, int32, uint8, uint16, int16, int8, complex64,
+ complex128, qint8, quint8, qint32, half.
+
+
+ A Tensor of type int32 (default: 0). Must be in the range
+ [-rank(x), rank(x)).
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Cumprod'.
+
+
+ Optional argument
+ If True, perform exclusive cumprod.
+
+
+ Optional argument
+ A bool (default: False).
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ By default, this op performs an inclusive cumprod, which means that the first
+ element of the input is identical to the first element of the output:
+
+
+ tf.cumprod([a, b, c]) # => [a, a * b, a * b * c]
+
+
+ By setting the exclusive kwarg to True, an exclusive cumprod is
+ performed instead:
+
+
+ tf.cumprod([a, b, c], exclusive=True) # => [1, a, a * b]
+
+
+ By setting the reverse kwarg to True, the cumprod is performed in the
+ opposite direction:
+
+
+ tf.cumprod([a, b, c], reverse=True) # => [a * b * c, b * c, c]
+
+
+ This is more efficient than using separate tf.reverse ops.
+
+ The reverse and exclusive kwargs can also be combined:
+
+
+ tf.cumprod([a, b, c], exclusive=True, reverse=True) # => [b * c, c, 1]
+
+
+
+
+
+ Compute the cumulative sum of the tensor x along axis.
+
+
+ A Tensor. Must be one of the following types: float32, float64,
+ int64, int32, uint8, uint16, int16, int8, complex64,
+ complex128, qint8, quint8, qint32, half.
+
+
+ A Tensor of type int32 (default: 0). Must be in the range
+ [-rank(x), rank(x)).
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Cumsum'.
+
+
+ Optional argument
+ If True, perform exclusive cumsum.
+
+
+ Optional argument
+ A bool (default: False).
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ By default, this op performs an inclusive cumsum, which means that the first
+ element of the input is identical to the first element of the output:
+
+
+ tf.cumsum([a, b, c]) # => [a, a + b, a + b + c]
+
+
+ By setting the exclusive kwarg to True, an exclusive cumsum is
+ performed instead:
+
+
+ tf.cumsum([a, b, c], exclusive=True) # => [0, a, a + b]
+
+
+ By setting the reverse kwarg to True, the cumsum is performed in the
+ opposite direction:
+
+
+ tf.cumsum([a, b, c], reverse=True) # => [a + b + c, b + c, c]
+
+
+ This is more efficient than using separate tf.reverse ops.
+
+ The reverse and exclusive kwargs can also be combined:
+
+
+ tf.cumsum([a, b, c], exclusive=True, reverse=True) # => [b + c, c, 0]
+
+
+
+
+
+ Returns the dimension index in the destination data format given the one in
+
+
+ A Tensor with each element as a dimension index in source data format.
+ Must be in the range [-4, 4).
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DataFormatDimMap'.
+
+
+ Optional argument
+ source data format.
+
+
+ Optional argument
+ destination data format.
+
+
+ A Tensor with each element as a dimension index in destination data format.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ the source data format.
+
+
+
+
+ Returns the permuted vector/tensor in the destination data format given the
+
+
+ Vector of size 4 or Tensor of shape (4, 2) in source data format.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DataFormatVecPermute'.
+
+
+ Optional argument
+ source data format.
+
+
+ Optional argument
+ destination data format.
+
+
+ Vector of size 4 or Tensor of shape (4, 2) in destination data format.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ one in the source data format.
+
+
+
+
+ Returns a serialized GraphDef representing input_dataset.
+
+
+ A variant tensor representing the dataset to return the graph representation for.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DatasetToGraph'.
+
+
+ The graph representation of the dataset (as serialized GraphDef).
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Returns a graph representation for input_dataset.
+
+
+
+
+ Outputs the single element from the given dataset.
+
+
+ A handle to a dataset that contains a single element.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DatasetToSingleElement'.
+
+
+
+
+
+
+ The components of the single element of input.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Identity op for gradient debugging.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DebugGradientIdentity'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This op is hidden from public in Python. It is used by TensorFlow Debugger to
+ register gradient tensors for gradient debugging.
+ This op operates on non-reference-type tensors.
+
+
+
+
+ Identity op for gradient debugging.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DebugGradientRefIdentity'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This op is hidden from public in Python. It is used by TensorFlow Debugger to
+ register gradient tensors for gradient debugging.
+ This op operates on reference-type tensors.
+
+
+
+
+ Debug Identity Op.
+
+
+ Input tensor, non-Reference type.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DebugIdentity'.
+
+
+ Optional argument
+
+
+ Optional argument
+ Name of the input tensor.
+
+
+ Optional argument
+ List of URLs to debug targets, e.g.,
+ file:///foo/tfdbg_dump, grpc:://localhost:11011
+
+
+ Optional argument
+ Whether this op will be gated. If any of the debug_urls of this
+ debug node is of the grpc:// scheme, when the value of this attribute is set
+ to True, the data will not actually be sent via the grpc stream unless this
+ debug op has been enabled at the debug_url. If all of the debug_urls of this
+ debug node are of the grpc:// scheme and the debug op is enabled at none of
+ them, the output will be an empty Tensor.
+
+
+ Output tensor that equals the input tensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Provides an identity mapping of the non-Ref type input tensor for debugging.
+
+
+
+
+ Debug NaN Value Counter Op
+
+
+ Input tensor, non-Reference type.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DebugNanCount'.
+
+
+ Optional argument
+
+
+ Optional argument
+ Name of the input tensor.
+
+
+ Optional argument
+ List of URLs to debug targets, e.g.,
+ file:///foo/tfdbg_dump, grpc:://localhost:11011.
+
+
+ Optional argument
+ Whether this op will be gated. If any of the debug_urls of this
+ debug node is of the grpc:// scheme, when the value of this attribute is set
+ to True, the data will not actually be sent via the grpc stream unless this
+ debug op has been enabled at the debug_url. If all of the debug_urls of this
+ debug node are of the grpc:// scheme and the debug op is enabled at none of
+ them, the output will be an empty Tensor.
+
+
+ An integer output tensor that is the number of NaNs in the input.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Counts number of NaNs in the input tensor, for debugging.
+
+
+
+
+ Debug Numeric Summary Op.
+
+
+ Input tensor, non-Reference type, float or double.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DebugNumericSummary'.
+
+
+ Optional argument
+
+
+ Optional argument
+ Name of the input tensor.
+
+
+ Optional argument
+ List of URLs to debug targets, e.g.,
+ file:///foo/tfdbg_dump, grpc:://localhost:11011
+
+
+ Optional argument
+ (float) The lower bound <= which values will be included in the
+ generalized -inf count. Default: -inf.
+
+
+ Optional argument
+ (float) The upper bound >= which values will be included in the
+ generalized +inf count. Default: +inf.
+
+
+ Optional argument
+ (bool) Do not send data to the debug URLs unless at least one
+ of elements [2], [3] and [7] (i.e., the nan count and the generalized -inf and
+ inf counts) is non-zero.
+
+
+ Optional argument
+ Whether this op will be gated. If any of the debug_urls of this
+ debug node is of the grpc:// scheme, when the value of this attribute is set
+ to True, the data will not actually be sent via the grpc stream unless this
+ debug op has been enabled at the debug_url. If all of the debug_urls of this
+ debug node are of the grpc:// scheme and the debug op is enabled at none of
+ them, the output will be an empty Tensor.
+
+
+ A double tensor of shape [14 + nDimensions], where nDimensions is the
+ the number of dimensions of the tensor's shape. The elements of output are:
+ [0]: is initialized (1.0) or not (0.0).
+ [1]: total number of elements
+ [2]: NaN element count
+ [3]: generalized -inf count: elements <= lower_bound. lower_bound is -inf by
+ default.
+ [4]: negative element count (excluding -inf), if lower_bound is the default
+ -inf. Otherwise, this is the count of elements > lower_bound and < 0.
+ [5]: zero element count
+ [6]: positive element count (excluding +inf), if upper_bound is the default
+ -inf. Otherwise, this is the count of elements < upper_bound and > 0.
+ [7]: generalized +inf count, elements >= upper_bound. upper_bound is +inf by
+ default.
+ Output elements [1:8] are all zero, if the tensor is uninitialized.
+ [8]: minimum of all non-inf and non-NaN elements.
+ If uninitialized or no such element exists: +inf.
+ [9]: maximum of all non-inf and non-NaN elements.
+ If uninitialized or no such element exists: -inf.
+ [10]: mean of all non-inf and non-NaN elements.
+ If uninitialized or no such element exists: NaN.
+ [11]: variance of all non-inf and non-NaN elements.
+ If uninitialized or no such element exists: NaN.
+ [12]: Data type of the tensor encoded as an enum integer. See the DataType
+ proto for more details.
+ [13]: Number of dimensions of the tensor (ndims).
+ [14+]: Sizes of the dimensions.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Provide a basic summary of numeric value types, range and distribution.
+
+
+
+
+ Decode and Crop a JPEG-encoded image to a uint8 tensor.
+
+
+ 0-D. The JPEG-encoded image.
+
+
+ 1-D. The crop window: [crop_y, crop_x, crop_height, crop_width].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DecodeAndCropJpeg'.
+
+
+ Optional argument
+ Number of color channels for the decoded image.
+
+
+ Optional argument
+ Downscaling ratio.
+
+
+ Optional argument
+ If true use a slower but nicer upscaling of the
+ chroma planes (yuv420/422 only).
+
+
+ Optional argument
+ If true try to recover an image from truncated input.
+
+
+ Optional argument
+ The minimum required fraction of lines before a truncated
+ input is accepted.
+
+
+ Optional argument
+ string specifying a hint about the algorithm used for
+ decompression. Defaults to "" which maps to a system-specific
+ default. Currently valid values are ["INTEGER_FAST",
+ "INTEGER_ACCURATE"]. The hint may be ignored (e.g., the internal
+ jpeg library changes to a version that does not have that specific
+ option.)
+
+
+ 3-D with shape [height, width, channels]..
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The attr channels indicates the desired number of color channels for the
+ decoded image.
+
+ Accepted values are:
+
+ * 0: Use the number of channels in the JPEG-encoded image.
+ * 1: output a grayscale image.
+ * 3: output an RGB image.
+
+ If needed, the JPEG-encoded image is transformed to match the requested number
+ of color channels.
+
+ The attr ratio allows downscaling the image by an integer factor during
+ decoding. Allowed values are: 1, 2, 4, and 8. This is much faster than
+ downscaling the image later.
+
+
+ It is equivalent to a combination of decode and crop, but much faster by only
+ decoding partial jpeg image.
+
+
+
+
+ Decode web-safe base64-encoded strings.
+
+
+ Base64 strings to decode.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DecodeBase64'.
+
+
+ Decoded strings.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Input may or may not have padding at the end. See EncodeBase64 for padding.
+ Web-safe means that input must use - and _ instead of + and /.
+
+
+
+
+ Decode the first frame of a BMP-encoded image to a uint8 tensor.
+
+
+ 0-D. The BMP-encoded image.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DecodeBmp'.
+
+
+ Optional argument
+
+
+ 3-D with shape [height, width, channels]. RGB order
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The attr channels indicates the desired number of color channels for the
+ decoded image.
+
+ Accepted values are:
+
+ * 0: Use the number of channels in the BMP-encoded image.
+ * 3: output an RGB image.
+ * 4: output an RGBA image.
+
+
+
+
+ Decompress strings.
+
+
+ A Tensor of string which is compressed.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DecodeCompressed'.
+
+
+ Optional argument
+ A scalar containing either (i) the empty string (no
+ compression), (ii) "ZLIB", or (iii) "GZIP".
+
+
+ A Tensor with the same shape as input bytes, uncompressed
+ from bytes.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This op decompresses each element of the bytes input Tensor, which
+ is assumed to be compressed using the given compression_type.
+
+ The output is a string Tensor of the same shape as bytes,
+ each element containing the decompressed data from the corresponding
+ element in bytes.
+
+
+
+
+ Convert CSV records to tensors. Each column maps to one tensor.
+
+
+ Each string is a record/row in the csv and all records should have
+ the same format.
+
+
+ One tensor per column of the input record, with either a
+ scalar default value for that column or an empty vector if the column is
+ required.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DecodeCSV'.
+
+
+ Optional argument
+ char delimiter to separate fields in a record.
+
+
+ Optional argument
+ If false, treats double quotation marks as regular
+ characters inside of the string fields (ignoring RFC 4180, Section 2,
+ Bullet 5).
+
+
+ Optional argument
+ Additional string to recognize as NA/NaN.
+
+
+ Optional argument
+
+
+ Each tensor will have the same shape as records.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ RFC 4180 format is expected for the CSV records.
+ (https://tools.ietf.org/html/rfc4180)
+ Note that we allow leading and trailing spaces with int or float field.
+
+
+
+
+ Decode the first frame of a GIF-encoded image to a uint8 tensor.
+
+
+ 0-D. The GIF-encoded image.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DecodeGif'.
+
+
+ 4-D with shape [num_frames, height, width, 3]. RGB order
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ GIF with frame or transparency compression are not supported
+ convert animated GIF from compressed to uncompressed by:
+
+ convert $src.gif -coalesce $dst.gif
+
+ This op also supports decoding JPEGs and PNGs, though it is cleaner to use
+ tf.image.decode_image.
+
+
+
+
+ Decode a JPEG-encoded image to a uint8 tensor.
+
+
+ 0-D. The JPEG-encoded image.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DecodeJpeg'.
+
+
+ Optional argument
+ Number of color channels for the decoded image.
+
+
+ Optional argument
+ Downscaling ratio.
+
+
+ Optional argument
+ If true use a slower but nicer upscaling of the
+ chroma planes (yuv420/422 only).
+
+
+ Optional argument
+ If true try to recover an image from truncated input.
+
+
+ Optional argument
+ The minimum required fraction of lines before a truncated
+ input is accepted.
+
+
+ Optional argument
+ string specifying a hint about the algorithm used for
+ decompression. Defaults to "" which maps to a system-specific
+ default. Currently valid values are ["INTEGER_FAST",
+ "INTEGER_ACCURATE"]. The hint may be ignored (e.g., the internal
+ jpeg library changes to a version that does not have that specific
+ option.)
+
+
+ 3-D with shape [height, width, channels]..
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The attr channels indicates the desired number of color channels for the
+ decoded image.
+
+ Accepted values are:
+
+ * 0: Use the number of channels in the JPEG-encoded image.
+ * 1: output a grayscale image.
+ * 3: output an RGB image.
+
+ If needed, the JPEG-encoded image is transformed to match the requested number
+ of color channels.
+
+ The attr ratio allows downscaling the image by an integer factor during
+ decoding. Allowed values are: 1, 2, 4, and 8. This is much faster than
+ downscaling the image later.
+
+
+ This op also supports decoding PNGs and non-animated GIFs since the interface is
+ the same, though it is cleaner to use tf.image.decode_image.
+
+
+
+
+ Convert JSON-encoded Example records to binary protocol buffer strings.
+
+
+ Each string is a JSON object serialized according to the JSON
+ mapping of the Example proto.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DecodeJSONExample'.
+
+
+ Each string is a binary Example protocol buffer corresponding
+ to the respective element of json_examples.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This op translates a tensor containing Example records, encoded using
+ the [standard JSON
+ mapping](https://developers.google.com/protocol-buffers/docs/proto3#json),
+ into a tensor containing the same records encoded as binary protocol
+ buffers. The resulting tensor can then be fed to any of the other
+ Example-parsing ops.
+
+
+
+
+ Decode a PNG-encoded image to a uint8 or uint16 tensor.
+
+
+ 0-D. The PNG-encoded image.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DecodePng'.
+
+
+ Optional argument
+ Number of color channels for the decoded image.
+
+
+ Optional argument
+
+
+ 3-D with shape [height, width, channels].
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The attr channels indicates the desired number of color channels for the
+ decoded image.
+
+ Accepted values are:
+
+ * 0: Use the number of channels in the PNG-encoded image.
+ * 1: output a grayscale image.
+ * 3: output an RGB image.
+ * 4: output an RGBA image.
+
+ If needed, the PNG-encoded image is transformed to match the requested number
+ of color channels.
+
+ This op also supports decoding JPEGs and non-animated GIFs since the interface
+ is the same, though it is cleaner to use tf.image.decode_image.
+
+
+
+
+ The op extracts fields from a serialized protocol buffers message into tensors.
+
+
+ Tensor of serialized protos with shape batch_shape.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DecodeProtoV2'.
+
+
+ Optional argument
+ Either the special value local:// or a path to a file containing
+ a serialized FileDescriptorSet.
+
+
+ Optional argument
+ Either binary or text.
+
+
+ Optional argument
+ Whether to sanitize the result or not.
+
+
+ Name of the proto message type to decode.
+
+
+ List of strings containing proto field names.
+
+
+ List of TF types to use for the respective field in field_names.
+
+
+ Returns a tuple with multiple values, as follows:
+ sizes: Tensor of int32 with shape [batch_shape, len(field_names)].
+ Each entry is the number of values found for the corresponding field.
+ Optional fields may have 0 or 1 values.
+ values: List of tensors containing values for the corresponding field.
+ values[i] has datatype output_types[i]
+ and shape [batch_shape, max(sizes[...,i])].
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ The decode_proto op extracts fields from a serialized protocol buffers
+ message into tensors. The fields in field_names are decoded and converted
+ to the corresponding output_types if possible.
+
+ A message_type name must be provided to give context for the field
+ names. The actual message descriptor can be looked up either in the
+ linked-in descriptor pool or a filename provided by the caller using
+ the descriptor_source attribute.
+
+ Each output tensor is a dense tensor. This means that it is padded to
+ hold the largest number of repeated elements seen in the input
+ minibatch. (The shape is also padded by one to prevent zero-sized
+ dimensions). The actual repeat counts for each example in the
+ minibatch can be found in the sizes output. In many cases the output
+ of decode_proto is fed immediately into tf.squeeze if missing values
+ are not a concern. When using tf.squeeze, always pass the squeeze
+ dimension explicitly to avoid surprises.
+
+ For the most part, the mapping between Proto field types and
+ TensorFlow dtypes is straightforward. However, there are a few
+ special cases:
+
+ - A proto field that contains a submessage or group can only be converted
+ to DT_STRING (the serialized submessage). This is to reduce the
+ complexity of the API. The resulting string can be used as input
+ to another instance of the decode_proto op.
+
+ - TensorFlow lacks support for unsigned integers. The ops represent uint64
+ types as a DT_INT64 with the same twos-complement bit pattern
+ (the obvious way). Unsigned int32 values can be represented exactly by
+ specifying type DT_INT64, or using twos-complement if the caller
+ specifies DT_INT32 in the output_types attribute.
+
+ The descriptor_source attribute selects a source of protocol
+ descriptors to consult when looking up message_type. This may be a
+ filename containing a serialized FileDescriptorSet message,
+ or the special value local://, in which case only descriptors linked
+ into the code will be searched; the filename can be on any filesystem
+ accessible to TensorFlow.
+
+ You can build a descriptor_source file using the --descriptor_set_out
+ and --include_imports options to the protocol compiler protoc.
+
+ The local:// database only covers descriptors linked into the
+ code via C++ libraries, not Python imports. You can link in a proto descriptor
+ by creating a cc_library target with alwayslink=1.
+
+ Both binary and text proto serializations are supported, and can be
+ chosen using the format attribute.
+
+
+
+
+ Reinterpret the bytes of a string as a vector of numbers.
+
+
+ All the elements must have the same length.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DecodeRaw'.
+
+
+ Optional argument
+ Whether the input bytes are in little-endian order.
+ Ignored for out_type values that are stored in a single byte like
+ uint8.
+
+
+
+
+ A Tensor with one more dimension than the input bytes. The
+ added dimension will have size equal to the length of the elements
+ of bytes divided by the number of bytes to represent out_type.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Decode a 16-bit PCM WAV file to a float tensor.
+
+
+ The WAV-encoded audio, usually from a file.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DecodeWav'.
+
+
+ Optional argument
+ Number of sample channels wanted.
+
+
+ Optional argument
+ Length of audio requested.
+
+
+ Returns a tuple with multiple values, as follows:
+ audio: 2-D with shape [length, channels].
+ sample_rate: Scalar holding the sample rate found in the WAV header.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ The -32768 to 32767 signed 16-bit values will be scaled to -1.0 to 1.0 in float.
+
+ When desired_channels is set, if the input contains fewer channels than this
+ then the last channel will be duplicated to give the requested number, else if
+ the input has more channels than requested then the additional channels will be
+ ignored.
+
+ If desired_samples is set, then the audio will be cropped or padded with zeroes
+ to the requested length.
+
+ The first output contains a Tensor with the content of the audio samples. The
+ lowest dimension will be the number of channels, and the second will be the
+ number of samples. For example, a ten-sample-long stereo WAV file should give an
+ output shape of [10, 2].
+
+
+
+
+ Makes a copy of x.
+
+
+ The source tensor of type T.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DeepCopy'.
+
+
+ y: A Tensor of type T. A copy of x. Guaranteed that y
+ is not an alias of x.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Delete the tensor specified by its handle in the session.
+
+
+ The handle for a tensor stored in the session state.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DeleteSessionTensor'.
+
+
+ Returns the description of the operation
+
+
+
+
+ Applies set operation along last dimension of 2 Tensor inputs.
+
+
+ Tensor with rank n. 1st n-1 dimensions must be the same as set2.
+ Dimension n contains values in a set, duplicates are allowed but ignored.
+
+
+ Tensor with rank n. 1st n-1 dimensions must be the same as set1.
+ Dimension n contains values in a set, duplicates are allowed but ignored.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DenseToDenseSetOperation'.
+
+
+ Optional argument
+
+
+
+
+ Returns a tuple with multiple values, as follows:
+ result_indices: 2D indices of a SparseTensor.
+ result_values: 1D values of a SparseTensor.
+ result_shape: 1D Tensor shape of a SparseTensor. result_shape[0...n-1] is
+ the same as the 1st n-1 dimensions of set1 and set2, result_shape[n]
+ is the max result set size across all 0...n-1 dimensions.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ See SetOperationOp::SetOperationFromContext for values of set_operation.
+
+ Output result is a SparseTensor represented by result_indices,
+ result_values, and result_shape. For set1 and set2 ranked n, this
+ has rank n and the same 1st n-1 dimensions as set1 and set2. The nth
+ dimension contains the result of set_operation applied to the corresponding
+ [0...n-1] dimension of set.
+
+
+
+
+ Applies set operation along last dimension of Tensor and SparseTensor.
+
+
+ Tensor with rank n. 1st n-1 dimensions must be the same as set2.
+ Dimension n contains values in a set, duplicates are allowed but ignored.
+
+
+ 2D Tensor, indices of a SparseTensor. Must be in row-major
+ order.
+
+
+ 1D Tensor, values of a SparseTensor. Must be in row-major
+ order.
+
+
+ 1D Tensor, shape of a SparseTensor. set2_shape[0...n-1] must
+ be the same as the 1st n-1 dimensions of set1, result_shape[n] is the
+ max set size across n-1 dimensions.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DenseToSparseSetOperation'.
+
+
+ Optional argument
+
+
+
+
+ Returns a tuple with multiple values, as follows:
+ result_indices: 2D indices of a SparseTensor.
+ result_values: 1D values of a SparseTensor.
+ result_shape: 1D Tensor shape of a SparseTensor. result_shape[0...n-1] is
+ the same as the 1st n-1 dimensions of set1 and set2, result_shape[n]
+ is the max result set size across all 0...n-1 dimensions.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ See SetOperationOp::SetOperationFromContext for values of set_operation.
+
+ Input set2 is a SparseTensor represented by set2_indices, set2_values,
+ and set2_shape. For set2 ranked n, 1st n-1 dimensions must be the same
+ as set1. Dimension n contains values in a set, duplicates are allowed but
+ ignored.
+
+ If validate_indices is True, this op validates the order and range of set2
+ indices.
+
+ Output result is a SparseTensor represented by result_indices,
+ result_values, and result_shape. For set1 and set2 ranked n, this
+ has rank n and the same 1st n-1 dimensions as set1 and set2. The nth
+ dimension contains the result of set_operation applied to the corresponding
+ [0...n-1] dimension of set.
+
+
+
+
+ DepthToSpace for tensors of type T.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DepthToSpace'.
+
+
+ Optional argument
+
+
+ The size of the spatial block, same as in Space2Depth.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Rearranges data from depth into blocks of spatial data.
+ This is the reverse transformation of SpaceToDepth. More specifically,
+ this op outputs a copy of the input tensor where values from the depth
+ dimension are moved in spatial blocks to the height and width dimensions.
+ The attr block_size indicates the input block size and how the data is moved.
+
+ * Chunks of data of size block_size * block_size from depth are rearranged
+ into non-overlapping blocks of size block_size x block_size
+ * The width the output tensor is input_depth * block_size, whereas the
+ height is input_height * block_size.
+ * The Y, X coordinates within each block of the output image are determined
+ by the high order component of the input channel index.
+ * The depth of the input tensor must be divisible by
+ block_size * block_size.
+
+ The data_format attr specifies the layout of the input and output tensors
+ with the following options:
+ "NHWC": [ batch, height, width, channels ]
+ "NCHW": [ batch, channels, height, width ]
+ "NCHW_VECT_C":
+ qint8 [ batch, channels / 4, height, width, 4 ]
+
+ It is useful to consider the operation as transforming a 6-D Tensor.
+ e.g. for data_format = NHWC,
+ Each element in the input tensor can be specified via 6 coordinates,
+ ordered by decreasing memory layout significance as:
+ n,iY,iX,bY,bX,oC (where n=batch index, iX, iY means X or Y coordinates
+ within the input image, bX, bY means coordinates
+ within the output block, oC means output channels).
+ The output would be the input transposed to the following layout:
+ n,iY,bY,iX,bX,oC
+
+ This operation is useful for resizing the activations between convolutions
+ (but keeping all data), e.g. instead of pooling. It is also useful for training
+ purely convolutional models.
+
+ For example, given an input of shape [1, 1, 1, 4], data_format = "NHWC" and
+ block_size = 2:
+
+
+ x = [[[[1, 2, 3, 4]]]]
+
+
+
+ This operation will output a tensor of shape [1, 2, 2, 1]:
+
+
+ [[[[1], [2]],
+ [[3], [4]]]]
+
+
+ Here, the input has a batch of 1 and each batch element has shape [1, 1, 4],
+ the corresponding output will have 2x2 elements and will have a depth of
+ 1 channel (1 = 4 / (block_size * block_size)).
+ The output element shape is [2, 2, 1].
+
+ For an input tensor with larger depth, here of shape [1, 1, 1, 12], e.g.
+
+
+ x = [[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]]
+
+
+ This operation, for block size of 2, will return the following tensor of shape
+ [1, 2, 2, 3]
+
+
+ [[[[1, 2, 3], [4, 5, 6]],
+ [[7, 8, 9], [10, 11, 12]]]]
+
+
+
+ Similarly, for the following input of shape [1 2 2 4], and a block size of 2:
+
+
+ x = [[[[1, 2, 3, 4],
+ [5, 6, 7, 8]],
+ [[9, 10, 11, 12],
+ [13, 14, 15, 16]]]]
+
+
+ the operator will return the following tensor of shape [1 4 4 1]:
+
+
+ x = [[[ [1], [2], [5], [6]],
+ [ [3], [4], [7], [8]],
+ [ [9], [10], [13], [14]],
+ [ [11], [12], [15], [16]]]]
+
+
+
+
+
+
+ Computes a 2-D depthwise convolution given 4-D input and filter tensors.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DepthwiseConv2dNative'.
+
+
+ Optional argument
+ Specify the data format of the input and output data. With the
+ default format "NHWC", the data is stored in the order of:
+ [batch, height, width, channels].
+ Alternatively, the format could be "NCHW", the data storage order of:
+ [batch, channels, height, width].
+
+
+ Optional argument
+ 1-D tensor of length 4. The dilation factor for each dimension of
+ input. If set to k > 1, there will be k-1 skipped cells between each filter
+ element on that dimension. The dimension order is determined by the value of
+ data_format, see above for details. Dilations in the batch and depth
+ dimensions must be 1.
+
+
+ 1-D of length 4. The stride of the sliding window for each dimension
+ of input.
+
+
+ The type of padding algorithm to use.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Given an input tensor of shape [batch, in_height, in_width, in_channels]
+ and a filter / kernel tensor of shape
+ [filter_height, filter_width, in_channels, channel_multiplier], containing
+ in_channels convolutional filters of depth 1, depthwise_conv2d applies
+ a different filter to each input channel (expanding from 1 channel to
+ channel_multiplier channels for each), then concatenates the results
+ together. Thus, the output has in_channels * channel_multiplier channels.
+
+
+ for k in 0..in_channels-1
+ for q in 0..channel_multiplier-1
+ output[b, i, j, k * channel_multiplier + q] =
+ sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] *
+ filter[di, dj, k, q]
+
+
+ Must have strides[0] = strides[3] = 1. For the most common case of the same
+ horizontal and vertices strides, strides = [1, stride, stride, 1].
+
+
+
+
+ Computes the gradients of depthwise convolution with respect to the filter.
+
+
+ 4-D with shape based on data_format. For example, if
+ data_format is 'NHWC' then input is a 4-D [batch, in_height,
+ in_width, in_channels] tensor.
+
+
+ An integer vector representing the tensor shape of filter,
+ where filter is a 4-D
+ [filter_height, filter_width, in_channels, depthwise_multiplier] tensor.
+
+
+ 4-D with shape based on data_format.
+ For example, if data_format is 'NHWC' then
+ out_backprop shape is [batch, out_height, out_width, out_channels].
+ Gradients w.r.t. the output of the convolution.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DepthwiseConv2dNativeBackpropFilter'.
+
+
+ Optional argument
+ Specify the data format of the input and output data. With the
+ default format "NHWC", the data is stored in the order of:
+ [batch, height, width, channels].
+ Alternatively, the format could be "NCHW", the data storage order of:
+ [batch, channels, height, width].
+
+
+ Optional argument
+ 1-D tensor of length 4. The dilation factor for each dimension of
+ input. If set to k > 1, there will be k-1 skipped cells between each filter
+ element on that dimension. The dimension order is determined by the value of
+ data_format, see above for details. Dilations in the batch and depth
+ dimensions must be 1.
+
+
+ The stride of the sliding window for each dimension of the input
+ of the convolution.
+
+
+ The type of padding algorithm to use.
+
+
+ 4-D with shape
+ [filter_height, filter_width, in_channels, out_channels]. Gradient w.r.t.
+ the filter input of the convolution.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes the gradients of depthwise convolution with respect to the input.
+
+
+ An integer vector representing the shape of input, based
+ on data_format. For example, if data_format is 'NHWC' then
+ input is a 4-D [batch, height, width, channels] tensor.
+
+
+ 4-D with shape
+ [filter_height, filter_width, in_channels, depthwise_multiplier].
+
+
+ 4-D with shape based on data_format.
+ For example, if data_format is 'NHWC' then
+ out_backprop shape is [batch, out_height, out_width, out_channels].
+ Gradients w.r.t. the output of the convolution.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DepthwiseConv2dNativeBackpropInput'.
+
+
+ Optional argument
+ Specify the data format of the input and output data. With the
+ default format "NHWC", the data is stored in the order of:
+ [batch, height, width, channels].
+ Alternatively, the format could be "NCHW", the data storage order of:
+ [batch, channels, height, width].
+
+
+ Optional argument
+ 1-D tensor of length 4. The dilation factor for each dimension of
+ input. If set to k > 1, there will be k-1 skipped cells between each filter
+ element on that dimension. The dimension order is determined by the value of
+ data_format, see above for details. Dilations in the batch and depth
+ dimensions must be 1.
+
+
+ The stride of the sliding window for each dimension of the input
+ of the convolution.
+
+
+ The type of padding algorithm to use.
+
+
+ 4-D with shape according to data_format. For example, if
+ data_format is 'NHWC', output shape is [batch, in_height,
+ in_width, in_channels]. Gradient w.r.t. the input of the
+ convolution.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Dequantize the 'input' tensor into a float Tensor.
+
+
+
+
+ The minimum scalar value possibly produced for the input.
+
+
+ The maximum scalar value possibly produced for the input.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Dequantize'.
+
+
+ Optional argument
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ [min_range, max_range] are scalar floats that specify the range for
+ the 'input' data. The 'mode' attribute controls exactly which calculations are
+ used to convert the float values to their quantized equivalents.
+
+ In 'MIN_COMBINED' mode, each value of the tensor will undergo the following:
+
+
+ if T == qint8: in[i] += (range(T) + 1)/ 2.0
+ out[i] = min_range + (in[i]* (max_range - min_range) / range(T))
+
+ here range(T) = numeric_limits<T>::max() - numeric_limits<T>::min()
+
+ *MIN_COMBINED Mode Example*
+
+ If the input comes from a QuantizedRelu6, the output type is
+ quint8 (range of 0-255) but the possible range of QuantizedRelu6 is
+ 0-6. The min_range and max_range values are therefore 0.0 and 6.0.
+ Dequantize on quint8 will take each value, cast to float, and multiply
+ by 6 / 255.
+ Note that if quantizedtype is qint8, the operation will additionally add
+ each value by 128 prior to casting.
+
+ If the mode is 'MIN_FIRST', then this approach is used:
+
+
+ num_discrete_values = 1 << (# of bits in T)
+ range_adjust = num_discrete_values / (num_discrete_values - 1)
+ range = (range_max - range_min) * range_adjust
+ range_scale = range / num_discrete_values
+ const double offset_input = static_cast<double>(input) - lowest_quantized;
+ result = range_min + ((input - numeric_limits<T>::min()) * range_scale)
+
+
+ *SCALED mode Example*
+
+ SCALED mode matches the quantization approach used in
+ QuantizeAndDequantize{V2|V3}.
+
+ If the mode is SCALED, we do not use the full range of the output type,
+ choosing to elide the lowest possible value for symmetry (e.g., output range is
+ -127 to 127, not -128 to 127 for signed 8 bit quantization), so that 0.0 maps to
+ 0.
+
+ We first find the range of values in our tensor. The
+ range we use is always centered on 0, so we find m such that
+
+ m = max(abs(input_min), abs(input_max))
+
+
+ Our input tensor range is then [-m, m].
+
+ Next, we choose our fixed-point quantization buckets, [min_fixed, max_fixed].
+ If T is signed, this is
+
+ num_bits = sizeof(T) * 8
+ [min_fixed, max_fixed] =
+ [-(1 << (num_bits - 1) - 1), (1 << (num_bits - 1)) - 1]
+
+
+ Otherwise, if T is unsigned, the fixed-point range is
+
+ [min_fixed, max_fixed] = [0, (1 << num_bits) - 1]
+
+
+ From this we compute our scaling factor, s:
+
+ s = (2 * m) / (max_fixed - min_fixed)
+
+
+ Now we can dequantize the elements of our tensor:
+
+ result = input * s
+
+
+
+
+
+ Converts the given variant tensor to an iterator and stores it in the given resource.
+
+
+ A handle to an iterator resource.
+
+
+ A variant tensor storing the state of the iterator contained in the
+ resource.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DeserializeIterator'.
+
+
+ Returns the description of the operation
+
+
+
+
+ Deserialize and concatenate SparseTensors from a serialized minibatch.
+
+
+ 2-D, The N serialized SparseTensor objects.
+ Must have 3 columns.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DeserializeManySparse'.
+
+
+ The dtype of the serialized SparseTensor objects.
+
+
+ Returns a tuple with multiple values, as follows:
+ sparse_indices:
+ sparse_values:
+ sparse_shape:
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ The input serialized_sparse must be a string matrix of shape [N x 3] where
+ N is the minibatch size and the rows correspond to packed outputs of
+ SerializeSparse. The ranks of the original SparseTensor objects
+ must all match. When the final SparseTensor is created, it has rank one
+ higher than the ranks of the incoming SparseTensor objects
+ (they have been concatenated along a new row dimension).
+
+ The output SparseTensor object's shape values for all dimensions but the
+ first are the max across the input SparseTensor objects' shape values
+ for the corresponding dimensions. Its first shape value is N, the minibatch
+ size.
+
+ The input SparseTensor objects' indices are assumed ordered in
+ standard lexicographic order. If this is not the case, after this
+ step run SparseReorder to restore index ordering.
+
+ For example, if the serialized input is a [2 x 3] matrix representing two
+ original SparseTensor objects:
+
+ index = [ 0]
+ [10]
+ [20]
+ values = [1, 2, 3]
+ shape = [50]
+
+ and
+
+ index = [ 2]
+ [10]
+ values = [4, 5]
+ shape = [30]
+
+ then the final deserialized SparseTensor will be:
+
+ index = [0 0]
+ [0 10]
+ [0 20]
+ [1 2]
+ [1 10]
+ values = [1, 2, 3, 4, 5]
+ shape = [2 50]
+
+
+
+
+ Deserialize SparseTensor objects.
+
+
+ The serialized SparseTensor objects. The last dimension
+ must have 3 columns.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DeserializeSparse'.
+
+
+ The dtype of the serialized SparseTensor objects.
+
+
+ Returns a tuple with multiple values, as follows:
+ sparse_indices:
+ sparse_values:
+ sparse_shape:
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ The input serialized_sparse must have the shape [?, ?, ..., ?, 3] where
+ the last dimension stores serialized SparseTensor objects and the other N
+ dimensions (N >= 0) correspond to a batch. The ranks of the original
+ SparseTensor objects must all match. When the final SparseTensor is
+ created, its rank is the rank of the incoming SparseTensor objects plus N;
+ the sparse tensors have been concatenated along new dimensions, one for each
+ batch.
+
+ The output SparseTensor object's shape values for the original dimensions
+ are the max across the input SparseTensor objects' shape values for the
+ corresponding dimensions. The new dimensions match the size of the batch.
+
+ The input SparseTensor objects' indices are assumed ordered in
+ standard lexicographic order. If this is not the case, after this
+ step run SparseReorder to restore index ordering.
+
+ For example, if the serialized input is a [2 x 3] matrix representing two
+ original SparseTensor objects:
+
+ index = [ 0]
+ [10]
+ [20]
+ values = [1, 2, 3]
+ shape = [50]
+
+ and
+
+ index = [ 2]
+ [10]
+ values = [4, 5]
+ shape = [30]
+
+ then the final deserialized SparseTensor will be:
+
+ index = [0 0]
+ [0 10]
+ [0 20]
+ [1 2]
+ [1 10]
+ values = [1, 2, 3, 4, 5]
+ shape = [2 50]
+
+
+
+
+ Deletes the resource specified by the handle.
+
+
+ handle to the resource to delete.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DestroyResourceOp'.
+
+
+ Optional argument
+ whether to ignore the error when the resource
+ doesn't exist.
+
+
+ Returns the description of the operation
+
+
+ All subsequent operations using the resource will result in a NotFound
+ error status.
+
+
+
+
+ Destroys the temporary variable and returns its final value.
+
+
+ A reference to the temporary variable tensor.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DestroyTemporaryVariable'.
+
+
+ Name of the temporary variable, usually the name of the matching
+ 'TemporaryVariable' op.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Sets output to the value of the Tensor pointed to by 'ref', then destroys
+ the temporary variable called 'var_name'.
+ All other uses of 'ref' *must* have executed before this op.
+ This is typically achieved by chaining the ref through each assign op, or by
+ using control dependencies.
+
+ Outputs the final value of the tensor pointed to by 'ref'.
+
+
+
+
+ Returns a diagonal tensor with a given diagonal values.
+
+
+ Rank k tensor where k is at most 1.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Diag'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Given a diagonal, this operation returns a tensor with the diagonal and
+ everything else padded with zeros. The diagonal is computed as follows:
+
+ Assume diagonal has dimensions [D1,..., Dk], then the output is a tensor of
+ rank 2k with dimensions [D1,..., Dk, D1,..., Dk] where:
+
+ output[i1,..., ik, i1,..., ik] = diagonal[i1, ..., ik] and 0 everywhere else.
+
+ For example:
+
+
+ # 'diagonal' is [1, 2, 3, 4]
+ tf.diag(diagonal) ==> [[1, 0, 0, 0]
+ [0, 2, 0, 0]
+ [0, 0, 3, 0]
+ [0, 0, 0, 4]]
+
+
+
+
+
+ Returns the diagonal part of the tensor.
+
+
+ Rank k tensor where k is even and not zero.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DiagPart'.
+
+
+ The extracted diagonal.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation returns a tensor with the diagonal part
+ of the input. The diagonal part is computed as follows:
+
+ Assume input has dimensions [D1,..., Dk, D1,..., Dk], then the output is a
+ tensor of rank k with dimensions [D1,..., Dk] where:
+
+ diagonal[i1,..., ik] = input[i1, ..., ik, i1,..., ik].
+
+ For example:
+
+
+ # 'input' is [[1, 0, 0, 0]
+ [0, 2, 0, 0]
+ [0, 0, 3, 0]
+ [0, 0, 0, 4]]
+
+ tf.diag_part(input) ==> [1, 2, 3, 4]
+
+
+
+
+
+ Computes Psi, the derivative of Lgamma (the log of the absolute value of
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Digamma'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Gamma(x)), element-wise.
+
+
+
+
+ Computes the grayscale dilation of 4-D input and 3-D filter tensors.
+
+
+ 4-D with shape [batch, in_height, in_width, depth].
+
+
+ 3-D with shape [filter_height, filter_width, depth].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Dilation2D'.
+
+
+ The stride of the sliding window for each dimension of the input
+ tensor. Must be: [1, stride_height, stride_width, 1].
+
+
+ The input stride for atrous morphological dilation. Must be:
+ [1, rate_height, rate_width, 1].
+
+
+ The type of padding algorithm to use.
+
+
+ 4-D with shape [batch, out_height, out_width, depth].
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The input tensor has shape [batch, in_height, in_width, depth] and the
+ filter tensor has shape [filter_height, filter_width, depth], i.e., each
+ input channel is processed independently of the others with its own structuring
+ function. The output tensor has shape
+ [batch, out_height, out_width, depth]. The spatial dimensions of the output
+ tensor depend on the padding algorithm. We currently only support the default
+ "NHWC" data_format.
+
+ In detail, the grayscale morphological 2-D dilation is the max-sum correlation
+ (for consistency with conv2d, we use unmirrored filters):
+
+ output[b, y, x, c] =
+ max_{dy, dx} input[b,
+ strides[1] * y + rates[1] * dy,
+ strides[2] * x + rates[2] * dx,
+ c] +
+ filter[dy, dx, c]
+
+ Max-pooling is a special case when the filter has size equal to the pooling
+ kernel size and contains all zeros.
+
+ Note on duality: The dilation of input by the filter is equal to the
+ negation of the erosion of -input by the reflected filter.
+
+
+
+
+ Computes the gradient of morphological 2-D dilation with respect to the filter.
+
+
+ 4-D with shape [batch, in_height, in_width, depth].
+
+
+ 3-D with shape [filter_height, filter_width, depth].
+
+
+ 4-D with shape [batch, out_height, out_width, depth].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Dilation2DBackpropFilter'.
+
+
+ 1-D of length 4. The stride of the sliding window for each dimension of
+ the input tensor. Must be: [1, stride_height, stride_width, 1].
+
+
+ 1-D of length 4. The input stride for atrous morphological dilation.
+ Must be: [1, rate_height, rate_width, 1].
+
+
+ The type of padding algorithm to use.
+
+
+ 3-D with shape [filter_height, filter_width, depth].
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes the gradient of morphological 2-D dilation with respect to the input.
+
+
+ 4-D with shape [batch, in_height, in_width, depth].
+
+
+ 3-D with shape [filter_height, filter_width, depth].
+
+
+ 4-D with shape [batch, out_height, out_width, depth].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Dilation2DBackpropInput'.
+
+
+ 1-D of length 4. The stride of the sliding window for each dimension of
+ the input tensor. Must be: [1, stride_height, stride_width, 1].
+
+
+ 1-D of length 4. The input stride for atrous morphological dilation.
+ Must be: [1, rate_height, rate_width, 1].
+
+
+ The type of padding algorithm to use.
+
+
+ 4-D with shape [batch, in_height, in_width, depth].
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Returns x / y element-wise.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Div'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ *NOTE*: Div supports broadcasting. More about broadcasting
+ [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+
+
+
+
+ Returns 0 if the denominator is zero.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DivNoNan'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+ *NOTE*: DivNoNan supports broadcasting. More about broadcasting
+ [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+
+
+
+
+ Draw bounding boxes on a batch of images.
+
+
+ 4-D with shape [batch, height, width, depth]. A batch of images.
+
+
+ 3-D with shape [batch, num_bounding_boxes, 4] containing bounding
+ boxes.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DrawBoundingBoxes'.
+
+
+ 4-D with the same shape as images. The batch of input images with
+ bounding boxes drawn on the images.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Outputs a copy of images but draws on top of the pixels zero or more bounding
+ boxes specified by the locations in boxes. The coordinates of the each
+ bounding box in boxes are encoded as [y_min, x_min, y_max, x_max]. The
+ bounding box coordinates are floats in [0.0, 1.0] relative to the width and
+ height of the underlying image.
+
+ For example, if an image is 100 x 200 pixels (height x width) and the bounding
+ box is [0.1, 0.2, 0.5, 0.9], the upper-left and bottom-right coordinates of
+ the bounding box will be (40, 10) to (180, 50) (in (x,y) coordinates).
+
+ Parts of the bounding box may fall outside the image.
+
+
+
+
+ Partitions data into num_partitions tensors using indices from partitions.
+
+
+
+
+ Any shape. Indices in the range [0, num_partitions).
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DynamicPartition'.
+
+
+ The number of partitions to output.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ For each index tuple js of size partitions.ndim, the slice data[js, ...]
+ becomes part of outputs[partitions[js]]. The slices with partitions[js] = i
+ are placed in outputs[i] in lexicographic order of js, and the first
+ dimension of outputs[i] is the number of entries in partitions equal to i.
+ In detail,
+
+
+ outputs[i].shape = [sum(partitions == i)] + data.shape[partitions.ndim:]
+
+ outputs[i] = pack([data[js, ...] for js if partitions[js] == i])
+
+
+ data.shape must start with partitions.shape.
+
+ For example:
+
+
+ # Scalar partitions.
+ partitions = 1
+ num_partitions = 2
+ data = [10, 20]
+ outputs[0] = [] # Empty with shape [0, 2]
+ outputs[1] = [[10, 20]]
+
+ # Vector partitions.
+ partitions = [0, 0, 1, 1, 0]
+ num_partitions = 2
+ data = [10, 20, 30, 40, 50]
+ outputs[0] = [10, 20, 50]
+ outputs[1] = [30, 40]
+
+
+ See dynamic_stitch for an example on how to merge partitions back.
+
+ <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+ <img style="width:100%" src="https://www.tensorflow.org/images/DynamicPartition.png" alt>
+ </div>
+
+
+
+
+ Interleave the values from the data tensors into a single tensor.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'DynamicStitch'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Builds a merged tensor such that
+
+
+ merged[indices[m][i, ..., j], ...] = data[m][i, ..., j, ...]
+
+
+ For example, if each indices[m] is scalar or vector, we have
+
+
+ # Scalar indices:
+ merged[indices[m], ...] = data[m][...]
+
+ # Vector indices:
+ merged[indices[m][i], ...] = data[m][i, ...]
+
+
+ Each data[i].shape must start with the corresponding indices[i].shape,
+ and the rest of data[i].shape must be constant w.r.t. i. That is, we
+ must have data[i].shape = indices[i].shape + constant. In terms of this
+ constant, the output shape is
+
+ merged.shape = [max(indices)] + constant
+
+ Values are merged in order, so if an index appears in both indices[m][i] and
+ indices[n][j] for (m,i) < (n,j) the slice data[n][j] will appear in the
+ merged result. If you do not need this guarantee, ParallelDynamicStitch might
+ perform better on some devices.
+
+ For example:
+
+
+ indices[0] = 6
+ indices[1] = [4, 1]
+ indices[2] = [[5, 2], [0, 3]]
+ data[0] = [61, 62]
+ data[1] = [[41, 42], [11, 12]]
+ data[2] = [[[51, 52], [21, 22]], [[1, 2], [31, 32]]]
+ merged = [[1, 2], [11, 12], [21, 22], [31, 32], [41, 42],
+ [51, 52], [61, 62]]
+
+
+ This method can be used to merge partitions created by dynamic_partition
+ as illustrated on the following example:
+
+
+ # Apply function (increments x_i) on elements for which a certain condition
+ # apply (x_i != -1 in this example).
+ x=tf.constant([0.1, -1., 5.2, 4.3, -1., 7.4])
+ condition_mask=tf.not_equal(x,tf.constant(-1.))
+ partitioned_data = tf.dynamic_partition(
+ x, tf.cast(condition_mask, tf.int32) , 2)
+ partitioned_data[1] = partitioned_data[1] + 1.0
+ condition_indices = tf.dynamic_partition(
+ tf.range(tf.shape(x)[0]), tf.cast(condition_mask, tf.int32) , 2)
+ x = tf.dynamic_stitch(condition_indices, partitioned_data)
+ # Here x=[1.1, -1., 6.2, 5.3, -1, 8.4], the -1. values remain
+ # unchanged.
+
+
+ <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+ <img style="width:100%" src="https://www.tensorflow.org/images/DynamicStitch.png" alt>
+ </div>
+
+
+
+
+ Computes the (possibly normalized) Levenshtein Edit Distance.
+
+
+ The indices of the hypothesis list SparseTensor.
+ This is an N x R int64 matrix.
+
+
+ The values of the hypothesis list SparseTensor.
+ This is an N-length vector.
+
+
+ The shape of the hypothesis list SparseTensor.
+ This is an R-length vector.
+
+
+ The indices of the truth list SparseTensor.
+ This is an M x R int64 matrix.
+
+
+ The values of the truth list SparseTensor.
+ This is an M-length vector.
+
+
+ truth indices, vector.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'EditDistance'.
+
+
+ Optional argument
+ boolean (if true, edit distances are normalized by length of truth).
+
+ The output is:
+
+
+ A dense float tensor with rank R - 1.
+
+ For the example input:
+
+ // hypothesis represents a 2x1 matrix with variable-length values:
+ // (0,0) = ["a"]
+ // (1,0) = ["b"]
+ hypothesis_indices = [[0, 0, 0],
+ [1, 0, 0]]
+ hypothesis_values = ["a", "b"]
+ hypothesis_shape = [2, 1, 1]
+
+ // truth represents a 2x2 matrix with variable-length values:
+ // (0,0) = []
+ // (0,1) = ["a"]
+ // (1,0) = ["b", "c"]
+ // (1,1) = ["a"]
+ truth_indices = [[0, 1, 0],
+ [1, 0, 0],
+ [1, 0, 1],
+ [1, 1, 0]]
+ truth_values = ["a", "b", "c", "a"]
+ truth_shape = [2, 2, 2]
+ normalize = true
+
+ The output will be:
+
+ // output is a 2x2 matrix with edit distances normalized by truth lengths.
+ output = [[inf, 1.0], // (0,0): no truth, (0,1): no hypothesis
+ [0.5, 1.0]] // (1,0): addition, (1,1): no hypothesis
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The inputs are variable-length sequences provided by SparseTensors
+ (hypothesis_indices, hypothesis_values, hypothesis_shape)
+ and
+ (truth_indices, truth_values, truth_shape).
+
+ The inputs are:
+
+
+
+
+ Computes exponential linear: exp(features) - 1 if < 0, features otherwise.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Elu'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ See [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
+ ](http://arxiv.org/abs/1511.07289)
+
+
+
+
+ Computes gradients for the exponential linear (Elu) operation.
+
+
+ The backpropagated gradients to the corresponding Elu operation.
+
+
+ The outputs of the corresponding Elu operation.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'EluGrad'.
+
+
+ The gradients: gradients * (outputs + 1) if outputs < 0,
+ gradients otherwise.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Creates a tensor with the given shape.
+
+ This operation creates a tensor of shape and dtype.
+
+
+ 1-D. Represents the shape of the output tensor.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Empty'.
+
+
+ Optional argument
+ If True, initialize the returned tensor with the default value of dtype. Otherwise, the implementation is free not to initializethe tensor's content.
+
+
+
+
+ A Tensor of type T.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Creates and returns an empty tensor list.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'EmptyTensorList'.
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ All list elements must be tensors of dtype element_dtype and shape compatible
+ with element_shape.
+
+ handle: an empty tensor list.
+ element_dtype: the type of elements in the list.
+ element_shape: a shape compatible with that of elements in the list.
+
+
+
+
+ Encode strings into web-safe base64 format.
+
+
+ Strings to be encoded.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'EncodeBase64'.
+
+
+ Optional argument
+ Bool whether padding is applied at the ends.
+
+
+ Input strings encoded in base64.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Refer to the following article for more information on base64 format:
+ en.wikipedia.org/wiki/Base64. Base64 strings may have padding with '=' at the
+ end so that the encoded has length multiple of 4. See Padding section of the
+ link above.
+
+ Web-safe means that the encoder uses - and _ instead of + and /.
+
+
+
+
+ JPEG-encode an image.
+
+
+ 3-D with shape [height, width, channels].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'EncodeJpeg'.
+
+
+ Optional argument
+ Per pixel image format.
+
+
+ Optional argument
+ Quality of the compression from 0 to 100 (higher is better and slower).
+
+
+ Optional argument
+ If True, create a JPEG that loads progressively (coarse to fine).
+
+
+ Optional argument
+ If True, spend CPU/RAM to reduce size with no quality change.
+
+
+ Optional argument
+ See http://en.wikipedia.org/wiki/Chroma_subsampling.
+
+
+ Optional argument
+ Unit used to specify x_density and y_density:
+ pixels per inch ('in') or centimeter ('cm').
+
+
+ Optional argument
+ Horizontal pixels per density unit.
+
+
+ Optional argument
+ Vertical pixels per density unit.
+
+
+ Optional argument
+ If not empty, embed this XMP metadata in the image header.
+
+
+ 0-D. JPEG-encoded image.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ image is a 3-D uint8 Tensor of shape [height, width, channels].
+
+ The attr format can be used to override the color format of the encoded
+ output. Values can be:
+
+ * '': Use a default format based on the number of channels in the image.
+ * grayscale: Output a grayscale JPEG image. The channels dimension
+ of image must be 1.
+ * rgb: Output an RGB JPEG image. The channels dimension
+ of image must be 3.
+
+ If format is not specified or is the empty string, a default format is picked
+ in function of the number of channels in image:
+
+ * 1: Output a grayscale image.
+ * 3: Output an RGB image.
+
+
+
+
+ PNG-encode an image.
+
+
+ 3-D with shape [height, width, channels].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'EncodePng'.
+
+
+ Optional argument
+ Compression level.
+
+
+ 0-D. PNG-encoded image.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ image is a 3-D uint8 or uint16 Tensor of shape [height, width, channels]
+ where channels is:
+
+ * 1: for grayscale.
+ * 2: for grayscale + alpha.
+ * 3: for RGB.
+ * 4: for RGBA.
+
+ The ZLIB compression level, compression, can be -1 for the PNG-encoder
+ default or a value from 0 to 9. 9 is the highest compression level, generating
+ the smallest output, but is slower.
+
+
+
+
+ The op serializes protobuf messages provided in the input tensors.
+
+
+ Tensor of int32 with shape [batch_shape, len(field_names)].
+
+
+ List of tensors containing values for the corresponding field.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'EncodeProto'.
+
+
+ Optional argument
+
+
+ List of strings containing proto field names.
+
+
+ Name of the proto message type to decode.
+
+
+ Tensor of serialized protos with shape batch_shape.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The types of the tensors in values must match the schema for the
+ fields specified in field_names. All the tensors in values must
+ have a common shape prefix, *batch_shape*.
+
+ The sizes tensor specifies repeat counts for each field. The repeat
+ count (last dimension) of a each tensor in values must be greater
+ than or equal to corresponding repeat count in sizes.
+
+ A message_type name must be provided to give context for the field
+ names. The actual message descriptor can be looked up either in the
+ linked-in descriptor pool or a filename provided by the caller using
+ the descriptor_source attribute.
+
+ The descriptor_source attribute selects a source of protocol
+ descriptors to consult when looking up message_type. This may be a
+ filename containing a serialized FileDescriptorSet message,
+ or the special value local://, in which case only descriptors linked
+ into the code will be searched; the filename can be on any filesystem
+ accessible to TensorFlow.
+
+ You can build a descriptor_source file using the --descriptor_set_out
+ and --include_imports options to the protocol compiler protoc.
+
+ The local:// database only covers descriptors linked into the
+ code via C++ libraries, not Python imports. You can link in a proto descriptor
+ by creating a cc_library target with alwayslink=1.
+
+ There are a few special cases in the value mapping:
+
+ Submessage and group fields must be pre-serialized as TensorFlow strings.
+
+ TensorFlow lacks support for unsigned int64s, so they must be
+ represented as tf.int64 with the same twos-complement bit pattern
+ (the obvious way).
+
+ Unsigned int32 values can be represented exactly with tf.int64, or
+ with sign wrapping if the input is of type tf.int32.
+
+
+
+
+ Encode audio data using the WAV file format.
+
+
+ 2-D with shape [length, channels].
+
+
+ Scalar containing the sample frequency.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'EncodeWav'.
+
+
+ 0-D. WAV-encoded file contents.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation will generate a string suitable to be saved out to create a .wav
+ audio file. It will be encoded in the 16-bit PCM format. It takes in float
+ values in the range -1.0f to 1.0f, and any outside that value will be clamped to
+ that range.
+
+ audio is a 2-D float Tensor of shape [length, channels].
+ sample_rate is a scalar Tensor holding the rate to use (e.g. 44100).
+
+
+
+
+ An op that enqueues a list of input batch tensors to TPUEmbedding.
+
+
+ A list of 1D tensors, one for each embedding table, containing the
+ indices into the tables.
+
+
+ A string input that overrides the mode specified in the
+ TPUEmbeddingConfiguration. Supported values are {'unspecified', 'inference',
+ 'training', 'backward_pass_only'}. When set to 'unspecified', the mode set
+ in TPUEmbeddingConfiguration is used, otherwise mode_override is used.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'EnqueueTPUEmbeddingIntegerBatch'.
+
+
+ Optional argument
+ The TPU device to use. Should be >= 0 and less than the number
+ of TPU cores in the task on which the node is placed.
+
+
+ Returns the description of the operation
+
+
+
+
+ An op that enqueues TPUEmbedding input indices from a SparseTensor.
+
+
+ A list of rank 1 Tensors specifying the training example and
+ feature to which the corresponding embedding_indices and aggregation_weights
+ values belong. sample_indices[i] must equal b * nf + f, where nf is the
+ number of features from the corresponding table, f is in [0, nf), and
+ b is in [0, batch size).
+
+
+ A list of rank 1 Tensors, indices into the embedding tables.
+
+
+ A list of rank 1 Tensors containing per sample -- i.e. per
+ (training example, feature) -- aggregation weights.
+
+
+ A string input that overrides the mode specified in the
+ TPUEmbeddingConfiguration. Supported values are {'unspecified', 'inference',
+ 'training', 'backward_pass_only'}. When set to 'unspecified', the mode set
+ in TPUEmbeddingConfiguration is used, otherwise mode_override is used.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'EnqueueTPUEmbeddingSparseBatch'.
+
+
+ Optional argument
+ The TPU device to use. Should be >= 0 and less than the number
+ of TPU cores in the task on which the node is placed.
+
+
+ Optional argument
+ A list of string scalars, one for each embedding table that specify
+ how to normalize the embedding activations after weighted summation.
+ Supported combiners are 'mean', 'sum', or 'sqrtn'. It is invalid to have
+ the sum of the weights be 0 for 'mean' or the sum of the squared weights be
+ 0 for 'sqrtn'. If combiners isn't passed, the default is to use 'sum' for
+ all tables.
+
+
+ Returns the description of the operation
+
+
+ This Op eases the porting of code that uses embedding_lookup_sparse(),
+ although some Python preprocessing of the SparseTensor arguments to
+ embedding_lookup_sparse() is required to produce the arguments to this Op,
+ since only a single EnqueueTPUEmbeddingSparseBatch Op is allowed per training
+ step.
+
+ The tensors at corresponding positions in the three input lists
+ must have the same shape, i.e. rank 1 with dim_size() equal to the total
+ number of lookups into the table described by the corresponding table_id.
+
+
+
+
+ This Op eases the porting of code that uses tf.nn.embedding_lookup_sparse().
+
+
+ A list of rank 1 Tensors specifying the training example to
+ which the corresponding embedding_indices and aggregation_weights values
+ belong. It corresponds to sp_ids.indices[:,0] in embedding_lookup_sparse().
+
+
+ A list of rank 1 Tensors, indices into the embedding tables.
+ It corresponds to sp_ids.values in embedding_lookup_sparse().
+
+
+ A list of rank 1 Tensors containing per training example
+ aggregation weights. It corresponds to sp_weights.values in
+ embedding_lookup_sparse().
+
+
+ A string input that overrides the mode specified in the
+ TPUEmbeddingConfiguration. Supported values are {'unspecified', 'inference',
+ 'training', 'backward_pass_only'}. When set to 'unspecified', the mode set
+ in TPUEmbeddingConfiguration is used, otherwise mode_override is used.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'EnqueueTPUEmbeddingSparseTensorBatch'.
+
+
+ Optional argument
+ The TPU device to use. Should be >= 0 and less than the number
+ of TPU cores in the task on which the node is placed.
+
+
+ Optional argument
+ A list of string scalars, one for each embedding table that specify
+ how to normalize the embedding activations after weighted summation.
+ Supported combiners are 'mean', 'sum', or 'sqrtn'. It is invalid to have
+ the sum of the weights be 0 for 'mean' or the sum of the squared weights be
+ 0 for 'sqrtn'. If combiners isn't passed, the default is to use 'sum' for
+ all tables.
+
+
+ A list of integers specifying the identifier of the embedding table
+ (offset of TableDescriptor in the TPUEmbeddingConfiguration) to lookup the
+ corresponding input. The ith input is looked up using table_ids[i]. The size
+ of the table_ids list must be equal to that of sample_indices,
+ embedding_indices and aggregation_weights.
+
+
+ Returns the description of the operation
+
+
+ sample_indices[i], embedding_indices[i] and aggregation_weights[i] correspond
+ to the ith feature. table_ids[i] indicates which embedding table to look up ith
+ feature.
+
+ The tensors at corresponding positions in the three input lists (sample_indices,
+ embedding_indices and aggregation_weights) must have the same shape, i.e. rank 1
+ with dim_size() equal to the total number of lookups into the table described by
+ the corresponding feature.
+
+
+
+
+ Ensures that the tensor's shape matches the expected shape.
+
+
+ A tensor, whose shape is to be validated.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'EnsureShape'.
+
+
+ The expected (possibly partially specified) shape of the input tensor.
+
+
+ A tensor with the same shape and contents as the input tensor or value.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Raises an error if the input tensor's shape does not match the specified shape.
+ Returns the input tensor otherwise.
+
+
+
+
+ Creates or finds a child frame, and makes data available to the child frame.
+
+
+ The tensor to be made available to the child frame.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Enter'.
+
+
+ Optional argument
+ If true, the output is constant within the child frame.
+
+
+ Optional argument
+ The number of iterations allowed to run in parallel.
+
+
+ The name of the child frame.
+
+
+ The same tensor as data.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This op is used together with Exit to create loops in the graph.
+ The unique frame_name is used by the Executor to identify frames. If
+ is_constant is true, output is a constant in the child frame; otherwise
+ it may be changed in the child frame. At most parallel_iterations iterations
+ are run in parallel in the child frame.
+
+
+
+
+ Returns the truth value of (x == y) element-wise.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Equal'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ *NOTE*: Equal supports broadcasting. More about broadcasting
+ [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+
+
+
+
+ Computes the Gauss error function of x element-wise.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Erf'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes the complementary error function of x element-wise.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Erfc'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Exits the current frame to its parent frame.
+
+
+ The tensor to be made available to the parent frame.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Exit'.
+
+
+ The same tensor as data.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Exit makes its input data available to the parent frame.
+
+
+
+
+ Computes exponential of x element-wise. \\(y = e^x\\).
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Exp'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Inserts a dimension of 1 into a tensor's shape.
+
+
+
+
+ 0-D (scalar). Specifies the dimension index at which to
+ expand the shape of input. Must be in the range
+ [-rank(input) - 1, rank(input)].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ExpandDims'.
+
+
+ Contains the same data as input, but its shape has an additional
+ dimension of size 1 added.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Given a tensor input, this operation inserts a dimension of 1 at the
+ dimension index axis of input's shape. The dimension index axis starts at
+ zero; if you specify a negative number for axis it is counted backward from
+ the end.
+
+ This operation is useful if you want to add a batch dimension to a single
+ element. For example, if you have a single image of shape [height, width,
+ channels], you can make it a batch of 1 image with expand_dims(image, 0),
+ which will make the shape [1, height, width, channels].
+
+ Other examples:
+
+
+ # 't' is a tensor of shape [2]
+ shape(expand_dims(t, 0)) ==> [1, 2]
+ shape(expand_dims(t, 1)) ==> [2, 1]
+ shape(expand_dims(t, -1)) ==> [2, 1]
+
+ # 't2' is a tensor of shape [2, 3, 5]
+ shape(expand_dims(t2, 0)) ==> [1, 2, 3, 5]
+ shape(expand_dims(t2, 2)) ==> [2, 3, 1, 5]
+ shape(expand_dims(t2, 3)) ==> [2, 3, 5, 1]
+
+
+ This operation requires that:
+
+ -1-input.dims() <= dim <= input.dims()
+
+ This operation is related to squeeze(), which removes dimensions of
+ size 1.
+
+
+
+
+ A substitute for InterleaveDataset on a fixed list of N datasets.
+
+
+ A dataset of scalar DT_INT64 elements that determines which of the
+ N data inputs should produce the next output element.
+
+
+ N datasets with the same type that will be interleaved according to
+ the values of selector_input_dataset.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ExperimentalDirectedInterleaveDataset'.
+
+
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Creates a dataset that contains the elements of input_dataset ignoring errors.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ExperimentalIgnoreErrorsDataset'.
+
+
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Returns the name of the device on which resource has been placed.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ExperimentalIteratorGetDevice'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Creates a dataset that uses a custom thread pool to compute input_dataset.
+
+
+
+
+ A resource produced by the ThreadPoolHandle op.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ExperimentalThreadPoolDataset'.
+
+
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Creates a dataset that uses a custom thread pool to compute input_dataset.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ExperimentalThreadPoolHandle'.
+
+
+ Optional argument
+ The maximum degree of parallelism to use within operations that execute on this
+ threadpool.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ The number of threads in the thread pool.
+
+
+ A human-readable name for the threads that may be visible in some
+ visualizations.
+ threadpool.
+
+
+ A resource that can be consumed by one or more ExperimentalThreadPoolDataset
+ ops.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Creates a dataset that contains the unique elements of input_dataset.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ExperimentalUniqueDataset'.
+
+
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes exponential of x - 1 element-wise.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Expm1'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ I.e., \\(y = (\exp x) - 1\\).
+
+
+
+
+ Extracts a glimpse from the input tensor.
+
+
+ A 4-D float tensor of shape [batch_size, height, width, channels].
+
+
+ A 1-D tensor of 2 elements containing the size of the glimpses
+ to extract. The glimpse height must be specified first, following
+ by the glimpse width.
+
+
+ A 2-D integer tensor of shape [batch_size, 2] containing
+ the y, x locations of the center of each window.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ExtractGlimpse'.
+
+
+ Optional argument
+ indicates if the offset coordinates are centered relative to
+ the image, in which case the (0, 0) offset is relative to the center
+ of the input images. If false, the (0,0) offset corresponds to the
+ upper left corner of the input images.
+
+
+ Optional argument
+ indicates if the offset coordinates are normalized.
+
+
+ Optional argument
+ indicates if the noise should be generated using a
+ uniform distribution or a Gaussian distribution.
+
+
+ A tensor representing the glimpses [batch_size,
+ glimpse_height, glimpse_width, channels].
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Returns a set of windows called glimpses extracted at location
+ offsets from the input tensor. If the windows only partially
+ overlaps the inputs, the non overlapping areas will be filled with
+ random noise.
+
+ The result is a 4-D tensor of shape [batch_size, glimpse_height,
+ glimpse_width, channels]. The channels and batch dimensions are the
+ same as that of the input tensor. The height and width of the output
+ windows are specified in the size parameter.
+
+ The argument normalized and centered controls how the windows are built:
+
+ * If the coordinates are normalized but not centered, 0.0 and 1.0
+ correspond to the minimum and maximum of each height and width
+ dimension.
+ * If the coordinates are both normalized and centered, they range from
+ -1.0 to 1.0. The coordinates (-1.0, -1.0) correspond to the upper
+ left corner, the lower right corner is located at (1.0, 1.0) and the
+ center is at (0, 0).
+ * If the coordinates are not normalized they are interpreted as
+ numbers of pixels.
+
+
+
+
+ Extract patches from images and put them in the "depth" output dimension.
+
+
+ 4-D Tensor with shape [batch, in_rows, in_cols, depth].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ExtractImagePatches'.
+
+
+ The size of the sliding window for each dimension of images.
+
+
+ 1-D of length 4. How far the centers of two consecutive patches are in
+ the images. Must be: [1, stride_rows, stride_cols, 1].
+
+
+ 1-D of length 4. Must be: [1, rate_rows, rate_cols, 1]. This is the
+ input stride, specifying how far two consecutive patch samples are in the
+ input. Equivalent to extracting patches with
+ patch_sizes_eff = patch_sizes + (patch_sizes - 1) * (rates - 1), followed by
+ subsampling them spatially by a factor of rates. This is equivalent to
+ rate in dilated (a.k.a. Atrous) convolutions.
+
+
+ The type of padding algorithm to use.
+
+ We specify the size-related attributes as:
+
+
+ ksizes = [1, ksize_rows, ksize_cols, 1]
+ strides = [1, strides_rows, strides_cols, 1]
+ rates = [1, rates_rows, rates_cols, 1]
+
+
+
+ 4-D Tensor with shape [batch, out_rows, out_cols, ksize_rows *
+ ksize_cols * depth] containing image patches with size
+ ksize_rows x ksize_cols x depth vectorized in the "depth" dimension. Note
+ out_rows and out_cols are the dimensions of the output patches.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Extract the shape information of a JPEG-encoded image.
+
+
+ 0-D. The JPEG-encoded image.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ExtractJpegShape'.
+
+
+ Optional argument
+ (Optional) The output type of the operation (int32 or int64).
+ Defaults to int32.
+
+
+ 1-D. The image shape with format [height, width, channels].
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This op only parses the image header, so it is much faster than DecodeJpeg.
+
+
+
+
+ Extract patches from input and put them in the "depth" output dimension. 3D extension of extract_image_patches.
+
+
+ 5-D Tensor with shape [batch, in_planes, in_rows, in_cols, depth].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ExtractVolumePatches'.
+
+
+ The size of the sliding window for each dimension of input.
+
+
+ 1-D of length 5. How far the centers of two consecutive patches are in
+ input. Must be: [1, stride_planes, stride_rows, stride_cols, 1].
+
+
+ The type of padding algorithm to use.
+
+ We specify the size-related attributes as:
+
+
+ ksizes = [1, ksize_planes, ksize_rows, ksize_cols, 1]
+ strides = [1, stride_planes, strides_rows, strides_cols, 1]
+
+
+
+ 5-D Tensor with shape [batch, out_planes, out_rows, out_cols,
+ ksize_planes * ksize_rows * ksize_cols * depth] containing patches
+ with size ksize_planes x ksize_rows x ksize_cols x depth vectorized
+ in the "depth" dimension. Note out_planes, out_rows and out_cols
+ are the dimensions of the output patches.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Output a fact about factorials.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Fact'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ This op is used as a placeholder in If branch functions. It doesn't provide a
+ valid output when run, so must either be removed (e.g. replaced with a
+ function input) or guaranteed not to be used (e.g. if mirroring an
+ intermediate output needed for the gradient computation of the other branch).
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'FakeParam'.
+
+
+ The type of the output.
+
+
+ The purported shape of the output. This is only used for shape inference;
+ the output will not necessarily have this shape. Can be a partial shape.
+
+
+ \"Fake\" output value. This should not be consumed by another op.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Fake-quantize the 'inputs' tensor, type float to 'outputs' tensor of same type.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'FakeQuantWithMinMaxArgs'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Attributes [min; max] define the clamping range for the inputs data.
+ inputs values are quantized into the quantization range ([0; 2^num_bits - 1]
+ when narrow_range is false and [1; 2^num_bits - 1] when it is true) and
+ then de-quantized and output as floats in [min; max] interval.
+ num_bits is the bitwidth of the quantization; between 2 and 16, inclusive.
+
+ Quantization is called fake since the output is still in floating point.
+
+
+
+
+ Compute gradients for a FakeQuantWithMinMaxArgs operation.
+
+
+ Backpropagated gradients above the FakeQuantWithMinMaxArgs operation.
+
+
+ Values passed as inputs to the FakeQuantWithMinMaxArgs operation.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'FakeQuantWithMinMaxArgsGradient'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Backpropagated gradients below the FakeQuantWithMinMaxArgs operation:
+ gradients * (inputs >= min && inputs <= max).
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Fake-quantize the 'inputs' tensor of type float via global float scalars min
+
+
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'FakeQuantWithMinMaxVars'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ and max to 'outputs' tensor of same shape as inputs.
+
+ [min; max] define the clamping range for the inputs data.
+ inputs values are quantized into the quantization range ([0; 2^num_bits - 1]
+ when narrow_range is false and [1; 2^num_bits - 1] when it is true) and
+ then de-quantized and output as floats in [min; max] interval.
+ num_bits is the bitwidth of the quantization; between 2 and 16, inclusive.
+
+ This operation has a gradient and thus allows for training min and max
+ values.
+
+
+
+
+ Compute gradients for a FakeQuantWithMinMaxVars operation.
+
+
+ Backpropagated gradients above the FakeQuantWithMinMaxVars operation.
+
+
+ Values passed as inputs to the FakeQuantWithMinMaxVars operation.
+ min, max: Quantization interval, scalar floats.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'FakeQuantWithMinMaxVarsGradient'.
+
+
+ Optional argument
+ The bitwidth of the quantization; between 2 and 8, inclusive.
+
+
+ Optional argument
+ Whether to quantize into 2^num_bits - 1 distinct values.
+
+
+ Returns a tuple with multiple values, as follows:
+ backprops_wrt_input: Backpropagated gradients w.r.t. inputs:
+ gradients * (inputs >= min && inputs <= max).
+ backprop_wrt_min: Backpropagated gradients w.r.t. min parameter:
+ sum(gradients * (inputs < min)).
+ backprop_wrt_max: Backpropagated gradients w.r.t. max parameter:
+ sum(gradients * (inputs > max)).
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+
+ Fake-quantize the 'inputs' tensor of type float and one of the shapes: [d],
+
+
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'FakeQuantWithMinMaxVarsPerChannel'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ [b, d] [b, h, w, d] via per-channel floats min and max of shape [d]
+ to 'outputs' tensor of same shape as inputs.
+
+ [min; max] define the clamping range for the inputs data.
+ inputs values are quantized into the quantization range ([0; 2^num_bits - 1]
+ when narrow_range is false and [1; 2^num_bits - 1] when it is true) and
+ then de-quantized and output as floats in [min; max] interval.
+ num_bits is the bitwidth of the quantization; between 2 and 16, inclusive.
+
+ This operation has a gradient and thus allows for training min and max
+ values.
+
+
+
+
+ Compute gradients for a FakeQuantWithMinMaxVarsPerChannel operation.
+
+
+ Backpropagated gradients above the FakeQuantWithMinMaxVars operation,
+ shape one of: [d], [b, d], [b, h, w, d].
+
+
+ Values passed as inputs to the FakeQuantWithMinMaxVars operation, shape
+ same as gradients.
+ min, max: Quantization interval, floats of shape [d].
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'FakeQuantWithMinMaxVarsPerChannelGradient'.
+
+
+ Optional argument
+ The bitwidth of the quantization; between 2 and 16, inclusive.
+
+
+ Optional argument
+ Whether to quantize into 2^num_bits - 1 distinct values.
+
+
+ Returns a tuple with multiple values, as follows:
+ backprops_wrt_input: Backpropagated gradients w.r.t. inputs, shape same as
+ inputs:
+ gradients * (inputs >= min && inputs <= max).
+ backprop_wrt_min: Backpropagated gradients w.r.t. min parameter, shape [d]:
+ sum_per_d(gradients * (inputs < min)).
+ backprop_wrt_max: Backpropagated gradients w.r.t. max parameter, shape [d]:
+ sum_per_d(gradients * (inputs > max)).
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+
+ Deprecated. Do not use.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'FakeQueue'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Fast Fourier transform.
+
+
+ A complex tensor.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'FFT'.
+
+
+ A complex tensor of the same shape as input. The inner-most
+ dimension of input is replaced with its 1D Fourier transform.
+
+ @compatibility(numpy)
+ Equivalent to np.fft.fft
+ @end_compatibility
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Computes the 1-dimensional discrete Fourier transform over the inner-most
+ dimension of input.
+
+
+
+
+ 2D fast Fourier transform.
+
+
+ A complex tensor.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'FFT2D'.
+
+
+ A complex tensor of the same shape as input. The inner-most 2
+ dimensions of input are replaced with their 2D Fourier transform.
+
+ @compatibility(numpy)
+ Equivalent to np.fft.fft2
+ @end_compatibility
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Computes the 2-dimensional discrete Fourier transform over the inner-most
+ 2 dimensions of input.
+
+
+
+
+ 3D fast Fourier transform.
+
+
+ A complex64 tensor.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'FFT3D'.
+
+
+ A complex64 tensor of the same shape as input. The inner-most 3
+ dimensions of input are replaced with their 3D Fourier transform.
+
+ @compatibility(numpy)
+ Equivalent to np.fft.fftn with 3 dimensions.
+ @end_compatibility
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Computes the 3-dimensional discrete Fourier transform over the inner-most 3
+ dimensions of input.
+
+
+
+
+ A queue that produces elements in first-in first-out order.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'FIFOQueue'.
+
+
+ Optional argument
+ The shape of each component in a value. The length of this attr must
+ be either 0 or the same as the length of component_types. If the length of
+ this attr is 0, the shapes of queue elements are not constrained, and
+ only one element may be dequeued at a time.
+
+
+ Optional argument
+ The upper bound on the number of elements in this queue.
+ Negative numbers mean no limit.
+
+
+ Optional argument
+ If non-empty, this queue is placed in the given container.
+ Otherwise, a default container is used.
+
+
+ Optional argument
+ If non-empty, this queue will be shared under the given name
+ across multiple sessions.
+
+
+ The type of each component in a value.
+
+
+ The handle to the queue.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ A queue that produces elements in first-in first-out order.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'FIFOQueueV2'.
+
+
+ Optional argument
+ The shape of each component in a value. The length of this attr must
+ be either 0 or the same as the length of component_types. If the length of
+ this attr is 0, the shapes of queue elements are not constrained, and
+ only one element may be dequeued at a time.
+
+
+ Optional argument
+ The upper bound on the number of elements in this queue.
+ Negative numbers mean no limit.
+
+
+ Optional argument
+ If non-empty, this queue is placed in the given container.
+ Otherwise, a default container is used.
+
+
+ Optional argument
+ If non-empty, this queue will be shared under the given name
+ across multiple sessions.
+
+
+ The type of each component in a value.
+
+
+ The handle to the queue.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Creates a tensor filled with a scalar value.
+
+
+ 1-D. Represents the shape of the output tensor.
+
+
+ 0-D (scalar). Value to fill the returned tensor.
+
+ @compatibility(numpy)
+ Equivalent to np.full
+ @end_compatibility
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Fill'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation creates a tensor of shape dims and fills it with value.
+
+ For example:
+
+
+ # Output tensor has shape [2, 3].
+ fill([2, 3], 9) ==> [[9, 9, 9]
+ [9, 9, 9]]
+
+
+ tf.fill differs from tf.constant in a few ways:
+
+ * tf.fill only supports scalar contents, whereas tf.constant supports
+ Tensor values.
+ * tf.fill creates an Op in the computation graph that constructs the actual
+ Tensor value at runtime. This is in contrast to tf.constant which embeds
+ the entire Tensor into the graph with a Const node.
+ * Because tf.fill evaluates at graph runtime, it supports dynamic shapes
+ based on other runtime Tensors, unlike tf.constant.
+
+
+
+
+ Creates a dataset containing elements of first component of input_dataset having true in the last component.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'FilterByLastComponentDataset'.
+
+
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Creates a dataset that emits the records from one or more binary files.
+
+
+ A scalar or a vector containing the name(s) of the file(s) to be
+ read.
+
+
+ A scalar representing the number of bytes to skip at the
+ beginning of a file.
+
+
+ A scalar representing the number of bytes in each record.
+
+
+ A scalar representing the number of bytes to skip at the end
+ of a file.
+
+
+ A scalar representing the number of bytes to buffer. Must be > 0.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'FixedLengthRecordDataset'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ A Reader that outputs fixed-length records from a file.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'FixedLengthRecordReader'.
+
+
+ Optional argument
+ Number of bytes in the header, defaults to 0.
+
+
+ Optional argument
+ Number of bytes in the footer, defaults to 0.
+
+
+ Optional argument
+ Number of bytes to hop before each read. Default of 0 means using
+ record_bytes.
+
+
+ Optional argument
+ If non-empty, this reader is placed in the given container.
+ Otherwise, a default container is used.
+
+
+ Optional argument
+ If non-empty, this reader is named in the given bucket
+ with this shared_name. Otherwise, the node name is used instead.
+
+
+ Number of bytes in the record.
+
+
+ The handle to reference the Reader.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ A Reader that outputs fixed-length records from a file.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'FixedLengthRecordReaderV2'.
+
+
+ Optional argument
+ Number of bytes in the header, defaults to 0.
+
+
+ Optional argument
+ Number of bytes in the footer, defaults to 0.
+
+
+ Optional argument
+ Number of bytes to hop before each read. Default of 0 means using
+ record_bytes.
+
+
+ Optional argument
+ If non-empty, this reader is placed in the given container.
+ Otherwise, a default container is used.
+
+
+ Optional argument
+ If non-empty, this reader is named in the given bucket
+ with this shared_name. Otherwise, the node name is used instead.
+
+
+ Optional argument
+ The type of encoding for the file. Currently ZLIB and GZIP
+ are supported. Defaults to none.
+
+
+ Number of bytes in the record.
+
+
+ The handle to reference the Reader.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Generates labels for candidate sampling with a learned unigram distribution.
+
+
+ A batch_size * num_true matrix, in which each row contains the
+ IDs of the num_true target_classes in the corresponding original label.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'FixedUnigramCandidateSampler'.
+
+
+ Optional argument
+ Each valid line in this file (which should have a CSV-like format)
+ corresponds to a valid word ID. IDs are in sequential order, starting from
+ num_reserved_ids. The last entry in each line is expected to be a value
+ corresponding to the count or relative probability. Exactly one of vocab_file
+ and unigrams needs to be passed to this op.
+
+
+ Optional argument
+ The distortion is used to skew the unigram probability distribution.
+ Each weight is first raised to the distortion's power before adding to the
+ internal unigram distribution. As a result, distortion = 1.0 gives regular
+ unigram sampling (as defined by the vocab file), and distortion = 0.0 gives
+ a uniform distribution.
+
+
+ Optional argument
+ Optionally some reserved IDs can be added in the range [0,
+ ..., num_reserved_ids) by the users. One use case is that a special unknown
+ word token is used as ID 0. These IDs will have a sampling probability of 0.
+
+
+ Optional argument
+ A sampler can be used to sample from a subset of the original range
+ in order to speed up the whole computation through parallelism. This parameter
+ (together with 'shard') indicates the number of partitions that are being
+ used in the overall computation.
+
+
+ Optional argument
+ A sampler can be used to sample from a subset of the original range
+ in order to speed up the whole computation through parallelism. This parameter
+ (together with 'num_shards') indicates the particular partition number of a
+ sampler op, when partitioning is being used.
+
+
+ Optional argument
+ A list of unigram counts or probabilities, one per ID in sequential
+ order. Exactly one of vocab_file and unigrams should be passed to this op.
+
+
+ Optional argument
+ If either seed or seed2 are set to be non-zero, the random number
+ generator is seeded by the given seed. Otherwise, it is seeded by a
+ random seed.
+
+
+ Optional argument
+ An second seed to avoid seed collision.
+
+
+ Number of true labels per context.
+
+
+ Number of candidates to randomly sample.
+
+
+ If unique is true, we sample with rejection, so that all sampled
+ candidates in a batch are unique. This requires some approximation to
+ estimate the post-rejection sampling probabilities.
+
+
+ The sampler will sample integers from the interval [0, range_max).
+
+
+ Returns a tuple with multiple values, as follows:
+ sampled_candidates: A vector of length num_sampled, in which each element is
+ the ID of a sampled candidate.
+ true_expected_count: A batch_size * num_true matrix, representing
+ the number of times each candidate is expected to occur in a batch
+ of sampled candidates. If unique=true, then this is a probability.
+ sampled_expected_count: A vector of length num_sampled, for each sampled
+ candidate representing the number of times the candidate is expected
+ to occur in a batch of sampled candidates. If unique=true, then this is a
+ probability.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ A unigram sampler could use a fixed unigram distribution read from a
+ file or passed in as an in-memory array instead of building up the distribution
+ from data on the fly. There is also an option to skew the distribution by
+ applying a distortion power to the weights.
+
+ The vocabulary file should be in CSV-like format, with the last field
+ being the weight associated with the word.
+
+ For each batch, this op picks a single set of sampled candidate labels.
+
+ The advantages of sampling candidates per-batch are simplicity and the
+ possibility of efficient dense matrix multiplication. The disadvantage is that
+ the sampled candidates must be chosen independently of the context and of the
+ true labels.
+
+
+
+
+ Returns element-wise largest integer not greater than x.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Floor'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Returns x // y element-wise.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'FloorDiv'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ *NOTE*: FloorDiv supports broadcasting. More about broadcasting
+ [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+
+
+
+
+ Returns element-wise remainder of division. When x < 0 xor y < 0 is
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'FloorMod'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ true, this follows Python semantics in that the result here is consistent
+ with a flooring divide. E.g. floor(x / y) * y + mod(x, y) = x.
+
+ *NOTE*: FloorMod supports broadcasting. More about broadcasting
+ [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+
+
+
+
+ Performs fractional average pooling on the input.
+
+
+ 4-D with shape [batch, height, width, channels].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'FractionalAvgPool'.
+
+
+ Optional argument
+ When set to True, generates the pooling sequence in a
+ pseudorandom fashion, otherwise, in a random fashion. Check paper [Benjamin
+ Graham, Fractional Max-Pooling](http://arxiv.org/abs/1412.6071) for
+ difference between pseudorandom and random.
+
+
+ Optional argument
+ When set to True, it means when pooling, the values at the boundary
+ of adjacent pooling cells are used by both cells. For example:
+
+ index 0 1 2 3 4
+
+ value 20 5 16 3 7
+
+ If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice.
+ The result would be [41/3, 26/3] for fractional avg pooling.
+
+
+ Optional argument
+ When set to True, a fixed pooling region will be used when
+ iterating over a FractionalAvgPool node in the computation graph. Mainly used
+ in unit test to make FractionalAvgPool deterministic.
+
+
+ Optional argument
+ If either seed or seed2 are set to be non-zero, the random number
+ generator is seeded by the given seed. Otherwise, it is seeded by a
+ random seed.
+
+
+ Optional argument
+ An second seed to avoid seed collision.
+
+
+ Pooling ratio for each dimension of value, currently only
+ supports row and col dimension and should be >= 1.0. For example, a valid
+ pooling ratio looks like [1.0, 1.44, 1.73, 1.0]. The first and last elements
+ must be 1.0 because we don't allow pooling on batch and channels
+ dimensions. 1.44 and 1.73 are pooling ratio on height and width dimensions
+ respectively.
+
+
+ Returns a tuple with multiple values, as follows:
+ output: output tensor after fractional avg pooling.
+ row_pooling_sequence: row pooling sequence, needed to calculate gradient.
+ col_pooling_sequence: column pooling sequence, needed to calculate gradient.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Fractional average pooling is similar to Fractional max pooling in the pooling
+ region generation step. The only difference is that after pooling regions are
+ generated, a mean operation is performed instead of a max operation in each
+ pooling region.
+
+
+
+
+ Computes gradient of the FractionalAvgPool function.
+
+
+ Original input tensor shape for fractional_avg_pool
+
+
+ 4-D with shape [batch, height, width, channels]. Gradients
+ w.r.t. the output of fractional_avg_pool.
+
+
+ row pooling sequence, form pooling region with
+ col_pooling_sequence.
+
+
+ column pooling sequence, form pooling region with
+ row_pooling sequence.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'FractionalAvgPoolGrad'.
+
+
+ Optional argument
+ When set to True, it means when pooling, the values at the boundary
+ of adjacent pooling cells are used by both cells. For example:
+
+ index 0 1 2 3 4
+
+ value 20 5 16 3 7
+
+ If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice.
+ The result would be [41/3, 26/3] for fractional avg pooling.
+
+
+ 4-D. Gradients w.r.t. the input of fractional_avg_pool.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Unlike FractionalMaxPoolGrad, we don't need to find arg_max for
+ FractionalAvgPoolGrad, we just need to evenly back-propagate each element of
+ out_backprop to those indices that form the same pooling cell. Therefore, we
+ just need to know the shape of original input tensor, instead of the whole
+ tensor.
+
+
+
+
+ Performs fractional max pooling on the input.
+
+
+ 4-D with shape [batch, height, width, channels].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'FractionalMaxPool'.
+
+
+ Optional argument
+ When set to True, generates the pooling sequence in a
+ pseudorandom fashion, otherwise, in a random fashion. Check paper [Benjamin
+ Graham, Fractional Max-Pooling](http://arxiv.org/abs/1412.6071) for
+ difference between pseudorandom and random.
+
+
+ Optional argument
+ When set to True, it means when pooling, the values at the boundary
+ of adjacent pooling cells are used by both cells. For example:
+
+ index 0 1 2 3 4
+
+ value 20 5 16 3 7
+
+ If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice.
+ The result would be [20, 16] for fractional max pooling.
+
+
+ Optional argument
+ When set to True, a fixed pooling region will be used when
+ iterating over a FractionalMaxPool node in the computation graph. Mainly used
+ in unit test to make FractionalMaxPool deterministic.
+
+
+ Optional argument
+ If either seed or seed2 are set to be non-zero, the random number
+ generator is seeded by the given seed. Otherwise, it is seeded by a
+ random seed.
+
+
+ Optional argument
+ An second seed to avoid seed collision.
+
+
+ Pooling ratio for each dimension of value, currently only
+ supports row and col dimension and should be >= 1.0. For example, a valid
+ pooling ratio looks like [1.0, 1.44, 1.73, 1.0]. The first and last elements
+ must be 1.0 because we don't allow pooling on batch and channels
+ dimensions. 1.44 and 1.73 are pooling ratio on height and width dimensions
+ respectively.
+
+
+ Returns a tuple with multiple values, as follows:
+ output: output tensor after fractional max pooling.
+ row_pooling_sequence: row pooling sequence, needed to calculate gradient.
+ col_pooling_sequence: column pooling sequence, needed to calculate gradient.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Fractional max pooling is slightly different than regular max pooling. In
+ regular max pooling, you downsize an input set by taking the maximum value of
+ smaller N x N subsections of the set (often 2x2), and try to reduce the set by
+ a factor of N, where N is an integer. Fractional max pooling, as you might
+ expect from the word "fractional", means that the overall reduction ratio N
+ does not have to be an integer.
+
+ The sizes of the pooling regions are generated randomly but are fairly uniform.
+ For example, let's look at the height dimension, and the constraints on the
+ list of rows that will be pool boundaries.
+
+ First we define the following:
+
+ 1. input_row_length : the number of rows from the input set
+ 2. output_row_length : which will be smaller than the input
+ 3. alpha = input_row_length / output_row_length : our reduction ratio
+ 4. K = floor(alpha)
+ 5. row_pooling_sequence : this is the result list of pool boundary rows
+
+ Then, row_pooling_sequence should satisfy:
+
+ 1. a[0] = 0 : the first value of the sequence is 0
+ 2. a[end] = input_row_length : the last value of the sequence is the size
+ 3. K <= (a[i+1] - a[i]) <= K+1 : all intervals are K or K+1 size
+ 4. length(row_pooling_sequence) = output_row_length+1
+
+ For more details on fractional max pooling, see this paper:
+ [Benjamin Graham, Fractional Max-Pooling](http://arxiv.org/abs/1412.6071)
+
+
+
+
+ Computes gradient of the FractionalMaxPool function.
+
+
+ Original input for fractional_max_pool
+
+
+ Original output for fractional_max_pool
+
+
+ 4-D with shape [batch, height, width, channels]. Gradients
+ w.r.t. the output of fractional_max_pool.
+
+
+ row pooling sequence, form pooling region with
+ col_pooling_sequence.
+
+
+ column pooling sequence, form pooling region with
+ row_pooling sequence.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'FractionalMaxPoolGrad'.
+
+
+ Optional argument
+ When set to True, it means when pooling, the values at the boundary
+ of adjacent pooling cells are used by both cells. For example:
+
+ index 0 1 2 3 4
+
+ value 20 5 16 3 7
+
+ If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice.
+ The result would be [20, 16] for fractional max pooling.
+
+
+ 4-D. Gradients w.r.t. the input of fractional_max_pool.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Batch normalization.
+
+
+ A 4D Tensor for input data.
+
+
+ A 1D Tensor for scaling factor, to scale the normalized x.
+
+
+ A 1D Tensor for offset, to shift to the normalized x.
+
+
+ A 1D Tensor for population mean. Used for inference only;
+ must be empty for training.
+
+
+ A 1D Tensor for population variance. Used for inference only;
+ must be empty for training.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'FusedBatchNorm'.
+
+
+ Optional argument
+ A small float number added to the variance of x.
+
+
+ Optional argument
+ The data format for x and y. Either "NHWC" (default) or "NCHW".
+
+
+ Optional argument
+ A bool value to indicate the operation is for training (default)
+ or inference.
+
+
+ Returns a tuple with multiple values, as follows:
+ y: A 4D Tensor for output data.
+ batch_mean: A 1D Tensor for the computed batch mean, to be used by TensorFlow
+ to compute the running mean.
+ batch_variance: A 1D Tensor for the computed batch variance, to be used by
+ TensorFlow to compute the running variance.
+ reserve_space_1: A 1D Tensor for the computed batch mean, to be reused
+ in the gradient computation.
+ reserve_space_2: A 1D Tensor for the computed batch variance (inverted variance
+ in the cuDNN case), to be reused in the gradient computation.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW".
+ The size of 1D Tensors matches the dimension C of the 4D Tensors.
+
+
+
+
+ Gradient for batch normalization.
+
+
+ A 4D Tensor for the gradient with respect to y.
+
+
+ A 4D Tensor for input data.
+
+
+ A 1D Tensor for scaling factor, to scale the normalized x.
+
+
+ When is_training is True, a 1D Tensor for the computed batch
+ mean to be reused in gradient computation. When is_training is
+ False, a 1D Tensor for the population mean to be reused in both
+ 1st and 2nd order gradient computation.
+
+
+ When is_training is True, a 1D Tensor for the computed batch
+ variance (inverted variance in the cuDNN case) to be reused in
+ gradient computation. When is_training is False, a 1D Tensor
+ for the population variance to be reused in both 1st and 2nd
+ order gradient computation.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'FusedBatchNormGrad'.
+
+
+ Optional argument
+ A small float number added to the variance of x.
+
+
+ Optional argument
+ The data format for y_backprop, x, x_backprop.
+ Either "NHWC" (default) or "NCHW".
+
+
+ Optional argument
+ A bool value to indicate the operation is for training (default)
+ or inference.
+
+
+ Returns a tuple with multiple values, as follows:
+ x_backprop: A 4D Tensor for the gradient with respect to x.
+ scale_backprop: A 1D Tensor for the gradient with respect to scale.
+ offset_backprop: A 1D Tensor for the gradient with respect to offset.
+ reserve_space_3: Unused placeholder to match the mean input in FusedBatchNorm.
+ reserve_space_4: Unused placeholder to match the variance input
+ in FusedBatchNorm.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW".
+ The size of 1D Tensors matches the dimension C of the 4D Tensors.
+
+
+
+
+ Gradient for batch normalization.
+
+
+ A 4D Tensor for the gradient with respect to y.
+
+
+ A 4D Tensor for input data.
+
+
+ A 1D Tensor for scaling factor, to scale the normalized x.
+
+
+ When is_training is True, a 1D Tensor for the computed batch
+ mean to be reused in gradient computation. When is_training is
+ False, a 1D Tensor for the population mean to be reused in both
+ 1st and 2nd order gradient computation.
+
+
+ When is_training is True, a 1D Tensor for the computed batch
+ variance (inverted variance in the cuDNN case) to be reused in
+ gradient computation. When is_training is False, a 1D Tensor
+ for the population variance to be reused in both 1st and 2nd
+ order gradient computation.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'FusedBatchNormGradV2'.
+
+
+ Optional argument
+ A small float number added to the variance of x.
+
+
+ Optional argument
+ The data format for y_backprop, x, x_backprop.
+ Either "NHWC" (default) or "NCHW".
+
+
+ Optional argument
+ A bool value to indicate the operation is for training (default)
+ or inference.
+
+
+ Returns a tuple with multiple values, as follows:
+ x_backprop: A 4D Tensor for the gradient with respect to x.
+ scale_backprop: A 1D Tensor for the gradient with respect to scale.
+ offset_backprop: A 1D Tensor for the gradient with respect to offset.
+ reserve_space_3: Unused placeholder to match the mean input in FusedBatchNorm.
+ reserve_space_4: Unused placeholder to match the variance input
+ in FusedBatchNorm.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW".
+ The size of 1D Tensors matches the dimension C of the 4D Tensors.
+
+
+
+
+ Batch normalization.
+
+
+ A 4D Tensor for input data.
+
+
+ A 1D Tensor for scaling factor, to scale the normalized x.
+
+
+ A 1D Tensor for offset, to shift to the normalized x.
+
+
+ A 1D Tensor for population mean. Used for inference only;
+ must be empty for training.
+
+
+ A 1D Tensor for population variance. Used for inference only;
+ must be empty for training.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'FusedBatchNormV2'.
+
+
+ Optional argument
+ A small float number added to the variance of x.
+
+
+ Optional argument
+ The data format for x and y. Either "NHWC" (default) or "NCHW".
+
+
+ Optional argument
+ A bool value to indicate the operation is for training (default)
+ or inference.
+
+
+ Returns a tuple with multiple values, as follows:
+ y: A 4D Tensor for output data.
+ batch_mean: A 1D Tensor for the computed batch mean, to be used by TensorFlow
+ to compute the running mean.
+ batch_variance: A 1D Tensor for the computed batch variance, to be used by
+ TensorFlow to compute the running variance.
+ reserve_space_1: A 1D Tensor for the computed batch mean, to be reused
+ in the gradient computation.
+ reserve_space_2: A 1D Tensor for the computed batch variance (inverted variance
+ in the cuDNN case), to be reused in the gradient computation.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW".
+ The size of 1D Tensors matches the dimension C of the 4D Tensors.
+
+
+
+
+ Performs a padding as a preprocess during a convolution.
+
+
+ 4-D with shape [batch, in_height, in_width, in_channels].
+
+
+ A two-column matrix specifying the padding sizes. The number of
+ rows must be the same as the rank of input.
+
+
+ 4-D with shape
+ [filter_height, filter_width, in_channels, out_channels].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'FusedPadConv2D'.
+
+
+
+
+ 1-D of length 4. The stride of the sliding window for each dimension
+ of input. Must be in the same order as the dimension specified with format.
+
+
+ The type of padding algorithm to use.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Similar to FusedResizeAndPadConv2d, this op allows for an optimized
+ implementation where the spatial padding transformation stage is fused with the
+ im2col lookup, but in this case without the bilinear filtering required for
+ resizing. Fusing the padding prevents the need to write out the intermediate
+ results as whole tensors, reducing memory pressure, and we can get some latency
+ gains by merging the transformation calculations.
+ The data_format attribute for Conv2D isn't supported by this op, and 'NHWC'
+ order is used instead.
+ Internally this op uses a single per-graph scratch buffer, which means that it
+ will block if multiple versions are being run in parallel. This is because this
+ operator is primarily an optimization to minimize memory usage.
+
+
+
+
+ Performs a resize and padding as a preprocess during a convolution.
+
+
+ 4-D with shape [batch, in_height, in_width, in_channels].
+
+
+ A 1-D int32 Tensor of 2 elements: new_height, new_width. The
+ new size for the images.
+
+
+ A two-column matrix specifying the padding sizes. The number of
+ rows must be the same as the rank of input.
+
+
+ 4-D with shape
+ [filter_height, filter_width, in_channels, out_channels].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'FusedResizeAndPadConv2D'.
+
+
+ Optional argument
+ If true, the centers of the 4 corner pixels of the input and output tensors are
+ aligned, preserving the values at the corner pixels. Defaults to false.
+
+
+
+
+ 1-D of length 4. The stride of the sliding window for each dimension
+ of input. Must be in the same order as the dimension specified with format.
+
+
+ The type of padding algorithm to use.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ It's often possible to do spatial transformations more efficiently as part of
+ the packing stage of a convolution, so this op allows for an optimized
+ implementation where these stages are fused together. This prevents the need to
+ write out the intermediate results as whole tensors, reducing memory pressure,
+ and we can get some latency gains by merging the transformation calculations.
+ The data_format attribute for Conv2D isn't supported by this op, and defaults to
+ 'NHWC' order.
+ Internally this op uses a single per-graph scratch buffer, which means that it
+ will block if multiple versions are being run in parallel. This is because this
+ operator is primarily an optimization to minimize memory usage.
+
+
+
+
+ Gather slices from params according to indices.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Gather'.
+
+
+ Optional argument
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ indices must be an integer tensor of any dimension (usually 0-D or 1-D).
+ Produces an output tensor with shape indices.shape + params.shape[1:] where:
+
+
+ # Scalar indices
+ output[:, ..., :] = params[indices, :, ... :]
+
+ # Vector indices
+ output[i, :, ..., :] = params[indices[i], :, ... :]
+
+ # Higher rank indices
+ output[i, ..., j, :, ... :] = params[indices[i, ..., j], :, ..., :]
+
+
+ If indices is a permutation and len(indices) == params.shape[0] then
+ this operation will permute params accordingly.
+
+ validate_indices: DEPRECATED. If this operation is assigned to CPU, values in
+ indices are always validated to be within range. If assigned to GPU,
+ out-of-bound indices result in safe but unspecified behavior, which may include
+ raising an error.
+
+ <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+ <img style="width:100%" src="https://www.tensorflow.org/images/Gather.png" alt>
+ </div>
+
+
+
+
+ Gather slices from params into a Tensor with shape specified by indices.
+
+
+ The tensor from which to gather values.
+
+
+ Index tensor.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'GatherNd'.
+
+
+ Values from params gathered from indices given by indices, with
+ shape indices.shape[:-1] + params.shape[indices.shape[-1]:].
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ indices is an K-dimensional integer tensor, best thought of as a
+ (K-1)-dimensional tensor of indices into params, where each element defines a
+ slice of params:
+
+ output[\\(i_0, ..., i_{K-2}\\)] = params[indices[\\(i_0, ..., i_{K-2}\\)]]
+
+ Whereas in tf.gather indices defines slices into the first
+ dimension of params, in tf.gather_nd, indices defines slices into the
+ first N dimensions of params, where N = indices.shape[-1].
+
+ The last dimension of indices can be at most the rank of
+ params:
+
+ indices.shape[-1] <= params.rank
+
+ The last dimension of indices corresponds to elements
+ (if indices.shape[-1] == params.rank) or slices
+ (if indices.shape[-1] < params.rank) along dimension indices.shape[-1]
+ of params. The output tensor has shape
+
+ indices.shape[:-1] + params.shape[indices.shape[-1]:]
+
+ Note that on CPU, if an out of bound index is found, an error is returned.
+ On GPU, if an out of bound index is found, a 0 is stored in the
+ corresponding output value.
+
+ Some examples below.
+
+ Simple indexing into a matrix:
+
+
+ indices = [[0, 0], [1, 1]]
+ params = [['a', 'b'], ['c', 'd']]
+ output = ['a', 'd']
+
+
+ Slice indexing into a matrix:
+
+
+ indices = [[1], [0]]
+ params = [['a', 'b'], ['c', 'd']]
+ output = [['c', 'd'], ['a', 'b']]
+
+
+ Indexing into a 3-tensor:
+
+
+ indices = [[1]]
+ params = [[['a0', 'b0'], ['c0', 'd0']],
+ [['a1', 'b1'], ['c1', 'd1']]]
+ output = [[['a1', 'b1'], ['c1', 'd1']]]
+
+
+ indices = [[0, 1], [1, 0]]
+ params = [[['a0', 'b0'], ['c0', 'd0']],
+ [['a1', 'b1'], ['c1', 'd1']]]
+ output = [['c0', 'd0'], ['a1', 'b1']]
+
+
+ indices = [[0, 0, 1], [1, 0, 1]]
+ params = [[['a0', 'b0'], ['c0', 'd0']],
+ [['a1', 'b1'], ['c1', 'd1']]]
+ output = ['b0', 'b1']
+
+
+ Batched indexing into a matrix:
+
+
+ indices = [[[0, 0]], [[0, 1]]]
+ params = [['a', 'b'], ['c', 'd']]
+ output = [['a'], ['b']]
+
+
+ Batched slice indexing into a matrix:
+
+
+ indices = [[[1]], [[0]]]
+ params = [['a', 'b'], ['c', 'd']]
+ output = [[['c', 'd']], [['a', 'b']]]
+
+
+ Batched indexing into a 3-tensor:
+
+
+ indices = [[[1]], [[0]]]
+ params = [[['a0', 'b0'], ['c0', 'd0']],
+ [['a1', 'b1'], ['c1', 'd1']]]
+ output = [[[['a1', 'b1'], ['c1', 'd1']]],
+ [[['a0', 'b0'], ['c0', 'd0']]]]
+
+ indices = [[[0, 1], [1, 0]], [[0, 0], [1, 1]]]
+ params = [[['a0', 'b0'], ['c0', 'd0']],
+ [['a1', 'b1'], ['c1', 'd1']]]
+ output = [[['c0', 'd0'], ['a1', 'b1']],
+ [['a0', 'b0'], ['c1', 'd1']]]
+
+
+ indices = [[[0, 0, 1], [1, 0, 1]], [[0, 1, 1], [1, 1, 0]]]
+ params = [[['a0', 'b0'], ['c0', 'd0']],
+ [['a1', 'b1'], ['c1', 'd1']]]
+ output = [['b0', 'b1'], ['d0', 'c1']]
+
+
+ See also tf.gather and tf.batch_gather.
+
+
+
+
+ Gather slices from params axis axis according to indices.
+
+
+ The tensor from which to gather values. Must be at least rank
+ axis + 1.
+
+
+ Index tensor. Must be in range [0, params.shape[axis]).
+
+
+ The axis in params to gather indices from. Defaults to the first
+ dimension. Supports negative indexes.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'GatherV2'.
+
+
+ Values from params gathered from indices given by indices, with
+ shape params.shape[:axis] + indices.shape + params.shape[axis + 1:].
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ indices must be an integer tensor of any dimension (usually 0-D or 1-D).
+ Produces an output tensor with shape params.shape[:axis] + indices.shape +
+ params.shape[axis + 1:] where:
+
+
+ # Scalar indices (output is rank(params) - 1).
+ output[a_0, ..., a_n, b_0, ..., b_n] =
+ params[a_0, ..., a_n, indices, b_0, ..., b_n]
+
+ # Vector indices (output is rank(params)).
+ output[a_0, ..., a_n, i, b_0, ..., b_n] =
+ params[a_0, ..., a_n, indices[i], b_0, ..., b_n]
+
+ # Higher rank indices (output is rank(params) + rank(indices) - 1).
+ output[a_0, ..., a_n, i, ..., j, b_0, ... b_n] =
+ params[a_0, ..., a_n, indices[i, ..., j], b_0, ..., b_n]
+
+
+ <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+ <img style="width:100%" src="https://www.tensorflow.org/images/Gather.png" alt>
+ </div>
+
+ Note that on CPU, if an out of bound index is found, an error is returned.
+ On GPU, if an out of bound index is found, a 0 is stored in the
+ corresponding output value.
+
+ See also tf.batch_gather and tf.gather_nd.
+
+
+
+
+ Re-configures the GCS block cache with the new configuration values.
+
+
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'GcsConfigureBlockCache'.
+
+
+ Returns the description of the operation
+
+
+ If the values are the same as already configured values, this op is a no-op. If
+ they are different, the current contents of the block cache is dropped, and a
+ new block cache is created fresh.
+
+
+
+
+ Configures the credentials used by the GCS client of the local TF runtime.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'GcsConfigureCredentials'.
+
+
+ Returns the description of the operation
+
+
+ The json input can be of the format:
+
+ 1. Refresh Token:
+ {
+ "client_id": "<redacted>",
+ "client_secret": "<redacted>",
+ "refresh_token: "<redacted>",
+ "type": "authorized_user",
+ }
+
+ 2. Service Account:
+ {
+ "type": "service_account",
+ "project_id": "<redacted>",
+ "private_key_id": "<redacted>",
+ "private_key": "------BEGIN PRIVATE KEY-----\n<REDACTED>\n-----END PRIVATE KEY------\n",
+ "client_email": "<REDACTED>@<REDACTED>.iam.gserviceaccount.com",
+ "client_id": "<REDACTED>",
+ # Some additional fields elided
+ }
+
+ Note the credentials established through this method are shared across all
+ sessions run on this runtime.
+
+ Note be sure to feed the inputs to this op to ensure the credentials are not
+ stored in a constant op within the graph that might accidentally be checkpointed
+ or in other ways be persisted or exfiltrated.
+
+
+
+
+ Generates serialized partition messages suitable for batch reads.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'GenerateBigQueryReaderPartitions'.
+
+
+ Optional argument
+ Do not use. For testing purposes only.
+
+
+ GCP project ID.
+
+
+ BigQuery Dataset ID.
+
+
+ Table to read.
+
+
+ List of columns to read. Leave empty to read all columns.
+
+
+ Table snapshot timestamp in millis since epoch. Relative
+ (negative or zero) snapshot times are not allowed. For more details, see
+ 'Table Decorators' in BigQuery docs.
+
+
+ Number of partitions to split the table into.
+
+
+ Serialized table partitions.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This op should not be used directly by clients. Instead, the
+ bigquery_reader_ops.py file defines a clean interface to the reader.
+
+
+
+
+ Given a path to new and old vocabulary files, returns a remapping Tensor of
+
+
+ Path to the new vocab file.
+
+
+ Path to the old vocab file.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'GenerateVocabRemapping'.
+
+
+ Optional argument
+ Number of entries in the old vocab file to consider. If -1,
+ use the entire old vocabulary.
+
+
+ How many entries into the new vocab file to start reading.
+
+
+ Number of entries in the new vocab file to remap.
+
+
+ Returns a tuple with multiple values, as follows:
+ remapping: A Tensor of length num_new_vocab where the element at index i
+ is equal to the old ID that maps to the new ID i. This element is -1 for any
+ new ID that is not found in the old vocabulary.
+ num_present: Number of new vocab entries found in old vocab.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ length num_new_vocab, where remapping[i] contains the row number in the old
+ vocabulary that corresponds to row i in the new vocabulary (starting at line
+ new_vocab_offset and up to num_new_vocab entities), or -1 if entry i
+ in the new vocabulary is not in the old vocabulary. The old vocabulary is
+ constrained to the first old_vocab_size entries if old_vocab_size is not the
+ default value of -1.
+
+ num_vocab_offset enables
+ use in the partitioned variable case, and should generally be set through
+ examining partitioning info. The format of the files should be a text file,
+ with each line containing a single entity within the vocabulary.
+
+ For example, with new_vocab_file a text file containing each of the following
+ elements on a single line: [f0, f1, f2, f3], old_vocab_file = [f1, f0, f3],
+ num_new_vocab = 3, new_vocab_offset = 1, the returned remapping would be
+ [0, -1, 2].
+
+ The op also returns a count of how many entries in the new vocabulary
+ were present in the old vocabulary, which is used to calculate the number of
+ values to initialize in a weight matrix remapping
+
+ This functionality can be used to remap both row vocabularies (typically,
+ features) and column vocabularies (typically, classes) from TensorFlow
+ checkpoints. Note that the partitioning logic relies on contiguous vocabularies
+ corresponding to div-partitioned variables. Moreover, the underlying remapping
+ uses an IndexTable (as opposed to an inexact CuckooTable), so client code should
+ use the corresponding index_table_from_file() as the FeatureColumn framework
+ does (as opposed to tf.feature_to_id(), which uses a CuckooTable).
+
+
+
+
+ Store the input tensor in the state of the current session.
+
+
+ The tensor to be stored.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'GetSessionHandle'.
+
+
+ The handle for the tensor stored in the session state, represented
+ as a string.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Store the input tensor in the state of the current session.
+
+
+ The tensor to be stored.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'GetSessionHandleV2'.
+
+
+ The handle for the tensor stored in the session state, represented
+ as a ResourceHandle object.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Get the value of the tensor specified by its handle.
+
+
+ The handle for a tensor stored in the session state.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'GetSessionTensor'.
+
+
+ The type of the output value.
+
+
+ The tensor for the given handle.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Returns the truth value of (x > y) element-wise.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Greater'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ *NOTE*: Greater supports broadcasting. More about broadcasting
+ [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+
+
+
+
+ Returns the truth value of (x >= y) element-wise.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'GreaterEqual'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ *NOTE*: GreaterEqual supports broadcasting. More about broadcasting
+ [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+
+
+
+
+ Gives a guarantee to the TF runtime that the input tensor is a constant.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'GuaranteeConst'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The runtime is then free to make optimizations based on this.
+
+ Only accepts value typed tensors as inputs and rejects resource variable handles
+ as input.
+
+ Returns the input tensor without modification.
+
+
+
+
+ Creates a non-initialized hash table.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'HashTable'.
+
+
+ Optional argument
+ If non-empty, this table is placed in the given container.
+ Otherwise, a default container is used.
+
+
+ Optional argument
+ If non-empty, this table is shared under the given name across
+ multiple sessions.
+
+
+ Optional argument
+ If true and shared_name is empty, the table is shared
+ using the node name.
+
+
+ Type of the table keys.
+
+
+ Type of the table values.
+
+
+ Handle to a table.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This op creates a hash table, specifying the type of its keys and values.
+ Before using the table you will have to initialize it. After initialization the
+ table will be immutable.
+
+
+
+
+ Creates a non-initialized hash table.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'HashTableV2'.
+
+
+ Optional argument
+ If non-empty, this table is placed in the given container.
+ Otherwise, a default container is used.
+
+
+ Optional argument
+ If non-empty, this table is shared under the given name across
+ multiple sessions.
+
+
+ Optional argument
+ If true and shared_name is empty, the table is shared
+ using the node name.
+
+
+ Type of the table keys.
+
+
+ Type of the table values.
+
+
+ Handle to a table.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This op creates a hash table, specifying the type of its keys and values.
+ Before using the table you will have to initialize it. After initialization the
+ table will be immutable.
+
+
+
+
+ Return histogram of values.
+
+
+ Numeric Tensor.
+
+
+ Shape [2] Tensor of same dtype as values.
+ values <= value_range[0] will be mapped to hist[0],
+ values >= value_range[1] will be mapped to hist[-1].
+
+
+ Scalar int32 Tensor. Number of histogram bins.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'HistogramFixedWidth'.
+
+
+ Optional argument
+
+
+ A 1-D Tensor holding histogram of values.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Given the tensor values, this operation returns a rank 1 histogram counting
+ the number of entries in values that fall into every bin. The bins are
+ equal width and determined by the arguments value_range and nbins.
+
+
+ # Bins will be: (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf)
+ nbins = 5
+ value_range = [0.0, 5.0]
+ new_values = [-1.0, 0.0, 1.5, 2.0, 5.0, 15]
+
+ with tf.get_default_session() as sess:
+ hist = tf.histogram_fixed_width(new_values, value_range, nbins=5)
+ variables.global_variables_initializer().run()
+ sess.run(hist) => [2, 1, 1, 0, 2]
+
+
+
+
+
+ Outputs a Summary protocol buffer with a histogram.
+
+
+ Scalar. Tag to use for the Summary.Value.
+
+
+ Any shape. Values to use to build the histogram.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'HistogramSummary'.
+
+
+ Scalar. Serialized Summary protocol buffer.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The generated
+ [Summary](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
+ has one summary value containing a histogram for values.
+
+ This op reports an InvalidArgument error if any value is not finite.
+
+
+
+
+ Returns a constant tensor on the host. Only for writing C++ tests.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'HostConst'.
+
+
+ Attr value is the tensor to return.
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Convert one or more images from HSV to RGB.
+
+
+ 1-D or higher rank. HSV data to convert. Last dimension must be size 3.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'HSVToRGB'.
+
+
+ images converted to RGB.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Outputs a tensor of the same shape as the images tensor, containing the RGB
+ value of the pixels. The output is only well defined if the value in images
+ are in [0,1].
+
+ See rgb_to_hsv for a description of the HSV encoding.
+
+
+
+
+ Return a tensor with the same shape and contents as the input tensor or value.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Identity'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Returns a list of tensors with the same shapes and contents as the input
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'IdentityN'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ tensors.
+
+ This op can be used to override the gradient for complicated functions. For
+ example, suppose y = f(x) and we wish to apply a custom function g for backprop
+ such that dx = g(dy). In Python,
+
+
+ with tf.get_default_graph().gradient_override_map(
+ {'IdentityN': 'OverrideGradientWithG'}):
+ y, _ = identity_n([f(x), x])
+
+ @tf.RegisterGradient('OverrideGradientWithG')
+ def ApplyG(op, dy, _):
+ return [None, g(dy)] # Do not backprop to f(x).
+
+
+
+
+
+ A Reader that outputs the queued work as both the key and value.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'IdentityReader'.
+
+
+ Optional argument
+ If non-empty, this reader is placed in the given container.
+ Otherwise, a default container is used.
+
+
+ Optional argument
+ If non-empty, this reader is named in the given bucket
+ with this shared_name. Otherwise, the node name is used instead.
+
+
+ The handle to reference the Reader.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ To use, enqueue strings in a Queue. ReaderRead will take the front
+ work string and output (work, work).
+
+
+
+
+ A Reader that outputs the queued work as both the key and value.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'IdentityReaderV2'.
+
+
+ Optional argument
+ If non-empty, this reader is placed in the given container.
+ Otherwise, a default container is used.
+
+
+ Optional argument
+ If non-empty, this reader is named in the given bucket
+ with this shared_name. Otherwise, the node name is used instead.
+
+
+ The handle to reference the Reader.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ To use, enqueue strings in a Queue. ReaderRead will take the front
+ work string and output (work, work).
+
+
+
+
+ Inverse fast Fourier transform.
+
+
+ A complex tensor.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'IFFT'.
+
+
+ A complex tensor of the same shape as input. The inner-most
+ dimension of input is replaced with its inverse 1D Fourier transform.
+
+ @compatibility(numpy)
+ Equivalent to np.fft.ifft
+ @end_compatibility
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Computes the inverse 1-dimensional discrete Fourier transform over the
+ inner-most dimension of input.
+
+
+
+
+ Inverse 2D fast Fourier transform.
+
+
+ A complex tensor.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'IFFT2D'.
+
+
+ A complex tensor of the same shape as input. The inner-most 2
+ dimensions of input are replaced with their inverse 2D Fourier transform.
+
+ @compatibility(numpy)
+ Equivalent to np.fft.ifft2
+ @end_compatibility
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Computes the inverse 2-dimensional discrete Fourier transform over the
+ inner-most 2 dimensions of input.
+
+
+
+
+ Inverse 3D fast Fourier transform.
+
+
+ A complex64 tensor.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'IFFT3D'.
+
+
+ A complex64 tensor of the same shape as input. The inner-most 3
+ dimensions of input are replaced with their inverse 3D Fourier transform.
+
+ @compatibility(numpy)
+ Equivalent to np.fft.ifftn with 3 dimensions.
+ @end_compatibility
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Computes the inverse 3-dimensional discrete Fourier transform over the
+ inner-most 3 dimensions of input.
+
+
+
+
+ Compute the lower regularized incomplete Gamma function P(a, x).
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Igamma'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The lower regularized incomplete Gamma function is defined as:
+
+
+ \\(P(a, x) = gamma(a, x) / Gamma(a) = 1 - Q(a, x)\\)
+
+ where
+
+ \\(gamma(a, x) = \\int_{0}^{x} t^{a-1} exp(-t) dt\\)
+
+ is the lower incomplete Gamma function.
+
+ Note, above Q(a, x) (Igammac) is the upper regularized complete
+ Gamma function.
+
+
+
+
+ Compute the upper regularized incomplete Gamma function Q(a, x).
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Igammac'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The upper regularized incomplete Gamma function is defined as:
+
+ \\(Q(a, x) = Gamma(a, x) / Gamma(a) = 1 - P(a, x)\\)
+
+ where
+
+ \\(Gamma(a, x) = int_{x}^{\infty} t^{a-1} exp(-t) dt\\)
+
+ is the upper incomplete Gama function.
+
+ Note, above P(a, x) (Igamma) is the lower regularized complete
+ Gamma function.
+
+
+
+
+ Computes the gradient of igamma(a, x) wrt a.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'IgammaGradA'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Returns the imaginary part of a complex number.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Imag'.
+
+
+ Optional argument
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Given a tensor input of complex numbers, this operation returns a tensor of
+ type float that is the imaginary part of each element in input. All
+ elements in input must be complex numbers of the form \\(a + bj\\), where *a*
+ is the real part and *b* is the imaginary part returned by this operation.
+
+ For example:
+
+
+ # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]
+ tf.imag(input) ==> [4.75, 5.75]
+
+
+
+
+
+ Outputs a Summary protocol buffer with images.
+
+
+ Scalar. Used to build the tag attribute of the summary values.
+
+
+ 4-D of shape [batch_size, height, width, channels] where
+ channels is 1, 3, or 4.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ImageSummary'.
+
+
+ Optional argument
+ Max number of batch elements to generate images for.
+
+
+ Optional argument
+ Color to use for pixels with non-finite values.
+
+
+ Scalar. Serialized Summary protocol buffer.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The summary has up to max_images summary values containing images. The
+ images are built from tensor which must be 4-D with shape [batch_size,
+ height, width, channels] and where channels can be:
+
+ * 1: tensor is interpreted as Grayscale.
+ * 3: tensor is interpreted as RGB.
+ * 4: tensor is interpreted as RGBA.
+
+ The images have the same number of channels as the input tensor. For float
+ input, the values are normalized one image at a time to fit in the range
+ [0, 255]. uint8 values are unchanged. The op uses two different
+ normalization algorithms:
+
+ * If the input values are all positive, they are rescaled so the largest one
+ is 255.
+
+ * If any input value is negative, the values are shifted so input value 0.0
+ is at 127. They are then rescaled so that either the smallest value is 0,
+ or the largest one is 255.
+
+ The tag argument is a scalar Tensor of type string. It is used to
+ build the tag of the summary values:
+
+ * If max_images is 1, the summary value tag is '*tag*/image'.
+ * If max_images is greater than 1, the summary value tags are
+ generated sequentially as '*tag*/image/0', '*tag*/image/1', etc.
+
+ The bad_color argument is the color to use in the generated images for
+ non-finite input values. It is a uint8 1-D tensor of length channels.
+ Each element must be in the range [0, 255] (It represents the value of a
+ pixel in the output image). Non-finite values in the input tensor are
+ replaced by this tensor in the output image. The default value is the color
+ red.
+
+
+
+
+ Returns immutable tensor from memory region.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ImmutableConst'.
+
+
+ Type of the returned tensor.
+
+
+ Shape of the returned tensor.
+
+
+ Name of readonly memory region used by the tensor, see
+ NewReadOnlyMemoryRegionFromFile in tensorflow::Env.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The current implementation memmaps the tensor from a file.
+
+
+
+
+ A placeholder op for a value that will be fed into the computation.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'InfeedDequeue'.
+
+
+ The type of elements in the tensor.
+
+
+ The shape of the tensor.
+
+
+ A tensor that will be provided using the infeed mechanism.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ A placeholder op for multiple values that will be fed into the computation
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'InfeedDequeueTuple'.
+
+
+ The element types of each element in outputs.
+
+
+ The shapes of each tensor in outputs.
+
+
+ A list of tensors that will be provided using the infeed mechanism.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ simultaneously as an XLA tuple.
+
+
+
+
+ An op which feeds a single Tensor value into the computation.
+
+
+ A tensor that will be provided using the infeed mechanism.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'InfeedEnqueue'.
+
+
+ Optional argument
+ The shape of the tensor.
+
+
+ Optional argument
+ The TPU device to use. This should be -1 when the Op
+ is running on a TPU device, and >= 0 when the Op is running on the CPU
+ device.
+
+
+ Returns the description of the operation
+
+
+
+
+ An op which feeds multiple Tensor values into the computation as an XLA tuple.
+
+
+ A list of tensors that will be provided using the infeed mechanism.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'InfeedEnqueueTuple'.
+
+
+ Optional argument
+ The TPU device to use. This should be -1 when the Op
+ is running on a TPU device, and >= 0 when the Op is running on the CPU
+ device.
+
+
+ The shapes of each tensor in inputs.
+
+
+ Returns the description of the operation
+
+
+
+
+ Table initializer that takes two tensors for keys and values respectively.
+
+
+ Handle to a table which will be initialized.
+
+
+ Keys of type Tkey.
+
+
+ Values of type Tval.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'InitializeTable'.
+
+
+ Returns the description of the operation
+
+
+
+
+ Initializes a table from a text file.
+
+
+ Handle to a table which will be initialized.
+
+
+ Filename of a vocabulary text file.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'InitializeTableFromTextFile'.
+
+
+ Optional argument
+ Number of elements of the file, use -1 if unknown.
+
+
+ Optional argument
+ Delimiter to separate fields in a line.
+
+
+ Column index in a line to get the table key values from.
+
+
+ Column index that represents information of a line to get the table
+ value values from.
+
+
+ Returns the description of the operation
+
+
+ It inserts one key-value pair into the table for each line of the file.
+ The key and value is extracted from the whole line content, elements from the
+ split line based on delimiter or the line number (starting from zero).
+ Where to extract the key and value from a line is specified by key_index and
+ value_index.
+
+ - A value of -1 means use the line number(starting from zero), expects int64.
+ - A value of -2 means use the whole line content, expects string.
+ - A value >= 0 means use the index (starting at zero) of the split line based
+ on delimiter.
+
+
+
+
+ Initializes a table from a text file.
+
+
+ Handle to a table which will be initialized.
+
+
+ Filename of a vocabulary text file.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'InitializeTableFromTextFileV2'.
+
+
+ Optional argument
+ Number of elements of the file, use -1 if unknown.
+
+
+ Optional argument
+ Delimiter to separate fields in a line.
+
+
+ Column index in a line to get the table key values from.
+
+
+ Column index that represents information of a line to get the table
+ value values from.
+
+
+ Returns the description of the operation
+
+
+ It inserts one key-value pair into the table for each line of the file.
+ The key and value is extracted from the whole line content, elements from the
+ split line based on delimiter or the line number (starting from zero).
+ Where to extract the key and value from a line is specified by key_index and
+ value_index.
+
+ - A value of -1 means use the line number(starting from zero), expects int64.
+ - A value of -2 means use the whole line content, expects string.
+ - A value >= 0 means use the index (starting at zero) of the split line based
+ on delimiter.
+
+
+
+
+ Table initializer that takes two tensors for keys and values respectively.
+
+
+ Handle to a table which will be initialized.
+
+
+ Keys of type Tkey.
+
+
+ Values of type Tval.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'InitializeTableV2'.
+
+
+ Returns the description of the operation
+
+
+
+
+ Adds v into specified rows of x.
+
+ Computes y = x; y[i, :] += v; return y.
+
+
+ A Tensor of type T.
+
+
+ A vector. Indices into the left-most dimension of x.
+
+
+ A Tensor of type T. Same dimension sizes as x except the first dimension, which must be the same as i's size.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'InplaceAdd'.
+
+
+ A Tensor of type T. An alias of x. The content of y is undefined if there are duplicates in i.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Subtracts v into specified rows of x.
+
+ Computes y = x; y[i, :] -= v; return y.
+
+
+ A Tensor of type T.
+
+
+ A vector. Indices into the left-most dimension of x.
+
+
+ A Tensor of type T. Same dimension sizes as x except the first dimension, which must be the same as i's size.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'InplaceSub'.
+
+
+ A Tensor of type T. An alias of x. The content of y is undefined if there are duplicates in i.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Updates specified rows with values in v.
+
+ Computes x[i, :] = v; return x.
+
+
+ A tensor of type T.
+
+
+ A vector. Indices into the left-most dimension of x.
+
+
+ A Tensor of type T. Same dimension sizes as x except the first dimension, which must be the same as i's size.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'InplaceUpdate'.
+
+
+ A Tensor of type T. An alias of x. The content of y is undefined if there are duplicates in i.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Says whether the targets are in the top K predictions.
+
+
+ A batch_size x classes tensor.
+
+
+ A batch_size vector of class ids.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'InTopK'.
+
+
+ Number of top elements to look at for computing precision.
+
+
+ Computed Precision at k as a bool Tensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This outputs a batch_size bool array, an entry out[i] is true if the
+ prediction for the target class is among the top k predictions among
+ all predictions for example i. Note that the behavior of InTopK differs
+ from the TopK op in its handling of ties; if multiple classes have the
+ same prediction value and straddle the top-k boundary, all of those
+ classes are considered to be in the top k.
+
+ More formally, let
+
+ \\(predictions_i\\) be the predictions for all classes for example i,
+ \\(targets_i\\) be the target class for example i,
+ \\(out_i\\) be the output for example i,
+
+ $$out_i = predictions_{i, targets_i} \in TopKIncludingTies(predictions_i)$$
+
+
+
+
+ Says whether the targets are in the top K predictions.
+
+
+ A batch_size x classes tensor.
+
+
+ A batch_size vector of class ids.
+
+
+ Number of top elements to look at for computing precision.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'InTopKV2'.
+
+
+ Computed precision at k as a bool Tensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This outputs a batch_size bool array, an entry out[i] is true if the
+ prediction for the target class is among the top k predictions among
+ all predictions for example i. Note that the behavior of InTopK differs
+ from the TopK op in its handling of ties; if multiple classes have the
+ same prediction value and straddle the top-k boundary, all of those
+ classes are considered to be in the top k.
+
+ More formally, let
+
+ \\(predictions_i\\) be the predictions for all classes for example i,
+ \\(targets_i\\) be the target class for example i,
+ \\(out_i\\) be the output for example i,
+
+ $$out_i = predictions_{i, targets_i} \in TopKIncludingTies(predictions_i)$$
+
+
+
+
+ Computes the reciprocal of x element-wise.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Inv'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ I.e., \\(y = 1 / x\\).
+
+
+
+
+ Flips all bits elementwise.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Invert'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The result will have exactly those bits set, that are not set in x. The
+ computation is performed on the underlying representation of x.
+
+
+
+
+ Computes the inverse permutation of a tensor.
+
+
+ 1-D.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'InvertPermutation'.
+
+
+ 1-D.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation computes the inverse of an index permutation. It takes a 1-D
+ integer tensor x, which represents the indices of a zero-based array, and
+ swaps each value with its index position. In other words, for an output tensor
+ y and an input tensor x, this operation computes the following:
+
+ y[x[i]] = i for i in [0, 1, ..., len(x) - 1]
+
+ The values must include 0. There can be no duplicate values or negative values.
+
+ For example:
+
+
+ # tensor x is [3, 4, 0, 2, 1]
+ invert_permutation(x) ==> [2, 4, 3, 0, 1]
+
+
+
+
+
+ Computes the gradient for the inverse of x wrt its input.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'InvGrad'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Specifically, grad = -dy * y*y, where y = 1/x, and dy
+ is the corresponding input gradient.
+
+
+
+
+ Inverse real-valued fast Fourier transform.
+
+
+ A complex64 tensor.
+
+
+ An int32 tensor of shape [1]. The FFT length.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'IRFFT'.
+
+
+ A float32 tensor of the same rank as input. The inner-most
+ dimension of input is replaced with the fft_length samples of its inverse
+ 1D Fourier transform.
+
+ @compatibility(numpy)
+ Equivalent to np.fft.irfft
+ @end_compatibility
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Computes the inverse 1-dimensional discrete Fourier transform of a real-valued
+ signal over the inner-most dimension of input.
+
+ The inner-most dimension of input is assumed to be the result of RFFT: the
+ fft_length / 2 + 1 unique components of the DFT of a real-valued signal. If
+ fft_length is not provided, it is computed from the size of the inner-most
+ dimension of input (fft_length = 2 * (inner - 1)). If the FFT length used to
+ compute input is odd, it should be provided since it cannot be inferred
+ properly.
+
+ Along the axis IRFFT is computed on, if fft_length / 2 + 1 is smaller
+ than the corresponding dimension of input, the dimension is cropped. If it is
+ larger, the dimension is padded with zeros.
+
+
+
+
+ Inverse 2D real-valued fast Fourier transform.
+
+
+ A complex64 tensor.
+
+
+ An int32 tensor of shape [2]. The FFT length for each dimension.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'IRFFT2D'.
+
+
+ A float32 tensor of the same rank as input. The inner-most 2
+ dimensions of input are replaced with the fft_length samples of their
+ inverse 2D Fourier transform.
+
+ @compatibility(numpy)
+ Equivalent to np.fft.irfft2
+ @end_compatibility
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Computes the inverse 2-dimensional discrete Fourier transform of a real-valued
+ signal over the inner-most 2 dimensions of input.
+
+ The inner-most 2 dimensions of input are assumed to be the result of RFFT2D:
+ The inner-most dimension contains the fft_length / 2 + 1 unique components of
+ the DFT of a real-valued signal. If fft_length is not provided, it is computed
+ from the size of the inner-most 2 dimensions of input. If the FFT length used
+ to compute input is odd, it should be provided since it cannot be inferred
+ properly.
+
+ Along each axis IRFFT2D is computed on, if fft_length (or
+ fft_length / 2 + 1 for the inner-most dimension) is smaller than the
+ corresponding dimension of input, the dimension is cropped. If it is larger,
+ the dimension is padded with zeros.
+
+
+
+
+ Inverse 3D real-valued fast Fourier transform.
+
+
+ A complex64 tensor.
+
+
+ An int32 tensor of shape [3]. The FFT length for each dimension.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'IRFFT3D'.
+
+
+ A float32 tensor of the same rank as input. The inner-most 3
+ dimensions of input are replaced with the fft_length samples of their
+ inverse 3D real Fourier transform.
+
+ @compatibility(numpy)
+ Equivalent to np.irfftn with 3 dimensions.
+ @end_compatibility
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Computes the inverse 3-dimensional discrete Fourier transform of a real-valued
+ signal over the inner-most 3 dimensions of input.
+
+ The inner-most 3 dimensions of input are assumed to be the result of RFFT3D:
+ The inner-most dimension contains the fft_length / 2 + 1 unique components of
+ the DFT of a real-valued signal. If fft_length is not provided, it is computed
+ from the size of the inner-most 3 dimensions of input. If the FFT length used
+ to compute input is odd, it should be provided since it cannot be inferred
+ properly.
+
+ Along each axis IRFFT3D is computed on, if fft_length (or
+ fft_length / 2 + 1 for the inner-most dimension) is smaller than the
+ corresponding dimension of input, the dimension is cropped. If it is larger,
+ the dimension is padded with zeros.
+
+
+
+
+ Checks whether a tree ensemble has been initialized.
+
+
+ Handle to the tree ensemble resouce.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'IsBoostedTreesEnsembleInitialized'.
+
+
+ output boolean on whether it is initialized or not.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Checks whether a quantile stream has been initialized.
+
+
+ resource; The reference to quantile stream resource handle.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'IsBoostedTreesQuantileStreamResourceInitialized'.
+
+
+ bool; True if the resource is initialized, False otherwise.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ An Op that checks if quantile stream resource is initialized.
+
+
+
+
+ Returns which elements of x are finite.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'IsFinite'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ @compatibility(numpy)
+ Equivalent to np.isfinite
+ @end_compatibility
+
+
+
+
+ Returns which elements of x are Inf.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'IsInf'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ @compatibility(numpy)
+ Equivalent to np.isinf
+ @end_compatibility
+
+
+
+
+ Returns which elements of x are NaN.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'IsNan'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ @compatibility(numpy)
+ Equivalent to np.isnan
+ @end_compatibility
+
+
+
+
+ Checks whether a tensor has been initialized.
+
+
+ Should be from a Variable node. May be uninitialized.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'IsVariableInitialized'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Outputs boolean scalar indicating whether the tensor has been initialized.
+
+
+
+
+ A container for an iterator resource.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Iterator'.
+
+
+
+
+
+
+
+
+
+
+ A handle to the iterator that can be passed to a "MakeIterator"
+ or "IteratorGetNext" op.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Converts the given string representing a handle to an iterator to a resource.
+
+
+ A string representation of the given handle.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'IteratorFromStringHandle'.
+
+
+ Optional argument
+ If specified, defines the type of each tuple component in an
+ element produced by the resulting iterator.
+
+
+ Optional argument
+ If specified, defines the shape of each tuple component in an
+ element produced by the resulting iterator.
+
+
+ A handle to an iterator resource.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Gets the next output from the given iterator .
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'IteratorGetNext'.
+
+
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Gets the next output from the given iterator as an Optional variant.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'IteratorGetNextAsOptional'.
+
+
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Gets the next output from the given iterator.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'IteratorGetNextSync'.
+
+
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation is a synchronous version IteratorGetNext. It should only be used
+ in situations where the iterator does not block the calling thread, or where
+ the calling thread is not a member of the thread pool used to execute parallel
+ operations (e.g. in eager mode).
+
+
+
+
+ Converts the given resource_handle representing an iterator to a string.
+
+
+ A handle to an iterator resource.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'IteratorToStringHandle'.
+
+
+ A string representation of the given handle.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ L2 Loss.
+
+
+ Typically 2-D, but may have any dimensions.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'L2Loss'.
+
+
+ 0-D.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Computes half the L2 norm of a tensor without the sqrt:
+
+ output = sum(t ** 2) / 2
+
+
+
+
+ Generates labels for candidate sampling with a learned unigram distribution.
+
+
+ A batch_size * num_true matrix, in which each row contains the
+ IDs of the num_true target_classes in the corresponding original label.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LearnedUnigramCandidateSampler'.
+
+
+ Optional argument
+ If either seed or seed2 are set to be non-zero, the random number
+ generator is seeded by the given seed. Otherwise, it is seeded by a
+ random seed.
+
+
+ Optional argument
+ An second seed to avoid seed collision.
+
+
+ Number of true labels per context.
+
+
+ Number of candidates to randomly sample.
+
+
+ If unique is true, we sample with rejection, so that all sampled
+ candidates in a batch are unique. This requires some approximation to
+ estimate the post-rejection sampling probabilities.
+
+
+ The sampler will sample integers from the interval [0, range_max).
+
+
+ Returns a tuple with multiple values, as follows:
+ sampled_candidates: A vector of length num_sampled, in which each element is
+ the ID of a sampled candidate.
+ true_expected_count: A batch_size * num_true matrix, representing
+ the number of times each candidate is expected to occur in a batch
+ of sampled candidates. If unique=true, then this is a probability.
+ sampled_expected_count: A vector of length num_sampled, for each sampled
+ candidate representing the number of times the candidate is expected
+ to occur in a batch of sampled candidates. If unique=true, then this is a
+ probability.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ See explanations of candidate sampling and the data formats at
+ go/candidate-sampling.
+
+ For each batch, this op picks a single set of sampled candidate labels.
+
+ The advantages of sampling candidates per-batch are simplicity and the
+ possibility of efficient dense matrix multiplication. The disadvantage is that
+ the sampled candidates must be chosen independently of the context and of the
+ true labels.
+
+
+
+
+ Elementwise computes the bitwise left-shift of x and y.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LeftShift'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ If y is negative, or greater than or equal to the width of x in bits the
+ result is implementation defined.
+
+
+
+
+ Returns the truth value of (x < y) element-wise.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Less'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ *NOTE*: Less supports broadcasting. More about broadcasting
+ [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+
+
+
+
+ Returns the truth value of (x <= y) element-wise.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LessEqual'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ *NOTE*: LessEqual supports broadcasting. More about broadcasting
+ [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+
+
+
+
+ Computes the log of the absolute value of Gamma(x) element-wise.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Lgamma'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Generates values in an interval.
+
+
+ 0-D tensor. First entry in the range.
+
+
+ 0-D tensor. Last entry in the range.
+
+
+ 0-D tensor. Number of values to generate.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LinSpace'.
+
+
+ 1-D. The generated values.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ A sequence of num evenly-spaced values are generated beginning at start.
+ If num > 1, the values in the sequence increase by stop - start / num - 1,
+ so that the last one is exactly stop.
+
+ For example:
+
+
+ tf.linspace(10.0, 12.0, 3, name="linspace") => [ 10.0 11.0 12.0]
+
+
+
+
+
+ Computes the difference between two lists of numbers or strings.
+
+
+ 1-D. Values to keep.
+
+
+ 1-D. Values to remove.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ListDiff'.
+
+
+ Optional argument
+
+
+ Returns a tuple with multiple values, as follows:
+ output: 1-D. Values present in x but not in y.
+ idx: 1-D. Positions of x values preserved in out.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Given a list x and a list y, this operation returns a list out that
+ represents all values that are in x but not in y. The returned list out
+ is sorted in the same order that the numbers appear in x (duplicates are
+ preserved). This operation also returns a list idx that represents the
+ position of each out element in x. In other words:
+
+ out[i] = x[idx[i]] for i in [0, 1, ..., len(out) - 1]
+
+ For example, given this input:
+
+
+ x = [1, 2, 3, 4, 5, 6]
+ y = [1, 3, 5]
+
+
+ This operation would return:
+
+
+ out ==> [2, 4, 6]
+ idx ==> [1, 3, 5]
+
+
+
+
+
+ A Reader that outputs the records from a LMDB file.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LMDBReader'.
+
+
+ Optional argument
+ If non-empty, this reader is placed in the given container.
+ Otherwise, a default container is used.
+
+
+ Optional argument
+ If non-empty, this reader is named in the given bucket
+ with this shared_name. Otherwise, the node name is used instead.
+
+
+ The handle to reference the Reader.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Loads a 2-D (matrix) Tensor with name old_tensor_name from the checkpoint
+
+
+ Path to the TensorFlow checkpoint (version 2, TensorBundle) from
+ which the old matrix Tensor will be loaded.
+
+
+ Name of the 2-D Tensor to load from checkpoint.
+
+
+ An int Tensor of row remappings (generally created by
+ generate_vocab_remapping). Even if no row remapping is needed, this must
+ still be an index-valued Tensor (e.g. [0, 1, 2, ...]), or a shifted
+ index-valued Tensor (e.g. [8, 9, 10, ...], for partitioned Variables).
+
+
+ An int Tensor of column remappings (generally created by
+ generate_vocab_remapping). May be a size-0 Tensor if only row remapping
+ is to be done (e.g. column ordering is the same).
+
+
+ A float Tensor containing values to fill in for cells
+ in the output matrix that are not loaded from the checkpoint. Length must be
+ exactly the same as the number of missing / new cells.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LoadAndRemapMatrix'.
+
+
+ Optional argument
+ The maximum number of rows to load from the checkpoint at
+ once. If less than or equal to 0, the entire matrix will be loaded into
+ memory. Setting this arg trades increased disk reads for lower memory usage.
+
+
+ Number of rows (length of the 1st dimension) in the output matrix.
+
+
+ Number of columns (length of the 2nd dimension) in the output matrix.
+
+
+ Output matrix containing existing values loaded from the
+ checkpoint, and with any missing values filled in from initializing_values.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ at ckpt_path and potentially reorders its rows and columns using the
+ specified remappings.
+
+ Most users should use one of the wrapper initializers (such as
+ tf.contrib.framework.load_and_remap_matrix_initializer) instead of this
+ function directly.
+
+ The remappings are 1-D tensors with the following properties:
+
+ * row_remapping must have exactly num_rows entries. Row i of the output
+ matrix will be initialized from the row corresponding to index
+ row_remapping[i] in the old Tensor from the checkpoint.
+ * col_remapping must have either 0 entries (indicating that no column
+ reordering is needed) or num_cols entries. If specified, column j of the
+ output matrix will be initialized from the column corresponding to index
+ col_remapping[j] in the old Tensor from the checkpoint.
+ * A value of -1 in either of the remappings signifies a "missing" entry. In that
+ case, values from the initializing_values tensor will be used to fill that
+ missing row or column. If row_remapping has r missing entries and
+ col_remapping has c missing entries, then the following condition must be
+ true:
+
+ (r * num_cols) + (c * num_rows) - (r * c) == len(initializing_values)
+
+ The remapping tensors can be generated using the GenerateVocabRemapping op.
+
+ As an example, with row_remapping = [1, 0, -1], col_remapping = [0, 2, -1],
+ initializing_values = [0.5, -0.5, 0.25, -0.25, 42], and w(i, j) representing
+ the value from row i, column j of the old tensor in the checkpoint, the output
+ matrix will look like the following:
+
+ [[w(1, 0), w(1, 2), 0.5],
+ [w(0, 0), w(0, 2), -0.5],
+ [0.25, -0.25, 42]]
+
+
+
+
+ Load embedding parameters for a single table.
+
+
+ Value of parameters used in the Adadelta optimization algorithm.
+
+
+ Value of accumulators used in the Adadelta optimization algorithm.
+
+
+ Value of updates used in the Adadelta optimization algorithm.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LoadTPUEmbeddingAdadeltaParameters'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+ Returns the description of the operation
+
+
+
+ An op that loads optimization parameters into HBM for embedding. Must be
+ preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
+ embedding table configuration. For example, this op is used to install
+ parameters that are loaded from a checkpoint before a training loop is
+ executed.
+
+ parameters: A tensor containing the initial embedding table parameters to use in embedding
+ lookups using the Adadelta optimization algorithm.
+ accumulators: A tensor containing the initial embedding table accumulators to use in embedding
+ lookups using the Adadelta optimization algorithm.
+ updates: A tensor containing the initial embedding table updates to use in embedding
+ lookups using the Adadelta optimization algorithm.
+ table_name: Name of this table; must match a name in the
+ TPUEmbeddingConfiguration proto (overrides table_id).
+ num_shards: Number of shards into which the embedding tables are divided.
+ shard_id: Identifier of shard for this operation.
+ table_id: Index of this table in the EmbeddingLayerConfiguration proto
+ (deprecated).
+
+
+
+
+
+ Load embedding parameters for a single table.
+
+
+ Value of parameters used in the Adadelta optimization algorithm.
+
+
+ Value of accumulators used in the Adadelta optimization algorithm.
+
+
+ Value of updates used in the Adadelta optimization algorithm.
+
+
+ Value of gradient_accumulators used in the Adadelta optimization algorithm.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LoadTPUEmbeddingAdadeltaParametersGradAccumDebug'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+ Returns the description of the operation
+
+
+
+ An op that loads optimization parameters into HBM for embedding. Must be
+ preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
+ embedding table configuration. For example, this op is used to install
+ parameters that are loaded from a checkpoint before a training loop is
+ executed.
+
+ parameters: A tensor containing the initial embedding table parameters to use in embedding
+ lookups using the Adadelta optimization algorithm.
+ accumulators: A tensor containing the initial embedding table accumulators to use in embedding
+ lookups using the Adadelta optimization algorithm.
+ updates: A tensor containing the initial embedding table updates to use in embedding
+ lookups using the Adadelta optimization algorithm.
+ gradient_accumulators: A tensor containing the initial embedding table gradient_accumulators to use in embedding
+ lookups using the Adadelta optimization algorithm.
+ table_name: Name of this table; must match a name in the
+ TPUEmbeddingConfiguration proto (overrides table_id).
+ num_shards: Number of shards into which the embedding tables are divided.
+ shard_id: Identifier of shard for this operation.
+ table_id: Index of this table in the EmbeddingLayerConfiguration proto
+ (deprecated).
+
+
+
+
+
+ Load embedding parameters for a single table.
+
+
+ Value of parameters used in the Adagrad optimization algorithm.
+
+
+ Value of accumulators used in the Adagrad optimization algorithm.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LoadTPUEmbeddingAdagradParameters'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+ Returns the description of the operation
+
+
+
+ An op that loads optimization parameters into HBM for embedding. Must be
+ preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
+ embedding table configuration. For example, this op is used to install
+ parameters that are loaded from a checkpoint before a training loop is
+ executed.
+
+ parameters: A tensor containing the initial embedding table parameters to use in embedding
+ lookups using the Adagrad optimization algorithm.
+ accumulators: A tensor containing the initial embedding table accumulators to use in embedding
+ lookups using the Adagrad optimization algorithm.
+ table_name: Name of this table; must match a name in the
+ TPUEmbeddingConfiguration proto (overrides table_id).
+ num_shards: Number of shards into which the embedding tables are divided.
+ shard_id: Identifier of shard for this operation.
+ table_id: Index of this table in the EmbeddingLayerConfiguration proto
+ (deprecated).
+
+
+
+
+
+ Load embedding parameters for a single table.
+
+
+ Value of parameters used in the Adagrad optimization algorithm.
+
+
+ Value of accumulators used in the Adagrad optimization algorithm.
+
+
+ Value of gradient_accumulators used in the Adagrad optimization algorithm.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LoadTPUEmbeddingAdagradParametersGradAccumDebug'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+ Returns the description of the operation
+
+
+
+ An op that loads optimization parameters into HBM for embedding. Must be
+ preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
+ embedding table configuration. For example, this op is used to install
+ parameters that are loaded from a checkpoint before a training loop is
+ executed.
+
+ parameters: A tensor containing the initial embedding table parameters to use in embedding
+ lookups using the Adagrad optimization algorithm.
+ accumulators: A tensor containing the initial embedding table accumulators to use in embedding
+ lookups using the Adagrad optimization algorithm.
+ gradient_accumulators: A tensor containing the initial embedding table gradient_accumulators to use in embedding
+ lookups using the Adagrad optimization algorithm.
+ table_name: Name of this table; must match a name in the
+ TPUEmbeddingConfiguration proto (overrides table_id).
+ num_shards: Number of shards into which the embedding tables are divided.
+ shard_id: Identifier of shard for this operation.
+ table_id: Index of this table in the EmbeddingLayerConfiguration proto
+ (deprecated).
+
+
+
+
+
+ Load embedding parameters for a single table.
+
+
+ Value of parameters used in the ADAM optimization algorithm.
+
+
+ Value of momenta used in the ADAM optimization algorithm.
+
+
+ Value of velocities used in the ADAM optimization algorithm.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LoadTPUEmbeddingADAMParameters'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+ Returns the description of the operation
+
+
+
+ An op that loads optimization parameters into HBM for embedding. Must be
+ preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
+ embedding table configuration. For example, this op is used to install
+ parameters that are loaded from a checkpoint before a training loop is
+ executed.
+
+ parameters: A tensor containing the initial embedding table parameters to use in embedding
+ lookups using the ADAM optimization algorithm.
+ momenta: A tensor containing the initial embedding table momenta to use in embedding
+ lookups using the ADAM optimization algorithm.
+ velocities: A tensor containing the initial embedding table velocities to use in embedding
+ lookups using the ADAM optimization algorithm.
+ table_name: Name of this table; must match a name in the
+ TPUEmbeddingConfiguration proto (overrides table_id).
+ num_shards: Number of shards into which the embedding tables are divided.
+ shard_id: Identifier of shard for this operation.
+ table_id: Index of this table in the EmbeddingLayerConfiguration proto
+ (deprecated).
+
+
+
+
+
+ Load embedding parameters for a single table.
+
+
+ Value of parameters used in the ADAM optimization algorithm.
+
+
+ Value of momenta used in the ADAM optimization algorithm.
+
+
+ Value of velocities used in the ADAM optimization algorithm.
+
+
+ Value of gradient_accumulators used in the ADAM optimization algorithm.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LoadTPUEmbeddingADAMParametersGradAccumDebug'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+ Returns the description of the operation
+
+
+
+ An op that loads optimization parameters into HBM for embedding. Must be
+ preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
+ embedding table configuration. For example, this op is used to install
+ parameters that are loaded from a checkpoint before a training loop is
+ executed.
+
+ parameters: A tensor containing the initial embedding table parameters to use in embedding
+ lookups using the ADAM optimization algorithm.
+ momenta: A tensor containing the initial embedding table momenta to use in embedding
+ lookups using the ADAM optimization algorithm.
+ velocities: A tensor containing the initial embedding table velocities to use in embedding
+ lookups using the ADAM optimization algorithm.
+ gradient_accumulators: A tensor containing the initial embedding table gradient_accumulators to use in embedding
+ lookups using the ADAM optimization algorithm.
+ table_name: Name of this table; must match a name in the
+ TPUEmbeddingConfiguration proto (overrides table_id).
+ num_shards: Number of shards into which the embedding tables are divided.
+ shard_id: Identifier of shard for this operation.
+ table_id: Index of this table in the EmbeddingLayerConfiguration proto
+ (deprecated).
+
+
+
+
+
+ Load embedding parameters for a single table.
+
+
+ Value of parameters used in the centered RMSProp optimization algorithm.
+
+
+ Value of ms used in the centered RMSProp optimization algorithm.
+
+
+ Value of mom used in the centered RMSProp optimization algorithm.
+
+
+ Value of mg used in the centered RMSProp optimization algorithm.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LoadTPUEmbeddingCenteredRMSPropParameters'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+ Returns the description of the operation
+
+
+
+ An op that loads optimization parameters into HBM for embedding. Must be
+ preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
+ embedding table configuration. For example, this op is used to install
+ parameters that are loaded from a checkpoint before a training loop is
+ executed.
+
+ parameters: A tensor containing the initial embedding table parameters to use in embedding
+ lookups using the centered RMSProp optimization algorithm.
+ ms: A tensor containing the initial embedding table ms to use in embedding
+ lookups using the centered RMSProp optimization algorithm.
+ mom: A tensor containing the initial embedding table mom to use in embedding
+ lookups using the centered RMSProp optimization algorithm.
+ mg: A tensor containing the initial embedding table mg to use in embedding
+ lookups using the centered RMSProp optimization algorithm.
+ table_name: Name of this table; must match a name in the
+ TPUEmbeddingConfiguration proto (overrides table_id).
+ num_shards: Number of shards into which the embedding tables are divided.
+ shard_id: Identifier of shard for this operation.
+ table_id: Index of this table in the EmbeddingLayerConfiguration proto
+ (deprecated).
+
+
+
+
+
+ Load embedding parameters for a single table.
+
+
+ Value of parameters used in the FTRL optimization algorithm.
+
+
+ Value of accumulators used in the FTRL optimization algorithm.
+
+
+ Value of linears used in the FTRL optimization algorithm.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LoadTPUEmbeddingFTRLParameters'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+ Returns the description of the operation
+
+
+
+ An op that loads optimization parameters into HBM for embedding. Must be
+ preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
+ embedding table configuration. For example, this op is used to install
+ parameters that are loaded from a checkpoint before a training loop is
+ executed.
+
+ parameters: A tensor containing the initial embedding table parameters to use in embedding
+ lookups using the FTRL optimization algorithm.
+ accumulators: A tensor containing the initial embedding table accumulators to use in embedding
+ lookups using the FTRL optimization algorithm.
+ linears: A tensor containing the initial embedding table linears to use in embedding
+ lookups using the FTRL optimization algorithm.
+ table_name: Name of this table; must match a name in the
+ TPUEmbeddingConfiguration proto (overrides table_id).
+ num_shards: Number of shards into which the embedding tables are divided.
+ shard_id: Identifier of shard for this operation.
+ table_id: Index of this table in the EmbeddingLayerConfiguration proto
+ (deprecated).
+
+
+
+
+
+ Load embedding parameters for a single table.
+
+
+ Value of parameters used in the FTRL optimization algorithm.
+
+
+ Value of accumulators used in the FTRL optimization algorithm.
+
+
+ Value of linears used in the FTRL optimization algorithm.
+
+
+ Value of gradient_accumulators used in the FTRL optimization algorithm.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LoadTPUEmbeddingFTRLParametersGradAccumDebug'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+ Returns the description of the operation
+
+
+
+ An op that loads optimization parameters into HBM for embedding. Must be
+ preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
+ embedding table configuration. For example, this op is used to install
+ parameters that are loaded from a checkpoint before a training loop is
+ executed.
+
+ parameters: A tensor containing the initial embedding table parameters to use in embedding
+ lookups using the FTRL optimization algorithm.
+ accumulators: A tensor containing the initial embedding table accumulators to use in embedding
+ lookups using the FTRL optimization algorithm.
+ linears: A tensor containing the initial embedding table linears to use in embedding
+ lookups using the FTRL optimization algorithm.
+ gradient_accumulators: A tensor containing the initial embedding table gradient_accumulators to use in embedding
+ lookups using the FTRL optimization algorithm.
+ table_name: Name of this table; must match a name in the
+ TPUEmbeddingConfiguration proto (overrides table_id).
+ num_shards: Number of shards into which the embedding tables are divided.
+ shard_id: Identifier of shard for this operation.
+ table_id: Index of this table in the EmbeddingLayerConfiguration proto
+ (deprecated).
+
+
+
+
+
+ Load embedding parameters for a single table.
+
+
+ Value of parameters used in the MDL Adagrad Light optimization algorithm.
+
+
+ Value of accumulators used in the MDL Adagrad Light optimization algorithm.
+
+
+ Value of weights used in the MDL Adagrad Light optimization algorithm.
+
+
+ Value of benefits used in the MDL Adagrad Light optimization algorithm.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LoadTPUEmbeddingMDLAdagradLightParameters'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+ Returns the description of the operation
+
+
+
+ An op that loads optimization parameters into HBM for embedding. Must be
+ preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
+ embedding table configuration. For example, this op is used to install
+ parameters that are loaded from a checkpoint before a training loop is
+ executed.
+
+ parameters: A tensor containing the initial embedding table parameters to use in embedding
+ lookups using the MDL Adagrad Light optimization algorithm.
+ accumulators: A tensor containing the initial embedding table accumulators to use in embedding
+ lookups using the MDL Adagrad Light optimization algorithm.
+ weights: A tensor containing the initial embedding table weights to use in embedding
+ lookups using the MDL Adagrad Light optimization algorithm.
+ benefits: A tensor containing the initial embedding table benefits to use in embedding
+ lookups using the MDL Adagrad Light optimization algorithm.
+ table_name: Name of this table; must match a name in the
+ TPUEmbeddingConfiguration proto (overrides table_id).
+ num_shards: Number of shards into which the embedding tables are divided.
+ shard_id: Identifier of shard for this operation.
+ table_id: Index of this table in the EmbeddingLayerConfiguration proto
+ (deprecated).
+
+
+
+
+
+ Load embedding parameters for a single table.
+
+
+ Value of parameters used in the Momentum optimization algorithm.
+
+
+ Value of momenta used in the Momentum optimization algorithm.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LoadTPUEmbeddingMomentumParameters'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+ Returns the description of the operation
+
+
+
+ An op that loads optimization parameters into HBM for embedding. Must be
+ preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
+ embedding table configuration. For example, this op is used to install
+ parameters that are loaded from a checkpoint before a training loop is
+ executed.
+
+ parameters: A tensor containing the initial embedding table parameters to use in embedding
+ lookups using the Momentum optimization algorithm.
+ momenta: A tensor containing the initial embedding table momenta to use in embedding
+ lookups using the Momentum optimization algorithm.
+ table_name: Name of this table; must match a name in the
+ TPUEmbeddingConfiguration proto (overrides table_id).
+ num_shards: Number of shards into which the embedding tables are divided.
+ shard_id: Identifier of shard for this operation.
+ table_id: Index of this table in the EmbeddingLayerConfiguration proto
+ (deprecated).
+
+
+
+
+
+ Load embedding parameters for a single table.
+
+
+ Value of parameters used in the Momentum optimization algorithm.
+
+
+ Value of momenta used in the Momentum optimization algorithm.
+
+
+ Value of gradient_accumulators used in the Momentum optimization algorithm.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LoadTPUEmbeddingMomentumParametersGradAccumDebug'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+ Returns the description of the operation
+
+
+
+ An op that loads optimization parameters into HBM for embedding. Must be
+ preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
+ embedding table configuration. For example, this op is used to install
+ parameters that are loaded from a checkpoint before a training loop is
+ executed.
+
+ parameters: A tensor containing the initial embedding table parameters to use in embedding
+ lookups using the Momentum optimization algorithm.
+ momenta: A tensor containing the initial embedding table momenta to use in embedding
+ lookups using the Momentum optimization algorithm.
+ gradient_accumulators: A tensor containing the initial embedding table gradient_accumulators to use in embedding
+ lookups using the Momentum optimization algorithm.
+ table_name: Name of this table; must match a name in the
+ TPUEmbeddingConfiguration proto (overrides table_id).
+ num_shards: Number of shards into which the embedding tables are divided.
+ shard_id: Identifier of shard for this operation.
+ table_id: Index of this table in the EmbeddingLayerConfiguration proto
+ (deprecated).
+
+
+
+
+
+ Load embedding parameters for a single table.
+
+
+ Value of parameters used in the proximal Adagrad optimization algorithm.
+
+
+ Value of accumulators used in the proximal Adagrad optimization algorithm.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LoadTPUEmbeddingProximalAdagradParameters'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+ Returns the description of the operation
+
+
+
+ An op that loads optimization parameters into HBM for embedding. Must be
+ preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
+ embedding table configuration. For example, this op is used to install
+ parameters that are loaded from a checkpoint before a training loop is
+ executed.
+
+ parameters: A tensor containing the initial embedding table parameters to use in embedding
+ lookups using the proximal Adagrad optimization algorithm.
+ accumulators: A tensor containing the initial embedding table accumulators to use in embedding
+ lookups using the proximal Adagrad optimization algorithm.
+ table_name: Name of this table; must match a name in the
+ TPUEmbeddingConfiguration proto (overrides table_id).
+ num_shards: Number of shards into which the embedding tables are divided.
+ shard_id: Identifier of shard for this operation.
+ table_id: Index of this table in the EmbeddingLayerConfiguration proto
+ (deprecated).
+
+
+
+
+
+ Load embedding parameters for a single table.
+
+
+ Value of parameters used in the proximal Adagrad optimization algorithm.
+
+
+ Value of accumulators used in the proximal Adagrad optimization algorithm.
+
+
+ Value of gradient_accumulators used in the proximal Adagrad optimization algorithm.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LoadTPUEmbeddingProximalAdagradParametersGradAccumDebug'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+ Returns the description of the operation
+
+
+
+ An op that loads optimization parameters into HBM for embedding. Must be
+ preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
+ embedding table configuration. For example, this op is used to install
+ parameters that are loaded from a checkpoint before a training loop is
+ executed.
+
+ parameters: A tensor containing the initial embedding table parameters to use in embedding
+ lookups using the proximal Adagrad optimization algorithm.
+ accumulators: A tensor containing the initial embedding table accumulators to use in embedding
+ lookups using the proximal Adagrad optimization algorithm.
+ gradient_accumulators: A tensor containing the initial embedding table gradient_accumulators to use in embedding
+ lookups using the proximal Adagrad optimization algorithm.
+ table_name: Name of this table; must match a name in the
+ TPUEmbeddingConfiguration proto (overrides table_id).
+ num_shards: Number of shards into which the embedding tables are divided.
+ shard_id: Identifier of shard for this operation.
+ table_id: Index of this table in the EmbeddingLayerConfiguration proto
+ (deprecated).
+
+
+
+
+
+ Load embedding parameters for a single table.
+
+
+ Value of parameters used in the RMSProp optimization algorithm.
+
+
+ Value of ms used in the RMSProp optimization algorithm.
+
+
+ Value of mom used in the RMSProp optimization algorithm.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LoadTPUEmbeddingRMSPropParameters'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+ Returns the description of the operation
+
+
+
+ An op that loads optimization parameters into HBM for embedding. Must be
+ preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
+ embedding table configuration. For example, this op is used to install
+ parameters that are loaded from a checkpoint before a training loop is
+ executed.
+
+ parameters: A tensor containing the initial embedding table parameters to use in embedding
+ lookups using the RMSProp optimization algorithm.
+ ms: A tensor containing the initial embedding table ms to use in embedding
+ lookups using the RMSProp optimization algorithm.
+ mom: A tensor containing the initial embedding table mom to use in embedding
+ lookups using the RMSProp optimization algorithm.
+ table_name: Name of this table; must match a name in the
+ TPUEmbeddingConfiguration proto (overrides table_id).
+ num_shards: Number of shards into which the embedding tables are divided.
+ shard_id: Identifier of shard for this operation.
+ table_id: Index of this table in the EmbeddingLayerConfiguration proto
+ (deprecated).
+
+
+
+
+
+ Load embedding parameters for a single table.
+
+
+ Value of parameters used in the RMSProp optimization algorithm.
+
+
+ Value of ms used in the RMSProp optimization algorithm.
+
+
+ Value of mom used in the RMSProp optimization algorithm.
+
+
+ Value of gradient_accumulators used in the RMSProp optimization algorithm.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LoadTPUEmbeddingRMSPropParametersGradAccumDebug'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+ Returns the description of the operation
+
+
+
+ An op that loads optimization parameters into HBM for embedding. Must be
+ preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
+ embedding table configuration. For example, this op is used to install
+ parameters that are loaded from a checkpoint before a training loop is
+ executed.
+
+ parameters: A tensor containing the initial embedding table parameters to use in embedding
+ lookups using the RMSProp optimization algorithm.
+ ms: A tensor containing the initial embedding table ms to use in embedding
+ lookups using the RMSProp optimization algorithm.
+ mom: A tensor containing the initial embedding table mom to use in embedding
+ lookups using the RMSProp optimization algorithm.
+ gradient_accumulators: A tensor containing the initial embedding table gradient_accumulators to use in embedding
+ lookups using the RMSProp optimization algorithm.
+ table_name: Name of this table; must match a name in the
+ TPUEmbeddingConfiguration proto (overrides table_id).
+ num_shards: Number of shards into which the embedding tables are divided.
+ shard_id: Identifier of shard for this operation.
+ table_id: Index of this table in the EmbeddingLayerConfiguration proto
+ (deprecated).
+
+
+
+
+
+ Load embedding parameters for a single table.
+
+
+ Value of parameters used in the stochastic gradient descent optimization algorithm.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LoadTPUEmbeddingStochasticGradientDescentParameters'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+ Returns the description of the operation
+
+
+
+ An op that loads optimization parameters into HBM for embedding. Must be
+ preceded by a ConfigureTPUEmbeddingHost op that sets up the correct
+ embedding table configuration. For example, this op is used to install
+ parameters that are loaded from a checkpoint before a training loop is
+ executed.
+
+ parameters: A tensor containing the initial embedding table parameters to use in embedding
+ lookups using the stochastic gradient descent optimization algorithm.
+ table_name: Name of this table; must match a name in the
+ TPUEmbeddingConfiguration proto (overrides table_id).
+ num_shards: Number of shards into which the embedding tables are divided.
+ shard_id: Identifier of shard for this operation.
+ table_id: Index of this table in the EmbeddingLayerConfiguration proto
+ (deprecated).
+
+
+
+
+
+ Computes natural logarithm of x element-wise.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Log'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ I.e., \\(y = \log_e x\\).
+
+
+
+
+ Computes natural logarithm of (1 + x) element-wise.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Log1p'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ I.e., \\(y = \log_e (1 + x)\\).
+
+
+
+
+ Returns the truth value of x AND y element-wise.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LogicalAnd'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ *NOTE*: LogicalAnd supports broadcasting. More about broadcasting
+ [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+
+
+
+
+ Returns the truth value of NOT x element-wise.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LogicalNot'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Returns the truth value of x OR y element-wise.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LogicalOr'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ *NOTE*: LogicalOr supports broadcasting. More about broadcasting
+ [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+
+
+
+
+ Computes the sign and the log of the absolute value of the determinant of
+
+
+ Shape is [N, M, M].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LogMatrixDeterminant'.
+
+
+ Returns a tuple with multiple values, as follows:
+ sign: The signs of the log determinants of the inputs. Shape is [N].
+ log_abs_determinant: The logs of the absolute values of the determinants
+ of the N input matrices. Shape is [N].
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ one or more square matrices.
+
+ The input is a tensor of shape [N, M, M] whose inner-most 2 dimensions
+ form square matrices. The outputs are two tensors containing the signs and
+ absolute values of the log determinants for all N input submatrices
+ [..., :, :] such that the determinant = sign*exp(log_abs_determinant).
+ The log_abs_determinant is computed as det(P)*sum(log(diag(LU))) where LU
+ is the LU decomposition of the input and P is the corresponding
+ permutation matrix.
+
+
+
+
+ Computes log softmax activations.
+
+
+ 2-D with shape [batch_size, num_classes].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LogSoftmax'.
+
+
+ Same shape as logits.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ For each batch i and class j we have
+
+ logsoftmax[i, j] = logits[i, j] - log(sum(exp(logits[i])))
+
+
+
+
+ Generates labels for candidate sampling with a log-uniform distribution.
+
+
+ A batch_size * num_true matrix, in which each row contains the
+ IDs of the num_true target_classes in the corresponding original label.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LogUniformCandidateSampler'.
+
+
+ Optional argument
+ If either seed or seed2 are set to be non-zero, the random number
+ generator is seeded by the given seed. Otherwise, it is seeded by a
+ random seed.
+
+
+ Optional argument
+ An second seed to avoid seed collision.
+
+
+ Number of true labels per context.
+
+
+ Number of candidates to randomly sample.
+
+
+ If unique is true, we sample with rejection, so that all sampled
+ candidates in a batch are unique. This requires some approximation to
+ estimate the post-rejection sampling probabilities.
+
+
+ The sampler will sample integers from the interval [0, range_max).
+
+
+ Returns a tuple with multiple values, as follows:
+ sampled_candidates: A vector of length num_sampled, in which each element is
+ the ID of a sampled candidate.
+ true_expected_count: A batch_size * num_true matrix, representing
+ the number of times each candidate is expected to occur in a batch
+ of sampled candidates. If unique=true, then this is a probability.
+ sampled_expected_count: A vector of length num_sampled, for each sampled
+ candidate representing the number of times the candidate is expected
+ to occur in a batch of sampled candidates. If unique=true, then this is a
+ probability.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ See explanations of candidate sampling and the data formats at
+ go/candidate-sampling.
+
+ For each batch, this op picks a single set of sampled candidate labels.
+
+ The advantages of sampling candidates per-batch are simplicity and the
+ possibility of efficient dense matrix multiplication. The disadvantage is that
+ the sampled candidates must be chosen independently of the context and of the
+ true labels.
+
+
+
+
+ Outputs all keys and values in the table.
+
+
+ Handle to the table.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LookupTableExport'.
+
+
+
+
+
+
+ Returns a tuple with multiple values, as follows:
+ keys: Vector of all keys present in the table.
+ values: Tensor of all values in the table. Indexed in parallel with keys.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+
+ Outputs all keys and values in the table.
+
+
+ Handle to the table.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LookupTableExportV2'.
+
+
+
+
+
+
+ Returns a tuple with multiple values, as follows:
+ keys: Vector of all keys present in the table.
+ values: Tensor of all values in the table. Indexed in parallel with keys.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+
+ Looks up keys in a table, outputs the corresponding values.
+
+
+ Handle to the table.
+
+
+ Any shape. Keys to look up.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LookupTableFind'.
+
+
+ Same shape as keys. Values found in the table, or default_values
+ for missing keys.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The tensor keys must of the same type as the keys of the table.
+ The output values is of the type of the table values.
+
+ The scalar default_value is the value output for keys not present in the
+ table. It must also be of the same type as the table values.
+
+
+
+
+ Looks up keys in a table, outputs the corresponding values.
+
+
+ Handle to the table.
+
+
+ Any shape. Keys to look up.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LookupTableFindV2'.
+
+
+ Same shape as keys. Values found in the table, or default_values
+ for missing keys.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The tensor keys must of the same type as the keys of the table.
+ The output values is of the type of the table values.
+
+ The scalar default_value is the value output for keys not present in the
+ table. It must also be of the same type as the table values.
+
+
+
+
+ Replaces the contents of the table with the specified keys and values.
+
+
+ Handle to the table.
+
+
+ Any shape. Keys to look up.
+
+
+ Values to associate with keys.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LookupTableImport'.
+
+
+ Returns the description of the operation
+
+
+ The tensor keys must be of the same type as the keys of the table.
+ The tensor values must be of the type of the table values.
+
+
+
+
+ Replaces the contents of the table with the specified keys and values.
+
+
+ Handle to the table.
+
+
+ Any shape. Keys to look up.
+
+
+ Values to associate with keys.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LookupTableImportV2'.
+
+
+ Returns the description of the operation
+
+
+ The tensor keys must be of the same type as the keys of the table.
+ The tensor values must be of the type of the table values.
+
+
+
+
+ Updates the table to associates keys with values.
+
+
+ Handle to the table.
+
+
+ Any shape. Keys to look up.
+
+
+ Values to associate with keys.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LookupTableInsert'.
+
+
+ Returns the description of the operation
+
+
+ The tensor keys must be of the same type as the keys of the table.
+ The tensor values must be of the type of the table values.
+
+
+
+
+ Updates the table to associates keys with values.
+
+
+ Handle to the table.
+
+
+ Any shape. Keys to look up.
+
+
+ Values to associate with keys.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LookupTableInsertV2'.
+
+
+ Returns the description of the operation
+
+
+ The tensor keys must be of the same type as the keys of the table.
+ The tensor values must be of the type of the table values.
+
+
+
+
+ Computes the number of elements in the given table.
+
+
+ Handle to the table.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LookupTableSize'.
+
+
+ Scalar that contains number of elements in the table.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes the number of elements in the given table.
+
+
+ Handle to the table.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LookupTableSizeV2'.
+
+
+ Scalar that contains number of elements in the table.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Forwards the input to the output.
+
+
+ A boolean scalar, representing the branch predicate of the Switch op.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LoopCond'.
+
+
+ The same tensor as input.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operator represents the loop termination condition used by the
+ "pivot" switches of a loop.
+
+
+
+
+ Applies lower_bound(sorted_search_values, values) along each row.
+
+
+ 2-D Tensor where each row is ordered.
+
+
+ 2-D Tensor with the same numbers of rows as sorted_search_values. Contains
+ the values that will be searched for in sorted_search_values.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LowerBound'.
+
+
+ Optional argument
+
+
+ A Tensor with the same shape as values. It contains the first scalar index
+ into the last dimension where values can be inserted without changing the
+ ordered property.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Each set of rows with the same index in (sorted_inputs, values) is treated
+ independently. The resulting row is the equivalent of calling
+ np.searchsorted(sorted_inputs, values, side='left').
+
+ The result is not a global index to the entire
+ Tensor, but rather just the index in the last dimension.
+
+ A 2-D example:
+ sorted_sequence = [[0, 3, 9, 9, 10],
+ [1, 2, 3, 4, 5]]
+ values = [[2, 4, 9],
+ [0, 2, 6]]
+
+ result = LowerBound(sorted_sequence, values)
+
+ result == [[1, 2, 2],
+ [0, 1, 5]]
+
+
+
+
+ Local Response Normalization.
+
+
+ 4-D.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LRN'.
+
+
+ Optional argument
+ 0-D. Half-width of the 1-D normalization window.
+
+
+ Optional argument
+ An offset (usually positive to avoid dividing by 0).
+
+
+ Optional argument
+ A scale factor, usually positive.
+
+
+ Optional argument
+ An exponent.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The 4-D input tensor is treated as a 3-D array of 1-D vectors (along the last
+ dimension), and each vector is normalized independently. Within a given vector,
+ each component is divided by the weighted, squared sum of inputs within
+ depth_radius. In detail,
+
+ sqr_sum[a, b, c, d] =
+ sum(input[a, b, c, d - depth_radius : d + depth_radius + 1] ** 2)
+ output = input / (bias + alpha * sqr_sum) ** beta
+
+ For details, see [Krizhevsky et al., ImageNet classification with deep
+ convolutional neural networks (NIPS 2012)](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks).
+
+
+
+
+ Gradients for Local Response Normalization.
+
+
+ 4-D with shape [batch, height, width, channels].
+
+
+ 4-D with shape [batch, height, width, channels].
+
+
+ 4-D with shape [batch, height, width, channels].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'LRNGrad'.
+
+
+ Optional argument
+ A depth radius.
+
+
+ Optional argument
+ An offset (usually > 0 to avoid dividing by 0).
+
+
+ Optional argument
+ A scale factor, usually positive.
+
+
+ Optional argument
+ An exponent.
+
+
+ The gradients for LRN.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Makes a new iterator from the given dataset and stores it in iterator.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MakeIterator'.
+
+
+ Returns the description of the operation
+
+
+ This operation may be executed multiple times. Each execution will reset the
+ iterator in iterator to the first element of dataset.
+
+
+
+
+ Op removes all elements in the underlying container.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MapClear'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+ Returns the description of the operation
+
+
+
+
+ Op returns the number of incomplete elements in the underlying container.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MapIncompleteSize'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Op peeks at the values at the specified key. If the
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MapPeek'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ underlying container does not contain this key
+ this op will block until it does.
+
+
+
+
+ Op returns the number of elements in the underlying container.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MapSize'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Stage (key, values) in the underlying container which behaves like a hashtable.
+
+
+ int64
+
+
+
+
+ a list of tensors
+ dtypes A list of data types that inserted values should adhere to.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MapStage'.
+
+
+ Optional argument
+ Maximum number of elements in the Staging Area. If > 0, inserts
+ on the container will block when the capacity is reached.
+
+
+ Optional argument
+
+
+ Optional argument
+ If non-empty, this queue is placed in the given container. Otherwise,
+ a default container is used.
+
+
+ Optional argument
+ It is necessary to match this name to the matching Unstage Op.
+
+
+
+
+ Returns the description of the operation
+
+
+
+
+ Op removes and returns the values associated with the key
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MapUnstage'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ from the underlying container. If the underlying container
+ does not contain this key, the op will block until it does.
+
+
+
+
+ Op removes and returns a random (key, value)
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MapUnstageNoKey'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+ Returns a tuple with multiple values, as follows:
+ key:
+ values:
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ from the underlying container. If the underlying container
+ does not contain elements, the op will block until it does.
+
+
+
+
+ Returns the set of files matching one or more glob patterns.
+
+
+ Shell wildcard pattern(s). Scalar or vector of type string.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MatchingFiles'.
+
+
+ A vector of matching filenames.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Note that this routine only supports wildcard characters in the
+ basename portion of the pattern, not in the directory portion.
+ Note also that the order of filenames returned can be non-deterministic.
+
+
+
+
+ Multiply the matrix "a" by the matrix "b".
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MatMul'.
+
+
+ Optional argument
+ If true, "a" is transposed before multiplication.
+
+
+ Optional argument
+ If true, "b" is transposed before multiplication.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The inputs must be two-dimensional matrices and the inner dimension of
+ "a" (after being transposed if transpose_a is true) must match the
+ outer dimension of "b" (after being transposed if transposed_b is
+ true).
+
+ *Note*: The default kernel implementation for MatMul on GPUs uses
+ cublas.
+
+
+
+
+ Copy a tensor setting everything outside a central band in each innermost matrix
+
+
+ Rank k tensor.
+
+
+ 0-D tensor. Number of subdiagonals to keep. If negative, keep entire
+ lower triangle.
+
+
+ 0-D tensor. Number of superdiagonals to keep. If negative, keep
+ entire upper triangle.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MatrixBandPart'.
+
+
+ Rank k tensor of the same shape as input. The extracted banded tensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ to zero.
+
+ The band part is computed as follows:
+ Assume input has k dimensions [I, J, K, ..., M, N], then the output is a
+ tensor with the same shape where
+
+ band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n].
+
+ The indicator function
+
+ in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower)) &&
+ (num_upper < 0 || (n-m) <= num_upper).
+
+ For example:
+
+
+ # if 'input' is [[ 0, 1, 2, 3]
+ [-1, 0, 1, 2]
+ [-2, -1, 0, 1]
+ [-3, -2, -1, 0]],
+
+ tf.matrix_band_part(input, 1, -1) ==> [[ 0, 1, 2, 3]
+ [-1, 0, 1, 2]
+ [ 0, -1, 0, 1]
+ [ 0, 0, -1, 0]],
+
+ tf.matrix_band_part(input, 2, 1) ==> [[ 0, 1, 0, 0]
+ [-1, 0, 1, 0]
+ [-2, -1, 0, 1]
+ [ 0, -2, -1, 0]]
+
+
+ Useful special cases:
+
+
+ tf.matrix_band_part(input, 0, -1) ==> Upper triangular part.
+ tf.matrix_band_part(input, -1, 0) ==> Lower triangular part.
+ tf.matrix_band_part(input, 0, 0) ==> Diagonal.
+
+
+
+
+
+ Computes the determinant of one or more square matrices.
+
+
+ Shape is [..., M, M].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MatrixDeterminant'.
+
+
+ Shape is [...].
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The input is a tensor of shape [..., M, M] whose inner-most 2 dimensions
+ form square matrices. The output is a tensor containing the determinants
+ for all input submatrices [..., :, :].
+
+
+
+
+
+ Returns the batched diagonal part of a batched tensor.
+
+
+ Rank k tensor where k >= 2.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MatrixDiagPart'.
+
+
+ The extracted diagonal(s) having shape
+ diagonal.shape = input.shape[:-2] + [min(input.shape[-2:])].
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation returns a tensor with the diagonal part
+ of the batched input. The diagonal part is computed as follows:
+
+ Assume input has k dimensions [I, J, K, ..., M, N], then the output is a
+ tensor of rank k - 1 with dimensions [I, J, K, ..., min(M, N)] where:
+
+ diagonal[i, j, k, ..., n] = input[i, j, k, ..., n, n].
+
+ The input must be at least a matrix.
+
+ For example:
+
+
+ # 'input' is [[[1, 0, 0, 0]
+ [0, 2, 0, 0]
+ [0, 0, 3, 0]
+ [0, 0, 0, 4]],
+ [[5, 0, 0, 0]
+ [0, 6, 0, 0]
+ [0, 0, 7, 0]
+ [0, 0, 0, 8]]]
+
+ and input.shape = (2, 4, 4)
+
+ tf.matrix_diag_part(input) ==> [[1, 2, 3, 4], [5, 6, 7, 8]]
+
+ which has shape (2, 4)
+
+
+
+
+
+ Deprecated, use python implementation tf.linalg.matrix_exponential.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MatrixExponential'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes the inverse of one or more square invertible matrices or their
+
+
+ Shape is [..., M, M].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MatrixInverse'.
+
+
+ Optional argument
+
+
+ Shape is [..., M, M].
+
+ @compatibility(numpy)
+ Equivalent to np.linalg.inv
+ @end_compatibility
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ adjoints (conjugate transposes).
+
+ The input is a tensor of shape [..., M, M] whose inner-most 2 dimensions
+ form square matrices. The output is a tensor of the same shape as the input
+ containing the inverse for all input submatrices [..., :, :].
+
+ The op uses LU decomposition with partial pivoting to compute the inverses.
+
+ If a matrix is not invertible there is no guarantee what the op does. It
+ may detect the condition and raise an exception or it may simply return a
+ garbage result.
+
+
+
+
+ Computes the matrix logarithm of one or more square matrices:
+
+
+ Shape is [..., M, M].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MatrixLogarithm'.
+
+
+ Shape is [..., M, M].
+
+ @compatibility(scipy)
+ Equivalent to scipy.linalg.logm
+ @end_compatibility
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+ \\(log(exp(A)) = A\\)
+
+ This op is only defined for complex matrices. If A is positive-definite and
+ real, then casting to a complex matrix, taking the logarithm and casting back
+ to a real matrix will give the correct result.
+
+ This function computes the matrix logarithm using the Schur-Parlett algorithm.
+ Details of the algorithm can be found in Section 11.6.2 of:
+ Nicholas J. Higham, Functions of Matrices: Theory and Computation, SIAM 2008.
+ ISBN 978-0-898716-46-7.
+
+ The input is a tensor of shape [..., M, M] whose inner-most 2 dimensions
+ form square matrices. The output is a tensor of the same shape as the input
+ containing the exponential for all input submatrices [..., :, :].
+
+
+
+
+ Returns a batched matrix tensor with new batched diagonal values.
+
+
+ Rank k+1, where k >= 1.
+
+
+ Rank k, where k >= 1.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MatrixSetDiag'.
+
+
+ Rank k+1, with output.shape = input.shape.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Given input and diagonal, this operation returns a tensor with the
+ same shape and values as input, except for the main diagonal of the
+ innermost matrices. These will be overwritten by the values in diagonal.
+
+ The output is computed as follows:
+
+ Assume input has k+1 dimensions [I, J, K, ..., M, N] and diagonal has
+ k dimensions [I, J, K, ..., min(M, N)]. Then the output is a
+ tensor of rank k+1 with dimensions [I, J, K, ..., M, N] where:
+
+ * output[i, j, k, ..., m, n] = diagonal[i, j, k, ..., n] for m == n.
+ * output[i, j, k, ..., m, n] = input[i, j, k, ..., m, n] for m != n.
+
+
+
+
+ Solves systems of linear equations.
+
+
+ Shape is [..., M, M].
+
+
+ Shape is [..., M, K].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MatrixSolve'.
+
+
+ Optional argument
+ Boolean indicating whether to solve with matrix or its (block-wise)
+ adjoint.
+
+
+ Shape is [..., M, K].
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Matrix is a tensor of shape [..., M, M] whose inner-most 2 dimensions
+ form square matrices. Rhs is a tensor of shape [..., M, K]. The output is
+ a tensor shape [..., M, K]. If adjoint is False then each output matrix
+ satisfies matrix[..., :, :] * output[..., :, :] = rhs[..., :, :].
+ If adjoint is True then each output matrix satisfies
+ adjoint(matrix[..., :, :]) * output[..., :, :] = rhs[..., :, :].
+
+
+
+
+ Solves one or more linear least-squares problems.
+
+
+ Shape is [..., M, N].
+
+
+ Shape is [..., M, K].
+
+
+ Scalar tensor.
+
+ @compatibility(numpy)
+ Equivalent to np.linalg.lstsq
+ @end_compatibility
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MatrixSolveLs'.
+
+
+ Optional argument
+
+
+ Shape is [..., N, K].
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ matrix is a tensor of shape [..., M, N] whose inner-most 2 dimensions
+ form real or complex matrices of size [M, N]. Rhs is a tensor of the same
+ type as matrix and shape [..., M, K].
+ The output is a tensor shape [..., N, K] where each output matrix solves
+ each of the equations
+ matrix[..., :, :] * output[..., :, :] = rhs[..., :, :]
+ in the least squares sense.
+
+ We use the following notation for (complex) matrix and right-hand sides
+ in the batch:
+
+ matrix=\\(A \in \mathbb{C}^{m \times n}\\),
+ rhs=\\(B \in \mathbb{C}^{m \times k}\\),
+ output=\\(X \in \mathbb{C}^{n \times k}\\),
+ l2_regularizer=\\(\lambda \in \mathbb{R}\\).
+
+ If fast is True, then the solution is computed by solving the normal
+ equations using Cholesky decomposition. Specifically, if \\(m \ge n\\) then
+ \\(X = (A^H A + \lambda I)^{-1} A^H B\\), which solves the least-squares
+ problem \\(X = \mathrm{argmin}_{Z \in \Re^{n \times k} } ||A Z - B||_F^2 + \lambda ||Z||_F^2\\).
+ If \\(m \lt n\\) then output is computed as
+ \\(X = A^H (A A^H + \lambda I)^{-1} B\\), which (for \\(\lambda = 0\\)) is the
+ minimum-norm solution to the under-determined linear system, i.e.
+ \\(X = \mathrm{argmin}_{Z \in \mathbb{C}^{n \times k} } ||Z||_F^2 \\),
+ subject to \\(A Z = B\\). Notice that the fast path is only numerically stable
+ when \\(A\\) is numerically full rank and has a condition number
+ \\(\mathrm{cond}(A) \lt \frac{1}{\sqrt{\epsilon_{mach} } }\\) or \\(\lambda\\) is
+ sufficiently large.
+
+ If fast is False an algorithm based on the numerically robust complete
+ orthogonal decomposition is used. This computes the minimum-norm
+ least-squares solution, even when \\(A\\) is rank deficient. This path is
+ typically 6-7 times slower than the fast path. If fast is False then
+ l2_regularizer is ignored.
+
+
+
+
+ Solves systems of linear equations with upper or lower triangular matrices by
+
+
+ Shape is [..., M, M].
+
+
+ Shape is [..., M, K].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MatrixTriangularSolve'.
+
+
+ Optional argument
+ Boolean indicating whether the innermost matrices in matrix are
+ lower or upper triangular.
+
+
+ Optional argument
+ Boolean indicating whether to solve with matrix or its (block-wise)
+ adjoint.
+
+ @compatibility(numpy)
+ Equivalent to scipy.linalg.solve_triangular
+ @end_compatibility
+
+
+ Shape is [..., M, K].
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ backsubstitution.
+
+ matrix is a tensor of shape [..., M, M] whose inner-most 2 dimensions form
+ square matrices. If lower is True then the strictly upper triangular part
+ of each inner-most matrix is assumed to be zero and not accessed.
+ If lower is False then the strictly lower triangular part of each inner-most
+ matrix is assumed to be zero and not accessed.
+ rhs is a tensor of shape [..., M, K].
+
+ The output is a tensor of shape [..., M, K]. If adjoint is
+ True then the innermost matrices in output satisfy matrix equations
+ matrix[..., :, :] * output[..., :, :] = rhs[..., :, :].
+ If adjoint is False then the strictly then the innermost matrices in
+ output satisfy matrix equations
+ adjoint(matrix[..., i, k]) * output[..., k, j] = rhs[..., i, j].
+
+
+
+
+ Computes the maximum of elements across dimensions of a tensor.
+
+
+ The tensor to reduce.
+
+
+ The dimensions to reduce. Must be in the range
+ [-rank(input), rank(input)).
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Max'.
+
+
+ Optional argument
+ If true, retain reduced dimensions with length 1.
+
+
+ The reduced tensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Reduces input along the dimensions given in axis. Unless
+ keep_dims is true, the rank of the tensor is reduced by 1 for each entry in
+ axis. If keep_dims is true, the reduced dimensions are
+ retained with length 1.
+
+
+
+
+ Returns the max of x and y (i.e. x > y ? x : y) element-wise.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Maximum'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ *NOTE*: Maximum supports broadcasting. More about broadcasting
+ [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+
+
+
+
+ Performs max pooling on the input.
+
+
+ 4-D input to pool over.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MaxPool'.
+
+
+ Optional argument
+ Specify the data format of the input and output data. With the
+ default format "NHWC", the data is stored in the order of:
+ [batch, in_height, in_width, in_channels].
+ Alternatively, the format could be "NCHW", the data storage order of:
+ [batch, in_channels, in_height, in_width].
+
+
+ The size of the window for each dimension of the input tensor.
+
+
+ The stride of the sliding window for each dimension of the
+ input tensor.
+
+
+ The type of padding algorithm to use.
+
+
+ The max pooled output tensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Performs 3D max pooling on the input.
+
+
+ Shape [batch, depth, rows, cols, channels] tensor to pool over.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MaxPool3D'.
+
+
+ Optional argument
+ The data format of the input and output data. With the
+ default format "NDHWC", the data is stored in the order of:
+ [batch, in_depth, in_height, in_width, in_channels].
+ Alternatively, the format could be "NCDHW", the data storage order is:
+ [batch, in_channels, in_depth, in_height, in_width].
+
+
+ 1-D tensor of length 5. The size of the window for each dimension of
+ the input tensor. Must have ksize[0] = ksize[4] = 1.
+
+
+ 1-D tensor of length 5. The stride of the sliding window for each
+ dimension of input. Must have strides[0] = strides[4] = 1.
+
+
+ The type of padding algorithm to use.
+
+
+ The max pooled output tensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes gradients of max pooling function.
+
+
+ The original input tensor.
+
+
+ The original output tensor.
+
+
+ Output backprop of shape [batch, depth, rows, cols, channels].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MaxPool3DGrad'.
+
+
+ Optional argument
+ The data format of the input and output data. With the
+ default format "NDHWC", the data is stored in the order of:
+ [batch, in_depth, in_height, in_width, in_channels].
+ Alternatively, the format could be "NCDHW", the data storage order is:
+ [batch, in_channels, in_depth, in_height, in_width].
+
+
+ 1-D tensor of length 5. The size of the window for each dimension of
+ the input tensor. Must have ksize[0] = ksize[4] = 1.
+
+
+ 1-D tensor of length 5. The stride of the sliding window for each
+ dimension of input. Must have strides[0] = strides[4] = 1.
+
+
+ The type of padding algorithm to use.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes second-order gradients of the maxpooling function.
+
+
+ The original input tensor.
+
+
+ The original output tensor.
+
+
+ Output backprop of shape [batch, depth, rows, cols, channels].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MaxPool3DGradGrad'.
+
+
+ Optional argument
+ The data format of the input and output data. With the
+ default format "NDHWC", the data is stored in the order of:
+ [batch, in_depth, in_height, in_width, in_channels].
+ Alternatively, the format could be "NCDHW", the data storage order is:
+ [batch, in_channels, in_depth, in_height, in_width].
+
+
+ 1-D tensor of length 5. The size of the window for each dimension of
+ the input tensor. Must have ksize[0] = ksize[4] = 1.
+
+
+ 1-D tensor of length 5. The stride of the sliding window for each
+ dimension of input. Must have strides[0] = strides[4] = 1.
+
+
+ The type of padding algorithm to use.
+
+
+ Gradients of gradients w.r.t. the input to max_pool.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes gradients of the maxpooling function.
+
+
+ The original input tensor.
+
+
+ The original output tensor.
+
+
+ 4-D. Gradients w.r.t. the output of max_pool.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MaxPoolGrad'.
+
+
+ Optional argument
+ Specify the data format of the input and output data. With the
+ default format "NHWC", the data is stored in the order of:
+ [batch, in_height, in_width, in_channels].
+ Alternatively, the format could be "NCHW", the data storage order of:
+ [batch, in_channels, in_height, in_width].
+
+
+ The size of the window for each dimension of the input tensor.
+
+
+ The stride of the sliding window for each dimension of the
+ input tensor.
+
+
+ The type of padding algorithm to use.
+
+
+ Gradients w.r.t. the input to max_pool.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes second-order gradients of the maxpooling function.
+
+
+ The original input tensor.
+
+
+ The original output tensor.
+
+
+ 4-D. Gradients of gradients w.r.t. the input of max_pool.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MaxPoolGradGrad'.
+
+
+ Optional argument
+ Specify the data format of the input and output data. With the
+ default format "NHWC", the data is stored in the order of:
+ [batch, in_height, in_width, in_channels].
+ Alternatively, the format could be "NCHW", the data storage order of:
+ [batch, in_channels, in_height, in_width].
+
+
+ The size of the window for each dimension of the input tensor.
+
+
+ The stride of the sliding window for each dimension of the
+ input tensor.
+
+
+ The type of padding algorithm to use.
+
+
+ Gradients of gradients w.r.t. the input to max_pool.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes second-order gradients of the maxpooling function.
+
+
+ The original input tensor.
+
+
+ The original output tensor.
+
+
+ 4-D. Gradients of gradients w.r.t. the input of max_pool.
+
+
+ The size of the window for each dimension of the input tensor.
+
+
+ The stride of the sliding window for each dimension of the
+ input tensor.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MaxPoolGradGradV2'.
+
+
+ Optional argument
+ Specify the data format of the input and output data. With the
+ default format "NHWC", the data is stored in the order of:
+ [batch, in_height, in_width, in_channels].
+ Alternatively, the format could be "NCHW", the data storage order of:
+ [batch, in_channels, in_height, in_width].
+
+
+ The type of padding algorithm to use.
+
+
+ Gradients of gradients w.r.t. the input to max_pool.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes second-order gradients of the maxpooling function.
+
+
+ The original input.
+
+
+ 4-D with shape [batch, height, width, channels]. Gradients w.r.t. the
+ input of max_pool.
+
+
+ The indices of the maximum values chosen for each output of max_pool.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MaxPoolGradGradWithArgmax'.
+
+
+ The size of the window for each dimension of the input tensor.
+
+
+ The stride of the sliding window for each dimension of the
+ input tensor.
+
+
+ The type of padding algorithm to use.
+
+
+ Gradients of gradients w.r.t. the input of max_pool.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes gradients of the maxpooling function.
+
+
+ The original input tensor.
+
+
+ The original output tensor.
+
+
+ 4-D. Gradients w.r.t. the output of max_pool.
+
+
+ The size of the window for each dimension of the input tensor.
+
+
+ The stride of the sliding window for each dimension of the
+ input tensor.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MaxPoolGradV2'.
+
+
+ Optional argument
+ Specify the data format of the input and output data. With the
+ default format "NHWC", the data is stored in the order of:
+ [batch, in_height, in_width, in_channels].
+ Alternatively, the format could be "NCHW", the data storage order of:
+ [batch, in_channels, in_height, in_width].
+
+
+ The type of padding algorithm to use.
+
+
+ Gradients w.r.t. the input to max_pool.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes gradients of the maxpooling function.
+
+
+ The original input.
+
+
+ 4-D with shape [batch, height, width, channels]. Gradients w.r.t. the
+ output of max_pool.
+
+
+ The indices of the maximum values chosen for each output of max_pool.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MaxPoolGradWithArgmax'.
+
+
+ The size of the window for each dimension of the input tensor.
+
+
+ The stride of the sliding window for each dimension of the
+ input tensor.
+
+
+ The type of padding algorithm to use.
+
+
+ Gradients w.r.t. the input of max_pool.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Performs max pooling on the input.
+
+
+ 4-D input to pool over.
+
+
+ The size of the window for each dimension of the input tensor.
+
+
+ The stride of the sliding window for each dimension of the
+ input tensor.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MaxPoolV2'.
+
+
+ Optional argument
+ Specify the data format of the input and output data. With the
+ default format "NHWC", the data is stored in the order of:
+ [batch, in_height, in_width, in_channels].
+ Alternatively, the format could be "NCHW", the data storage order of:
+ [batch, in_channels, in_height, in_width].
+
+
+ The type of padding algorithm to use.
+
+
+ The max pooled output tensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Performs max pooling on the input and outputs both max values and indices.
+
+
+ 4-D with shape [batch, height, width, channels]. Input to pool over.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MaxPoolWithArgmax'.
+
+
+ Optional argument
+
+
+ The size of the window for each dimension of the input tensor.
+
+
+ The stride of the sliding window for each dimension of the
+ input tensor.
+
+
+ The type of padding algorithm to use.
+
+
+ Returns a tuple with multiple values, as follows:
+ output: The max pooled output tensor.
+ argmax: 4-D. The flattened indices of the max values chosen for each output.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ The indices in argmax are flattened, so that a maximum value at position
+ [b, y, x, c] becomes flattened index
+ ((b * height + y) * width + x) * channels + c.
+
+ The indices returned are always in [0, height) x [0, width) before flattening,
+ even if padding is involved and the mathematically correct answer is outside
+ (either negative or too large). This is a bug, but fixing it is difficult to do
+ in a safe backwards compatible way, especially due to flattening.
+
+
+
+
+ Computes the mean of elements across dimensions of a tensor.
+
+
+ The tensor to reduce.
+
+
+ The dimensions to reduce. Must be in the range
+ [-rank(input), rank(input)).
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Mean'.
+
+
+ Optional argument
+ If true, retain reduced dimensions with length 1.
+
+
+ The reduced tensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Reduces input along the dimensions given in axis. Unless
+ keep_dims is true, the rank of the tensor is reduced by 1 for each entry in
+ axis. If keep_dims is true, the reduced dimensions are
+ retained with length 1.
+
+
+
+
+ Forwards the value of an available tensor from inputs to output.
+
+
+ The input tensors, exactly one of which will become available.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Merge'.
+
+
+ Returns a tuple with multiple values, as follows:
+ output: Will be set to the available input tensor.
+ value_index: The index of the chosen input tensor in inputs.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Merge waits for at least one of the tensors in inputs to become available.
+ It is usually combined with Switch to implement branching.
+
+ Merge forwards the first tensor to become available to output, and sets
+ value_index to its index in inputs.
+
+
+
+
+ Merges summaries.
+
+
+ Can be of any shape. Each must contain serialized Summary protocol
+ buffers.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MergeSummary'.
+
+
+ Scalar. Serialized Summary protocol buffer.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This op creates a
+ [Summary](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
+ protocol buffer that contains the union of all the values in the input
+ summaries.
+
+ When the Op is run, it reports an InvalidArgument error if multiple values
+ in the summaries to merge use the same tag.
+
+
+
+
+ V2 format specific: merges the metadata files of sharded checkpoints. The
+
+
+ prefixes of V2 checkpoints to merge.
+
+
+ scalar. The desired final prefix. Allowed to be the same
+ as one of the checkpoint_prefixes.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MergeV2Checkpoints'.
+
+
+ Optional argument
+ see above.
+
+
+ Returns the description of the operation
+
+
+ result is one logical checkpoint, with one physical metadata file and renamed
+ data files.
+
+ Intended for "grouping" multiple checkpoints in a sharded checkpoint setup.
+
+ If delete_old_dirs is true, attempts to delete recursively the dirname of each
+ path in the input checkpoint_prefixes. This is useful when those paths are non
+ user-facing temporary locations.
+
+
+
+
+ Transforms a spectrogram into a form that's useful for speech recognition.
+
+
+ Typically produced by the Spectrogram op, with magnitude_squared
+ set to true.
+
+
+ How many samples per second the source audio used.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Mfcc'.
+
+
+ Optional argument
+ The highest frequency to use when calculating the
+ ceptstrum.
+
+
+ Optional argument
+ The lowest frequency to use when calculating the
+ ceptstrum.
+
+
+ Optional argument
+ Resolution of the Mel bank used internally.
+
+
+ Optional argument
+ How many output channels to produce per time slice.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Mel Frequency Cepstral Coefficients are a way of representing audio data that's
+ been effective as an input feature for machine learning. They are created by
+ taking the spectrum of a spectrogram (a 'cepstrum'), and discarding some of the
+ higher frequencies that are less significant to the human ear. They have a long
+ history in the speech recognition world, and https://en.wikipedia.org/wiki/Mel-frequency_cepstrum
+ is a good resource to learn more.
+
+
+
+
+ Computes the minimum of elements across dimensions of a tensor.
+
+
+ The tensor to reduce.
+
+
+ The dimensions to reduce. Must be in the range
+ [-rank(input), rank(input)).
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Min'.
+
+
+ Optional argument
+ If true, retain reduced dimensions with length 1.
+
+
+ The reduced tensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Reduces input along the dimensions given in axis. Unless
+ keep_dims is true, the rank of the tensor is reduced by 1 for each entry in
+ axis. If keep_dims is true, the reduced dimensions are
+ retained with length 1.
+
+
+
+
+ Returns the min of x and y (i.e. x < y ? x : y) element-wise.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Minimum'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ *NOTE*: Minimum supports broadcasting. More about broadcasting
+ [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+
+
+
+
+ Pads a tensor with mirrored values.
+
+
+ The input tensor to be padded.
+
+
+ A two-column matrix specifying the padding sizes. The number of
+ rows must be the same as the rank of input.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MirrorPad'.
+
+
+ Either REFLECT or SYMMETRIC. In reflect mode the padded regions
+ do not include the borders, while in symmetric mode the padded regions
+ do include the borders. For example, if input is [1, 2, 3] and paddings
+ is [0, 2], then the output is [1, 2, 3, 2, 1] in reflect mode, and
+ it is [1, 2, 3, 3, 2] in symmetric mode.
+
+
+ The padded tensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation pads a input with mirrored values according to the paddings
+ you specify. paddings is an integer tensor with shape [n, 2], where n is
+ the rank of input. For each dimension D of input, paddings[D, 0] indicates
+ how many values to add before the contents of input in that dimension, and
+ paddings[D, 1] indicates how many values to add after the contents of input
+ in that dimension. Both paddings[D, 0] and paddings[D, 1] must be no greater
+ than input.dim_size(D) (or input.dim_size(D) - 1) if copy_border is true
+ (if false, respectively).
+
+ The padded size of each dimension D of the output is:
+
+ paddings(D, 0) + input.dim_size(D) + paddings(D, 1)
+
+ For example:
+
+
+ # 't' is [[1, 2, 3], [4, 5, 6]].
+ # 'paddings' is [[1, 1]], [2, 2]].
+ # 'mode' is SYMMETRIC.
+ # rank of 't' is 2.
+ pad(t, paddings) ==> [[2, 1, 1, 2, 3, 3, 2]
+ [2, 1, 1, 2, 3, 3, 2]
+ [5, 4, 4, 5, 6, 6, 5]
+ [5, 4, 4, 5, 6, 6, 5]]
+
+
+
+
+
+ Gradient op for MirrorPad op. This op folds a mirror-padded tensor.
+
+
+ The input tensor to be folded.
+
+
+ A two-column matrix specifying the padding sizes. The number of
+ rows must be the same as the rank of input.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MirrorPadGrad'.
+
+
+ The mode used in the MirrorPad op.
+
+
+ The folded tensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation folds the padded areas of input by MirrorPad according to the
+ paddings you specify. paddings must be the same as paddings argument
+ given to the corresponding MirrorPad op.
+
+ The folded size of each dimension D of the output is:
+
+ input.dim_size(D) - paddings(D, 0) - paddings(D, 1)
+
+ For example:
+
+
+ # 't' is [[1, 2, 3], [4, 5, 6], [7, 8, 9]].
+ # 'paddings' is [[0, 1]], [0, 1]].
+ # 'mode' is SYMMETRIC.
+ # rank of 't' is 2.
+ pad(t, paddings) ==> [[ 1, 5]
+ [11, 28]]
+
+
+
+
+
+ Returns element-wise remainder of division. This emulates C semantics in that
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Mod'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ the result here is consistent with a truncating divide. E.g.
+ tf.truncatediv(x, y) * y + truncate_mod(x, y) = x.
+
+ *NOTE*: Mod supports broadcasting. More about broadcasting
+ [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+
+
+
+
+ Identity transformation that models performance.
+
+
+ A variant tensor representing the input dataset.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ModelDataset'.
+
+
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Identity transformation that models performance.
+
+
+
+
+ Returns x * y element-wise.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Mul'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ *NOTE*: Multiply supports broadcasting. More about broadcasting
+ [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+
+
+
+
+ Creates a MultiDeviceIterator resource.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MultiDeviceIterator'.
+
+
+ A list of devices the iterator works across.
+
+
+ If non-empty, this resource will be shared under the given name
+ across multiple sessions.
+
+
+ If non-empty, this resource is placed in the given container.
+ Otherwise, a default container is used.
+
+
+ The type list for the return values.
+
+
+ The list of shapes being produced.
+
+
+ Handle to the resource created.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Generates a MultiDeviceIterator resource from its provided string handle.
+
+
+ String representing the resource.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MultiDeviceIteratorFromStringHandle'.
+
+
+ Optional argument
+ The type list for the return values.
+
+
+ Optional argument
+ The list of shapes being produced.
+
+
+ A MultiDeviceIterator resource.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Gets next element for the provided shard number.
+
+
+ A MultiDeviceIterator resource.
+
+
+ Integer representing which shard to fetch data for.
+
+
+ Which incarnation of the MultiDeviceIterator is running.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MultiDeviceIteratorGetNextFromShard'.
+
+
+ The type list for the return values.
+
+
+ The list of shapes being produced.
+
+
+ Result of the get_next on the dataset.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Initializes the multi device iterator with the given dataset.
+
+
+ Dataset to be iterated upon.
+
+
+ A MultiDeviceIteratorResource.
+
+
+ The maximum size of the host side per device buffer to keep.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MultiDeviceIteratorInit'.
+
+
+ An int64 indicating which incarnation of the MultiDeviceIterator
+ is running.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Produces a string handle for the given MultiDeviceIterator.
+
+
+ A MultiDeviceIterator resource.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MultiDeviceIteratorToStringHandle'.
+
+
+ A string representing the resource.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Draws samples from a multinomial distribution.
+
+
+ 2-D Tensor with shape [batch_size, num_classes]. Each slice [i, :]
+ represents the unnormalized log probabilities for all classes.
+
+
+ 0-D. Number of independent samples to draw for each row slice.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Multinomial'.
+
+
+ Optional argument
+ If either seed or seed2 is set to be non-zero, the internal random number
+ generator is seeded by the given seed. Otherwise, a random seed is used.
+
+
+ Optional argument
+ A second seed to avoid seed collision.
+
+
+ Optional argument
+
+
+ 2-D Tensor with shape [batch_size, num_samples]. Each slice [i, :]
+ contains the drawn class labels with range [0, num_classes).
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Creates an empty hash table that uses tensors as the backing store.
+
+
+ The key used to represent empty key buckets internally. Must not
+ be used in insert or lookup operations.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MutableDenseHashTable'.
+
+
+ Optional argument
+ If non-empty, this table is placed in the given container.
+ Otherwise, a default container is used.
+
+
+ Optional argument
+ If non-empty, this table is shared under the given name across
+ multiple sessions.
+
+
+ Optional argument
+
+
+ Optional argument
+ The shape of each value.
+
+
+ Optional argument
+ The initial number of hash table buckets. Must be a power
+ to 2.
+
+
+ Optional argument
+ The maximum ratio between number of entries and number of
+ buckets before growing the table. Must be between 0 and 1.
+
+
+ Type of the table values.
+
+
+ Handle to a table.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ It uses "open addressing" with quadratic reprobing to resolve
+ collisions.
+
+ This op creates a mutable hash table, specifying the type of its keys and
+ values. Each value must be a scalar. Data can be inserted into the table using
+ the insert operations. It does not support the initialization operation.
+
+
+
+
+ Creates an empty hash table that uses tensors as the backing store.
+
+
+ The key used to represent empty key buckets internally. Must not
+ be used in insert or lookup operations.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MutableDenseHashTableV2'.
+
+
+ Optional argument
+ If non-empty, this table is placed in the given container.
+ Otherwise, a default container is used.
+
+
+ Optional argument
+ If non-empty, this table is shared under the given name across
+ multiple sessions.
+
+
+ Optional argument
+
+
+ Optional argument
+ The shape of each value.
+
+
+ Optional argument
+ The initial number of hash table buckets. Must be a power
+ to 2.
+
+
+ Optional argument
+ The maximum ratio between number of entries and number of
+ buckets before growing the table. Must be between 0 and 1.
+
+
+ Type of the table values.
+
+
+ Handle to a table.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ It uses "open addressing" with quadratic reprobing to resolve
+ collisions.
+
+ This op creates a mutable hash table, specifying the type of its keys and
+ values. Each value must be a scalar. Data can be inserted into the table using
+ the insert operations. It does not support the initialization operation.
+
+
+
+
+ Creates an empty hash table.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MutableHashTable'.
+
+
+ Optional argument
+ If non-empty, this table is placed in the given container.
+ Otherwise, a default container is used.
+
+
+ Optional argument
+ If non-empty, this table is shared under the given name across
+ multiple sessions.
+
+
+ Optional argument
+ If true and shared_name is empty, the table is shared
+ using the node name.
+
+
+ Type of the table keys.
+
+
+ Type of the table values.
+
+
+ Handle to a table.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This op creates a mutable hash table, specifying the type of its keys and
+ values. Each value must be a scalar. Data can be inserted into the table using
+ the insert operations. It does not support the initialization operation.
+
+
+
+
+ Creates an empty hash table.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MutableHashTableOfTensors'.
+
+
+ Optional argument
+ If non-empty, this table is placed in the given container.
+ Otherwise, a default container is used.
+
+
+ Optional argument
+ If non-empty, this table is shared under the given name across
+ multiple sessions.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Type of the table keys.
+
+
+ Type of the table values.
+
+
+ Handle to a table.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This op creates a mutable hash table, specifying the type of its keys and
+ values. Each value must be a vector. Data can be inserted into the table using
+ the insert operations. It does not support the initialization operation.
+
+
+
+
+ Creates an empty hash table.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MutableHashTableOfTensorsV2'.
+
+
+ Optional argument
+ If non-empty, this table is placed in the given container.
+ Otherwise, a default container is used.
+
+
+ Optional argument
+ If non-empty, this table is shared under the given name across
+ multiple sessions.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Type of the table keys.
+
+
+ Type of the table values.
+
+
+ Handle to a table.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This op creates a mutable hash table, specifying the type of its keys and
+ values. Each value must be a vector. Data can be inserted into the table using
+ the insert operations. It does not support the initialization operation.
+
+
+
+
+ Creates an empty hash table.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MutableHashTableV2'.
+
+
+ Optional argument
+ If non-empty, this table is placed in the given container.
+ Otherwise, a default container is used.
+
+
+ Optional argument
+ If non-empty, this table is shared under the given name across
+ multiple sessions.
+
+
+ Optional argument
+ If true and shared_name is empty, the table is shared
+ using the node name.
+
+
+ Type of the table keys.
+
+
+ Type of the table values.
+
+
+ Handle to a table.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This op creates a mutable hash table, specifying the type of its keys and
+ values. Each value must be a scalar. Data can be inserted into the table using
+ the insert operations. It does not support the initialization operation.
+
+
+
+
+ Locks a mutex resource. The output is the lock. So long as the lock tensor
+
+
+ The mutex resource to lock.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MutexLock'.
+
+
+ A tensor that keeps a shared pointer to a lock on the mutex;
+ when the Tensor is destroyed, the use count on the shared pointer is decreased
+ by 1. When it reaches 0, the lock is released.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ is alive, any other request to use MutexLock with this mutex will wait.
+
+ This is particularly useful for creating a critical section when used in
+ conjunction with MutexLockIdentity:
+
+
+
+ mutex = mutex_v2(
+ shared_name=handle_name, container=container, name=name)
+
+ def execute_in_critical_section(fn, *args, **kwargs):
+ lock = gen_resource_variable_ops.mutex_lock(mutex)
+
+ with ops.control_dependencies([lock]):
+ r = fn(*args, **kwargs)
+
+ with ops.control_dependencies(nest.flatten(r)):
+ with ops.colocate_with(mutex):
+ ensure_lock_exists = mutex_lock_identity(lock)
+
+ # Make sure that if any element of r is accessed, all of
+ # them are executed together.
+ r = nest.map_structure(tf.identity, r)
+
+ with ops.control_dependencies([ensure_lock_exists]):
+ return nest.map_structure(tf.identity, r)
+
+
+ While fn is running in the critical section, no other functions which wish to
+ use this critical section may run.
+
+ Often the use case is that two executions of the same graph, in parallel,
+ wish to run fn; and we wish to ensure that only one of them executes
+ at a time. This is especially important if fn modifies one or more
+ variables at a time.
+
+ It is also useful if two separate functions must share a resource, but we
+ wish to ensure the usage is exclusive.
+
+
+
+
+ Creates a Mutex resource that can be locked by MutexLock.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'MutexV2'.
+
+
+ Optional argument
+ If non-empty, this variable is placed in the given container.
+ Otherwise, a default container is used.
+
+
+ Optional argument
+ If non-empty, this variable is named in the given bucket
+ with this shared_name. Otherwise, the node name is used instead.
+
+
+ The mutex resource.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes numerical negative value element-wise.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Neg'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ I.e., \\(y = -x\\).
+
+
+
+
+ Training via negative sampling.
+
+
+ input word embedding.
+
+
+ output word embedding.
+
+
+ A vector of word ids.
+
+
+ A vector of word ids.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'NegTrain'.
+
+
+ Count of words in the vocabulary.
+
+
+ Number of negative samples per example.
+
+
+ Returns the description of the operation
+
+
+
+
+ Makes its input available to the next iteration.
+
+
+ The tensor to be made available to the next iteration.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'NextIteration'.
+
+
+ The same tensor as data.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Greedily selects a subset of bounding boxes in descending order of score,
+
+
+ A 2-D float tensor of shape [num_boxes, 4].
+
+
+ A 1-D float tensor of shape [num_boxes] representing a single
+ score corresponding to each box (each row of boxes).
+
+
+ A scalar integer tensor representing the maximum number of
+ boxes to be selected by non max suppression.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'NonMaxSuppression'.
+
+
+ Optional argument
+ A float representing the threshold for deciding whether boxes
+ overlap too much with respect to IOU.
+
+
+ A 1-D integer tensor of shape [M] representing the selected
+ indices from the boxes tensor, where M <= max_output_size.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ pruning away boxes that have high intersection-over-union (IOU) overlap
+ with previously selected boxes. Bounding boxes are supplied as
+ [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any
+ diagonal pair of box corners and the coordinates can be provided as normalized
+ (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm
+ is agnostic to where the origin is in the coordinate system. Note that this
+ algorithm is invariant to orthogonal transformations and translations
+ of the coordinate system; thus translating or reflections of the coordinate
+ system result in the same boxes being selected by the algorithm.
+ The output of this operation is a set of integers indexing into the input
+ collection of bounding boxes representing the selected boxes. The bounding
+ box coordinates corresponding to the selected indices can then be obtained
+ using the tf.gather operation. For example:
+ selected_indices = tf.image.non_max_suppression(
+ boxes, scores, max_output_size, iou_threshold)
+ selected_boxes = tf.gather(boxes, selected_indices)
+
+
+
+
+ Greedily selects a subset of bounding boxes in descending order of score,
+
+
+ A 2-D float tensor of shape [num_boxes, 4].
+
+
+ A 1-D float tensor of shape [num_boxes] representing a single
+ score corresponding to each box (each row of boxes).
+
+
+ A scalar integer tensor representing the maximum number of
+ boxes to be selected by non max suppression.
+
+
+ A 0-D float tensor representing the threshold for deciding whether
+ boxes overlap too much with respect to IOU.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'NonMaxSuppressionV2'.
+
+
+ A 1-D integer tensor of shape [M] representing the selected
+ indices from the boxes tensor, where M <= max_output_size.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ pruning away boxes that have high intersection-over-union (IOU) overlap
+ with previously selected boxes. Bounding boxes are supplied as
+ [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any
+ diagonal pair of box corners and the coordinates can be provided as normalized
+ (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm
+ is agnostic to where the origin is in the coordinate system. Note that this
+ algorithm is invariant to orthogonal transformations and translations
+ of the coordinate system; thus translating or reflections of the coordinate
+ system result in the same boxes being selected by the algorithm.
+
+ The output of this operation is a set of integers indexing into the input
+ collection of bounding boxes representing the selected boxes. The bounding
+ box coordinates corresponding to the selected indices can then be obtained
+ using the tf.gather operation. For example:
+
+ selected_indices = tf.image.non_max_suppression_v2(
+ boxes, scores, max_output_size, iou_threshold)
+ selected_boxes = tf.gather(boxes, selected_indices)
+
+
+
+
+ Greedily selects a subset of bounding boxes in descending order of score,
+
+
+ A 2-D float tensor of shape [num_boxes, 4].
+
+
+ A 1-D float tensor of shape [num_boxes] representing a single
+ score corresponding to each box (each row of boxes).
+
+
+ A scalar integer tensor representing the maximum number of
+ boxes to be selected by non max suppression.
+
+
+ A 0-D float tensor representing the threshold for deciding whether
+ boxes overlap too much with respect to IOU.
+
+
+ A 0-D float tensor representing the threshold for deciding when to remove
+ boxes based on score.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'NonMaxSuppressionV3'.
+
+
+ A 1-D integer tensor of shape [M] representing the selected
+ indices from the boxes tensor, where M <= max_output_size.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ pruning away boxes that have high intersection-over-union (IOU) overlap
+ with previously selected boxes. Bounding boxes with score less than
+ score_threshold are removed. Bounding boxes are supplied as
+ [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any
+ diagonal pair of box corners and the coordinates can be provided as normalized
+ (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm
+ is agnostic to where the origin is in the coordinate system and more
+ generally is invariant to orthogonal transformations and translations
+ of the coordinate system; thus translating or reflections of the coordinate
+ system result in the same boxes being selected by the algorithm.
+ The output of this operation is a set of integers indexing into the input
+ collection of bounding boxes representing the selected boxes. The bounding
+ box coordinates corresponding to the selected indices can then be obtained
+ using the tf.gather operation. For example:
+ selected_indices = tf.image.non_max_suppression_v2(
+ boxes, scores, max_output_size, iou_threshold, score_threshold)
+ selected_boxes = tf.gather(boxes, selected_indices)
+
+
+
+
+ Greedily selects a subset of bounding boxes in descending order of score,
+
+
+ A 2-D float tensor of shape [num_boxes, 4].
+
+
+ A 1-D float tensor of shape [num_boxes] representing a single
+ score corresponding to each box (each row of boxes).
+
+
+ A scalar integer tensor representing the maximum number of
+ boxes to be selected by non max suppression.
+
+
+ A 0-D float tensor representing the threshold for deciding whether
+ boxes overlap too much with respect to IOU.
+
+
+ A 0-D float tensor representing the threshold for deciding when to remove
+ boxes based on score.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'NonMaxSuppressionV4'.
+
+
+ Optional argument
+ If true, the output selected_indices is padded to be of length
+ max_output_size. Defaults to false.
+
+
+ Returns a tuple with multiple values, as follows:
+ selected_indices: A 1-D integer tensor of shape [M] representing the selected
+ indices from the boxes tensor, where M <= max_output_size.
+ valid_outputs: A 0-D integer tensor representing the number of valid elements in
+ selected_indices, with the valid elements appearing first.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ pruning away boxes that have high intersection-over-union (IOU) overlap
+ with previously selected boxes. Bounding boxes with score less than
+ score_threshold are removed. Bounding boxes are supplied as
+ [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any
+ diagonal pair of box corners and the coordinates can be provided as normalized
+ (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm
+ is agnostic to where the origin is in the coordinate system and more
+ generally is invariant to orthogonal transformations and translations
+ of the coordinate system; thus translating or reflections of the coordinate
+ system result in the same boxes being selected by the algorithm.
+ The output of this operation is a set of integers indexing into the input
+ collection of bounding boxes representing the selected boxes. The bounding
+ box coordinates corresponding to the selected indices can then be obtained
+ using the tf.gather operation. For example:
+ selected_indices = tf.image.non_max_suppression_v2(
+ boxes, scores, max_output_size, iou_threshold, score_threshold)
+ selected_boxes = tf.gather(boxes, selected_indices)
+
+
+
+
+ Greedily selects a subset of bounding boxes in descending order of score,
+
+
+ A 2-D float tensor of shape [num_boxes, num_boxes] representing
+ the n-by-n box overlap values.
+
+
+ A 1-D float tensor of shape [num_boxes] representing a single
+ score corresponding to each box (each row of boxes).
+
+
+ A scalar integer tensor representing the maximum number of
+ boxes to be selected by non max suppression.
+
+
+ A 0-D float tensor representing the threshold for deciding whether
+ boxes overlap too.
+
+
+ A 0-D float tensor representing the threshold for deciding when to remove
+ boxes based on score.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'NonMaxSuppressionWithOverlaps'.
+
+
+ A 1-D integer tensor of shape [M] representing the selected
+ indices from the boxes tensor, where M <= max_output_size.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ pruning away boxes that have high overlaps
+ with previously selected boxes. Bounding boxes with score less than
+ score_threshold are removed. N-by-n overlap values are supplied as square matrix,
+ which allows for defining a custom overlap criterium (eg. intersection over union,
+ intersection over area, etc.).
+
+ The output of this operation is a set of integers indexing into the input
+ collection of bounding boxes representing the selected boxes. The bounding
+ box coordinates corresponding to the selected indices can then be obtained
+ using the tf.gather operation. For example:
+
+ selected_indices = tf.image.non_max_suppression_with_overlaps(
+ overlaps, scores, max_output_size, overlap_threshold, score_threshold)
+ selected_boxes = tf.gather(boxes, selected_indices)
+
+
+
+
+ Does nothing. Only useful as a placeholder for control edges.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'NoOp'.
+
+
+ Returns the description of the operation
+
+
+
+
+ Returns the truth value of (x != y) element-wise.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'NotEqual'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ *NOTE*: NotEqual supports broadcasting. More about broadcasting
+ [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+
+
+
+
+ Finds values of the n-th order statistic for the last dimension.
+
+
+ 1-D or higher with last dimension at least n+1.
+
+
+ 0-D. Position of sorted vector to select along the last dimension (along
+ each row for matrices). Valid range of n is [0, input.shape[:-1])
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'NthElement'.
+
+
+ Optional argument
+ When set to True, find the nth-largest value in the vector and vice
+ versa.
+
+
+ The n-th order statistic along each last dimensional slice.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ If the input is a vector (rank-1), finds the entries which is the nth-smallest
+ value in the vector and outputs their values as scalar tensor.
+
+ For matrices (resp. higher rank input), computes the entries which is the
+ nth-smallest value in each row (resp. vector along the last dimension). Thus,
+
+ values.shape = input.shape[:-1]
+
+
+
+
+
+ Returns a tensor of ones with the same shape and type as x.
+
+
+ a tensor of type T.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'OnesLike'.
+
+
+ a tensor of the same shape and type as x but filled with ones.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Creates a dataset by applying optimizations to input_dataset.
+
+
+ A variant tensor representing the input dataset.
+
+
+ A tf.string vector tf.Tensor identifying optimizations to use.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'OptimizeDataset'.
+
+
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Creates a dataset by applying optimizations to input_dataset.
+
+
+
+
+ Constructs an Optional variant from a tuple of tensors.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'OptionalFromValue'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Returns the value stored in an Optional variant or raises an error if none exists.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'OptionalGetValue'.
+
+
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Returns true if and only if the given Optional variant has a value.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'OptionalHasValue'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Creates an Optional variant with no value.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'OptionalNone'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Op removes all elements in the underlying container.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'OrderedMapClear'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+ Returns the description of the operation
+
+
+
+
+ Op returns the number of incomplete elements in the underlying container.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'OrderedMapIncompleteSize'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Op peeks at the values at the specified key. If the
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'OrderedMapPeek'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ underlying container does not contain this key
+ this op will block until it does. This Op is optimized for
+ performance.
+
+
+
+
+ Op returns the number of elements in the underlying container.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'OrderedMapSize'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Stage (key, values) in the underlying container which behaves like a ordered
+
+
+ int64
+
+
+
+
+ a list of tensors
+ dtypes A list of data types that inserted values should adhere to.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'OrderedMapStage'.
+
+
+ Optional argument
+ Maximum number of elements in the Staging Area. If > 0, inserts
+ on the container will block when the capacity is reached.
+
+
+ Optional argument
+
+
+ Optional argument
+ If non-empty, this queue is placed in the given container. Otherwise,
+ a default container is used.
+
+
+ Optional argument
+ It is necessary to match this name to the matching Unstage Op.
+
+
+
+
+ Returns the description of the operation
+
+
+ associative container. Elements are ordered by key.
+
+
+
+
+ Op removes and returns the values associated with the key
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'OrderedMapUnstage'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ from the underlying container. If the underlying container
+ does not contain this key, the op will block until it does.
+
+
+
+
+ Op removes and returns the (key, value) element with the smallest
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'OrderedMapUnstageNoKey'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+ Returns a tuple with multiple values, as follows:
+ key:
+ values:
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ key from the underlying container. If the underlying container
+ does not contain elements, the op will block until it does.
+
+
+
+
+ Retrieves a single tensor from the computation outfeed. This operation will
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'OutfeedDequeue'.
+
+
+ Optional argument
+ The TPU device to use. This should be -1 when the Op
+ is running on a TPU device, and >= 0 when the Op is running on the CPU
+ device.
+
+
+ The type of elements in the tensor.
+
+
+ The shape of the tensor.
+
+
+ A tensor that will be read from the device outfeed.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ block indefinitely until data is available.
+
+
+
+
+ Retrieve multiple values that will be emitted by the computation as an XLA
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'OutfeedDequeueTuple'.
+
+
+ Optional argument
+ The TPU device to use. This should be -1 when the Op
+ is running on a TPU device, and >= 0 when the Op is running on the CPU
+ device.
+
+
+ The element types of each element in outputs.
+
+
+ The shapes of each tensor in outputs.
+
+
+ A list of tensors that will be read from the outfeed.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ tuple. This operations will block indefinitely until data is available.
+ Output i corresponds to XLA tuple element i.
+
+
+
+
+ An op which emits a single Tensor value from an XLA computation.
+
+
+ A tensor that will be inserted into the outfeed queue.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'OutfeedEnqueue'.
+
+
+ Returns the description of the operation
+
+
+
+
+ An op which emits multiple Tensor values from an XLA computation.
+
+
+ A list of tensors that will be inserted into the outfeed queue as an
+ XLA tuple.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'OutfeedEnqueueTuple'.
+
+
+ Returns the description of the operation
+
+
+
+
+ Packs a list of N rank-R tensors into one rank-(R+1) tensor.
+
+
+ Must be of same shape and type.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Pack'.
+
+
+ Optional argument
+ Dimension along which to pack. Negative values wrap around, so the
+ valid range is [-(R+1), R+1).
+
+
+ The packed tensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Packs the N tensors in values into a tensor with rank one higher than each
+ tensor in values, by packing them along the axis dimension.
+ Given a list of tensors of shape (A, B, C);
+
+ if axis == 0 then the output tensor will have the shape (N, A, B, C).
+ if axis == 1 then the output tensor will have the shape (A, N, B, C).
+ Etc.
+
+ For example:
+
+
+ # 'x' is [1, 4]
+ # 'y' is [2, 5]
+ # 'z' is [3, 6]
+ pack([x, y, z]) => [[1, 4], [2, 5], [3, 6]] # Pack along first dim.
+ pack([x, y, z], axis=1) => [[1, 2, 3], [4, 5, 6]]
+
+
+ This is the opposite of unpack.
+
+
+
+
+ Pads a tensor with zeros.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Pad'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation pads a input with zeros according to the paddings you
+ specify. paddings is an integer tensor with shape [Dn, 2], where n is the
+ rank of input. For each dimension D of input, paddings[D, 0] indicates
+ how many zeros to add before the contents of input in that dimension, and
+ paddings[D, 1] indicates how many zeros to add after the contents of input
+ in that dimension.
+
+ The padded size of each dimension D of the output is:
+
+ paddings(D, 0) + input.dim_size(D) + paddings(D, 1)
+
+ For example:
+
+
+ # 't' is [[1, 1], [2, 2]]
+ # 'paddings' is [[1, 1], [2, 2]]
+ # rank of 't' is 2
+ pad(t, paddings) ==> [[0, 0, 0, 0, 0, 0]
+ [0, 0, 1, 1, 0, 0]
+ [0, 0, 2, 2, 0, 0]
+ [0, 0, 0, 0, 0, 0]]
+
+
+
+
+
+
+ Creates a dataset that batches and pads batch_size elements from the input.
+
+
+
+
+ A scalar representing the number of elements to accumulate in a
+ batch.
+
+
+ A list of int64 tensors representing the desired padded shapes
+ of the corresponding output components. These shapes may be partially
+ specified, using -1 to indicate that a particular dimension should be
+ padded to the maximum size of all batch elements.
+
+
+ A list of scalars containing the padding value to use for
+ each of the outputs.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'PaddedBatchDataset'.
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Creates a dataset that batches and pads batch_size elements from the input.
+
+
+
+
+ A scalar representing the number of elements to accumulate in a
+ batch.
+
+
+ A list of int64 tensors representing the desired padded shapes
+ of the corresponding output components. These shapes may be partially
+ specified, using -1 to indicate that a particular dimension should be
+ padded to the maximum size of all batch elements.
+
+
+ A list of scalars containing the padding value to use for
+ each of the outputs.
+
+
+ A scalar representing whether the last batch should be dropped in case its size
+ is smaller than desired.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'PaddedBatchDatasetV2'.
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ A queue that produces elements in first-in first-out order.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'PaddingFIFOQueue'.
+
+
+ Optional argument
+ The shape of each component in a value. The length of this attr must
+ be either 0 or the same as the length of component_types.
+ Shapes of fixed rank but variable size are allowed by setting
+ any shape dimension to -1. In this case, the inputs' shape may vary along
+ the given dimension, and DequeueMany will pad the given dimension with
+ zeros up to the maximum shape of all elements in the given batch.
+ If the length of this attr is 0, different queue elements may have
+ different ranks and shapes, but only one element may be dequeued at a time.
+
+
+ Optional argument
+ The upper bound on the number of elements in this queue.
+ Negative numbers mean no limit.
+
+
+ Optional argument
+ If non-empty, this queue is placed in the given container.
+ Otherwise, a default container is used.
+
+
+ Optional argument
+ If non-empty, this queue will be shared under the given name
+ across multiple sessions.
+
+
+ The type of each component in a value.
+
+
+ The handle to the queue.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Variable-size shapes are allowed by setting the corresponding shape dimensions
+ to 0 in the shape attr. In this case DequeueMany will pad up to the maximum
+ size of any given element in the minibatch. See below for details.
+
+
+
+
+ A queue that produces elements in first-in first-out order.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'PaddingFIFOQueueV2'.
+
+
+ Optional argument
+ The shape of each component in a value. The length of this attr must
+ be either 0 or the same as the length of component_types.
+ Shapes of fixed rank but variable size are allowed by setting
+ any shape dimension to -1. In this case, the inputs' shape may vary along
+ the given dimension, and DequeueMany will pad the given dimension with
+ zeros up to the maximum shape of all elements in the given batch.
+ If the length of this attr is 0, different queue elements may have
+ different ranks and shapes, but only one element may be dequeued at a time.
+
+
+ Optional argument
+ The upper bound on the number of elements in this queue.
+ Negative numbers mean no limit.
+
+
+ Optional argument
+ If non-empty, this queue is placed in the given container.
+ Otherwise, a default container is used.
+
+
+ Optional argument
+ If non-empty, this queue will be shared under the given name
+ across multiple sessions.
+
+
+ The type of each component in a value.
+
+
+ The handle to the queue.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Variable-size shapes are allowed by setting the corresponding shape dimensions
+ to 0 in the shape attr. In this case DequeueMany will pad up to the maximum
+ size of any given element in the minibatch. See below for details.
+
+
+
+
+ Pads a tensor.
+
+
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'PadV2'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation pads input according to the paddings and constant_values
+ you specify. paddings is an integer tensor with shape [Dn, 2], where n is
+ the rank of input. For each dimension D of input, paddings[D, 0] indicates
+ how many padding values to add before the contents of input in that dimension,
+ and paddings[D, 1] indicates how many padding values to add after the contents
+ of input in that dimension. constant_values is a scalar tensor of the same
+ type as input that indicates the value to use for padding input.
+
+ The padded size of each dimension D of the output is:
+
+ paddings(D, 0) + input.dim_size(D) + paddings(D, 1)
+
+ For example:
+
+
+ # 't' is [[1, 1], [2, 2]]
+ # 'paddings' is [[1, 1], [2, 2]]
+ # 'constant_values' is 0
+ # rank of 't' is 2
+ pad(t, paddings) ==> [[0, 0, 0, 0, 0, 0]
+ [0, 0, 1, 1, 0, 0]
+ [0, 0, 2, 2, 0, 0]
+ [0, 0, 0, 0, 0, 0]]
+
+
+
+
+
+ Concatenates a list of N tensors along the first dimension.
+
+
+ Tensors to be concatenated. All must have size 1 in the first dimension
+ and same shape.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ParallelConcat'.
+
+
+ the final shape of the result; should be equal to the shapes of any input
+ but with the number of input values in the first dimension.
+
+
+ The concatenated tensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The input tensors are all required to have size 1 in the first dimension.
+
+ For example:
+
+
+ # 'x' is [[1, 4]]
+ # 'y' is [[2, 5]]
+ # 'z' is [[3, 6]]
+ parallel_concat([x, y, z]) => [[1, 4], [2, 5], [3, 6]] # Pack along first dim.
+
+
+ The difference between concat and parallel_concat is that concat requires all
+ of the inputs be computed before the operation will begin but doesn't require
+ that the input shapes be known during graph construction. Parallel concat
+ will copy pieces of the input into the output as they become available, in
+ some situations this can provide a performance benefit.
+
+
+
+
+ Interleave the values from the data tensors into a single tensor.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ParallelDynamicStitch'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Builds a merged tensor such that
+
+
+ merged[indices[m][i, ..., j], ...] = data[m][i, ..., j, ...]
+
+
+ For example, if each indices[m] is scalar or vector, we have
+
+
+ # Scalar indices:
+ merged[indices[m], ...] = data[m][...]
+
+ # Vector indices:
+ merged[indices[m][i], ...] = data[m][i, ...]
+
+
+ Each data[i].shape must start with the corresponding indices[i].shape,
+ and the rest of data[i].shape must be constant w.r.t. i. That is, we
+ must have data[i].shape = indices[i].shape + constant. In terms of this
+ constant, the output shape is
+
+ merged.shape = [max(indices)] + constant
+
+ Values may be merged in parallel, so if an index appears in both indices[m][i]
+ and indices[n][j], the result may be invalid. This differs from the normal
+ DynamicStitch operator that defines the behavior in that case.
+
+ For example:
+
+
+ indices[0] = 6
+ indices[1] = [4, 1]
+ indices[2] = [[5, 2], [0, 3]]
+ data[0] = [61, 62]
+ data[1] = [[41, 42], [11, 12]]
+ data[2] = [[[51, 52], [21, 22]], [[1, 2], [31, 32]]]
+ merged = [[1, 2], [11, 12], [21, 22], [31, 32], [41, 42],
+ [51, 52], [61, 62]]
+
+
+ This method can be used to merge partitions created by dynamic_partition
+ as illustrated on the following example:
+
+
+ # Apply function (increments x_i) on elements for which a certain condition
+ # apply (x_i != -1 in this example).
+ x=tf.constant([0.1, -1., 5.2, 4.3, -1., 7.4])
+ condition_mask=tf.not_equal(x,tf.constant(-1.))
+ partitioned_data = tf.dynamic_partition(
+ x, tf.cast(condition_mask, tf.int32) , 2)
+ partitioned_data[1] = partitioned_data[1] + 1.0
+ condition_indices = tf.dynamic_partition(
+ tf.range(tf.shape(x)[0]), tf.cast(condition_mask, tf.int32) , 2)
+ x = tf.dynamic_stitch(condition_indices, partitioned_data)
+ # Here x=[1.1, -1., 6.2, 5.3, -1, 8.4], the -1. values remain
+ # unchanged.
+
+
+ <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+ <img style="width:100%" src="https://www.tensorflow.org/images/DynamicStitch.png" alt>
+ </div>
+
+
+
+
+ Outputs random values from a normal distribution. The parameters may each be a
+
+
+ The shape of the output tensor. Batches are indexed by the 0th dimension.
+
+
+ The mean parameter of each batch.
+
+
+ The standard deviation parameter of each batch. Must be greater than 0.
+
+
+ The minimum cutoff. May be -infinity.
+
+
+ The maximum cutoff. May be +infinity, and must be more than the minval
+ for each batch.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ParameterizedTruncatedNormal'.
+
+
+ Optional argument
+ If either seed or seed2 are set to be non-zero, the random number
+ generator is seeded by the given seed. Otherwise, it is seeded by a
+ random seed.
+
+
+ Optional argument
+ A second seed to avoid seed collision.
+
+
+ A matrix of shape num_batches x samples_per_batch, filled with random
+ truncated normal values using the parameters for each row.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ scalar which applies to the entire output, or a vector of length shape[0] which
+ stores the parameters for each batch.
+
+
+
+
+ Transforms a vector of brain.Example protos (as strings) into typed tensors.
+
+
+ A vector containing a batch of binary serialized Example protos.
+
+
+ A vector containing the names of the serialized protos.
+ May contain, for example, table key (descriptive) names for the
+ corresponding serialized protos. These are purely useful for debugging
+ purposes, and the presence of values here has no effect on the output.
+ May also be an empty vector if no names are available.
+ If non-empty, this vector must be the same length as "serialized".
+
+
+ A list of Nsparse string Tensors (scalars).
+ The keys expected in the Examples' features associated with sparse values.
+
+
+ A list of Ndense string Tensors (scalars).
+ The keys expected in the Examples' features associated with dense values.
+
+
+ A list of Ndense Tensors (some may be empty).
+ dense_defaults[j] provides default values
+ when the example's feature_map lacks dense_key[j]. If an empty Tensor is
+ provided for dense_defaults[j], then the Feature dense_keys[j] is required.
+ The input type is inferred from dense_defaults[j], even when it's empty.
+ If dense_defaults[j] is not empty, and dense_shapes[j] is fully defined,
+ then the shape of dense_defaults[j] must match that of dense_shapes[j].
+ If dense_shapes[j] has an undefined major dimension (variable strides dense
+ feature), dense_defaults[j] must contain a single element:
+ the padding element.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ParseExample'.
+
+
+ A list of Nsparse types; the data types of data in each Feature
+ given in sparse_keys.
+ Currently the ParseExample supports DT_FLOAT (FloatList),
+ DT_INT64 (Int64List), and DT_STRING (BytesList).
+
+
+ A list of Ndense shapes; the shapes of data in each Feature
+ given in dense_keys.
+ The number of elements in the Feature corresponding to dense_key[j]
+ must always equal dense_shapes[j].NumEntries().
+ If dense_shapes[j] == (D0, D1, ..., DN) then the shape of output
+ Tensor dense_values[j] will be (|serialized|, D0, D1, ..., DN):
+ The dense outputs are just the inputs row-stacked by batch.
+ This works for dense_shapes[j] = (-1, D1, ..., DN). In this case
+ the shape of the output Tensor dense_values[j] will be
+ (|serialized|, M, D1, .., DN), where M is the maximum number of blocks
+ of elements of length D1 * .... * DN, across all minibatch entries
+ in the input. Any minibatch entry with less than M blocks of elements of
+ length D1 * ... * DN will be padded with the corresponding default_value
+ scalar element along the second dimension.
+
+
+ Returns a tuple with multiple values, as follows:
+ sparse_indices:
+ sparse_values:
+ sparse_shapes:
+ dense_values:
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+
+ Transforms a vector of brain.SequenceExample protos (as strings) into typed tensors.
+
+
+ A vector containing binary serialized SequenceExample protos.
+
+
+ A vector containing the names of the serialized protos.
+ May contain, for example, table key (descriptive) name for the
+ corresponding serialized proto. This is purely useful for debugging
+ purposes, and the presence of values here has no effect on the output.
+ May also be an empty vector if no name is available.
+
+
+ A list of Ncontext_dense Tensors (some may be empty).
+ context_dense_defaults[j] provides default values
+ when the SequenceExample's context map lacks context_dense_key[j].
+ If an empty Tensor is provided for context_dense_defaults[j],
+ then the Feature context_dense_keys[j] is required.
+ The input type is inferred from context_dense_defaults[j], even when it's
+ empty. If context_dense_defaults[j] is not empty, its shape must match
+ context_dense_shapes[j].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ParseSequenceExample'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+ A list of Ncontext_sparse types; the data types of data in
+ each context Feature given in context_sparse_keys.
+ Currently the ParseSingleSequenceExample supports DT_FLOAT (FloatList),
+ DT_INT64 (Int64List), and DT_STRING (BytesList).
+
+
+ Optional argument
+
+
+ Optional argument
+ A list of Ncontext_dense shapes; the shapes of data in
+ each context Feature given in context_dense_keys.
+ The number of elements in the Feature corresponding to context_dense_key[j]
+ must always equal context_dense_shapes[j].NumEntries().
+ The shape of context_dense_values[j] will match context_dense_shapes[j].
+
+
+ Optional argument
+ A list of Nfeature_list_sparse types; the data types
+ of data in each FeatureList given in feature_list_sparse_keys.
+ Currently the ParseSingleSequenceExample supports DT_FLOAT (FloatList),
+ DT_INT64 (Int64List), and DT_STRING (BytesList).
+
+
+ Optional argument
+ A list of Nfeature_list_dense shapes; the shapes of
+ data in each FeatureList given in feature_list_dense_keys.
+ The shape of each Feature in the FeatureList corresponding to
+ feature_list_dense_key[j] must always equal
+ feature_list_dense_shapes[j].NumEntries().
+
+
+ A vector listing the
+ FeatureList keys which may be missing from the SequenceExamples. If the
+ associated FeatureList is missing, it is treated as empty. By default,
+ any FeatureList not listed in this vector must exist in the SequenceExamples.
+
+
+ A list of Ncontext_sparse string Tensors (scalars).
+ The keys expected in the Examples' features associated with context_sparse
+ values.
+
+
+ A list of Ncontext_dense string Tensors (scalars).
+ The keys expected in the SequenceExamples' context features associated with
+ dense values.
+
+
+ A list of Nfeature_list_sparse string Tensors
+ (scalars). The keys expected in the FeatureLists associated with sparse
+ values.
+
+
+ A list of Nfeature_list_dense string Tensors (scalars).
+ The keys expected in the SequenceExamples' feature_lists associated
+ with lists of dense values.
+
+
+ Returns a tuple with multiple values, as follows:
+ context_sparse_indices:
+ context_sparse_values:
+ context_sparse_shapes:
+ context_dense_values:
+ feature_list_sparse_indices:
+ feature_list_sparse_values:
+ feature_list_sparse_shapes:
+ feature_list_dense_values:
+ feature_list_dense_lengths:
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+
+ Transforms a tf.Example proto (as a string) into typed tensors.
+
+
+ A vector containing a batch of binary serialized Example protos.
+
+
+ A list of Tensors (some may be empty), whose length matches
+ the length of dense_keys. dense_defaults[j] provides default values
+ when the example's feature_map lacks dense_key[j]. If an empty Tensor is
+ provided for dense_defaults[j], then the Feature dense_keys[j] is required.
+ The input type is inferred from dense_defaults[j], even when it's empty.
+ If dense_defaults[j] is not empty, and dense_shapes[j] is fully defined,
+ then the shape of dense_defaults[j] must match that of dense_shapes[j].
+ If dense_shapes[j] has an undefined major dimension (variable strides dense
+ feature), dense_defaults[j] must contain a single element:
+ the padding element.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ParseSingleExample'.
+
+
+ The number of sparse features to be parsed from the example. This
+ must match the lengths of sparse_keys and sparse_types.
+
+
+ A list of num_sparse strings.
+ The keys expected in the Examples' features associated with sparse values.
+
+
+ The keys expected in the Examples' features associated with dense
+ values.
+
+
+ A list of num_sparse types; the data types of data in each
+ Feature given in sparse_keys.
+ Currently the ParseSingleExample op supports DT_FLOAT (FloatList),
+ DT_INT64 (Int64List), and DT_STRING (BytesList).
+
+
+ The shapes of data in each Feature given in dense_keys.
+ The length of this list must match the length of dense_keys. The
+ number of elements in the Feature corresponding to dense_key[j] must
+ always equal dense_shapes[j].NumEntries(). If dense_shapes[j] ==
+ (D0, D1, ..., DN) then the shape of output Tensor dense_values[j]
+ will be (D0, D1, ..., DN): In the case dense_shapes[j] = (-1, D1,
+ ..., DN), the shape of the output Tensor dense_values[j] will be (M,
+ D1, .., DN), where M is the number of blocks of elements of length
+ D1 * .... * DN, in the input.
+
+
+ Returns a tuple with multiple values, as follows:
+ sparse_indices:
+ sparse_values:
+ sparse_shapes:
+ dense_values:
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+
+ Transforms a scalar brain.SequenceExample proto (as strings) into typed tensors.
+
+
+ A scalar containing a binary serialized SequenceExample proto.
+
+
+ A vector listing the
+ FeatureList keys which may be missing from the SequenceExample. If the
+ associated FeatureList is missing, it is treated as empty. By default,
+ any FeatureList not listed in this vector must exist in the SequenceExample.
+
+
+ A list of Ncontext_sparse string Tensors (scalars).
+ The keys expected in the Examples' features associated with context_sparse
+ values.
+
+
+ A list of Ncontext_dense string Tensors (scalars).
+ The keys expected in the SequenceExamples' context features associated with
+ dense values.
+
+
+ A list of Nfeature_list_sparse string Tensors
+ (scalars). The keys expected in the FeatureLists associated with sparse
+ values.
+
+
+ A list of Nfeature_list_dense string Tensors (scalars).
+ The keys expected in the SequenceExamples' feature_lists associated
+ with lists of dense values.
+
+
+ A list of Ncontext_dense Tensors (some may be empty).
+ context_dense_defaults[j] provides default values
+ when the SequenceExample's context map lacks context_dense_key[j].
+ If an empty Tensor is provided for context_dense_defaults[j],
+ then the Feature context_dense_keys[j] is required.
+ The input type is inferred from context_dense_defaults[j], even when it's
+ empty. If context_dense_defaults[j] is not empty, its shape must match
+ context_dense_shapes[j].
+
+
+ A scalar containing the name of the serialized proto.
+ May contain, for example, table key (descriptive) name for the
+ corresponding serialized proto. This is purely useful for debugging
+ purposes, and the presence of values here has no effect on the output.
+ May also be an empty scalar if no name is available.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ParseSingleSequenceExample'.
+
+
+ Optional argument
+ A list of Ncontext_sparse types; the data types of data in
+ each context Feature given in context_sparse_keys.
+ Currently the ParseSingleSequenceExample supports DT_FLOAT (FloatList),
+ DT_INT64 (Int64List), and DT_STRING (BytesList).
+
+
+ Optional argument
+
+
+ Optional argument
+ A list of Ncontext_dense shapes; the shapes of data in
+ each context Feature given in context_dense_keys.
+ The number of elements in the Feature corresponding to context_dense_key[j]
+ must always equal context_dense_shapes[j].NumEntries().
+ The shape of context_dense_values[j] will match context_dense_shapes[j].
+
+
+ Optional argument
+ A list of Nfeature_list_sparse types; the data types
+ of data in each FeatureList given in feature_list_sparse_keys.
+ Currently the ParseSingleSequenceExample supports DT_FLOAT (FloatList),
+ DT_INT64 (Int64List), and DT_STRING (BytesList).
+
+
+ Optional argument
+ A list of Nfeature_list_dense shapes; the shapes of
+ data in each FeatureList given in feature_list_dense_keys.
+ The shape of each Feature in the FeatureList corresponding to
+ feature_list_dense_key[j] must always equal
+ feature_list_dense_shapes[j].NumEntries().
+
+
+ Returns a tuple with multiple values, as follows:
+ context_sparse_indices:
+ context_sparse_values:
+ context_sparse_shapes:
+ context_dense_values:
+ feature_list_sparse_indices:
+ feature_list_sparse_values:
+ feature_list_sparse_shapes:
+ feature_list_dense_values:
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+
+ Transforms a serialized tensorflow.TensorProto proto into a Tensor.
+
+
+ A scalar string containing a serialized TensorProto proto.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ParseTensor'.
+
+
+ The type of the serialized tensor. The provided type must match the
+ type of the serialized tensor and no implicit conversion will take place.
+
+
+ A Tensor of type out_type.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ A placeholder op for a value that will be fed into the computation.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Placeholder'.
+
+
+ Optional argument
+ (Optional) The shape of the tensor. If the shape has 0 dimensions, the
+ shape is unconstrained.
+
+
+ The type of elements in the tensor.
+
+
+ A placeholder tensor that must be replaced using the feed mechanism.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ N.B. This operation will fail with an error if it is executed. It is
+ intended as a way to represent a value that will always be fed, and to
+ provide attrs that enable the fed value to be checked at runtime.
+
+
+
+
+ A placeholder op for a value that will be fed into the computation.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'PlaceholderV2'.
+
+
+ The type of elements in the tensor.
+
+
+ The shape of the tensor. The shape can be any partially-specified
+ shape. To be unconstrained, pass in a shape with unknown rank.
+
+
+ A placeholder tensor that must be replaced using the feed mechanism.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ N.B. This operation will fail with an error if it is executed. It is
+ intended as a way to represent a value that will always be fed, and to
+ provide attrs that enable the fed value to be checked at runtime.
+
+
+
+
+ A placeholder op that passes through input when its output is not fed.
+
+
+ The default value to produce when output is not fed.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'PlaceholderWithDefault'.
+
+
+ The (possibly partial) shape of the tensor.
+
+
+ A placeholder tensor that defaults to input if it is not fed.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Compute the polygamma function \\(\psi^{(n)}(x)\\).
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Polygamma'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The polygamma function is defined as:
+
+
+ \\(\psi^{(n)}(x) = \frac{d^n}{dx^n} \psi(x)\\)
+
+ where \\(\psi(x)\\) is the digamma function.
+
+
+
+
+ Computes element-wise population count (a.k.a. popcount, bitsum, bitcount).
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'PopulationCount'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ For each entry in x, calculates the number of 1 (on) bits in the binary
+ representation of that entry.
+
+ **NOTE**: It is more efficient to first tf.bitcast your tensors into
+ int32 or int64 and perform the bitcount on the result, than to feed in
+ 8- or 16-bit inputs and then aggregate the resulting counts.
+
+
+
+
+ Computes the power of one value to another.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Pow'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Given a tensor x and a tensor y, this operation computes \\(x^y\\) for
+ corresponding elements in x and y. For example:
+
+
+ # tensor 'x' is [[2, 2]], [3, 3]]
+ # tensor 'y' is [[8, 16], [2, 3]]
+ tf.pow(x, y) ==> [[256, 65536], [9, 27]]
+
+
+
+
+
+ Creates a dataset that asynchronously prefetches elements from input_dataset.
+
+
+
+
+ The maximum number of elements to buffer in an iterator over
+ this dataset.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'PrefetchDataset'.
+
+
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ An identity op that triggers an error if a gradient is requested.
+
+
+ any tensor.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'PreventGradient'.
+
+
+ Optional argument
+ Will be printed in the error when anyone tries to differentiate
+ this operation.
+
+
+ the same input tensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ When executed in a graph, this op outputs its input tensor as-is.
+
+ When building ops to compute gradients, the TensorFlow gradient system
+ will return an error when trying to lookup the gradient of this op,
+ because no gradient must ever be registered for this function. This
+ op exists to prevent subtle bugs from silently returning unimplemented
+ gradients in some corner cases.
+
+
+
+
+ Prints a list of tensors.
+
+
+ The tensor passed to output
+
+
+ A list of tensors to print out when op is evaluated.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Print'.
+
+
+ Optional argument
+ A string, prefix of the error message.
+
+
+ Optional argument
+ Only log first_n number of times. -1 disables logging.
+
+
+ Optional argument
+ Only print this many entries of each tensor.
+
+
+ = The unmodified input tensor
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Passes input through to output and prints data when evaluating.
+
+
+
+
+ Prints a string scalar.
+
+
+ The string scalar to print.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'PrintV2'.
+
+
+ Optional argument
+ A string specifying the output stream or logging level to print to.
+
+
+ Returns the description of the operation
+
+
+ Prints a string scalar to the desired output_stream.
+
+
+
+
+ A queue that produces elements sorted by the first component value.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'PriorityQueue'.
+
+
+ Optional argument
+ The type of each component in a value.
+
+
+ Optional argument
+ The upper bound on the number of elements in this queue.
+ Negative numbers mean no limit.
+
+
+ Optional argument
+ If non-empty, this queue is placed in the given container.
+ Otherwise, a default container is used.
+
+
+ Optional argument
+ If non-empty, this queue will be shared under the given name
+ across multiple sessions.
+
+
+ The shape of each component in a value. The length of this attr must
+ be either 0 or the same as the length of component_types. If the length of
+ this attr is 0, the shapes of queue elements are not constrained, and
+ only one element may be dequeued at a time.
+
+
+ The handle to the queue.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Note that the PriorityQueue requires the first component of any element
+ to be a scalar int64, in addition to the other elements declared by
+ component_types. Therefore calls to Enqueue and EnqueueMany (resp. Dequeue
+ and DequeueMany) on a PriorityQueue will all require (resp. output) one extra
+ entry in their input (resp. output) lists.
+
+
+
+
+ A queue that produces elements sorted by the first component value.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'PriorityQueueV2'.
+
+
+ Optional argument
+ The type of each component in a value.
+
+
+ Optional argument
+ The upper bound on the number of elements in this queue.
+ Negative numbers mean no limit.
+
+
+ Optional argument
+ If non-empty, this queue is placed in the given container.
+ Otherwise, a default container is used.
+
+
+ Optional argument
+ If non-empty, this queue will be shared under the given name
+ across multiple sessions.
+
+
+ The shape of each component in a value. The length of this attr must
+ be either 0 or the same as the length of component_types. If the length of
+ this attr is 0, the shapes of queue elements are not constrained, and
+ only one element may be dequeued at a time.
+
+
+ The handle to the queue.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Note that the PriorityQueue requires the first component of any element
+ to be a scalar int64, in addition to the other elements declared by
+ component_types. Therefore calls to Enqueue and EnqueueMany (resp. Dequeue
+ and DequeueMany) on a PriorityQueue will all require (resp. output) one extra
+ entry in their input (resp. output) lists.
+
+
+
+
+ Computes the product of elements across dimensions of a tensor.
+
+
+ The tensor to reduce.
+
+
+ The dimensions to reduce. Must be in the range
+ [-rank(input), rank(input)).
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Prod'.
+
+
+ Optional argument
+ If true, retain reduced dimensions with length 1.
+
+
+ The reduced tensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Reduces input along the dimensions given in axis. Unless
+ keep_dims is true, the rank of the tensor is reduced by 1 for each entry in
+ axis. If keep_dims is true, the reduced dimensions are
+ retained with length 1.
+
+
+
+
+ Computes the QR decompositions of one or more matrices.
+
+
+ A tensor of shape [..., M, N] whose inner-most 2 dimensions
+ form matrices of size [M, N]. Let P be the minimum of M and N.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Qr'.
+
+
+ Optional argument
+ If true, compute full-sized q and r. If false
+ (the default), compute only the leading P columns of q.
+
+
+ Returns a tuple with multiple values, as follows:
+ q: Orthonormal basis for range of a. If full_matrices is False then
+ shape is [..., M, P]; if full_matrices is True then shape is
+ [..., M, M].
+ r: Triangular factor. If full_matrices is False then shape is
+ [..., P, N]. If full_matrices is True then shape is [..., M, N].
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Computes the QR decomposition of each inner matrix in tensor such that
+ tensor[..., :, :] = q[..., :, :] * r[..., :,:])
+
+
+ # a is a tensor.
+ # q is a tensor of orthonormal matrices.
+ # r is a tensor of upper triangular matrices.
+ q, r = qr(a)
+ q_full, r_full = qr(a, full_matrices=True)
+
+
+
+
+
+ Use QuantizeAndDequantizeV2 instead.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QuantizeAndDequantize'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Quantizes then dequantizes a tensor.
+
+
+
+
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QuantizeAndDequantizeV3'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This is almost identical to QuantizeAndDequantizeV2, except that num_bits is a
+ tensor, so its value can change during training.
+
+
+
+
+ Returns x + y element-wise, working on quantized buffers.
+
+
+
+
+
+
+ The float value that the lowest quantized x value represents.
+
+
+ The float value that the highest quantized x value represents.
+
+
+ The float value that the lowest quantized y value represents.
+
+
+ The float value that the highest quantized y value represents.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QuantizedAdd'.
+
+
+ Optional argument
+
+
+ Returns a tuple with multiple values, as follows:
+ z:
+ min_z: The float value that the lowest quantized output value represents.
+ max_z: The float value that the highest quantized output value represents.
+
+ *NOTE*: QuantizedAdd supports limited forms of broadcasting. More about
+ broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+
+ Produces the average pool of the input tensor for quantized types.
+
+
+ 4-D with shape [batch, height, width, channels].
+
+
+ The float value that the lowest quantized input value represents.
+
+
+ The float value that the highest quantized input value represents.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QuantizedAvgPool'.
+
+
+ The size of the window for each dimension of the input tensor.
+ The length must be 4 to match the number of dimensions of the input.
+
+
+ The stride of the sliding window for each dimension of the input
+ tensor. The length must be 4 to match the number of dimensions of the input.
+
+
+ The type of padding algorithm to use.
+
+
+ Returns a tuple with multiple values, as follows:
+ output:
+ min_output: The float value that the lowest quantized output value represents.
+ max_output: The float value that the highest quantized output value represents.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+
+ Quantized Batch normalization.
+
+
+ A 4D input Tensor.
+
+
+ The value represented by the lowest quantized input.
+
+
+ The value represented by the highest quantized input.
+
+
+ A 1D mean Tensor with size matching the last dimension of t.
+ This is the first output from tf.nn.moments,
+ or a saved moving average thereof.
+
+
+ The value represented by the lowest quantized mean.
+
+
+ The value represented by the highest quantized mean.
+
+
+ A 1D variance Tensor with size matching the last dimension of t.
+ This is the second output from tf.nn.moments,
+ or a saved moving average thereof.
+
+
+ The value represented by the lowest quantized variance.
+
+
+ The value represented by the highest quantized variance.
+
+
+ A 1D beta Tensor with size matching the last dimension of t.
+ An offset to be added to the normalized tensor.
+
+
+ The value represented by the lowest quantized offset.
+
+
+ The value represented by the highest quantized offset.
+
+
+ A 1D gamma Tensor with size matching the last dimension of t.
+ If "scale_after_normalization" is true, this tensor will be multiplied
+ with the normalized tensor.
+
+
+ The value represented by the lowest quantized gamma.
+
+
+ The value represented by the highest quantized gamma.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QuantizedBatchNormWithGlobalNormalization'.
+
+
+
+
+ A small float number to avoid dividing by 0.
+
+
+ A bool indicating whether the resulted tensor
+ needs to be multiplied with gamma.
+
+
+ Returns a tuple with multiple values, as follows:
+ result:
+ result_min:
+ result_max:
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ This op is deprecated and will be removed in the future. Prefer
+ tf.nn.batch_normalization.
+
+
+
+
+ Adds Tensor 'bias' to Tensor 'input' for Quantized types.
+
+
+
+
+ A 1D bias Tensor with size matching the last dimension of 'input'.
+
+
+ The float value that the lowest quantized input value represents.
+
+
+ The float value that the highest quantized input value represents.
+
+
+ The float value that the lowest quantized bias value represents.
+
+
+ The float value that the highest quantized bias value represents.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QuantizedBiasAdd'.
+
+
+
+
+ Returns a tuple with multiple values, as follows:
+ output:
+ min_out: The float value that the lowest quantized output value represents.
+ max_out: The float value that the highest quantized output value represents.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Broadcasts the values of bias on dimensions 0..N-2 of 'input'.
+
+
+
+
+ Concatenates quantized tensors along one dimension.
+
+
+ 0-D. The dimension along which to concatenate. Must be in the
+ range [0, rank(values)).
+
+
+ The N Tensors to concatenate. Their ranks and types must match,
+ and their sizes must match in all dimensions except concat_dim.
+
+
+ The minimum scalar values for each of the input tensors.
+
+
+ The maximum scalar values for each of the input tensors.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QuantizedConcat'.
+
+
+ Returns a tuple with multiple values, as follows:
+ output: A Tensor with the concatenation of values stacked along the
+ concat_dim dimension. This tensor's shape matches that of values except
+ in concat_dim where it has the sum of the sizes.
+ output_min: The float value that the minimum quantized output value represents.
+ output_max: The float value that the maximum quantized output value represents.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+
+ Computes a 2D convolution given quantized 4D input and filter tensors.
+
+
+
+
+ filter's input_depth dimension must match input's depth dimensions.
+
+
+ The float value that the lowest quantized input value represents.
+
+
+ The float value that the highest quantized input value represents.
+
+
+ The float value that the lowest quantized filter value represents.
+
+
+ The float value that the highest quantized filter value represents.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QuantizedConv2D'.
+
+
+ Optional argument
+
+
+ Optional argument
+ 1-D tensor of length 4. The dilation factor for each dimension of
+ input. If set to k > 1, there will be k-1 skipped cells between each
+ filter element on that dimension. The dimension order is determined by the
+ value of data_format, see above for details. Dilations in the batch and
+ depth dimensions must be 1.
+
+
+ The stride of the sliding window for each dimension of the input
+ tensor.
+
+
+ The type of padding algorithm to use.
+
+
+ Returns a tuple with multiple values, as follows:
+ output:
+ min_output: The float value that the lowest quantized output value represents.
+ max_output: The float value that the highest quantized output value represents.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ The inputs are quantized tensors where the lowest value represents the real
+ number of the associated minimum, and the highest represents the maximum.
+ This means that you can only interpret the quantized output in the same way, by
+ taking the returned minimum and maximum values into account.
+
+
+
+
+ Quantized Instance normalization.
+
+
+ A 4D input Tensor.
+
+
+ The value represented by the lowest quantized input.
+
+
+ The value represented by the highest quantized input.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QuantizedInstanceNorm'.
+
+
+ Optional argument
+ If True, given_y_min and given_y_min
+ and given_y_max are used as the output range. Otherwise,
+ the implementation computes the output range.
+
+
+ Optional argument
+ Output in y_min if output_range_given is True.
+
+
+ Optional argument
+ Output in y_max if output_range_given is True.
+
+
+ Optional argument
+ A small float number to avoid dividing by 0.
+
+
+ Optional argument
+ Minimum value of y_max - y_min
+
+
+ Returns a tuple with multiple values, as follows:
+ y: A 4D Tensor.
+ y_min: The value represented by the lowest quantized output.
+ y_max: The value represented by the highest quantized output.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+
+ Perform a quantized matrix multiplication of a by the matrix b.
+
+
+ Must be a two-dimensional tensor.
+
+
+ Must be a two-dimensional tensor.
+
+
+ The float value that the lowest quantized a value represents.
+
+
+ The float value that the highest quantized a value represents.
+
+
+ The float value that the lowest quantized b value represents.
+
+
+ The float value that the highest quantized b value represents.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QuantizedMatMul'.
+
+
+ Optional argument
+
+
+ Optional argument
+ If true, a is transposed before multiplication.
+
+
+ Optional argument
+ If true, b is transposed before multiplication.
+
+
+ Optional argument
+ The type of output produced by activation function
+ following this operation.
+
+
+ Returns a tuple with multiple values, as follows:
+ output:
+ min_out: The float value that the lowest quantized output value represents.
+ max_out: The float value that the highest quantized output value represents.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ The inputs must be two-dimensional matrices and the inner dimension of
+ a (after being transposed if transpose_a is non-zero) must match the
+ outer dimension of b (after being transposed if transposed_b is
+ non-zero).
+
+
+
+
+ Produces the max pool of the input tensor for quantized types.
+
+
+ The 4D (batch x rows x cols x depth) Tensor to MaxReduce over.
+
+
+ The float value that the lowest quantized input value represents.
+
+
+ The float value that the highest quantized input value represents.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QuantizedMaxPool'.
+
+
+ The size of the window for each dimension of the input tensor.
+ The length must be 4 to match the number of dimensions of the input.
+
+
+ The stride of the sliding window for each dimension of the input
+ tensor. The length must be 4 to match the number of dimensions of the input.
+
+
+ The type of padding algorithm to use.
+
+
+ Returns a tuple with multiple values, as follows:
+ output:
+ min_output: The float value that the lowest quantized output value represents.
+ max_output: The float value that the highest quantized output value represents.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+
+ Returns x * y element-wise, working on quantized buffers.
+
+
+
+
+
+
+ The float value that the lowest quantized x value represents.
+
+
+ The float value that the highest quantized x value represents.
+
+
+ The float value that the lowest quantized y value represents.
+
+
+ The float value that the highest quantized y value represents.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QuantizedMul'.
+
+
+ Optional argument
+
+
+ Returns a tuple with multiple values, as follows:
+ z:
+ min_z: The float value that the lowest quantized output value represents.
+ max_z: The float value that the highest quantized output value represents.
+
+ *NOTE*: QuantizedMul supports limited forms of broadcasting. More about
+ broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+
+ Convert the quantized 'input' tensor into a lower-precision 'output', using the
+
+
+
+
+ The float value that the minimum quantized input value represents.
+
+
+ The float value that the maximum quantized input value represents.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QuantizeDownAndShrinkRange'.
+
+
+ The type of the output. Should be a lower bit depth than Tinput.
+
+
+ Returns a tuple with multiple values, as follows:
+ output:
+ output_min: The float value that the minimum quantized output value represents.
+ output_max: The float value that the maximum quantized output value represents.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ actual distribution of the values to maximize the usage of the lower bit depth
+ and adjusting the output min and max ranges accordingly.
+
+ [input_min, input_max] are scalar floats that specify the range for the float
+ interpretation of the 'input' data. For example, if input_min is -1.0f and
+ input_max is 1.0f, and we are dealing with quint16 quantized data, then a 0
+ value in the 16-bit data should be interpreted as -1.0f, and a 65535 means 1.0f.
+
+ This operator tries to squeeze as much precision as possible into an output with
+ a lower bit depth by calculating the actual min and max values found in the
+ data. For example, maybe that quint16 input has no values lower than 16,384 and
+ none higher than 49,152. That means only half the range is actually needed, all
+ the float interpretations are between -0.5f and 0.5f, so if we want to compress
+ the data into a quint8 output, we can use that range rather than the theoretical
+ -1.0f to 1.0f that is suggested by the input min and max.
+
+ In practice, this is most useful for taking output from operations like
+ QuantizedMatMul that can produce higher bit-depth outputs than their inputs and
+ may have large potential output ranges, but in practice have a distribution of
+ input values that only uses a small fraction of the possible range. By feeding
+ that output into this operator, we can reduce it from 32 bits down to 8 with
+ minimal loss of accuracy.
+
+
+
+
+ Computes Quantized Rectified Linear: max(features, 0)
+
+
+
+
+ The float value that the lowest quantized value represents.
+
+
+ The float value that the highest quantized value represents.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QuantizedRelu'.
+
+
+ Optional argument
+
+
+ Returns a tuple with multiple values, as follows:
+ activations: Has the same output shape as "features".
+ min_activations: The float value that the lowest quantized value represents.
+ max_activations: The float value that the highest quantized value represents.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+
+ Computes Quantized Rectified Linear 6: min(max(features, 0), 6)
+
+
+
+
+ The float value that the lowest quantized value represents.
+
+
+ The float value that the highest quantized value represents.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QuantizedRelu6'.
+
+
+ Optional argument
+
+
+ Returns a tuple with multiple values, as follows:
+ activations: Has the same output shape as "features".
+ min_activations: The float value that the lowest quantized value represents.
+ max_activations: The float value that the highest quantized value represents.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+
+ Computes Quantized Rectified Linear X: min(max(features, 0), max_value)
+
+
+
+
+
+
+ The float value that the lowest quantized value represents.
+
+
+ The float value that the highest quantized value represents.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QuantizedReluX'.
+
+
+ Optional argument
+
+
+ Returns a tuple with multiple values, as follows:
+ activations: Has the same output shape as "features".
+ min_activations: The float value that the lowest quantized value represents.
+ max_activations: The float value that the highest quantized value represents.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+
+
+ Resize quantized images to size using quantized bilinear interpolation.
+
+
+ 4-D with shape [batch, height, width, channels].
+
+
+ = A 1-D int32 Tensor of 2 elements: new_height, new_width. The
+ new size for the images.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QuantizedResizeBilinear'.
+
+
+ Optional argument
+ If true, the centers of the 4 corner pixels of the input and output tensors are
+ aligned, preserving the values at the corner pixels. Defaults to false.
+
+
+ Returns a tuple with multiple values, as follows:
+ resized_images: 4-D with shape
+ [batch, new_height, new_width, channels].
+ out_min:
+ out_max:
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Input images and output images must be quantized types.
+
+
+
+
+ Quantize the 'input' tensor of type float to 'output' tensor of type 'T'.
+
+
+
+
+ The minimum scalar value possibly produced for the input.
+
+
+ The maximum scalar value possibly produced for the input.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QuantizeV2'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+ Returns a tuple with multiple values, as follows:
+ output: The quantized data produced from the float input.
+ output_min: The actual minimum scalar value used for the output.
+ output_max: The actual maximum scalar value used for the output.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ [min_range, max_range] are scalar floats that specify the range for
+ the 'input' data. The 'mode' attribute controls exactly which calculations are
+ used to convert the float values to their quantized equivalents. The
+ 'round_mode' attribute controls which rounding tie-breaking algorithm is used
+ when rounding float values to their quantized equivalents.
+
+ In 'MIN_COMBINED' mode, each value of the tensor will undergo the following:
+
+
+ out[i] = (in[i] - min_range) * range(T) / (max_range - min_range)
+ if T == qint8: out[i] -= (range(T) + 1) / 2.0
+
+
+ here range(T) = numeric_limits<T>::max() - numeric_limits<T>::min()
+
+ *MIN_COMBINED Mode Example*
+
+ Assume the input is type float and has a possible range of [0.0, 6.0] and the
+ output type is quint8 ([0, 255]). The min_range and max_range values should be
+ specified as 0.0 and 6.0. Quantizing from float to quint8 will multiply each
+ value of the input by 255/6 and cast to quint8.
+
+ If the output type was qint8 ([-128, 127]), the operation will additionally
+ subtract each value by 128 prior to casting, so that the range of values aligns
+ with the range of qint8.
+
+ If the mode is 'MIN_FIRST', then this approach is used:
+
+
+ num_discrete_values = 1 << (# of bits in T)
+ range_adjust = num_discrete_values / (num_discrete_values - 1)
+ range = (range_max - range_min) * range_adjust
+ range_scale = num_discrete_values / range
+ quantized = round(input * range_scale) - round(range_min * range_scale) +
+ numeric_limits<T>::min()
+ quantized = max(quantized, numeric_limits<T>::min())
+ quantized = min(quantized, numeric_limits<T>::max())
+
+
+ The biggest difference between this and MIN_COMBINED is that the minimum range
+ is rounded first, before it's subtracted from the rounded value. With
+ MIN_COMBINED, a small bias is introduced where repeated iterations of quantizing
+ and dequantizing will introduce a larger and larger error.
+
+ *SCALED mode Example*
+
+ SCALED mode matches the quantization approach used in
+ QuantizeAndDequantize{V2|V3}.
+
+ If the mode is SCALED, we do not use the full range of the output type,
+ choosing to elide the lowest possible value for symmetry (e.g., output range is
+ -127 to 127, not -128 to 127 for signed 8 bit quantization), so that 0.0 maps to
+ 0.
+
+ We first find the range of values in our tensor. The
+ range we use is always centered on 0, so we find m such that
+
+
+ m = max(abs(input_min), abs(input_max))
+
+
+ Our input tensor range is then [-m, m].
+
+ Next, we choose our fixed-point quantization buckets, [min_fixed, max_fixed].
+ If T is signed, this is
+
+
+ num_bits = sizeof(T) * 8
+ [min_fixed, max_fixed] =
+ [-(1 << (num_bits - 1) - 1), (1 << (num_bits - 1)) - 1]
+
+
+ Otherwise, if T is unsigned, the fixed-point range is
+
+
+ [min_fixed, max_fixed] = [0, (1 << num_bits) - 1]
+
+
+ From this we compute our scaling factor, s:
+
+
+ s = (max_fixed - min_fixed) / (2 * m)
+
+
+ Now we can quantize the elements of our tensor:
+
+
+ result = round(input * s)
+
+
+ One thing to watch out for is that the operator may choose to adjust the
+ requested minimum and maximum values slightly during the quantization process,
+ so you should always use the output ports as the range for further calculations.
+ For example, if the requested minimum and maximum values are close to equal,
+ they will be separated by a small epsilon value to prevent ill-formed quantized
+ buffers from being created. Otherwise, you can end up with buffers where all the
+ quantized values map to the same float value, which causes problems for
+ operations that have to perform further calculations on them.
+
+
+
+
+ Closes the given queue.
+
+
+ The handle to a queue.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QueueClose'.
+
+
+ Optional argument
+ If true, all pending enqueue requests that are
+ blocked on the given queue will be canceled.
+
+
+ Returns the description of the operation
+
+
+ This operation signals that no more elements will be enqueued in the
+ given queue. Subsequent Enqueue(Many) operations will fail.
+ Subsequent Dequeue(Many) operations will continue to succeed if
+ sufficient elements remain in the queue. Subsequent Dequeue(Many)
+ operations that would block will fail immediately.
+
+
+
+
+ Closes the given queue.
+
+
+ The handle to a queue.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QueueCloseV2'.
+
+
+ Optional argument
+ If true, all pending enqueue requests that are
+ blocked on the given queue will be canceled.
+
+
+ Returns the description of the operation
+
+
+ This operation signals that no more elements will be enqueued in the
+ given queue. Subsequent Enqueue(Many) operations will fail.
+ Subsequent Dequeue(Many) operations will continue to succeed if
+ sufficient elements remain in the queue. Subsequent Dequeue(Many)
+ operations that would block will fail immediately.
+
+
+
+
+ Dequeues a tuple of one or more tensors from the given queue.
+
+
+ The handle to a queue.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QueueDequeue'.
+
+
+ Optional argument
+ If the queue is empty, this operation will block for up to
+ timeout_ms milliseconds.
+ Note: This option is not supported yet.
+
+
+ The type of each component in a tuple.
+
+
+ One or more tensors that were dequeued as a tuple.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation has k outputs, where k is the number of components
+ in the tuples stored in the given queue, and output i is the ith
+ component of the dequeued tuple.
+
+ N.B. If the queue is empty, this operation will block until an element
+ has been dequeued (or 'timeout_ms' elapses, if specified).
+
+
+
+
+ Dequeues n tuples of one or more tensors from the given queue.
+
+
+ The handle to a queue.
+
+
+ The number of tuples to dequeue.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QueueDequeueMany'.
+
+
+ Optional argument
+ If the queue has fewer than n elements, this operation
+ will block for up to timeout_ms milliseconds.
+ Note: This option is not supported yet.
+
+
+ The type of each component in a tuple.
+
+
+ One or more tensors that were dequeued as a tuple.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ If the queue is closed and there are fewer than n elements, then an
+ OutOfRange error is returned.
+
+ This operation concatenates queue-element component tensors along the
+ 0th dimension to make a single component tensor. All of the components
+ in the dequeued tuple will have size n in the 0th dimension.
+
+ This operation has k outputs, where k is the number of components in
+ the tuples stored in the given queue, and output i is the ith
+ component of the dequeued tuple.
+
+ N.B. If the queue is empty, this operation will block until n elements
+ have been dequeued (or 'timeout_ms' elapses, if specified).
+
+
+
+
+ Dequeues n tuples of one or more tensors from the given queue.
+
+
+ The handle to a queue.
+
+
+ The number of tuples to dequeue.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QueueDequeueManyV2'.
+
+
+ Optional argument
+ If the queue has fewer than n elements, this operation
+ will block for up to timeout_ms milliseconds.
+ Note: This option is not supported yet.
+
+
+ The type of each component in a tuple.
+
+
+ One or more tensors that were dequeued as a tuple.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ If the queue is closed and there are fewer than n elements, then an
+ OutOfRange error is returned.
+
+ This operation concatenates queue-element component tensors along the
+ 0th dimension to make a single component tensor. All of the components
+ in the dequeued tuple will have size n in the 0th dimension.
+
+ This operation has k outputs, where k is the number of components in
+ the tuples stored in the given queue, and output i is the ith
+ component of the dequeued tuple.
+
+ N.B. If the queue is empty, this operation will block until n elements
+ have been dequeued (or 'timeout_ms' elapses, if specified).
+
+
+
+
+ Dequeues n tuples of one or more tensors from the given queue.
+
+
+ The handle to a queue.
+
+
+ The number of tuples to dequeue.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QueueDequeueUpTo'.
+
+
+ Optional argument
+ If the queue has fewer than n elements, this operation
+ will block for up to timeout_ms milliseconds.
+ Note: This option is not supported yet.
+
+
+ The type of each component in a tuple.
+
+
+ One or more tensors that were dequeued as a tuple.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation is not supported by all queues. If a queue does not support
+ DequeueUpTo, then an Unimplemented error is returned.
+
+ If the queue is closed and there are more than 0 but less than n
+ elements remaining, then instead of returning an OutOfRange error like
+ QueueDequeueMany, less than n elements are returned immediately. If
+ the queue is closed and there are 0 elements left in the queue, then
+ an OutOfRange error is returned just like in QueueDequeueMany.
+ Otherwise the behavior is identical to QueueDequeueMany:
+
+ This operation concatenates queue-element component tensors along the
+ 0th dimension to make a single component tensor. All of the components
+ in the dequeued tuple will have size n in the 0th dimension.
+
+ This operation has k outputs, where k is the number of components in
+ the tuples stored in the given queue, and output i is the ith
+ component of the dequeued tuple.
+
+
+
+
+ Dequeues n tuples of one or more tensors from the given queue.
+
+
+ The handle to a queue.
+
+
+ The number of tuples to dequeue.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QueueDequeueUpToV2'.
+
+
+ Optional argument
+ If the queue has fewer than n elements, this operation
+ will block for up to timeout_ms milliseconds.
+ Note: This option is not supported yet.
+
+
+ The type of each component in a tuple.
+
+
+ One or more tensors that were dequeued as a tuple.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation is not supported by all queues. If a queue does not support
+ DequeueUpTo, then an Unimplemented error is returned.
+
+ If the queue is closed and there are more than 0 but less than n
+ elements remaining, then instead of returning an OutOfRange error like
+ QueueDequeueMany, less than n elements are returned immediately. If
+ the queue is closed and there are 0 elements left in the queue, then
+ an OutOfRange error is returned just like in QueueDequeueMany.
+ Otherwise the behavior is identical to QueueDequeueMany:
+
+ This operation concatenates queue-element component tensors along the
+ 0th dimension to make a single component tensor. All of the components
+ in the dequeued tuple will have size n in the 0th dimension.
+
+ This operation has k outputs, where k is the number of components in
+ the tuples stored in the given queue, and output i is the ith
+ component of the dequeued tuple.
+
+
+
+
+ Dequeues a tuple of one or more tensors from the given queue.
+
+
+ The handle to a queue.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QueueDequeueV2'.
+
+
+ Optional argument
+ If the queue is empty, this operation will block for up to
+ timeout_ms milliseconds.
+ Note: This option is not supported yet.
+
+
+ The type of each component in a tuple.
+
+
+ One or more tensors that were dequeued as a tuple.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation has k outputs, where k is the number of components
+ in the tuples stored in the given queue, and output i is the ith
+ component of the dequeued tuple.
+
+ N.B. If the queue is empty, this operation will block until an element
+ has been dequeued (or 'timeout_ms' elapses, if specified).
+
+
+
+
+ Enqueues a tuple of one or more tensors in the given queue.
+
+
+ The handle to a queue.
+
+
+ One or more tensors from which the enqueued tensors should be taken.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QueueEnqueue'.
+
+
+ Optional argument
+ If the queue is full, this operation will block for up to
+ timeout_ms milliseconds.
+ Note: This option is not supported yet.
+
+
+ Returns the description of the operation
+
+
+ The components input has k elements, which correspond to the components of
+ tuples stored in the given queue.
+
+ N.B. If the queue is full, this operation will block until the given
+ element has been enqueued (or 'timeout_ms' elapses, if specified).
+
+
+
+
+ Enqueues zero or more tuples of one or more tensors in the given queue.
+
+
+ The handle to a queue.
+
+
+ One or more tensors from which the enqueued tensors should
+ be taken.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QueueEnqueueMany'.
+
+
+ Optional argument
+ If the queue is too full, this operation will block for up
+ to timeout_ms milliseconds.
+ Note: This option is not supported yet.
+
+
+ Returns the description of the operation
+
+
+ This operation slices each component tensor along the 0th dimension to
+ make multiple queue elements. All of the tuple components must have the
+ same size in the 0th dimension.
+
+ The components input has k elements, which correspond to the components of
+ tuples stored in the given queue.
+
+ N.B. If the queue is full, this operation will block until the given
+ elements have been enqueued (or 'timeout_ms' elapses, if specified).
+
+
+
+
+ Enqueues zero or more tuples of one or more tensors in the given queue.
+
+
+ The handle to a queue.
+
+
+ One or more tensors from which the enqueued tensors should
+ be taken.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QueueEnqueueManyV2'.
+
+
+ Optional argument
+ If the queue is too full, this operation will block for up
+ to timeout_ms milliseconds.
+ Note: This option is not supported yet.
+
+
+ Returns the description of the operation
+
+
+ This operation slices each component tensor along the 0th dimension to
+ make multiple queue elements. All of the tuple components must have the
+ same size in the 0th dimension.
+
+ The components input has k elements, which correspond to the components of
+ tuples stored in the given queue.
+
+ N.B. If the queue is full, this operation will block until the given
+ elements have been enqueued (or 'timeout_ms' elapses, if specified).
+
+
+
+
+ Enqueues a tuple of one or more tensors in the given queue.
+
+
+ The handle to a queue.
+
+
+ One or more tensors from which the enqueued tensors should be taken.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QueueEnqueueV2'.
+
+
+ Optional argument
+ If the queue is full, this operation will block for up to
+ timeout_ms milliseconds.
+ Note: This option is not supported yet.
+
+
+ Returns the description of the operation
+
+
+ The components input has k elements, which correspond to the components of
+ tuples stored in the given queue.
+
+ N.B. If the queue is full, this operation will block until the given
+ element has been enqueued (or 'timeout_ms' elapses, if specified).
+
+
+
+
+ Returns true if queue is closed.
+
+
+ The handle to a queue.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QueueIsClosed'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation returns true if the queue is closed and false if the queue
+ is open.
+
+
+
+
+ Returns true if queue is closed.
+
+
+ The handle to a queue.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QueueIsClosedV2'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation returns true if the queue is closed and false if the queue
+ is open.
+
+
+
+
+ Computes the number of elements in the given queue.
+
+
+ The handle to a queue.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QueueSize'.
+
+
+ The number of elements in the given queue.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes the number of elements in the given queue.
+
+
+ The handle to a queue.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QueueSizeV2'.
+
+
+ The number of elements in the given queue.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Randomly crop image.
+
+
+ 3-D of shape [height, width, channels].
+
+
+ 1-D of length 2 containing: crop_height, crop_width..
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RandomCrop'.
+
+
+ Optional argument
+ If either seed or seed2 are set to be non-zero, the random number
+ generator is seeded by the given seed. Otherwise, it is seeded by a
+ random seed.
+
+
+ Optional argument
+ An second seed to avoid seed collision.
+
+
+ 3-D of shape [crop_height, crop_width, channels].
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ size is a 1-D int64 tensor with 2 elements representing the crop height and
+ width. The values must be non negative.
+
+ This Op picks a random location in image and crops a height by width
+ rectangle from that location. The random location is picked so the cropped
+ area will fit inside the original image.
+
+
+
+
+ Outputs random values from the Gamma distribution(s) described by alpha.
+
+
+ 1-D integer tensor. Shape of independent samples to draw from each
+ distribution described by the shape parameters given in alpha.
+
+
+ A tensor in which each scalar is a "shape" parameter describing the
+ associated gamma distribution.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RandomGamma'.
+
+
+ Optional argument
+ If either seed or seed2 are set to be non-zero, the random number
+ generator is seeded by the given seed. Otherwise, it is seeded by a
+ random seed.
+
+
+ Optional argument
+ A second seed to avoid seed collision.
+
+
+ A tensor with shape shape + shape(alpha). Each slice
+ [:, ..., :, i0, i1, ...iN] contains the samples drawn for
+ alpha[i0, i1, ...iN]. The dtype of the output matches the dtype of alpha.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This op uses the algorithm by Marsaglia et al. to acquire samples via
+ transformation-rejection from pairs of uniform and normal random variables.
+ See http://dl.acm.org/citation.cfm?id=358414
+
+
+
+
+ Computes the derivative of a Gamma random sample w.r.t. alpha.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RandomGammaGrad'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Use RandomPoissonV2 instead.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RandomPoisson'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Outputs random values from the Poisson distribution(s) described by rate.
+
+
+ 1-D integer tensor. Shape of independent samples to draw from each
+ distribution described by the shape parameters given in rate.
+
+
+ A tensor in which each scalar is a "rate" parameter describing the
+ associated poisson distribution.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RandomPoissonV2'.
+
+
+ Optional argument
+ If either seed or seed2 are set to be non-zero, the random number
+ generator is seeded by the given seed. Otherwise, it is seeded by a
+ random seed.
+
+
+ Optional argument
+ A second seed to avoid seed collision.
+
+
+ Optional argument
+
+
+ A tensor with shape shape + shape(rate). Each slice
+ [:, ..., :, i0, i1, ...iN] contains the samples drawn for
+ rate[i0, i1, ...iN].
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This op uses two algorithms, depending on rate. If rate >= 10, then
+ the algorithm by Hormann is used to acquire samples via
+ transformation-rejection.
+ See http://www.sciencedirect.com/science/article/pii/0167668793909974.
+
+ Otherwise, Knuth's algorithm is used to acquire samples via multiplying uniform
+ random variables.
+ See Donald E. Knuth (1969). Seminumerical Algorithms. The Art of Computer
+ Programming, Volume 2. Addison Wesley
+
+
+
+
+ Randomly shuffles a tensor along its first dimension.
+
+
+ The tensor to be shuffled.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RandomShuffle'.
+
+
+ Optional argument
+ If either seed or seed2 are set to be non-zero, the random number
+ generator is seeded by the given seed. Otherwise, it is seeded by a
+ random seed.
+
+
+ Optional argument
+ A second seed to avoid seed collision.
+
+
+ A tensor of same shape and type as value, shuffled along its first
+ dimension.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The tensor is shuffled along dimension 0, such that each value[j] is mapped
+ to one and only one output[i]. For example, a mapping that might occur for a
+ 3x2 tensor is:
+
+
+ [[1, 2], [[5, 6],
+ [3, 4], ==> [1, 2],
+ [5, 6]] [3, 4]]
+
+
+
+
+
+ A queue that randomizes the order of elements.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RandomShuffleQueue'.
+
+
+ Optional argument
+ The shape of each component in a value. The length of this attr must
+ be either 0 or the same as the length of component_types. If the length of
+ this attr is 0, the shapes of queue elements are not constrained, and
+ only one element may be dequeued at a time.
+
+
+ Optional argument
+ The upper bound on the number of elements in this queue.
+ Negative numbers mean no limit.
+
+
+ Optional argument
+ Dequeue will block unless there would be this
+ many elements after the dequeue or the queue is closed. This
+ ensures a minimum level of mixing of elements.
+
+
+ Optional argument
+ If either seed or seed2 is set to be non-zero, the random number
+ generator is seeded by the given seed. Otherwise, a random seed is used.
+
+
+ Optional argument
+ A second seed to avoid seed collision.
+
+
+ Optional argument
+ If non-empty, this queue is placed in the given container.
+ Otherwise, a default container is used.
+
+
+ Optional argument
+ If non-empty, this queue will be shared under the given name
+ across multiple sessions.
+
+
+ The type of each component in a value.
+
+
+ The handle to the queue.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ A queue that randomizes the order of elements.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RandomShuffleQueueV2'.
+
+
+ Optional argument
+ The shape of each component in a value. The length of this attr must
+ be either 0 or the same as the length of component_types. If the length of
+ this attr is 0, the shapes of queue elements are not constrained, and
+ only one element may be dequeued at a time.
+
+
+ Optional argument
+ The upper bound on the number of elements in this queue.
+ Negative numbers mean no limit.
+
+
+ Optional argument
+ Dequeue will block unless there would be this
+ many elements after the dequeue or the queue is closed. This
+ ensures a minimum level of mixing of elements.
+
+
+ Optional argument
+ If either seed or seed2 is set to be non-zero, the random number
+ generator is seeded by the given seed. Otherwise, a random seed is used.
+
+
+ Optional argument
+ A second seed to avoid seed collision.
+
+
+ Optional argument
+ If non-empty, this queue is placed in the given container.
+ Otherwise, a default container is used.
+
+
+ Optional argument
+ If non-empty, this queue will be shared under the given name
+ across multiple sessions.
+
+
+ The type of each component in a value.
+
+
+ The handle to the queue.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Outputs random values from a normal distribution.
+
+
+ The shape of the output tensor.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RandomStandardNormal'.
+
+
+ Optional argument
+ If either seed or seed2 are set to be non-zero, the random number
+ generator is seeded by the given seed. Otherwise, it is seeded by a
+ random seed.
+
+
+ Optional argument
+ A second seed to avoid seed collision.
+
+
+ The type of the output.
+
+
+ A tensor of the specified shape filled with random normal values.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The generated values will have mean 0 and standard deviation 1.
+
+
+
+
+ Outputs random values from a uniform distribution.
+
+
+ The shape of the output tensor.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RandomUniform'.
+
+
+ Optional argument
+ If either seed or seed2 are set to be non-zero, the random number
+ generator is seeded by the given seed. Otherwise, it is seeded by a
+ random seed.
+
+
+ Optional argument
+ A second seed to avoid seed collision.
+
+
+ The type of the output.
+
+
+ A tensor of the specified shape filled with uniform random values.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The generated values follow a uniform distribution in the range [0, 1). The
+ lower bound 0 is included in the range, while the upper bound 1 is excluded.
+
+
+
+
+ Outputs random integers from a uniform distribution.
+
+
+ The shape of the output tensor.
+
+
+ 0-D. Inclusive lower bound on the generated integers.
+
+
+ 0-D. Exclusive upper bound on the generated integers.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RandomUniformInt'.
+
+
+ Optional argument
+ If either seed or seed2 are set to be non-zero, the random number
+ generator is seeded by the given seed. Otherwise, it is seeded by a
+ random seed.
+
+
+ Optional argument
+ A second seed to avoid seed collision.
+
+
+ A tensor of the specified shape filled with uniform random integers.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The generated values are uniform integers in the range [minval, maxval).
+ The lower bound minval is included in the range, while the upper bound
+ maxval is excluded.
+
+ The random integers are slightly biased unless maxval - minval is an exact
+ power of two. The bias is small for values of maxval - minval significantly
+ smaller than the range of the output (either 2^32 or 2^64).
+
+
+
+
+ Creates a sequence of numbers.
+
+
+ 0-D (scalar). First entry in the sequence.
+
+
+ 0-D (scalar). Upper limit of sequence, exclusive.
+
+
+ 0-D (scalar). Optional. Default is 1. Number that increments start.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Range'.
+
+
+ 1-D.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation creates a sequence of numbers that begins at start and
+ extends by increments of delta up to but not including limit.
+
+ For example:
+
+
+ # 'start' is 3
+ # 'limit' is 18
+ # 'delta' is 3
+ tf.range(start, limit, delta) ==> [3, 6, 9, 12, 15]
+
+
+
+
+
+ Creates a dataset with a range of values. Corresponds to python's xrange.
+
+
+ corresponds to start in python's xrange().
+
+
+ corresponds to stop in python's xrange().
+
+
+ corresponds to step in python's xrange().
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RangeDataset'.
+
+
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Returns the rank of a tensor.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Rank'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation returns an integer representing the rank of input.
+
+ For example:
+
+
+ # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]
+ # shape of tensor 't' is [2, 2, 3]
+ rank(t) ==> 3
+
+
+ **Note**: The rank of a tensor is not the same as the rank of a matrix. The rank
+ of a tensor is the number of indices required to uniquely select each element
+ of the tensor. Rank is also known as "order", "degree", or "ndims."
+
+
+
+
+ Returns the number of records this Reader has produced.
+
+
+ Handle to a Reader.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ReaderNumRecordsProduced'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This is the same as the number of ReaderRead executions that have
+ succeeded.
+
+
+
+
+ Returns the number of records this Reader has produced.
+
+
+ Handle to a Reader.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ReaderNumRecordsProducedV2'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This is the same as the number of ReaderRead executions that have
+ succeeded.
+
+
+
+
+ Returns the number of work units this Reader has finished processing.
+
+
+ Handle to a Reader.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ReaderNumWorkUnitsCompleted'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Returns the number of work units this Reader has finished processing.
+
+
+ Handle to a Reader.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ReaderNumWorkUnitsCompletedV2'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Returns the next record (key, value pair) produced by a Reader.
+
+
+ Handle to a Reader.
+
+
+ Handle to a Queue, with string work items.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ReaderRead'.
+
+
+ Returns a tuple with multiple values, as follows:
+ key: A scalar.
+ value: A scalar.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Will dequeue from the input queue if necessary (e.g. when the
+ Reader needs to start reading from a new file since it has finished
+ with the previous file).
+
+
+
+
+ Returns up to num_records (key, value) pairs produced by a Reader.
+
+
+ Handle to a Reader.
+
+
+ Handle to a Queue, with string work items.
+
+
+ number of records to read from Reader.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ReaderReadUpTo'.
+
+
+ Returns a tuple with multiple values, as follows:
+ keys: A 1-D tensor.
+ values: A 1-D tensor.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Will dequeue from the input queue if necessary (e.g. when the
+ Reader needs to start reading from a new file since it has finished
+ with the previous file).
+ It may return less than num_records even before the last batch.
+
+
+
+
+ Returns up to num_records (key, value) pairs produced by a Reader.
+
+
+ Handle to a Reader.
+
+
+ Handle to a Queue, with string work items.
+
+
+ number of records to read from Reader.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ReaderReadUpToV2'.
+
+
+ Returns a tuple with multiple values, as follows:
+ keys: A 1-D tensor.
+ values: A 1-D tensor.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Will dequeue from the input queue if necessary (e.g. when the
+ Reader needs to start reading from a new file since it has finished
+ with the previous file).
+ It may return less than num_records even before the last batch.
+
+
+
+
+ Returns the next record (key, value pair) produced by a Reader.
+
+
+ Handle to a Reader.
+
+
+ Handle to a Queue, with string work items.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ReaderReadV2'.
+
+
+ Returns a tuple with multiple values, as follows:
+ key: A scalar.
+ value: A scalar.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Will dequeue from the input queue if necessary (e.g. when the
+ Reader needs to start reading from a new file since it has finished
+ with the previous file).
+
+
+
+
+ Restore a Reader to its initial clean state.
+
+
+ Handle to a Reader.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ReaderReset'.
+
+
+ Returns the description of the operation
+
+
+
+
+ Restore a Reader to its initial clean state.
+
+
+ Handle to a Reader.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ReaderResetV2'.
+
+
+ Returns the description of the operation
+
+
+
+
+ Restore a reader to a previously saved state.
+
+
+ Handle to a Reader.
+
+
+ Result of a ReaderSerializeState of a Reader with type
+ matching reader_handle.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ReaderRestoreState'.
+
+
+ Returns the description of the operation
+
+
+ Not all Readers support being restored, so this can produce an
+ Unimplemented error.
+
+
+
+
+ Restore a reader to a previously saved state.
+
+
+ Handle to a Reader.
+
+
+ Result of a ReaderSerializeState of a Reader with type
+ matching reader_handle.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ReaderRestoreStateV2'.
+
+
+ Returns the description of the operation
+
+
+ Not all Readers support being restored, so this can produce an
+ Unimplemented error.
+
+
+
+
+ Produce a string tensor that encodes the state of a Reader.
+
+
+ Handle to a Reader.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ReaderSerializeState'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Not all Readers support being serialized, so this can produce an
+ Unimplemented error.
+
+
+
+
+ Produce a string tensor that encodes the state of a Reader.
+
+
+ Handle to a Reader.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ReaderSerializeStateV2'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Not all Readers support being serialized, so this can produce an
+ Unimplemented error.
+
+
+
+
+ Reads and outputs the entire contents of the input filename.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ReadFile'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Reads the value of a variable.
+
+
+ handle to the resource in which to store the variable.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ReadVariableOp'.
+
+
+ the dtype of the value.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The tensor returned by this operation is immutable.
+
+ The value returned by this operation is guaranteed to be influenced by all the
+ writes on which this operation depends directly or indirectly, and to not be
+ influenced by any of the writes which depend directly or indirectly on this
+ operation.
+
+
+
+
+ Returns the real part of a complex number.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Real'.
+
+
+ Optional argument
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Given a tensor input of complex numbers, this operation returns a tensor of
+ type float that is the real part of each element in input. All elements in
+ input must be complex numbers of the form \\(a + bj\\), where *a* is the real
+ part returned by this operation and *b* is the imaginary part.
+
+ For example:
+
+
+ # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]
+ tf.real(input) ==> [-2.25, 3.25]
+
+
+
+
+
+ Returns x / y element-wise for real types.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RealDiv'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ If x and y are reals, this will return the floating-point division.
+
+ *NOTE*: Div supports broadcasting. More about broadcasting
+ [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+
+
+
+
+ Computes the reciprocal of x element-wise.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Reciprocal'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ I.e., \\(y = 1 / x\\).
+
+
+
+
+ Computes the gradient for the inverse of x wrt its input.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ReciprocalGrad'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Specifically, grad = -dy * y*y, where y = 1/x, and dy
+ is the corresponding input gradient.
+
+
+
+
+ Emits randomized records.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RecordInput'.
+
+
+ Optional argument
+ Random seeds used to produce randomized records.
+
+
+ Optional argument
+ Shifts the list of files after the list is randomly
+ shuffled.
+
+
+ Optional argument
+ The randomization shuffling buffer.
+
+
+ Optional argument
+ How many sstables are opened and concurrently iterated over.
+
+
+ Optional argument
+ The batch size.
+
+
+ Optional argument
+ The type of compression for the file. Currently ZLIB and
+ GZIP are supported. Defaults to none.
+
+
+ Glob pattern for the data files.
+
+
+ A tensor of shape [batch_size].
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ An op that receives embedding activations on the TPU.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RecvTPUEmbeddingActivations'.
+
+
+ The number of output activation tensors, equal to the number of
+ embedding tables in the model.
+
+
+ Serialized TPUEmbeddingConfiguration proto.
+
+
+ A TensorList of embedding activations containing one Tensor per
+ embedding table in the model.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The TPU system performs the embedding lookups and aggregations specified by
+ the arguments to TPUEmbeddingEnqueue(Integer/Sparse/SparseTensor)Batch. The
+ results of these aggregations are visible to the Tensorflow Graph as the
+ outputs of a RecvTPUEmbeddingActivations op. This op returns a list containing
+ one Tensor of activations per table specified in the model. There can be at
+ most one RecvTPUEmbeddingActivations op in the TPU graph.
+
+
+
+
+ Joins a string Tensor across the given dimensions.
+
+
+ The input to be joined. All reduced indices must have non-zero size.
+
+
+ The dimensions to reduce over. Dimensions are reduced in the
+ order specified. Omitting reduction_indices is equivalent to passing
+ [n-1, n-2, ..., 0]. Negative indices from -n to -1 are supported.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ReduceJoin'.
+
+
+ Optional argument
+ If True, retain reduced dimensions with length 1.
+
+
+ Optional argument
+ The separator to use when joining.
+
+
+ Has shape equal to that of the input with reduced dimensions removed or
+ set to 1 depending on keep_dims.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Computes the string join across dimensions in the given string Tensor of shape
+ [\\(d_0, d_1, ..., d_{n-1}\\)]. Returns a new Tensor created by joining the input
+ strings with the given separator (default: empty string). Negative indices are
+ counted backwards from the end, with -1 being equivalent to n - 1. If
+ indices are not specified, joins across all dimensions beginning from n - 1
+ through 0.
+
+ For example:
+
+
+ # tensor a is [["a", "b"], ["c", "d"]]
+ tf.reduce_join(a, 0) ==> ["ac", "bd"]
+ tf.reduce_join(a, 1) ==> ["ab", "cd"]
+ tf.reduce_join(a, -2) = tf.reduce_join(a, 0) ==> ["ac", "bd"]
+ tf.reduce_join(a, -1) = tf.reduce_join(a, 1) ==> ["ab", "cd"]
+ tf.reduce_join(a, 0, keep_dims=True) ==> [["ac", "bd"]]
+ tf.reduce_join(a, 1, keep_dims=True) ==> [["ab"], ["cd"]]
+ tf.reduce_join(a, 0, separator=".") ==> ["a.c", "b.d"]
+ tf.reduce_join(a, [0, 1]) ==> "acbd"
+ tf.reduce_join(a, [1, 0]) ==> "abcd"
+ tf.reduce_join(a, []) ==> [["a", "b"], ["c", "d"]]
+ tf.reduce_join(a) = tf.reduce_join(a, [1, 0]) ==> "abcd"
+
+
+
+
+
+ Creates or finds a child frame, and makes data available to the child frame.
+
+
+ The tensor to be made available to the child frame.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RefEnter'.
+
+
+ Optional argument
+ If true, the output is constant within the child frame.
+
+
+ Optional argument
+ The number of iterations allowed to run in parallel.
+
+
+ The name of the child frame.
+
+
+ The same tensor as data.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The unique frame_name is used by the Executor to identify frames. If
+ is_constant is true, output is a constant in the child frame; otherwise
+ it may be changed in the child frame. At most parallel_iterations iterations
+ are run in parallel in the child frame.
+
+
+
+
+ Exits the current frame to its parent frame.
+
+
+ The tensor to be made available to the parent frame.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RefExit'.
+
+
+ The same tensor as data.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Exit makes its input data available to the parent frame.
+
+
+
+
+ Return the same ref tensor as the input ref tensor.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RefIdentity'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Forwards the value of an available tensor from inputs to output.
+
+
+ The input tensors, exactly one of which will become available.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RefMerge'.
+
+
+ Returns a tuple with multiple values, as follows:
+ output: Will be set to the available input tensor.
+ value_index: The index of the chosen input tensor in inputs.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Merge waits for at least one of the tensors in inputs to become available.
+ It is usually combined with Switch to implement branching.
+
+ Merge forwards the first tensor for become available to output, and sets
+ value_index to its index in inputs.
+
+
+
+
+ Makes its input available to the next iteration.
+
+
+ The tensor to be made available to the next iteration.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RefNextIteration'.
+
+
+ The same tensor as data.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Forwards the indexth element of inputs to output.
+
+
+ A scalar that determines the input that gets selected.
+
+
+ A list of ref tensors, one of which will be forwarded to output.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RefSelect'.
+
+
+ The forwarded tensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Forwards the ref tensor data to the output port determined by pred.
+
+
+ The ref tensor to be forwarded to the appropriate output.
+
+
+ A scalar that specifies which output port will receive data.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RefSwitch'.
+
+
+ Returns a tuple with multiple values, as follows:
+ output_false: If pred is false, data will be forwarded to this output.
+ output_true: If pred is true, data will be forwarded to this output.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ If pred is true, the data input is forwarded to output_true. Otherwise,
+ the data goes to output_false.
+
+ See also Switch and Merge.
+
+
+
+
+ Check if the input matches the regex pattern.
+
+
+ A string tensor of the text to be processed.
+
+
+ A scalar string tensor containing the regular expression to match the input.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RegexFullMatch'.
+
+
+ A bool tensor with the same shape as input.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The input is a string tensor of any shape. The pattern is a scalar
+ string tensor which is applied to every element of the input tensor.
+ The boolean values (True or False) of the output tensor indicate
+ if the input matches the regex pattern provided.
+
+ The pattern follows the re2 syntax (https://github.com/google/re2/wiki/Syntax)
+
+
+
+
+ Replaces the match of pattern in input with rewrite.
+
+
+ The text to be processed.
+
+
+ The regular expression to match the input.
+
+
+ The rewrite to be applied to the matched expresion.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RegexReplace'.
+
+
+ Optional argument
+ If True, the replacement is global, otherwise the replacement
+ is done only on the first match.
+
+
+ The text after applying pattern and rewrite.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ It follows the re2 syntax (https://github.com/google/re2/wiki/Syntax)
+
+
+
+
+ Computes rectified linear: max(features, 0).
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Relu'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes rectified linear 6: min(max(features, 0), 6).
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Relu6'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes rectified linear 6 gradients for a Relu6 operation.
+
+
+ The backpropagated gradients to the corresponding Relu6 operation.
+
+
+ The features passed as input to the corresponding Relu6 operation, or
+ its output; using either one produces the same result.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Relu6Grad'.
+
+
+ The gradients:
+ gradients * (features > 0) * (features < 6).
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes rectified linear gradients for a Relu operation.
+
+
+ The backpropagated gradients to the corresponding Relu operation.
+
+
+ The features passed as input to the corresponding Relu operation, OR
+ the outputs of that operation (both work equivalently).
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ReluGrad'.
+
+
+ gradients * (features > 0).
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Execute a sub graph on a remote processor.
+
+
+ Arbitrary number of tensors with arbitrary data types
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RemoteFusedGraphExecute'.
+
+
+
+
+ Serialized protocol buffer
+ of RemoteFusedGraphExecuteInfo which contains graph specifications.
+
+
+ Arbitrary number of tensors with arbitrary data types
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The graph specifications(such as graph itself, input tensors and output names)
+ are stored as a serialized protocol buffer of RemoteFusedGraphExecuteInfo
+ as serialized_remote_fused_graph_execute_info.
+ The specifications will be passed to a dedicated registered
+ remote fused graph executor. The executor will send the graph specifications
+ to a remote processor and execute that graph. The execution results
+ will be passed to consumer nodes as outputs of this node.
+
+
+
+
+ Creates a dataset that emits the outputs of input_dataset count times.
+
+
+
+
+ A scalar representing the number of times that input_dataset should
+ be repeated. A value of -1 indicates that it should be repeated infinitely.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RepeatDataset'.
+
+
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Given a quantized tensor described by (input, input_min, input_max), outputs a
+
+
+
+
+ The float value that the minimum quantized input value represents.
+
+
+ The float value that the maximum quantized input value represents.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RequantizationRange'.
+
+
+ Returns a tuple with multiple values, as follows:
+ output_min: The computed min output.
+ output_max: the computed max output.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ range that covers the actual values present in that tensor. This op is
+ typically used to produce the requested_output_min and requested_output_max for
+ Requantize.
+
+
+
+
+ Convert the quantized 'input' tensor into a lower-precision 'output', using the
+
+
+
+
+ The float value that the minimum quantized input value represents.
+
+
+ The float value that the maximum quantized input value represents.
+
+
+ The float value that the minimum quantized output value represents.
+
+
+ The float value that the maximum quantized output value represents.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Requantize'.
+
+
+ The type of the output. Should be a lower bit depth than Tinput.
+
+
+ Returns a tuple with multiple values, as follows:
+ output:
+ output_min: The requested_output_min value is copied into this output.
+ output_max: The requested_output_max value is copied into this output.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ output range specified with 'requested_output_min' and 'requested_output_max'.
+
+ [input_min, input_max] are scalar floats that specify the range for the float
+ interpretation of the 'input' data. For example, if input_min is -1.0f and
+ input_max is 1.0f, and we are dealing with quint16 quantized data, then a 0
+ value in the 16-bit data should be interpreted as -1.0f, and a 65535 means 1.0f.
+
+
+
+
+ Reshapes a tensor.
+
+
+
+
+ Defines the shape of the output tensor.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Reshape'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Given tensor, this operation returns a tensor that has the same values
+ as tensor with shape shape.
+
+ If one component of shape is the special value -1, the size of that dimension
+ is computed so that the total size remains constant. In particular, a shape
+ of [-1] flattens into 1-D. At most one component of shape can be -1.
+
+ If shape is 1-D or higher, then the operation returns a tensor with shape
+ shape filled with the values of tensor. In this case, the number of elements
+ implied by shape must be the same as the number of elements in tensor.
+
+ For example:
+
+
+ # tensor 't' is [1, 2, 3, 4, 5, 6, 7, 8, 9]
+ # tensor 't' has shape [9]
+ reshape(t, [3, 3]) ==> [[1, 2, 3],
+ [4, 5, 6],
+ [7, 8, 9]]
+
+ # tensor 't' is [[[1, 1], [2, 2]],
+ # [[3, 3], [4, 4]]]
+ # tensor 't' has shape [2, 2, 2]
+ reshape(t, [2, 4]) ==> [[1, 1, 2, 2],
+ [3, 3, 4, 4]]
+
+ # tensor 't' is [[[1, 1, 1],
+ # [2, 2, 2]],
+ # [[3, 3, 3],
+ # [4, 4, 4]],
+ # [[5, 5, 5],
+ # [6, 6, 6]]]
+ # tensor 't' has shape [3, 2, 3]
+ # pass '[-1]' to flatten 't'
+ reshape(t, [-1]) ==> [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6]
+
+ # -1 can also be used to infer the shape
+
+ # -1 is inferred to be 9:
+ reshape(t, [2, -1]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],
+ [4, 4, 4, 5, 5, 5, 6, 6, 6]]
+ # -1 is inferred to be 2:
+ reshape(t, [-1, 9]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],
+ [4, 4, 4, 5, 5, 5, 6, 6, 6]]
+ # -1 is inferred to be 3:
+ reshape(t, [ 2, -1, 3]) ==> [[[1, 1, 1],
+ [2, 2, 2],
+ [3, 3, 3]],
+ [[4, 4, 4],
+ [5, 5, 5],
+ [6, 6, 6]]]
+
+ # tensor 't' is [7]
+ # shape [] reshapes to a scalar
+ reshape(t, []) ==> 7
+
+
+
+
+
+ Resize images to size using area interpolation.
+
+
+ 4-D with shape [batch, height, width, channels].
+
+
+ = A 1-D int32 Tensor of 2 elements: new_height, new_width. The
+ new size for the images.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResizeArea'.
+
+
+ Optional argument
+ If true, the centers of the 4 corner pixels of the input and output tensors are
+ aligned, preserving the values at the corner pixels. Defaults to false.
+
+
+ 4-D with shape
+ [batch, new_height, new_width, channels].
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Input images can be of different types but output images are always float.
+
+ The range of pixel values for the output image might be slightly different
+ from the range for the input image because of limited numerical precision.
+ To guarantee an output range, for example [0.0, 1.0], apply
+ tf.clip_by_value to the output.
+
+ Each output pixel is computed by first transforming the pixel's footprint into
+ the input tensor and then averaging the pixels that intersect the footprint. An
+ input pixel's contribution to the average is weighted by the fraction of its
+ area that intersects the footprint. This is the same as OpenCV's INTER_AREA.
+
+
+
+
+ Resize images to size using bicubic interpolation.
+
+
+ 4-D with shape [batch, height, width, channels].
+
+
+ = A 1-D int32 Tensor of 2 elements: new_height, new_width. The
+ new size for the images.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResizeBicubic'.
+
+
+ Optional argument
+ If true, the centers of the 4 corner pixels of the input and output tensors are
+ aligned, preserving the values at the corner pixels. Defaults to false.
+
+
+ 4-D with shape
+ [batch, new_height, new_width, channels].
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Input images can be of different types but output images are always float.
+
+
+
+
+ Computes the gradient of bicubic interpolation.
+
+
+ 4-D with shape [batch, height, width, channels].
+
+
+ 4-D with shape [batch, orig_height, orig_width, channels],
+ The image tensor that was resized.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResizeBicubicGrad'.
+
+
+ Optional argument
+ If true, the centers of the 4 corner pixels of the input and grad tensors are
+ aligned. Defaults to false.
+
+
+ 4-D with shape [batch, orig_height, orig_width, channels].
+ Gradients with respect to the input image. Input image must have been
+ float or double.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Resize images to size using bilinear interpolation.
+
+
+ 4-D with shape [batch, height, width, channels].
+
+
+ = A 1-D int32 Tensor of 2 elements: new_height, new_width. The
+ new size for the images.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResizeBilinear'.
+
+
+ Optional argument
+ If true, the centers of the 4 corner pixels of the input and output tensors are
+ aligned, preserving the values at the corner pixels. Defaults to false.
+
+
+ 4-D with shape
+ [batch, new_height, new_width, channels].
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Input images can be of different types but output images are always float.
+
+
+
+
+ Computes the gradient of bilinear interpolation.
+
+
+ 4-D with shape [batch, height, width, channels].
+
+
+ 4-D with shape [batch, orig_height, orig_width, channels],
+ The image tensor that was resized.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResizeBilinearGrad'.
+
+
+ Optional argument
+ If true, the centers of the 4 corner pixels of the input and grad tensors are
+ aligned. Defaults to false.
+
+
+ 4-D with shape [batch, orig_height, orig_width, channels].
+ Gradients with respect to the input image. Input image must have been
+ float or double.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Resize images to size using nearest neighbor interpolation.
+
+
+ 4-D with shape [batch, height, width, channels].
+
+
+ = A 1-D int32 Tensor of 2 elements: new_height, new_width. The
+ new size for the images.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResizeNearestNeighbor'.
+
+
+ Optional argument
+ If true, the centers of the 4 corner pixels of the input and output tensors are
+ aligned, preserving the values at the corner pixels. Defaults to false.
+
+
+ 4-D with shape
+ [batch, new_height, new_width, channels].
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes the gradient of nearest neighbor interpolation.
+
+
+ 4-D with shape [batch, height, width, channels].
+
+
+ = A 1-D int32 Tensor of 2 elements: orig_height, orig_width. The
+ original input size.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResizeNearestNeighborGrad'.
+
+
+ Optional argument
+ If true, the centers of the 4 corner pixels of the input and grad tensors are
+ aligned. Defaults to false.
+
+
+ 4-D with shape [batch, orig_height, orig_width, channels]. Gradients
+ with respect to the input image.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Update '*var' according to the adadelta scheme.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Scaling factor. Must be a scalar.
+
+
+ Decay factor. Must be a scalar.
+
+
+ Constant factor. Must be a scalar.
+
+
+ The gradient.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResourceApplyAdadelta'.
+
+
+ Optional argument
+ If True, updating of the var, accum and update_accum tensors will be protected by
+ a lock; otherwise the behavior is undefined, but may exhibit less contention.
+
+
+ Returns the description of the operation
+
+
+ accum = rho() * accum + (1 - rho()) * grad.square();
+ update = (update_accum + epsilon).sqrt() * (accum + epsilon()).rsqrt() * grad;
+ update_accum = rho() * update_accum + (1 - rho()) * update.square();
+ var -= update;
+
+
+
+
+ Update '*var' according to the adagrad scheme.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Scaling factor. Must be a scalar.
+
+
+ The gradient.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResourceApplyAdagrad'.
+
+
+ Optional argument
+ If True, updating of the var and accum tensors will be protected
+ by a lock; otherwise the behavior is undefined, but may exhibit less
+ contention.
+
+
+ Optional argument
+
+
+ Returns the description of the operation
+
+
+ accum += grad * grad
+ var -= lr * grad * (1 / sqrt(accum))
+
+
+
+
+ Update '*var' according to the proximal adagrad scheme.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ The gradient.
+
+
+ Scaling factor. Must be a scalar.
+
+
+ L1 regularization. Must be a scalar.
+
+
+ L2 regularization. Must be a scalar.
+
+
+ Training step number. Must be a scalar.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResourceApplyAdagradDA'.
+
+
+ Optional argument
+ If True, updating of the var and accum tensors will be protected by
+ a lock; otherwise the behavior is undefined, but may exhibit less contention.
+
+
+ Returns the description of the operation
+
+
+
+
+ Update '*var' according to the Adam algorithm.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Must be a scalar.
+
+
+ Must be a scalar.
+
+
+ Scaling factor. Must be a scalar.
+
+
+ Momentum factor. Must be a scalar.
+
+
+ Momentum factor. Must be a scalar.
+
+
+ Ridge term. Must be a scalar.
+
+
+ The gradient.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResourceApplyAdam'.
+
+
+ Optional argument
+ If True, updating of the var, m, and v tensors will be protected
+ by a lock; otherwise the behavior is undefined, but may exhibit less
+ contention.
+
+
+ Optional argument
+ If True, uses the nesterov update.
+
+
+ Returns the description of the operation
+
+
+ $$lr_t := \text{learning\_rate} * \sqrt{1 - beta_2^t} / (1 - beta_1^t)$$
+ $$m_t := beta_1 * m_{t-1} + (1 - beta_1) * g$$
+ $$v_t := beta_2 * v_{t-1} + (1 - beta_2) * g * g$$
+ $$variable := variable - lr_t * m_t / (\sqrt{v_t} + \epsilon)$$
+
+
+
+
+ Update '*var' according to the AdaMax algorithm.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Must be a scalar.
+
+
+ Scaling factor. Must be a scalar.
+
+
+ Momentum factor. Must be a scalar.
+
+
+ Momentum factor. Must be a scalar.
+
+
+ Ridge term. Must be a scalar.
+
+
+ The gradient.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResourceApplyAdaMax'.
+
+
+ Optional argument
+ If True, updating of the var, m, and v tensors will be protected
+ by a lock; otherwise the behavior is undefined, but may exhibit less
+ contention.
+
+
+ Returns the description of the operation
+
+
+ m_t <- beta1 * m_{t-1} + (1 - beta1) * g
+ v_t <- max(beta2 * v_{t-1}, abs(g))
+ variable <- variable - learning_rate / (1 - beta1^t) * m_t / (v_t + epsilon)
+
+
+
+
+ Update '*var' according to the AddSign update.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Scaling factor. Must be a scalar.
+
+
+ Must be a scalar.
+
+
+ Must be a scalar.
+
+
+ Must be a scalar.
+
+
+ The gradient.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResourceApplyAddSign'.
+
+
+ Optional argument
+ If True, updating of the var and m tensors is
+ protected by a lock; otherwise the behavior is undefined, but may exhibit less
+ contention.
+
+
+ Returns the description of the operation
+
+
+ m_t <- beta1 * m_{t-1} + (1 - beta1) * g
+ update <- (alpha + sign_decay * sign(g) *sign(m)) * g
+ variable <- variable - lr_t * update
+
+
+
+
+ Update '*var' according to the centered RMSProp algorithm.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Scaling factor. Must be a scalar.
+
+
+ Decay rate. Must be a scalar.
+
+
+
+
+ Ridge term. Must be a scalar.
+
+
+ The gradient.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResourceApplyCenteredRMSProp'.
+
+
+ Optional argument
+ If True, updating of the var, mg, ms, and mom tensors is
+ protected by a lock; otherwise the behavior is undefined, but may exhibit less
+ contention.
+
+
+ Returns the description of the operation
+
+
+ The centered RMSProp algorithm uses an estimate of the centered second moment
+ (i.e., the variance) for normalization, as opposed to regular RMSProp, which
+ uses the (uncentered) second moment. This often helps with training, but is
+ slightly more expensive in terms of computation and memory.
+
+ Note that in dense implementation of this algorithm, mg, ms, and mom will
+ update even if the grad is zero, but in this sparse implementation, mg, ms,
+ and mom will not update in iterations during which the grad is zero.
+
+ mean_square = decay * mean_square + (1-decay) * gradient ** 2
+ mean_grad = decay * mean_grad + (1-decay) * gradient
+
+ Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2)
+
+ mg <- rho * mg_{t-1} + (1-rho) * grad
+ ms <- rho * ms_{t-1} + (1-rho) * grad * grad
+ mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms - mg * mg + epsilon)
+ var <- var - mom
+
+
+
+
+ Update '*var' according to the Ftrl-proximal scheme.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ The gradient.
+
+
+ Scaling factor. Must be a scalar.
+
+
+ L1 regulariation. Must be a scalar.
+
+
+ L2 regulariation. Must be a scalar.
+
+
+ Scaling factor. Must be a scalar.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResourceApplyFtrl'.
+
+
+ Optional argument
+ If True, updating of the var and accum tensors will be protected
+ by a lock; otherwise the behavior is undefined, but may exhibit less
+ contention.
+
+
+ Returns the description of the operation
+
+
+ accum_new = accum + grad * grad
+ linear += grad - (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var
+ quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2
+ var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0
+ accum = accum_new
+
+
+
+
+ Update '*var' according to the Ftrl-proximal scheme.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ The gradient.
+
+
+ Scaling factor. Must be a scalar.
+
+
+ L1 regulariation. Must be a scalar.
+
+
+ L2 shrinkage regulariation. Must be a scalar.
+
+
+
+
+ Scaling factor. Must be a scalar.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResourceApplyFtrlV2'.
+
+
+ Optional argument
+ If True, updating of the var and accum tensors will be protected
+ by a lock; otherwise the behavior is undefined, but may exhibit less
+ contention.
+
+
+ Returns the description of the operation
+
+
+ grad_with_shrinkage = grad + 2 * l2_shrinkage * var
+ accum_new = accum + grad_with_shrinkage * grad_with_shrinkage
+ linear += grad_with_shrinkage +
+ (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var
+ quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2
+ var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0
+ accum = accum_new
+
+
+
+
+ Update '*var' by subtracting 'alpha' * 'delta' from it.
+
+
+ Should be from a Variable().
+
+
+ Scaling factor. Must be a scalar.
+
+
+ The change.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResourceApplyGradientDescent'.
+
+
+ Optional argument
+ If True, the subtraction will be protected by a lock;
+ otherwise the behavior is undefined, but may exhibit less contention.
+
+
+ Returns the description of the operation
+
+
+
+
+ Update '*var' according to the momentum scheme. Set use_nesterov = True if you
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Scaling factor. Must be a scalar.
+
+
+ The gradient.
+
+
+ Momentum. Must be a scalar.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResourceApplyMomentum'.
+
+
+ Optional argument
+ If True, updating of the var and accum tensors will be protected
+ by a lock; otherwise the behavior is undefined, but may exhibit less
+ contention.
+
+
+ Optional argument
+ If True, the tensor passed to compute grad will be
+ var - lr * momentum * accum, so in the end, the var you get is actually
+ var - lr * momentum * accum.
+
+
+ Returns the description of the operation
+
+
+ want to use Nesterov momentum.
+
+ accum = accum * momentum + grad
+ var -= lr * accum
+
+
+
+
+ Update '*var' according to the AddSign update.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Scaling factor. Must be a scalar.
+
+
+ Must be a scalar.
+
+
+ Must be a scalar.
+
+
+ Must be a scalar.
+
+
+ The gradient.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResourceApplyPowerSign'.
+
+
+ Optional argument
+ If True, updating of the var and m tensors is
+ protected by a lock; otherwise the behavior is undefined, but may exhibit less
+ contention.
+
+
+ Returns the description of the operation
+
+
+ m_t <- beta1 * m_{t-1} + (1 - beta1) * g
+ update <- exp(logbase * sign_decay * sign(g) * sign(m_t)) * g
+ variable <- variable - lr_t * update
+
+
+
+
+ Update '*var' and '*accum' according to FOBOS with Adagrad learning rate.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Scaling factor. Must be a scalar.
+
+
+ L1 regularization. Must be a scalar.
+
+
+ L2 regularization. Must be a scalar.
+
+
+ The gradient.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResourceApplyProximalAdagrad'.
+
+
+ Optional argument
+ If True, updating of the var and accum tensors will be protected by
+ a lock; otherwise the behavior is undefined, but may exhibit less contention.
+
+
+ Returns the description of the operation
+
+
+ accum += grad * grad
+ prox_v = var - lr * grad * (1 / sqrt(accum))
+ var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0}
+
+
+
+
+ Update '*var' as FOBOS algorithm with fixed learning rate.
+
+
+ Should be from a Variable().
+
+
+ Scaling factor. Must be a scalar.
+
+
+ L1 regularization. Must be a scalar.
+
+
+ L2 regularization. Must be a scalar.
+
+
+ The change.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResourceApplyProximalGradientDescent'.
+
+
+ Optional argument
+ If True, the subtraction will be protected by a lock;
+ otherwise the behavior is undefined, but may exhibit less contention.
+
+
+ Returns the description of the operation
+
+
+ prox_v = var - alpha * delta
+ var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0}
+
+
+
+
+ Update '*var' according to the RMSProp algorithm.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Scaling factor. Must be a scalar.
+
+
+ Decay rate. Must be a scalar.
+
+
+
+
+ Ridge term. Must be a scalar.
+
+
+ The gradient.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResourceApplyRMSProp'.
+
+
+ Optional argument
+ If True, updating of the var, ms, and mom tensors is protected
+ by a lock; otherwise the behavior is undefined, but may exhibit less
+ contention.
+
+
+ Returns the description of the operation
+
+
+ Note that in dense implementation of this algorithm, ms and mom will
+ update even if the grad is zero, but in this sparse implementation, ms
+ and mom will not update in iterations during which the grad is zero.
+
+ mean_square = decay * mean_square + (1-decay) * gradient ** 2
+ Delta = learning_rate * gradient / sqrt(mean_square + epsilon)
+
+ ms <- rho * ms_{t-1} + (1-rho) * grad * grad
+ mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon)
+ var <- var - mom
+
+
+
+
+ Increments variable pointed to by 'resource' until it reaches 'limit'.
+
+
+ Should be from a scalar Variable node.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResourceCountUpTo'.
+
+
+ If incrementing ref would bring it above limit, instead generates an
+ 'OutOfRange' error.
+
+
+
+
+ A copy of the input before increment. If nothing else modifies the
+ input, the values produced will all be distinct.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Gather slices from the variable pointed to by resource according to indices.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResourceGather'.
+
+
+ Optional argument
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ indices must be an integer tensor of any dimension (usually 0-D or 1-D).
+ Produces an output tensor with shape indices.shape + params.shape[1:] where:
+
+
+ # Scalar indices
+ output[:, ..., :] = params[indices, :, ... :]
+
+ # Vector indices
+ output[i, :, ..., :] = params[indices[i], :, ... :]
+
+ # Higher rank indices
+ output[i, ..., j, :, ... :] = params[indices[i, ..., j], :, ..., :]
+
+
+
+
+
+ Adds sparse updates to the variable referenced by resource.
+
+
+ Should be from a Variable node.
+
+
+ A tensor of indices into the first dimension of ref.
+
+
+ A tensor of updated values to add to ref.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResourceScatterAdd'.
+
+
+ Returns the description of the operation
+
+
+ This operation computes
+
+ # Scalar indices
+ ref[indices, ...] += updates[...]
+
+ # Vector indices (for each i)
+ ref[indices[i], ...] += updates[i, ...]
+
+ # High rank indices (for each i, ..., j)
+ ref[indices[i, ..., j], ...] += updates[i, ..., j, ...]
+
+ Duplicate entries are handled correctly: if multiple indices reference
+ the same location, their contributions add.
+
+ Requires updates.shape = indices.shape + ref.shape[1:] or updates.shape = [].
+
+ <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+ <img style="width:100%" src='https://www.tensorflow.org/images/ScatterAdd.png' alt>
+ </div>
+
+
+
+
+ Divides sparse updates into the variable referenced by resource.
+
+
+ Should be from a Variable node.
+
+
+ A tensor of indices into the first dimension of ref.
+
+
+ A tensor of updated values to add to ref.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResourceScatterDiv'.
+
+
+ Returns the description of the operation
+
+
+ This operation computes
+
+ # Scalar indices
+ ref[indices, ...] /= updates[...]
+
+ # Vector indices (for each i)
+ ref[indices[i], ...] /= updates[i, ...]
+
+ # High rank indices (for each i, ..., j)
+ ref[indices[i, ..., j], ...] /= updates[i, ..., j, ...]
+
+ Duplicate entries are handled correctly: if multiple indices reference
+ the same location, their contributions multiply.
+
+ Requires updates.shape = indices.shape + ref.shape[1:] or updates.shape = [].
+
+ <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+ <img style="width:100%" src='https://www.tensorflow.org/images/ScatterAdd.png' alt>
+ </div>
+
+
+
+
+ Reduces sparse updates into the variable referenced by resource using the max operation.
+
+
+ Should be from a Variable node.
+
+
+ A tensor of indices into the first dimension of ref.
+
+
+ A tensor of updated values to add to ref.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResourceScatterMax'.
+
+
+ Returns the description of the operation
+
+
+ This operation computes
+
+ # Scalar indices
+ ref[indices, ...] = max(ref[indices, ...], updates[...])
+
+ # Vector indices (for each i)
+ ref[indices[i], ...] = max(ref[indices[i], ...], updates[i, ...])
+
+ # High rank indices (for each i, ..., j)
+ ref[indices[i, ..., j], ...] = max(ref[indices[i, ..., j], ...], updates[i, ..., j, ...])
+
+ Duplicate entries are handled correctly: if multiple indices reference
+ the same location, their contributions are combined.
+
+ Requires updates.shape = indices.shape + ref.shape[1:] or updates.shape = [].
+
+ <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+ <img style="width:100%" src='https://www.tensorflow.org/images/ScatterAdd.png' alt>
+ </div>
+
+
+
+
+ Reduces sparse updates into the variable referenced by resource using the min operation.
+
+
+ Should be from a Variable node.
+
+
+ A tensor of indices into the first dimension of ref.
+
+
+ A tensor of updated values to add to ref.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResourceScatterMin'.
+
+
+ Returns the description of the operation
+
+
+ This operation computes
+
+ # Scalar indices
+ ref[indices, ...] = min(ref[indices, ...], updates[...])
+
+ # Vector indices (for each i)
+ ref[indices[i], ...] = min(ref[indices[i], ...], updates[i, ...])
+
+ # High rank indices (for each i, ..., j)
+ ref[indices[i, ..., j], ...] = min(ref[indices[i, ..., j], ...], updates[i, ..., j, ...])
+
+ Duplicate entries are handled correctly: if multiple indices reference
+ the same location, their contributions are combined.
+
+ Requires updates.shape = indices.shape + ref.shape[1:] or updates.shape = [].
+
+ <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+ <img style="width:100%" src='https://www.tensorflow.org/images/ScatterAdd.png' alt>
+ </div>
+
+
+
+
+ Multiplies sparse updates into the variable referenced by resource.
+
+
+ Should be from a Variable node.
+
+
+ A tensor of indices into the first dimension of ref.
+
+
+ A tensor of updated values to add to ref.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResourceScatterMul'.
+
+
+ Returns the description of the operation
+
+
+ This operation computes
+
+ # Scalar indices
+ ref[indices, ...] *= updates[...]
+
+ # Vector indices (for each i)
+ ref[indices[i], ...] *= updates[i, ...]
+
+ # High rank indices (for each i, ..., j)
+ ref[indices[i, ..., j], ...] *= updates[i, ..., j, ...]
+
+ Duplicate entries are handled correctly: if multiple indices reference
+ the same location, their contributions multiply.
+
+ Requires updates.shape = indices.shape + ref.shape[1:] or updates.shape = [].
+
+ <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+ <img style="width:100%" src='https://www.tensorflow.org/images/ScatterAdd.png' alt>
+ </div>
+
+
+
+
+ Adds sparse updates to individual values or slices within a given
+
+
+ A resource handle. Must be from a VarHandleOp.
+
+
+ A Tensor. Must be one of the following types: int32, int64.
+ A tensor of indices into ref.
+
+
+ A Tensor. Must have the same type as ref. A tensor of
+ values to add to ref.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResourceScatterNdAdd'.
+
+
+ Optional argument
+ An optional bool. Defaults to True. If True, the assignment will
+ be protected by a lock; otherwise the behavior is undefined,
+ but may exhibit less contention.
+
+
+ Returns the description of the operation
+
+
+ variable according to indices.
+
+ ref is a Tensor with rank P and indices is a Tensor of rank Q.
+
+ indices must be integer tensor, containing indices into ref.
+ It must be shape [d_0, ..., d_{Q-2}, K] where 0 < K <= P.
+
+ The innermost dimension of indices (with length K) corresponds to
+ indices into elements (if K = P) or slices (if K < P) along the Kth
+ dimension of ref.
+
+ updates is Tensor of rank Q-1+P-K with shape:
+
+
+ [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].
+
+
+ For example, say we want to update 4 scattered elements to a rank-1 tensor to
+ 8 elements. In Python, that update would look like this:
+
+
+ ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8], use_resource=True)
+ indices = tf.constant([[4], [3], [1] ,[7]])
+ updates = tf.constant([9, 10, 11, 12])
+ update = tf.scatter_nd_add(ref, indices, updates)
+ with tf.Session() as sess:
+ print sess.run(update)
+
+
+ The resulting update to ref would look like this:
+
+ [1, 12, 3, 14, 14, 6, 7, 20]
+
+ See tf.scatter_nd for more details about how to make updates to
+ slices.
+
+
+
+
+ Applies sparse updates to individual values or slices within a given
+
+
+ A resource handle. Must be from a VarHandleOp.
+
+
+ A Tensor. Must be one of the following types: int32, int64.
+ A tensor of indices into ref.
+
+
+ A Tensor. Must have the same type as ref. A tensor of updated
+ values to add to ref.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResourceScatterNdUpdate'.
+
+
+ Optional argument
+ An optional bool. Defaults to True. If True, the assignment will
+ be protected by a lock; otherwise the behavior is undefined,
+ but may exhibit less contention.
+
+
+ Returns the description of the operation
+
+
+ variable according to indices.
+
+ ref is a Tensor with rank P and indices is a Tensor of rank Q.
+
+ indices must be integer tensor, containing indices into ref.
+ It must be shape [d_0, ..., d_{Q-2}, K] where 0 < K <= P.
+
+ The innermost dimension of indices (with length K) corresponds to
+ indices into elements (if K = P) or slices (if K < P) along the Kth
+ dimension of ref.
+
+ updates is Tensor of rank Q-1+P-K with shape:
+
+
+ [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].
+
+
+ For example, say we want to update 4 scattered elements to a rank-1 tensor to
+ 8 elements. In Python, that update would look like this:
+
+
+ ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
+ indices = tf.constant([[4], [3], [1] ,[7]])
+ updates = tf.constant([9, 10, 11, 12])
+ update = tf.scatter_nd_update(ref, indices, updates)
+ with tf.Session() as sess:
+ print sess.run(update)
+
+
+ The resulting update to ref would look like this:
+
+ [1, 11, 3, 10, 9, 6, 7, 12]
+
+ See tf.scatter_nd for more details about how to make updates to
+ slices.
+
+
+
+
+ Subtracts sparse updates from the variable referenced by resource.
+
+
+ Should be from a Variable node.
+
+
+ A tensor of indices into the first dimension of ref.
+
+
+ A tensor of updated values to add to ref.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResourceScatterSub'.
+
+
+ Returns the description of the operation
+
+
+ This operation computes
+
+ # Scalar indices
+ ref[indices, ...] -= updates[...]
+
+ # Vector indices (for each i)
+ ref[indices[i], ...] -= updates[i, ...]
+
+ # High rank indices (for each i, ..., j)
+ ref[indices[i, ..., j], ...] -= updates[i, ..., j, ...]
+
+ Duplicate entries are handled correctly: if multiple indices reference
+ the same location, their contributions add.
+
+ Requires updates.shape = indices.shape + ref.shape[1:] or updates.shape = [].
+
+ <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+ <img style="width:100%" src='https://www.tensorflow.org/images/ScatterAdd.png' alt>
+ </div>
+
+
+
+
+ Assigns sparse updates to the variable referenced by resource.
+
+
+ Should be from a Variable node.
+
+
+ A tensor of indices into the first dimension of ref.
+
+
+ A tensor of updated values to add to ref.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResourceScatterUpdate'.
+
+
+ Returns the description of the operation
+
+
+ This operation computes
+
+ # Scalar indices
+ ref[indices, ...] = updates[...]
+
+ # Vector indices (for each i)
+ ref[indices[i], ...] = updates[i, ...]
+
+ # High rank indices (for each i, ..., j)
+ ref[indices[i, ..., j], ...] = updates[i, ..., j, ...]
+
+
+
+
+ var: Should be from a Variable().
+
+
+
+
+ Should be from a Variable().
+
+
+ : Should be from a Variable().
+
+
+ Learning rate. Must be a scalar.
+
+
+ Decay factor. Must be a scalar.
+
+
+ Constant factor. Must be a scalar.
+
+
+ The gradient.
+
+
+ A vector of indices into the first dimension of var and accum.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResourceSparseApplyAdadelta'.
+
+
+ Optional argument
+ If True, updating of the var and accum tensors will be protected by
+ a lock; otherwise the behavior is undefined, but may exhibit less contention.
+
+
+ Returns the description of the operation
+
+
+
+
+ Update relevant entries in '*var' and '*accum' according to the adagrad scheme.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Learning rate. Must be a scalar.
+
+
+ The gradient.
+
+
+ A vector of indices into the first dimension of var and accum.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResourceSparseApplyAdagrad'.
+
+
+ Optional argument
+ If True, updating of the var and accum tensors will be protected
+ by a lock; otherwise the behavior is undefined, but may exhibit less
+ contention.
+
+
+ Optional argument
+
+
+ Returns the description of the operation
+
+
+ That is for rows we have grad for, we update var and accum as follows:
+ accum += grad * grad
+ var -= lr * grad * (1 / sqrt(accum))
+
+
+
+
+ Update entries in '*var' and '*accum' according to the proximal adagrad scheme.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ The gradient.
+
+
+ A vector of indices into the first dimension of var and accum.
+
+
+ Learning rate. Must be a scalar.
+
+
+ L1 regularization. Must be a scalar.
+
+
+ L2 regularization. Must be a scalar.
+
+
+ Training step number. Must be a scalar.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResourceSparseApplyAdagradDA'.
+
+
+ Optional argument
+ If True, updating of the var and accum tensors will be protected by
+ a lock; otherwise the behavior is undefined, but may exhibit less contention.
+
+
+ Returns the description of the operation
+
+
+
+
+ Update '*var' according to the centered RMSProp algorithm.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Scaling factor. Must be a scalar.
+
+
+ Decay rate. Must be a scalar.
+
+
+
+
+ Ridge term. Must be a scalar.
+
+
+ The gradient.
+
+
+ A vector of indices into the first dimension of var, ms and mom.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResourceSparseApplyCenteredRMSProp'.
+
+
+ Optional argument
+ If True, updating of the var, mg, ms, and mom tensors is
+ protected by a lock; otherwise the behavior is undefined, but may exhibit less
+ contention.
+
+
+ Returns the description of the operation
+
+
+ The centered RMSProp algorithm uses an estimate of the centered second moment
+ (i.e., the variance) for normalization, as opposed to regular RMSProp, which
+ uses the (uncentered) second moment. This often helps with training, but is
+ slightly more expensive in terms of computation and memory.
+
+ Note that in dense implementation of this algorithm, mg, ms, and mom will
+ update even if the grad is zero, but in this sparse implementation, mg, ms,
+ and mom will not update in iterations during which the grad is zero.
+
+ mean_square = decay * mean_square + (1-decay) * gradient ** 2
+ mean_grad = decay * mean_grad + (1-decay) * gradient
+ Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2)
+
+ ms <- rho * ms_{t-1} + (1-rho) * grad * grad
+ mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon)
+ var <- var - mom
+
+
+
+
+ Update relevant entries in '*var' according to the Ftrl-proximal scheme.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ The gradient.
+
+
+ A vector of indices into the first dimension of var and accum.
+
+
+ Scaling factor. Must be a scalar.
+
+
+ L1 regularization. Must be a scalar.
+
+
+ L2 regularization. Must be a scalar.
+
+
+ Scaling factor. Must be a scalar.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResourceSparseApplyFtrl'.
+
+
+ Optional argument
+ If True, updating of the var and accum tensors will be protected
+ by a lock; otherwise the behavior is undefined, but may exhibit less
+ contention.
+
+
+ Returns the description of the operation
+
+
+ That is for rows we have grad for, we update var, accum and linear as follows:
+ accum_new = accum + grad * grad
+ linear += grad + (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var
+ quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2
+ var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0
+ accum = accum_new
+
+
+
+
+ Update relevant entries in '*var' according to the Ftrl-proximal scheme.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ The gradient.
+
+
+ A vector of indices into the first dimension of var and accum.
+
+
+ Scaling factor. Must be a scalar.
+
+
+ L1 regularization. Must be a scalar.
+
+
+ L2 shrinkage regulariation. Must be a scalar.
+
+
+
+
+ Scaling factor. Must be a scalar.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResourceSparseApplyFtrlV2'.
+
+
+ Optional argument
+ If True, updating of the var and accum tensors will be protected
+ by a lock; otherwise the behavior is undefined, but may exhibit less
+ contention.
+
+
+ Returns the description of the operation
+
+
+ That is for rows we have grad for, we update var, accum and linear as follows:
+ grad_with_shrinkage = grad + 2 * l2_shrinkage * var
+ accum_new = accum + grad_with_shrinkage * grad_with_shrinkage
+ linear += grad_with_shrinkage +
+ (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var
+ quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2
+ var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0
+ accum = accum_new
+
+
+
+
+ Update relevant entries in '*var' and '*accum' according to the momentum scheme.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Learning rate. Must be a scalar.
+
+
+ The gradient.
+
+
+ A vector of indices into the first dimension of var and accum.
+
+
+ Momentum. Must be a scalar.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResourceSparseApplyMomentum'.
+
+
+ Optional argument
+ If True, updating of the var and accum tensors will be protected
+ by a lock; otherwise the behavior is undefined, but may exhibit less
+ contention.
+
+
+ Optional argument
+ If True, the tensor passed to compute grad will be
+ var - lr * momentum * accum, so in the end, the var you get is actually
+ var - lr * momentum * accum.
+
+
+ Returns the description of the operation
+
+
+ Set use_nesterov = True if you want to use Nesterov momentum.
+
+ That is for rows we have grad for, we update var and accum as follows:
+
+ accum = accum * momentum + grad
+ var -= lr * accum
+
+
+
+
+ Sparse update entries in '*var' and '*accum' according to FOBOS algorithm.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Learning rate. Must be a scalar.
+
+
+ L1 regularization. Must be a scalar.
+
+
+ L2 regularization. Must be a scalar.
+
+
+ The gradient.
+
+
+ A vector of indices into the first dimension of var and accum.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResourceSparseApplyProximalAdagrad'.
+
+
+ Optional argument
+ If True, updating of the var and accum tensors will be protected by
+ a lock; otherwise the behavior is undefined, but may exhibit less contention.
+
+
+ Returns the description of the operation
+
+
+ That is for rows we have grad for, we update var and accum as follows:
+ accum += grad * grad
+ prox_v = var
+ prox_v -= lr * grad * (1 / sqrt(accum))
+ var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0}
+
+
+
+
+ Sparse update '*var' as FOBOS algorithm with fixed learning rate.
+
+
+ Should be from a Variable().
+
+
+ Scaling factor. Must be a scalar.
+
+
+ L1 regularization. Must be a scalar.
+
+
+ L2 regularization. Must be a scalar.
+
+
+ The gradient.
+
+
+ A vector of indices into the first dimension of var and accum.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResourceSparseApplyProximalGradientDescent'.
+
+
+ Optional argument
+ If True, the subtraction will be protected by a lock;
+ otherwise the behavior is undefined, but may exhibit less contention.
+
+
+ Returns the description of the operation
+
+
+ That is for rows we have grad for, we update var as follows:
+ prox_v = var - alpha * grad
+ var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0}
+
+
+
+
+ Update '*var' according to the RMSProp algorithm.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Scaling factor. Must be a scalar.
+
+
+ Decay rate. Must be a scalar.
+
+
+
+
+ Ridge term. Must be a scalar.
+
+
+ The gradient.
+
+
+ A vector of indices into the first dimension of var, ms and mom.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ResourceSparseApplyRMSProp'.
+
+
+ Optional argument
+ If True, updating of the var, ms, and mom tensors is protected
+ by a lock; otherwise the behavior is undefined, but may exhibit less
+ contention.
+
+
+ Returns the description of the operation
+
+
+ Note that in dense implementation of this algorithm, ms and mom will
+ update even if the grad is zero, but in this sparse implementation, ms
+ and mom will not update in iterations during which the grad is zero.
+
+ mean_square = decay * mean_square + (1-decay) * gradient ** 2
+ Delta = learning_rate * gradient / sqrt(mean_square + epsilon)
+
+ ms <- rho * ms_{t-1} + (1-rho) * grad * grad
+ mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon)
+ var <- var - mom
+
+
+
+
+
+ Restores a tensor from checkpoint files.
+
+
+ Must have a single element. The pattern of the files from
+ which we read the tensor.
+
+
+ Must have a single element. The name of the tensor to be
+ restored.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Restore'.
+
+
+ Optional argument
+ Index of file to open first if multiple files match
+ file_pattern.
+
+
+ The type of the tensor to be restored.
+
+
+ The restored tensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Reads a tensor stored in one or several files. If there are several files (for
+ instance because a tensor was saved as slices), file_pattern may contain
+ wildcard symbols (* and ?) in the filename portion only, not in the
+ directory portion.
+
+ If a file_pattern matches several files, preferred_shard can be used to hint
+ in which file the requested tensor is likely to be found. This op will first
+ open the file at index preferred_shard in the list of matching files and try
+ to restore tensors from that file. Only if some tensors or tensor slices are
+ not found in that first file, then the Op opens all the files. Setting
+ preferred_shard to match the value passed as the shard input
+ of a matching Save Op may speed up Restore. This attribute only affects
+ performance, not correctness. The default value -1 means files are processed in
+ order.
+
+ See also RestoreSlice.
+
+
+
+
+ Restores a tensor from checkpoint files.
+
+
+ Must have a single element. The pattern of the files from
+ which we read the tensor.
+
+
+ Must have a single element. The name of the tensor to be
+ restored.
+
+
+ Scalar. The shapes and slice specifications to use when
+ restoring a tensors.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RestoreSlice'.
+
+
+ Optional argument
+ Index of file to open first if multiple files match
+ file_pattern. See the documentation for Restore.
+
+
+ The type of the tensor to be restored.
+
+
+ The restored tensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This is like Restore except that restored tensor can be listed as filling
+ only a slice of a larger tensor. shape_and_slice specifies the shape of the
+ larger tensor and the slice that the restored tensor covers.
+
+ The shape_and_slice input has the same format as the
+ elements of the shapes_and_slices input of the SaveSlices op.
+
+
+
+
+ Restores tensors from a V2 checkpoint.
+
+
+ Must have a single element. The prefix of a V2 checkpoint.
+
+
+ shape {N}. The names of the tensors to be restored.
+
+
+ shape {N}. The slice specs of the tensors to be restored.
+ Empty strings indicate that they are non-partitioned tensors.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RestoreV2'.
+
+
+ shape {N}. The list of expected dtype for the tensors. Must match
+ those stored in the checkpoint.
+
+
+ shape {N}. The restored tensors, whose shapes are read from the
+ checkpoint directly.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ For backward compatibility with the V1 format, this Op currently allows
+ restoring from a V1 checkpoint as well:
+ - This Op first attempts to find the V2 index file pointed to by "prefix", and
+ if found proceed to read it as a V2 checkpoint;
+ - Otherwise the V1 read path is invoked.
+ Relying on this behavior is not recommended, as the ability to fall back to read
+ V1 might be deprecated and eventually removed.
+
+ By default, restores the named tensors in full. If the caller wishes to restore
+ specific slices of stored tensors, "shape_and_slices" should be non-empty
+ strings and correspondingly well-formed.
+
+ Callers must ensure all the named tensors are indeed stored in the checkpoint.
+
+
+
+
+ Retrieve embedding parameters for a single table.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RetrieveTPUEmbeddingAdadeltaParameters'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+ Returns a tuple with multiple values, as follows:
+ parameters: Parameter parameters updated by the Adadelta optimization algorithm.
+ accumulators: Parameter accumulators updated by the Adadelta optimization algorithm.
+ updates: Parameter updates updated by the Adadelta optimization algorithm.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+ An op that retrieves optimization parameters from embedding to host
+ memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
+ the correct embedding table configuration. For example, this op is
+ used to retrieve updated parameters before saving a checkpoint.
+
+ parameters: A tensor containing the embedding table parameters to store with the
+ parameters from embedding updates using the Adadelta optimization algorithm.
+ accumulators: A tensor containing the embedding table accumulators to store with the
+ parameters from embedding updates using the Adadelta optimization algorithm.
+ updates: A tensor containing the embedding table updates to store with the
+ parameters from embedding updates using the Adadelta optimization algorithm.
+ table_name: Name of this table; must match a name in the
+ TPUEmbeddingConfiguration proto (overrides table_id).
+ num_shards: Number of shards into which the embedding tables are divided.
+ shard_id: Identifier of shard for this operation.
+ table_id: Index of this table in the EmbeddingLayerConfiguration proto
+ (deprecated).
+
+
+
+
+
+ Retrieve embedding parameters for a single table.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+ Returns a tuple with multiple values, as follows:
+ parameters: Parameter parameters updated by the Adadelta optimization algorithm.
+ accumulators: Parameter accumulators updated by the Adadelta optimization algorithm.
+ updates: Parameter updates updated by the Adadelta optimization algorithm.
+ gradient_accumulators: Parameter gradient_accumulators updated by the Adadelta optimization algorithm.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+ An op that retrieves optimization parameters from embedding to host
+ memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
+ the correct embedding table configuration. For example, this op is
+ used to retrieve updated parameters before saving a checkpoint.
+
+ parameters: A tensor containing the embedding table parameters to store with the
+ parameters from embedding updates using the Adadelta optimization algorithm.
+ accumulators: A tensor containing the embedding table accumulators to store with the
+ parameters from embedding updates using the Adadelta optimization algorithm.
+ updates: A tensor containing the embedding table updates to store with the
+ parameters from embedding updates using the Adadelta optimization algorithm.
+ gradient_accumulators: A tensor containing the embedding table gradient_accumulators to store with the
+ parameters from embedding updates using the Adadelta optimization algorithm.
+ table_name: Name of this table; must match a name in the
+ TPUEmbeddingConfiguration proto (overrides table_id).
+ num_shards: Number of shards into which the embedding tables are divided.
+ shard_id: Identifier of shard for this operation.
+ table_id: Index of this table in the EmbeddingLayerConfiguration proto
+ (deprecated).
+
+
+
+
+
+ Retrieve embedding parameters for a single table.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RetrieveTPUEmbeddingAdagradParameters'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+ Returns a tuple with multiple values, as follows:
+ parameters: Parameter parameters updated by the Adagrad optimization algorithm.
+ accumulators: Parameter accumulators updated by the Adagrad optimization algorithm.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+ An op that retrieves optimization parameters from embedding to host
+ memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
+ the correct embedding table configuration. For example, this op is
+ used to retrieve updated parameters before saving a checkpoint.
+
+ parameters: A tensor containing the embedding table parameters to store with the
+ parameters from embedding updates using the Adagrad optimization algorithm.
+ accumulators: A tensor containing the embedding table accumulators to store with the
+ parameters from embedding updates using the Adagrad optimization algorithm.
+ table_name: Name of this table; must match a name in the
+ TPUEmbeddingConfiguration proto (overrides table_id).
+ num_shards: Number of shards into which the embedding tables are divided.
+ shard_id: Identifier of shard for this operation.
+ table_id: Index of this table in the EmbeddingLayerConfiguration proto
+ (deprecated).
+
+
+
+
+
+ Retrieve embedding parameters for a single table.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RetrieveTPUEmbeddingAdagradParametersGradAccumDebug'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+ Returns a tuple with multiple values, as follows:
+ parameters: Parameter parameters updated by the Adagrad optimization algorithm.
+ accumulators: Parameter accumulators updated by the Adagrad optimization algorithm.
+ gradient_accumulators: Parameter gradient_accumulators updated by the Adagrad optimization algorithm.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+ An op that retrieves optimization parameters from embedding to host
+ memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
+ the correct embedding table configuration. For example, this op is
+ used to retrieve updated parameters before saving a checkpoint.
+
+ parameters: A tensor containing the embedding table parameters to store with the
+ parameters from embedding updates using the Adagrad optimization algorithm.
+ accumulators: A tensor containing the embedding table accumulators to store with the
+ parameters from embedding updates using the Adagrad optimization algorithm.
+ gradient_accumulators: A tensor containing the embedding table gradient_accumulators to store with the
+ parameters from embedding updates using the Adagrad optimization algorithm.
+ table_name: Name of this table; must match a name in the
+ TPUEmbeddingConfiguration proto (overrides table_id).
+ num_shards: Number of shards into which the embedding tables are divided.
+ shard_id: Identifier of shard for this operation.
+ table_id: Index of this table in the EmbeddingLayerConfiguration proto
+ (deprecated).
+
+
+
+
+
+ Retrieve embedding parameters for a single table.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RetrieveTPUEmbeddingADAMParameters'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+ Returns a tuple with multiple values, as follows:
+ parameters: Parameter parameters updated by the ADAM optimization algorithm.
+ momenta: Parameter momenta updated by the ADAM optimization algorithm.
+ velocities: Parameter velocities updated by the ADAM optimization algorithm.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+ An op that retrieves optimization parameters from embedding to host
+ memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
+ the correct embedding table configuration. For example, this op is
+ used to retrieve updated parameters before saving a checkpoint.
+
+ parameters: A tensor containing the embedding table parameters to store with the
+ parameters from embedding updates using the ADAM optimization algorithm.
+ momenta: A tensor containing the embedding table momenta to store with the
+ parameters from embedding updates using the ADAM optimization algorithm.
+ velocities: A tensor containing the embedding table velocities to store with the
+ parameters from embedding updates using the ADAM optimization algorithm.
+ table_name: Name of this table; must match a name in the
+ TPUEmbeddingConfiguration proto (overrides table_id).
+ num_shards: Number of shards into which the embedding tables are divided.
+ shard_id: Identifier of shard for this operation.
+ table_id: Index of this table in the EmbeddingLayerConfiguration proto
+ (deprecated).
+
+
+
+
+
+ Retrieve embedding parameters for a single table.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RetrieveTPUEmbeddingADAMParametersGradAccumDebug'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+ Returns a tuple with multiple values, as follows:
+ parameters: Parameter parameters updated by the ADAM optimization algorithm.
+ momenta: Parameter momenta updated by the ADAM optimization algorithm.
+ velocities: Parameter velocities updated by the ADAM optimization algorithm.
+ gradient_accumulators: Parameter gradient_accumulators updated by the ADAM optimization algorithm.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+ An op that retrieves optimization parameters from embedding to host
+ memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
+ the correct embedding table configuration. For example, this op is
+ used to retrieve updated parameters before saving a checkpoint.
+
+ parameters: A tensor containing the embedding table parameters to store with the
+ parameters from embedding updates using the ADAM optimization algorithm.
+ momenta: A tensor containing the embedding table momenta to store with the
+ parameters from embedding updates using the ADAM optimization algorithm.
+ velocities: A tensor containing the embedding table velocities to store with the
+ parameters from embedding updates using the ADAM optimization algorithm.
+ gradient_accumulators: A tensor containing the embedding table gradient_accumulators to store with the
+ parameters from embedding updates using the ADAM optimization algorithm.
+ table_name: Name of this table; must match a name in the
+ TPUEmbeddingConfiguration proto (overrides table_id).
+ num_shards: Number of shards into which the embedding tables are divided.
+ shard_id: Identifier of shard for this operation.
+ table_id: Index of this table in the EmbeddingLayerConfiguration proto
+ (deprecated).
+
+
+
+
+
+ Retrieve embedding parameters for a single table.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RetrieveTPUEmbeddingCenteredRMSPropParameters'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+ Returns a tuple with multiple values, as follows:
+ parameters: Parameter parameters updated by the centered RMSProp optimization algorithm.
+ ms: Parameter ms updated by the centered RMSProp optimization algorithm.
+ mom: Parameter mom updated by the centered RMSProp optimization algorithm.
+ mg: Parameter mg updated by the centered RMSProp optimization algorithm.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+ An op that retrieves optimization parameters from embedding to host
+ memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
+ the correct embedding table configuration. For example, this op is
+ used to retrieve updated parameters before saving a checkpoint.
+
+ parameters: A tensor containing the embedding table parameters to store with the
+ parameters from embedding updates using the centered RMSProp optimization algorithm.
+ ms: A tensor containing the embedding table ms to store with the
+ parameters from embedding updates using the centered RMSProp optimization algorithm.
+ mom: A tensor containing the embedding table mom to store with the
+ parameters from embedding updates using the centered RMSProp optimization algorithm.
+ mg: A tensor containing the embedding table mg to store with the
+ parameters from embedding updates using the centered RMSProp optimization algorithm.
+ table_name: Name of this table; must match a name in the
+ TPUEmbeddingConfiguration proto (overrides table_id).
+ num_shards: Number of shards into which the embedding tables are divided.
+ shard_id: Identifier of shard for this operation.
+ table_id: Index of this table in the EmbeddingLayerConfiguration proto
+ (deprecated).
+
+
+
+
+
+ Retrieve embedding parameters for a single table.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RetrieveTPUEmbeddingFTRLParameters'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+ Returns a tuple with multiple values, as follows:
+ parameters: Parameter parameters updated by the FTRL optimization algorithm.
+ accumulators: Parameter accumulators updated by the FTRL optimization algorithm.
+ linears: Parameter linears updated by the FTRL optimization algorithm.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+ An op that retrieves optimization parameters from embedding to host
+ memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
+ the correct embedding table configuration. For example, this op is
+ used to retrieve updated parameters before saving a checkpoint.
+
+ parameters: A tensor containing the embedding table parameters to store with the
+ parameters from embedding updates using the FTRL optimization algorithm.
+ accumulators: A tensor containing the embedding table accumulators to store with the
+ parameters from embedding updates using the FTRL optimization algorithm.
+ linears: A tensor containing the embedding table linears to store with the
+ parameters from embedding updates using the FTRL optimization algorithm.
+ table_name: Name of this table; must match a name in the
+ TPUEmbeddingConfiguration proto (overrides table_id).
+ num_shards: Number of shards into which the embedding tables are divided.
+ shard_id: Identifier of shard for this operation.
+ table_id: Index of this table in the EmbeddingLayerConfiguration proto
+ (deprecated).
+
+
+
+
+
+ Retrieve embedding parameters for a single table.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RetrieveTPUEmbeddingFTRLParametersGradAccumDebug'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+ Returns a tuple with multiple values, as follows:
+ parameters: Parameter parameters updated by the FTRL optimization algorithm.
+ accumulators: Parameter accumulators updated by the FTRL optimization algorithm.
+ linears: Parameter linears updated by the FTRL optimization algorithm.
+ gradient_accumulators: Parameter gradient_accumulators updated by the FTRL optimization algorithm.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+ An op that retrieves optimization parameters from embedding to host
+ memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
+ the correct embedding table configuration. For example, this op is
+ used to retrieve updated parameters before saving a checkpoint.
+
+ parameters: A tensor containing the embedding table parameters to store with the
+ parameters from embedding updates using the FTRL optimization algorithm.
+ accumulators: A tensor containing the embedding table accumulators to store with the
+ parameters from embedding updates using the FTRL optimization algorithm.
+ linears: A tensor containing the embedding table linears to store with the
+ parameters from embedding updates using the FTRL optimization algorithm.
+ gradient_accumulators: A tensor containing the embedding table gradient_accumulators to store with the
+ parameters from embedding updates using the FTRL optimization algorithm.
+ table_name: Name of this table; must match a name in the
+ TPUEmbeddingConfiguration proto (overrides table_id).
+ num_shards: Number of shards into which the embedding tables are divided.
+ shard_id: Identifier of shard for this operation.
+ table_id: Index of this table in the EmbeddingLayerConfiguration proto
+ (deprecated).
+
+
+
+
+
+ Retrieve embedding parameters for a single table.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RetrieveTPUEmbeddingMDLAdagradLightParameters'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+ Returns a tuple with multiple values, as follows:
+ parameters: Parameter parameters updated by the MDL Adagrad Light optimization algorithm.
+ accumulators: Parameter accumulators updated by the MDL Adagrad Light optimization algorithm.
+ weights: Parameter weights updated by the MDL Adagrad Light optimization algorithm.
+ benefits: Parameter benefits updated by the MDL Adagrad Light optimization algorithm.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+ An op that retrieves optimization parameters from embedding to host
+ memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
+ the correct embedding table configuration. For example, this op is
+ used to retrieve updated parameters before saving a checkpoint.
+
+ parameters: A tensor containing the embedding table parameters to store with the
+ parameters from embedding updates using the MDL Adagrad Light optimization algorithm.
+ accumulators: A tensor containing the embedding table accumulators to store with the
+ parameters from embedding updates using the MDL Adagrad Light optimization algorithm.
+ weights: A tensor containing the embedding table weights to store with the
+ parameters from embedding updates using the MDL Adagrad Light optimization algorithm.
+ benefits: A tensor containing the embedding table benefits to store with the
+ parameters from embedding updates using the MDL Adagrad Light optimization algorithm.
+ table_name: Name of this table; must match a name in the
+ TPUEmbeddingConfiguration proto (overrides table_id).
+ num_shards: Number of shards into which the embedding tables are divided.
+ shard_id: Identifier of shard for this operation.
+ table_id: Index of this table in the EmbeddingLayerConfiguration proto
+ (deprecated).
+
+
+
+
+
+ Retrieve embedding parameters for a single table.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RetrieveTPUEmbeddingMomentumParameters'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+ Returns a tuple with multiple values, as follows:
+ parameters: Parameter parameters updated by the Momentum optimization algorithm.
+ momenta: Parameter momenta updated by the Momentum optimization algorithm.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+ An op that retrieves optimization parameters from embedding to host
+ memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
+ the correct embedding table configuration. For example, this op is
+ used to retrieve updated parameters before saving a checkpoint.
+
+ parameters: A tensor containing the embedding table parameters to store with the
+ parameters from embedding updates using the Momentum optimization algorithm.
+ momenta: A tensor containing the embedding table momenta to store with the
+ parameters from embedding updates using the Momentum optimization algorithm.
+ table_name: Name of this table; must match a name in the
+ TPUEmbeddingConfiguration proto (overrides table_id).
+ num_shards: Number of shards into which the embedding tables are divided.
+ shard_id: Identifier of shard for this operation.
+ table_id: Index of this table in the EmbeddingLayerConfiguration proto
+ (deprecated).
+
+
+
+
+
+ Retrieve embedding parameters for a single table.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RetrieveTPUEmbeddingMomentumParametersGradAccumDebug'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+ Returns a tuple with multiple values, as follows:
+ parameters: Parameter parameters updated by the Momentum optimization algorithm.
+ momenta: Parameter momenta updated by the Momentum optimization algorithm.
+ gradient_accumulators: Parameter gradient_accumulators updated by the Momentum optimization algorithm.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+ An op that retrieves optimization parameters from embedding to host
+ memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
+ the correct embedding table configuration. For example, this op is
+ used to retrieve updated parameters before saving a checkpoint.
+
+ parameters: A tensor containing the embedding table parameters to store with the
+ parameters from embedding updates using the Momentum optimization algorithm.
+ momenta: A tensor containing the embedding table momenta to store with the
+ parameters from embedding updates using the Momentum optimization algorithm.
+ gradient_accumulators: A tensor containing the embedding table gradient_accumulators to store with the
+ parameters from embedding updates using the Momentum optimization algorithm.
+ table_name: Name of this table; must match a name in the
+ TPUEmbeddingConfiguration proto (overrides table_id).
+ num_shards: Number of shards into which the embedding tables are divided.
+ shard_id: Identifier of shard for this operation.
+ table_id: Index of this table in the EmbeddingLayerConfiguration proto
+ (deprecated).
+
+
+
+
+
+ Retrieve embedding parameters for a single table.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RetrieveTPUEmbeddingProximalAdagradParameters'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+ Returns a tuple with multiple values, as follows:
+ parameters: Parameter parameters updated by the proximal Adagrad optimization algorithm.
+ accumulators: Parameter accumulators updated by the proximal Adagrad optimization algorithm.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+ An op that retrieves optimization parameters from embedding to host
+ memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
+ the correct embedding table configuration. For example, this op is
+ used to retrieve updated parameters before saving a checkpoint.
+
+ parameters: A tensor containing the embedding table parameters to store with the
+ parameters from embedding updates using the proximal Adagrad optimization algorithm.
+ accumulators: A tensor containing the embedding table accumulators to store with the
+ parameters from embedding updates using the proximal Adagrad optimization algorithm.
+ table_name: Name of this table; must match a name in the
+ TPUEmbeddingConfiguration proto (overrides table_id).
+ num_shards: Number of shards into which the embedding tables are divided.
+ shard_id: Identifier of shard for this operation.
+ table_id: Index of this table in the EmbeddingLayerConfiguration proto
+ (deprecated).
+
+
+
+
+
+ Retrieve embedding parameters for a single table.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+ Returns a tuple with multiple values, as follows:
+ parameters: Parameter parameters updated by the proximal Adagrad optimization algorithm.
+ accumulators: Parameter accumulators updated by the proximal Adagrad optimization algorithm.
+ gradient_accumulators: Parameter gradient_accumulators updated by the proximal Adagrad optimization algorithm.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+ An op that retrieves optimization parameters from embedding to host
+ memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
+ the correct embedding table configuration. For example, this op is
+ used to retrieve updated parameters before saving a checkpoint.
+
+ parameters: A tensor containing the embedding table parameters to store with the
+ parameters from embedding updates using the proximal Adagrad optimization algorithm.
+ accumulators: A tensor containing the embedding table accumulators to store with the
+ parameters from embedding updates using the proximal Adagrad optimization algorithm.
+ gradient_accumulators: A tensor containing the embedding table gradient_accumulators to store with the
+ parameters from embedding updates using the proximal Adagrad optimization algorithm.
+ table_name: Name of this table; must match a name in the
+ TPUEmbeddingConfiguration proto (overrides table_id).
+ num_shards: Number of shards into which the embedding tables are divided.
+ shard_id: Identifier of shard for this operation.
+ table_id: Index of this table in the EmbeddingLayerConfiguration proto
+ (deprecated).
+
+
+
+
+
+ Retrieve embedding parameters for a single table.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RetrieveTPUEmbeddingRMSPropParameters'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+ Returns a tuple with multiple values, as follows:
+ parameters: Parameter parameters updated by the RMSProp optimization algorithm.
+ ms: Parameter ms updated by the RMSProp optimization algorithm.
+ mom: Parameter mom updated by the RMSProp optimization algorithm.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+ An op that retrieves optimization parameters from embedding to host
+ memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
+ the correct embedding table configuration. For example, this op is
+ used to retrieve updated parameters before saving a checkpoint.
+
+ parameters: A tensor containing the embedding table parameters to store with the
+ parameters from embedding updates using the RMSProp optimization algorithm.
+ ms: A tensor containing the embedding table ms to store with the
+ parameters from embedding updates using the RMSProp optimization algorithm.
+ mom: A tensor containing the embedding table mom to store with the
+ parameters from embedding updates using the RMSProp optimization algorithm.
+ table_name: Name of this table; must match a name in the
+ TPUEmbeddingConfiguration proto (overrides table_id).
+ num_shards: Number of shards into which the embedding tables are divided.
+ shard_id: Identifier of shard for this operation.
+ table_id: Index of this table in the EmbeddingLayerConfiguration proto
+ (deprecated).
+
+
+
+
+
+ Retrieve embedding parameters for a single table.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+ Returns a tuple with multiple values, as follows:
+ parameters: Parameter parameters updated by the RMSProp optimization algorithm.
+ ms: Parameter ms updated by the RMSProp optimization algorithm.
+ mom: Parameter mom updated by the RMSProp optimization algorithm.
+ gradient_accumulators: Parameter gradient_accumulators updated by the RMSProp optimization algorithm.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+ An op that retrieves optimization parameters from embedding to host
+ memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
+ the correct embedding table configuration. For example, this op is
+ used to retrieve updated parameters before saving a checkpoint.
+
+ parameters: A tensor containing the embedding table parameters to store with the
+ parameters from embedding updates using the RMSProp optimization algorithm.
+ ms: A tensor containing the embedding table ms to store with the
+ parameters from embedding updates using the RMSProp optimization algorithm.
+ mom: A tensor containing the embedding table mom to store with the
+ parameters from embedding updates using the RMSProp optimization algorithm.
+ gradient_accumulators: A tensor containing the embedding table gradient_accumulators to store with the
+ parameters from embedding updates using the RMSProp optimization algorithm.
+ table_name: Name of this table; must match a name in the
+ TPUEmbeddingConfiguration proto (overrides table_id).
+ num_shards: Number of shards into which the embedding tables are divided.
+ shard_id: Identifier of shard for this operation.
+ table_id: Index of this table in the EmbeddingLayerConfiguration proto
+ (deprecated).
+
+
+
+
+
+ Retrieve embedding parameters for a single table.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RetrieveTPUEmbeddingStochasticGradientDescentParameters'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+ Parameter parameters updated by the stochastic gradient descent optimization algorithm.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+ An op that retrieves optimization parameters from embedding to host
+ memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up
+ the correct embedding table configuration. For example, this op is
+ used to retrieve updated parameters before saving a checkpoint.
+
+ parameters: A tensor containing the embedding table parameters to store with the
+ parameters from embedding updates using the stochastic gradient descent optimization algorithm.
+ table_name: Name of this table; must match a name in the
+ TPUEmbeddingConfiguration proto (overrides table_id).
+ num_shards: Number of shards into which the embedding tables are divided.
+ shard_id: Identifier of shard for this operation.
+ table_id: Index of this table in the EmbeddingLayerConfiguration proto
+ (deprecated).
+
+
+
+
+
+ Reverses specific dimensions of a tensor.
+
+
+ Up to 8-D.
+
+
+ 1-D. The dimensions to reverse.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Reverse'.
+
+
+ The same shape as tensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Given a tensor, and a bool tensor dims representing the dimensions
+ of tensor, this operation reverses each dimension i of tensor where
+ dims[i] is True.
+
+ tensor can have up to 8 dimensions. The number of dimensions
+ of tensor must equal the number of elements in dims. In other words:
+
+ rank(tensor) = size(dims)
+
+ For example:
+
+
+ # tensor 't' is [[[[ 0, 1, 2, 3],
+ # [ 4, 5, 6, 7],
+ # [ 8, 9, 10, 11]],
+ # [[12, 13, 14, 15],
+ # [16, 17, 18, 19],
+ # [20, 21, 22, 23]]]]
+ # tensor 't' shape is [1, 2, 3, 4]
+
+ # 'dims' is [False, False, False, True]
+ reverse(t, dims) ==> [[[[ 3, 2, 1, 0],
+ [ 7, 6, 5, 4],
+ [ 11, 10, 9, 8]],
+ [[15, 14, 13, 12],
+ [19, 18, 17, 16],
+ [23, 22, 21, 20]]]]
+
+ # 'dims' is [False, True, False, False]
+ reverse(t, dims) ==> [[[[12, 13, 14, 15],
+ [16, 17, 18, 19],
+ [20, 21, 22, 23]
+ [[ 0, 1, 2, 3],
+ [ 4, 5, 6, 7],
+ [ 8, 9, 10, 11]]]]
+
+ # 'dims' is [False, False, True, False]
+ reverse(t, dims) ==> [[[[8, 9, 10, 11],
+ [4, 5, 6, 7],
+ [0, 1, 2, 3]]
+ [[20, 21, 22, 23],
+ [16, 17, 18, 19],
+ [12, 13, 14, 15]]]]
+
+
+
+
+
+ Reverses variable length slices.
+
+
+ The input to reverse.
+
+
+ 1-D with length input.dims(batch_dim) and
+ max(seq_lengths) <= input.dims(seq_dim)
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ReverseSequence'.
+
+
+ Optional argument
+ The dimension along which reversal is performed.
+
+
+ The dimension which is partially reversed.
+
+
+ The partially reversed input. It has the same shape as input.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This op first slices input along the dimension batch_dim, and for each
+ slice i, reverses the first seq_lengths[i] elements along
+ the dimension seq_dim.
+
+ The elements of seq_lengths must obey seq_lengths[i] <= input.dims[seq_dim],
+ and seq_lengths must be a vector of length input.dims[batch_dim].
+
+ The output slice i along dimension batch_dim is then given by input
+ slice i, with the first seq_lengths[i] slices along dimension
+ seq_dim reversed.
+
+ For example:
+
+
+ # Given this:
+ batch_dim = 0
+ seq_dim = 1
+ input.dims = (4, 8, ...)
+ seq_lengths = [7, 2, 3, 5]
+
+ # then slices of input are reversed on seq_dim, but only up to seq_lengths:
+ output[0, 0:7, :, ...] = input[0, 7:0:-1, :, ...]
+ output[1, 0:2, :, ...] = input[1, 2:0:-1, :, ...]
+ output[2, 0:3, :, ...] = input[2, 3:0:-1, :, ...]
+ output[3, 0:5, :, ...] = input[3, 5:0:-1, :, ...]
+
+ # while entries past seq_lens are copied through:
+ output[0, 7:, :, ...] = input[0, 7:, :, ...]
+ output[1, 2:, :, ...] = input[1, 2:, :, ...]
+ output[2, 3:, :, ...] = input[2, 3:, :, ...]
+ output[3, 2:, :, ...] = input[3, 2:, :, ...]
+
+
+ In contrast, if:
+
+
+ # Given this:
+ batch_dim = 2
+ seq_dim = 0
+ input.dims = (8, ?, 4, ...)
+ seq_lengths = [7, 2, 3, 5]
+
+ # then slices of input are reversed on seq_dim, but only up to seq_lengths:
+ output[0:7, :, 0, :, ...] = input[7:0:-1, :, 0, :, ...]
+ output[0:2, :, 1, :, ...] = input[2:0:-1, :, 1, :, ...]
+ output[0:3, :, 2, :, ...] = input[3:0:-1, :, 2, :, ...]
+ output[0:5, :, 3, :, ...] = input[5:0:-1, :, 3, :, ...]
+
+ # while entries past seq_lens are copied through:
+ output[7:, :, 0, :, ...] = input[7:, :, 0, :, ...]
+ output[2:, :, 1, :, ...] = input[2:, :, 1, :, ...]
+ output[3:, :, 2, :, ...] = input[3:, :, 2, :, ...]
+ output[2:, :, 3, :, ...] = input[2:, :, 3, :, ...]
+
+
+
+
+
+ Reverses specific dimensions of a tensor.
+
+
+ Up to 8-D.
+
+
+ 1-D. The indices of the dimensions to reverse. Must be in the range
+ [-rank(tensor), rank(tensor)).
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ReverseV2'.
+
+
+ The same shape as tensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ NOTE tf.reverse has now changed behavior in preparation for 1.0.
+ tf.reverse_v2 is currently an alias that will be deprecated before TF 1.0.
+
+ Given a tensor, and a int32 tensor axis representing the set of
+ dimensions of tensor to reverse. This operation reverses each dimension
+ i for which there exists j s.t. axis[j] == i.
+
+ tensor can have up to 8 dimensions. The number of dimensions specified
+ in axis may be 0 or more entries. If an index is specified more than
+ once, a InvalidArgument error is raised.
+
+ For example:
+
+
+ # tensor 't' is [[[[ 0, 1, 2, 3],
+ # [ 4, 5, 6, 7],
+ # [ 8, 9, 10, 11]],
+ # [[12, 13, 14, 15],
+ # [16, 17, 18, 19],
+ # [20, 21, 22, 23]]]]
+ # tensor 't' shape is [1, 2, 3, 4]
+
+ # 'dims' is [3] or 'dims' is [-1]
+ reverse(t, dims) ==> [[[[ 3, 2, 1, 0],
+ [ 7, 6, 5, 4],
+ [ 11, 10, 9, 8]],
+ [[15, 14, 13, 12],
+ [19, 18, 17, 16],
+ [23, 22, 21, 20]]]]
+
+ # 'dims' is '[1]' (or 'dims' is '[-3]')
+ reverse(t, dims) ==> [[[[12, 13, 14, 15],
+ [16, 17, 18, 19],
+ [20, 21, 22, 23]
+ [[ 0, 1, 2, 3],
+ [ 4, 5, 6, 7],
+ [ 8, 9, 10, 11]]]]
+
+ # 'dims' is '[2]' (or 'dims' is '[-2]')
+ reverse(t, dims) ==> [[[[8, 9, 10, 11],
+ [4, 5, 6, 7],
+ [0, 1, 2, 3]]
+ [[20, 21, 22, 23],
+ [16, 17, 18, 19],
+ [12, 13, 14, 15]]]]
+
+
+
+
+
+ Real-valued fast Fourier transform.
+
+
+ A float32 tensor.
+
+
+ An int32 tensor of shape [1]. The FFT length.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RFFT'.
+
+
+ A complex64 tensor of the same rank as input. The inner-most
+ dimension of input is replaced with the fft_length / 2 + 1 unique
+ frequency components of its 1D Fourier transform.
+
+ @compatibility(numpy)
+ Equivalent to np.fft.rfft
+ @end_compatibility
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Computes the 1-dimensional discrete Fourier transform of a real-valued signal
+ over the inner-most dimension of input.
+
+ Since the DFT of a real signal is Hermitian-symmetric, RFFT only returns the
+ fft_length / 2 + 1 unique components of the FFT: the zero-frequency term,
+ followed by the fft_length / 2 positive-frequency terms.
+
+ Along the axis RFFT is computed on, if fft_length is smaller than the
+ corresponding dimension of input, the dimension is cropped. If it is larger,
+ the dimension is padded with zeros.
+
+
+
+
+ 2D real-valued fast Fourier transform.
+
+
+ A float32 tensor.
+
+
+ An int32 tensor of shape [2]. The FFT length for each dimension.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RFFT2D'.
+
+
+ A complex64 tensor of the same rank as input. The inner-most 2
+ dimensions of input are replaced with their 2D Fourier transform. The
+ inner-most dimension contains fft_length / 2 + 1 unique frequency
+ components.
+
+ @compatibility(numpy)
+ Equivalent to np.fft.rfft2
+ @end_compatibility
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Computes the 2-dimensional discrete Fourier transform of a real-valued signal
+ over the inner-most 2 dimensions of input.
+
+ Since the DFT of a real signal is Hermitian-symmetric, RFFT2D only returns the
+ fft_length / 2 + 1 unique components of the FFT for the inner-most dimension
+ of output: the zero-frequency term, followed by the fft_length / 2
+ positive-frequency terms.
+
+ Along each axis RFFT2D is computed on, if fft_length is smaller than the
+ corresponding dimension of input, the dimension is cropped. If it is larger,
+ the dimension is padded with zeros.
+
+
+
+
+ 3D real-valued fast Fourier transform.
+
+
+ A float32 tensor.
+
+
+ An int32 tensor of shape [3]. The FFT length for each dimension.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RFFT3D'.
+
+
+ A complex64 tensor of the same rank as input. The inner-most 3
+ dimensions of input are replaced with the their 3D Fourier transform. The
+ inner-most dimension contains fft_length / 2 + 1 unique frequency
+ components.
+
+ @compatibility(numpy)
+ Equivalent to np.fft.rfftn with 3 dimensions.
+ @end_compatibility
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Computes the 3-dimensional discrete Fourier transform of a real-valued signal
+ over the inner-most 3 dimensions of input.
+
+ Since the DFT of a real signal is Hermitian-symmetric, RFFT3D only returns the
+ fft_length / 2 + 1 unique components of the FFT for the inner-most dimension
+ of output: the zero-frequency term, followed by the fft_length / 2
+ positive-frequency terms.
+
+ Along each axis RFFT3D is computed on, if fft_length is smaller than the
+ corresponding dimension of input, the dimension is cropped. If it is larger,
+ the dimension is padded with zeros.
+
+
+
+
+ Converts one or more images from RGB to HSV.
+
+
+ 1-D or higher rank. RGB data to convert. Last dimension must be size 3.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RGBToHSV'.
+
+
+ images converted to HSV.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Outputs a tensor of the same shape as the images tensor, containing the HSV
+ value of the pixels. The output is only well defined if the value in images
+ are in [0,1].
+
+ output[..., 0] contains hue, output[..., 1] contains saturation, and
+ output[..., 2] contains value. All HSV values are in [0,1]. A hue of 0
+ corresponds to pure red, hue 1/3 is pure green, and 2/3 is pure blue.
+
+
+
+
+ Elementwise computes the bitwise right-shift of x and y.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RightShift'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Performs a logical shift for unsigned integer types, and an arithmetic shift
+ for signed integer types.
+
+ If y is negative, or greater than or equal to than the width of x in bits
+ the result is implementation defined.
+
+
+
+
+ Returns element-wise integer closest to x.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Rint'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ If the result is midway between two representable values,
+ the even representable is chosen.
+ For example:
+
+
+ rint(-1.5) ==> -2.0
+ rint(0.5000001) ==> 1.0
+ rint([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]) ==> [-2., -2., -0., 0., 2., 2., 2.]
+
+
+
+
+
+ Rolls the elements of a tensor along an axis.
+
+
+
+
+ Dimension must be 0-D or 1-D. shift[i] specifies the number of places by which
+ elements are shifted positively (towards larger indices) along the dimension
+ specified by axis[i]. Negative shifts will roll the elements in the opposite
+ direction.
+
+
+ Dimension must be 0-D or 1-D. axis[i] specifies the dimension that the shift
+ shift[i] should occur. If the same axis is referenced more than once, the
+ total shift for that axis will be the sum of all the shifts that belong to that
+ axis.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Roll'.
+
+
+ Has the same shape and size as the input. The elements are shifted
+ positively (towards larger indices) by the offsets of shift along the
+ dimensions of axis.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The elements are shifted positively (towards larger indices) by the offset of
+ shift along the dimension of axis. Negative shift values will shift
+ elements in the opposite direction. Elements that roll passed the last position
+ will wrap around to the first and vice versa. Multiple shifts along multiple
+ axes may be specified.
+
+ For example:
+
+
+ # 't' is [0, 1, 2, 3, 4]
+ roll(t, shift=2, axis=0) ==> [3, 4, 0, 1, 2]
+
+ # shifting along multiple dimensions
+ # 't' is [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]
+ roll(t, shift=[1, -2], axis=[0, 1]) ==> [[7, 8, 9, 5, 6], [2, 3, 4, 0, 1]]
+
+ # shifting along the same axis multiple times
+ # 't' is [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]
+ roll(t, shift=[2, -3], axis=[1, 1]) ==> [[1, 2, 3, 4, 0], [6, 7, 8, 9, 5]]
+
+
+
+
+
+ Rounds the values of a tensor to the nearest integer, element-wise.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Round'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Rounds half to even. Also known as bankers rounding. If you want to round
+ according to the current system rounding mode use std::cint.
+
+
+
+
+ Perform batches of RPC requests.
+
+
+ 0-D or 1-D. The address (i.e. host_name:port) of the RPC server.
+ If this tensor has more than 1 element, then multiple parallel rpc requests
+ are sent. This argument broadcasts with method and request.
+
+
+ 0-D or 1-D. The method address on the RPC server.
+ If this tensor has more than 1 element, then multiple parallel rpc requests
+ are sent. This argument broadcasts with address and request.
+
+
+ 0-D or 1-D. Serialized proto strings: the rpc request argument.
+ If this tensor has more than 1 element, then multiple parallel rpc requests
+ are sent. This argument broadcasts with address and method.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Rpc'.
+
+
+ Optional argument
+ RPC protocol to use. Empty string means use the default protocol.
+ Options include 'grpc'.
+
+
+ Optional argument
+ boolean. If true (default), then failures to connect
+ (i.e., the server does not immediately respond) cause an RPC failure.
+
+
+ Optional argument
+ int. If 0 (default), then the kernel will run the RPC
+ request and only time out if the RPC deadline passes or the session times out.
+ If this value is greater than 0, then the op will raise an exception if
+ the RPC takes longer than timeout_in_ms.
+
+
+ Same shape as request. Serialized proto strings: the rpc responses.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This op asynchronously performs either a single RPC request, or a batch
+ of requests. RPC requests are defined by three main parameters:
+
+ - address (the host+port or BNS address of the request)
+ - method (the RPC method name for the request)
+ - request (the serialized proto string, or vector of strings,
+ of the RPC request argument).
+
+ For example, if you have an RPC service running on port localhost:2345,
+ and its interface is configured with the following proto declaration:
+
+
+ service MyService {
+ rpc MyMethod(MyRequestProto) returns (MyResponseProto) {
+ }
+ };
+
+
+ then call this op with arguments:
+
+
+ address = "localhost:2345"
+ method = "MyService/MyMethod"
+
+
+ The request tensor is a string tensor representing serialized MyRequestProto
+ strings; and the output string tensor response will have the same shape
+ and contain (upon successful completion) corresponding serialized
+ MyResponseProto strings.
+
+ For example, to send a single, empty, MyRequestProto, call
+ this op with request = "". To send 5 **parallel** empty requests,
+ call this op with request = ["", "", "", "", ""].
+
+ More generally, one can create a batch of MyRequestProto serialized protos
+ from regular batched tensors using the encode_proto op, and convert
+ the response MyResponseProto serialized protos to batched tensors
+ using the decode_proto op.
+
+ **NOTE** Working with serialized proto strings is faster than instantiating
+ actual proto objects in memory, so no performance degradation is expected
+ compared to writing custom kernels for this workflow.
+
+ If the connection fails or the remote worker returns an error
+ status, the op reraises this exception locally.
+
+ See the TryRpc op if you prefer to handle RPC failures manually in the graph.
+
+
+
+
+ Computes reciprocal of square root of x element-wise.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Rsqrt'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ I.e., \\(y = 1 / \sqrt{x}\\).
+
+
+
+
+ Computes the gradient for the rsqrt of x wrt its input.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'RsqrtGrad'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Specifically, grad = dy * -0.5 * y^3, where y = rsqrt(x), and dy
+ is the corresponding input gradient.
+
+
+
+
+ Generate a single randomly distorted bounding box for an image.
+
+
+ 1-D, containing [height, width, channels].
+
+
+ 3-D with shape [batch, N, 4] describing the N bounding boxes
+ associated with the image.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SampleDistortedBoundingBox'.
+
+
+ Optional argument
+ If either seed or seed2 are set to non-zero, the random number
+ generator is seeded by the given seed. Otherwise, it is seeded by a random
+ seed.
+
+
+ Optional argument
+ A second seed to avoid seed collision.
+
+
+ Optional argument
+ The cropped area of the image must contain at least this
+ fraction of any bounding box supplied. The value of this parameter should be
+ non-negative. In the case of 0, the cropped area does not need to overlap
+ any of the bounding boxes supplied.
+
+
+ Optional argument
+ The cropped area of the image must have an aspect ratio =
+ width / height within this range.
+
+
+ Optional argument
+ The cropped area of the image must contain a fraction of the
+ supplied image within this range.
+
+
+ Optional argument
+ Number of attempts at generating a cropped region of the image
+ of the specified constraints. After max_attempts failures, return the entire
+ image.
+
+
+ Optional argument
+ Controls behavior if no bounding boxes supplied.
+ If true, assume an implicit bounding box covering the whole input. If false,
+ raise an error.
+
+
+ Returns a tuple with multiple values, as follows:
+ begin: 1-D, containing [offset_height, offset_width, 0]. Provide as input to
+ tf.slice.
+ size: 1-D, containing [target_height, target_width, -1]. Provide as input to
+ tf.slice.
+ bboxes: 3-D with shape [1, 1, 4] containing the distorted bounding box.
+ Provide as input to tf.image.draw_bounding_boxes.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Bounding box annotations are often supplied in addition to ground-truth labels
+ in image recognition or object localization tasks. A common technique for
+ training such a system is to randomly distort an image while preserving
+ its content, i.e. *data augmentation*. This Op outputs a randomly distorted
+ localization of an object, i.e. bounding box, given an image_size,
+ bounding_boxes and a series of constraints.
+
+ The output of this Op is a single bounding box that may be used to crop the
+ original image. The output is returned as 3 tensors: begin, size and
+ bboxes. The first 2 tensors can be fed directly into tf.slice to crop the
+ image. The latter may be supplied to tf.image.draw_bounding_boxes to visualize
+ what the bounding box looks like.
+
+ Bounding boxes are supplied and returned as [y_min, x_min, y_max, x_max]. The
+ bounding box coordinates are floats in [0.0, 1.0] relative to the width and
+ height of the underlying image.
+
+ For example,
+
+
+ # Generate a single distorted bounding box.
+ begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box(
+ tf.shape(image),
+ bounding_boxes=bounding_boxes)
+
+ # Draw the bounding box in an image summary.
+ image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0),
+ bbox_for_draw)
+ tf.summary.image('images_with_box', image_with_box)
+
+ # Employ the bounding box to distort the image.
+ distorted_image = tf.slice(image, begin, size)
+
+
+ Note that if no bounding box information is available, setting
+ use_image_if_no_bounding_boxes = true will assume there is a single implicit
+ bounding box covering the whole image. If use_image_if_no_bounding_boxes is
+ false and no bounding boxes are supplied, an error is raised.
+
+
+
+
+ Generate a single randomly distorted bounding box for an image.
+
+
+ 1-D, containing [height, width, channels].
+
+
+ 3-D with shape [batch, N, 4] describing the N bounding boxes
+ associated with the image.
+
+
+ The cropped area of the image must contain at least this
+ fraction of any bounding box supplied. The value of this parameter should be
+ non-negative. In the case of 0, the cropped area does not need to overlap
+ any of the bounding boxes supplied.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SampleDistortedBoundingBoxV2'.
+
+
+ Optional argument
+ If either seed or seed2 are set to non-zero, the random number
+ generator is seeded by the given seed. Otherwise, it is seeded by a random
+ seed.
+
+
+ Optional argument
+ A second seed to avoid seed collision.
+
+
+ Optional argument
+ The cropped area of the image must have an aspect ratio =
+ width / height within this range.
+
+
+ Optional argument
+ The cropped area of the image must contain a fraction of the
+ supplied image within this range.
+
+
+ Optional argument
+ Number of attempts at generating a cropped region of the image
+ of the specified constraints. After max_attempts failures, return the entire
+ image.
+
+
+ Optional argument
+ Controls behavior if no bounding boxes supplied.
+ If true, assume an implicit bounding box covering the whole input. If false,
+ raise an error.
+
+
+ Returns a tuple with multiple values, as follows:
+ begin: 1-D, containing [offset_height, offset_width, 0]. Provide as input to
+ tf.slice.
+ size: 1-D, containing [target_height, target_width, -1]. Provide as input to
+ tf.slice.
+ bboxes: 3-D with shape [1, 1, 4] containing the distorted bounding box.
+ Provide as input to tf.image.draw_bounding_boxes.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Bounding box annotations are often supplied in addition to ground-truth labels
+ in image recognition or object localization tasks. A common technique for
+ training such a system is to randomly distort an image while preserving
+ its content, i.e. *data augmentation*. This Op outputs a randomly distorted
+ localization of an object, i.e. bounding box, given an image_size,
+ bounding_boxes and a series of constraints.
+
+ The output of this Op is a single bounding box that may be used to crop the
+ original image. The output is returned as 3 tensors: begin, size and
+ bboxes. The first 2 tensors can be fed directly into tf.slice to crop the
+ image. The latter may be supplied to tf.image.draw_bounding_boxes to visualize
+ what the bounding box looks like.
+
+ Bounding boxes are supplied and returned as [y_min, x_min, y_max, x_max]. The
+ bounding box coordinates are floats in [0.0, 1.0] relative to the width and
+ height of the underlying image.
+
+ For example,
+
+
+ # Generate a single distorted bounding box.
+ begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box(
+ tf.shape(image),
+ bounding_boxes=bounding_boxes)
+
+ # Draw the bounding box in an image summary.
+ image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0),
+ bbox_for_draw)
+ tf.summary.image('images_with_box', image_with_box)
+
+ # Employ the bounding box to distort the image.
+ distorted_image = tf.slice(image, begin, size)
+
+
+ Note that if no bounding box information is available, setting
+ use_image_if_no_bounding_boxes = true will assume there is a single implicit
+ bounding box covering the whole image. If use_image_if_no_bounding_boxes is
+ false and no bounding boxes are supplied, an error is raised.
+
+
+
+
+ Saves the input tensors to disk.
+
+
+ Must have a single element. The name of the file to which we write
+ the tensor.
+
+
+ Shape [N]. The names of the tensors to be saved.
+
+
+ N tensors to save.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Save'.
+
+
+ Returns the description of the operation
+
+
+ The size of tensor_names must match the number of tensors in data. data[i]
+ is written to filename with name tensor_names[i].
+
+ See also SaveSlices.
+
+
+
+
+ Saves input tensors slices to disk.
+
+
+ Must have a single element. The name of the file to which we write the
+ tensor.
+
+
+ Shape [N]. The names of the tensors to be saved.
+
+
+ Shape [N]. The shapes and slice specifications to use when
+ saving the tensors.
+
+
+ N tensors to save.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SaveSlices'.
+
+
+ Returns the description of the operation
+
+
+ This is like Save except that tensors can be listed in the saved file as being
+ a slice of a larger tensor. shapes_and_slices specifies the shape of the
+ larger tensor and the slice that this tensor covers. shapes_and_slices must
+ have as many elements as tensor_names.
+
+ Elements of the shapes_and_slices input must either be:
+
+ * The empty string, in which case the corresponding tensor is
+ saved normally.
+ * A string of the form dim0 dim1 ... dimN-1 slice-spec where the
+ dimI are the dimensions of the larger tensor and slice-spec
+ specifies what part is covered by the tensor to save.
+
+ slice-spec itself is a :-separated list: slice0:slice1:...:sliceN-1
+ where each sliceI is either:
+
+ * The string - meaning that the slice covers all indices of this dimension
+ * start,length where start and length are integers. In that
+ case the slice covers length indices starting at start.
+
+ See also Save.
+
+
+
+
+ Saves tensors in V2 checkpoint format.
+
+
+ Must have a single element. The prefix of the V2 checkpoint to which we
+ write the tensors.
+
+
+ shape {N}. The names of the tensors to be saved.
+
+
+ shape {N}. The slice specs of the tensors to be saved.
+ Empty strings indicate that they are non-partitioned tensors.
+
+
+ N tensors to save.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SaveV2'.
+
+
+ Returns the description of the operation
+
+
+ By default, saves the named tensors in full. If the caller wishes to save
+ specific slices of full tensors, "shape_and_slices" should be non-empty strings
+ and correspondingly well-formed.
+
+
+
+
+
+ Adds sparse updates to a variable reference.
+
+
+ Should be from a Variable node.
+
+
+ A tensor of indices into the first dimension of ref.
+
+
+ A tensor of updated values to add to ref.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ScatterAdd'.
+
+
+ Optional argument
+ If True, the addition will be protected by a lock;
+ otherwise the behavior is undefined, but may exhibit less contention.
+
+
+ = Same as ref. Returned as a convenience for operations that want
+ to use the updated values after the update is done.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation computes
+
+ # Scalar indices
+ ref[indices, ...] += updates[...]
+
+ # Vector indices (for each i)
+ ref[indices[i], ...] += updates[i, ...]
+
+ # High rank indices (for each i, ..., j)
+ ref[indices[i, ..., j], ...] += updates[i, ..., j, ...]
+
+ This operation outputs ref after the update is done.
+ This makes it easier to chain operations that need to use the reset value.
+
+ Duplicate entries are handled correctly: if multiple indices reference
+ the same location, their contributions add.
+
+ Requires updates.shape = indices.shape + ref.shape[1:] or updates.shape = [].
+
+ <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+ <img style="width:100%" src="https://www.tensorflow.org/images/ScatterAdd.png" alt>
+ </div>
+
+
+
+
+ Divides a variable reference by sparse updates.
+
+
+ Should be from a Variable node.
+
+
+ A tensor of indices into the first dimension of ref.
+
+
+ A tensor of values that ref is divided by.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ScatterDiv'.
+
+
+ Optional argument
+ If True, the operation will be protected by a lock;
+ otherwise the behavior is undefined, but may exhibit less contention.
+
+
+ = Same as ref. Returned as a convenience for operations that want
+ to use the updated values after the update is done.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation computes
+
+
+ # Scalar indices
+ ref[indices, ...] /= updates[...]
+
+ # Vector indices (for each i)
+ ref[indices[i], ...] /= updates[i, ...]
+
+ # High rank indices (for each i, ..., j)
+ ref[indices[i, ..., j], ...] /= updates[i, ..., j, ...]
+
+
+ This operation outputs ref after the update is done.
+ This makes it easier to chain operations that need to use the reset value.
+
+ Duplicate entries are handled correctly: if multiple indices reference
+ the same location, their contributions divide.
+
+ Requires updates.shape = indices.shape + ref.shape[1:] or updates.shape = [].
+
+
+
+
+ Reduces sparse updates into a variable reference using the max operation.
+
+
+ Should be from a Variable node.
+
+
+ A tensor of indices into the first dimension of ref.
+
+
+ A tensor of updated values to reduce into ref.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ScatterMax'.
+
+
+ Optional argument
+ If True, the update will be protected by a lock;
+ otherwise the behavior is undefined, but may exhibit less contention.
+
+
+ = Same as ref. Returned as a convenience for operations that want
+ to use the updated values after the update is done.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation computes
+
+ # Scalar indices
+ ref[indices, ...] = max(ref[indices, ...], updates[...])
+
+ # Vector indices (for each i)
+ ref[indices[i], ...] = max(ref[indices[i], ...], updates[i, ...])
+
+ # High rank indices (for each i, ..., j)
+ ref[indices[i, ..., j], ...] = max(ref[indices[i, ..., j], ...], updates[i, ..., j, ...])
+
+ This operation outputs ref after the update is done.
+ This makes it easier to chain operations that need to use the reset value.
+
+ Duplicate entries are handled correctly: if multiple indices reference
+ the same location, their contributions combine.
+
+ Requires updates.shape = indices.shape + ref.shape[1:] or updates.shape = [].
+
+ <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+ <img style="width:100%" src="https://www.tensorflow.org/images/ScatterAdd.png" alt>
+ </div>
+
+
+
+
+ Reduces sparse updates into a variable reference using the min operation.
+
+
+ Should be from a Variable node.
+
+
+ A tensor of indices into the first dimension of ref.
+
+
+ A tensor of updated values to reduce into ref.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ScatterMin'.
+
+
+ Optional argument
+ If True, the update will be protected by a lock;
+ otherwise the behavior is undefined, but may exhibit less contention.
+
+
+ = Same as ref. Returned as a convenience for operations that want
+ to use the updated values after the update is done.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation computes
+
+ # Scalar indices
+ ref[indices, ...] = min(ref[indices, ...], updates[...])
+
+ # Vector indices (for each i)
+ ref[indices[i], ...] = min(ref[indices[i], ...], updates[i, ...])
+
+ # High rank indices (for each i, ..., j)
+ ref[indices[i, ..., j], ...] = min(ref[indices[i, ..., j], ...], updates[i, ..., j, ...])
+
+ This operation outputs ref after the update is done.
+ This makes it easier to chain operations that need to use the reset value.
+
+ Duplicate entries are handled correctly: if multiple indices reference
+ the same location, their contributions combine.
+
+ Requires updates.shape = indices.shape + ref.shape[1:] or updates.shape = [].
+
+ <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+ <img style="width:100%" src="https://www.tensorflow.org/images/ScatterAdd.png" alt>
+ </div>
+
+
+
+
+ Multiplies sparse updates into a variable reference.
+
+
+ Should be from a Variable node.
+
+
+ A tensor of indices into the first dimension of ref.
+
+
+ A tensor of updated values to multiply to ref.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ScatterMul'.
+
+
+ Optional argument
+ If True, the operation will be protected by a lock;
+ otherwise the behavior is undefined, but may exhibit less contention.
+
+
+ = Same as ref. Returned as a convenience for operations that want
+ to use the updated values after the update is done.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation computes
+
+
+ # Scalar indices
+ ref[indices, ...] *= updates[...]
+
+ # Vector indices (for each i)
+ ref[indices[i], ...] *= updates[i, ...]
+
+ # High rank indices (for each i, ..., j)
+ ref[indices[i, ..., j], ...] *= updates[i, ..., j, ...]
+
+
+ This operation outputs ref after the update is done.
+ This makes it easier to chain operations that need to use the reset value.
+
+ Duplicate entries are handled correctly: if multiple indices reference
+ the same location, their contributions multiply.
+
+ Requires updates.shape = indices.shape + ref.shape[1:] or updates.shape = [].
+
+
+
+
+ Scatter updates into a new tensor according to indices.
+
+
+ Index tensor.
+
+
+ Updates to scatter into output.
+
+
+ 1-D. The shape of the resulting tensor.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ScatterNd'.
+
+
+ A new tensor with the given shape and updates applied according
+ to the indices.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Creates a new tensor by applying sparse updates to individual values or
+ slices within a tensor (initially zero for numeric, empty for string) of
+ the given shape according to indices. This operator is the inverse of the
+ tf.gather_nd operator which extracts values or slices from a given tensor.
+
+ This operation is similar to tensor_scatter_add, except that the tensor is
+ zero-initialized. Calling tf.scatter_nd(indices, values, shape) is identical
+ to tensor_scatter_add(tf.zeros(shape, values.dtype), indices, values)
+
+ If indices contains duplicates, then their updates are accumulated (summed).
+
+ **WARNING**: The order in which updates are applied is nondeterministic, so the
+ output will be nondeterministic if indices contains duplicates -- because
+ of some numerical approximation issues, numbers summed in different order
+ may yield different results.
+
+ indices is an integer tensor containing indices into a new tensor of shape
+ shape. The last dimension of indices can be at most the rank of shape:
+
+ indices.shape[-1] <= shape.rank
+
+ The last dimension of indices corresponds to indices into elements
+ (if indices.shape[-1] = shape.rank) or slices
+ (if indices.shape[-1] < shape.rank) along dimension indices.shape[-1] of
+ shape. updates is a tensor with shape
+
+ indices.shape[:-1] + shape[indices.shape[-1]:]
+
+ The simplest form of scatter is to insert individual elements in a tensor by
+ index. For example, say we want to insert 4 scattered elements in a rank-1
+ tensor with 8 elements.
+
+ <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+ <img style="width:100%" src="https://www.tensorflow.org/images/ScatterNd1.png" alt>
+ </div>
+
+ In Python, this scatter operation would look like this:
+
+
+ indices = tf.constant([[4], [3], [1], [7]])
+ updates = tf.constant([9, 10, 11, 12])
+ shape = tf.constant([8])
+ scatter = tf.scatter_nd(indices, updates, shape)
+ with tf.Session() as sess:
+ print(sess.run(scatter))
+
+
+ The resulting tensor would look like this:
+
+ [0, 11, 0, 10, 9, 0, 0, 12]
+
+ We can also, insert entire slices of a higher rank tensor all at once. For
+ example, if we wanted to insert two slices in the first dimension of a
+ rank-3 tensor with two matrices of new values.
+
+ <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+ <img style="width:100%" src="https://www.tensorflow.org/images/ScatterNd2.png" alt>
+ </div>
+
+ In Python, this scatter operation would look like this:
+
+
+ indices = tf.constant([[0], [2]])
+ updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6],
+ [7, 7, 7, 7], [8, 8, 8, 8]],
+ [[5, 5, 5, 5], [6, 6, 6, 6],
+ [7, 7, 7, 7], [8, 8, 8, 8]]])
+ shape = tf.constant([4, 4, 4])
+ scatter = tf.scatter_nd(indices, updates, shape)
+ with tf.Session() as sess:
+ print(sess.run(scatter))
+
+
+ The resulting tensor would look like this:
+
+ [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+ [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
+ [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
+ [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]]
+
+ Note that on CPU, if an out of bound index is found, an error is returned.
+ On GPU, if an out of bound index is found, the index is ignored.
+
+
+
+
+ Applies sparse addition between updates and individual values or slices
+
+
+ A mutable Tensor. Should be from a Variable node.
+
+
+ A Tensor. Must be one of the following types: int32, int64.
+ A tensor of indices into ref.
+
+
+ A Tensor. Must have the same type as ref. A tensor of updated values
+ to add to ref.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ScatterNdAdd'.
+
+
+ Optional argument
+ An optional bool. Defaults to True. If True, the assignment will
+ be protected by a lock; otherwise the behavior is undefined,
+ but may exhibit less contention.
+
+
+ Same as ref. Returned as a convenience for operations that want
+ to use the updated values after the update is done.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ within a given variable according to indices.
+
+ ref is a Tensor with rank P and indices is a Tensor of rank Q.
+
+ indices must be integer tensor, containing indices into ref.
+ It must be shape \\([d_0, ..., d_{Q-2}, K]\\) where 0 < K <= P.
+
+ The innermost dimension of indices (with length K) corresponds to
+ indices into elements (if K = P) or slices (if K < P) along the Kth
+ dimension of ref.
+
+ updates is Tensor of rank Q-1+P-K with shape:
+
+ $$[d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].$$
+
+ For example, say we want to add 4 scattered elements to a rank-1 tensor to 8
+ elements. In Python, that addition would look like this:
+
+ ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
+ indices = tf.constant([[4], [3], [1], [7]])
+ updates = tf.constant([9, 10, 11, 12])
+ add = tf.scatter_nd_add(ref, indices, updates)
+ with tf.Session() as sess:
+ print sess.run(add)
+
+ The resulting update to ref would look like this:
+
+ [1, 13, 3, 14, 14, 6, 7, 20]
+
+ See tf.scatter_nd for more details about how to make updates to
+ slices.
+
+
+
+
+ Applies sparse addition to input using individual values or slices
+
+
+ A Tensor.
+
+
+ A Tensor. Must be one of the following types: int32, int64.
+ A tensor of indices into input.
+
+
+ A Tensor. Must have the same type as ref. A tensor of updated values
+ to add to input.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ScatterNdNonAliasingAdd'.
+
+
+ A Tensor with the same shape as input, containing values of input
+ updated with updates.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ from updates according to indices indices. The updates are non-aliasing:
+ input is only modified in-place if no other operations will use it.
+ Otherwise, a copy of input is made. This operation has a gradient with
+ respect to both input and updates.
+
+ input is a Tensor with rank P and indices is a Tensor of rank Q.
+
+ indices must be integer tensor, containing indices into input.
+ It must be shape \\([d_0, ..., d_{Q-2}, K]\\) where 0 < K <= P.
+
+ The innermost dimension of indices (with length K) corresponds to
+ indices into elements (if K = P) or (P-K)-dimensional slices
+ (if K < P) along the Kth dimension of input.
+
+ updates is Tensor of rank Q-1+P-K with shape:
+
+ $$[d_0, ..., d_{Q-2}, input.shape[K], ..., input.shape[P-1]].$$
+
+ For example, say we want to add 4 scattered elements to a rank-1 tensor to 8
+ elements. In Python, that addition would look like this:
+
+ input = tf.constant([1, 2, 3, 4, 5, 6, 7, 8])
+ indices = tf.constant([[4], [3], [1], [7]])
+ updates = tf.constant([9, 10, 11, 12])
+ output = tf.scatter_nd_non_aliasing_add(input, indices, updates)
+ with tf.Session() as sess:
+ print(sess.run(output))
+
+ The resulting value output would look like this:
+
+ [1, 13, 3, 14, 14, 6, 7, 20]
+
+ See tf.scatter_nd for more details about how to make updates to slices.
+
+
+
+
+ Applies sparse subtraction between updates and individual values or slices
+
+
+ A mutable Tensor. Should be from a Variable node.
+
+
+ A Tensor. Must be one of the following types: int32, int64.
+ A tensor of indices into ref.
+
+
+ A Tensor. Must have the same type as ref. A tensor of updated values
+ to subtract from ref.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ScatterNdSub'.
+
+
+ Optional argument
+ An optional bool. Defaults to True. If True, the assignment will
+ be protected by a lock; otherwise the behavior is undefined,
+ but may exhibit less contention.
+
+
+ Same as ref. Returned as a convenience for operations that want
+ to use the updated values after the update is done.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ within a given variable according to indices.
+
+ ref is a Tensor with rank P and indices is a Tensor of rank Q.
+
+ indices must be integer tensor, containing indices into ref.
+ It must be shape \\([d_0, ..., d_{Q-2}, K]\\) where 0 < K <= P.
+
+ The innermost dimension of indices (with length K) corresponds to
+ indices into elements (if K = P) or slices (if K < P) along the Kth
+ dimension of ref.
+
+ updates is Tensor of rank Q-1+P-K with shape:
+
+ $$[d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].$$
+
+ For example, say we want to subtract 4 scattered elements from a rank-1 tensor
+ with 8 elements. In Python, that subtraction would look like this:
+
+ ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
+ indices = tf.constant([[4], [3], [1], [7]])
+ updates = tf.constant([9, 10, 11, 12])
+ sub = tf.scatter_nd_sub(ref, indices, updates)
+ with tf.Session() as sess:
+ print sess.run(sub)
+
+ The resulting update to ref would look like this:
+
+ [1, -9, 3, -6, -4, 6, 7, -4]
+
+ See tf.scatter_nd for more details about how to make updates to
+ slices.
+
+
+
+
+ Applies sparse updates to individual values or slices within a given
+
+
+ A mutable Tensor. Should be from a Variable node.
+
+
+ A Tensor. Must be one of the following types: int32, int64.
+ A tensor of indices into ref.
+
+
+ A Tensor. Must have the same type as ref. A tensor of updated
+ values to add to ref.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ScatterNdUpdate'.
+
+
+ Optional argument
+ An optional bool. Defaults to True. If True, the assignment will
+ be protected by a lock; otherwise the behavior is undefined,
+ but may exhibit less contention.
+
+
+ Same as ref. Returned as a convenience for operations that want to
+ use the updated values after the update is done.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ variable according to indices.
+
+ ref is a Tensor with rank P and indices is a Tensor of rank Q.
+
+ indices must be integer tensor, containing indices into ref.
+ It must be shape \\([d_0, ..., d_{Q-2}, K]\\) where 0 < K <= P.
+
+ The innermost dimension of indices (with length K) corresponds to
+ indices into elements (if K = P) or slices (if K < P) along the Kth
+ dimension of ref.
+
+ updates is Tensor of rank Q-1+P-K with shape:
+
+ $$[d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].$$
+
+ For example, say we want to update 4 scattered elements to a rank-1 tensor to
+ 8 elements. In Python, that update would look like this:
+
+
+ ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
+ indices = tf.constant([[4], [3], [1] ,[7]])
+ updates = tf.constant([9, 10, 11, 12])
+ update = tf.scatter_nd_update(ref, indices, updates)
+ with tf.Session() as sess:
+ print sess.run(update)
+
+
+ The resulting update to ref would look like this:
+
+ [1, 11, 3, 10, 9, 6, 7, 12]
+
+ See tf.scatter_nd for more details about how to make updates to
+ slices.
+
+ See also tf.scatter_update and tf.batch_scatter_update.
+
+
+
+
+ Subtracts sparse updates to a variable reference.
+
+
+ Should be from a Variable node.
+
+
+ A tensor of indices into the first dimension of ref.
+
+
+ A tensor of updated values to subtract from ref.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ScatterSub'.
+
+
+ Optional argument
+ If True, the subtraction will be protected by a lock;
+ otherwise the behavior is undefined, but may exhibit less contention.
+
+
+ = Same as ref. Returned as a convenience for operations that want
+ to use the updated values after the update is done.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+ # Scalar indices
+ ref[indices, ...] -= updates[...]
+
+ # Vector indices (for each i)
+ ref[indices[i], ...] -= updates[i, ...]
+
+ # High rank indices (for each i, ..., j)
+ ref[indices[i, ..., j], ...] -= updates[i, ..., j, ...]
+
+
+ This operation outputs ref after the update is done.
+ This makes it easier to chain operations that need to use the reset value.
+
+ Duplicate entries are handled correctly: if multiple indices reference
+ the same location, their (negated) contributions add.
+
+ Requires updates.shape = indices.shape + ref.shape[1:] or updates.shape = [].
+
+ <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+ <img style="width:100%" src="https://www.tensorflow.org/images/ScatterSub.png" alt>
+ </div>
+
+
+
+
+ Applies sparse updates to a variable reference.
+
+
+ Should be from a Variable node.
+
+
+ A tensor of indices into the first dimension of ref.
+
+
+ A tensor of updated values to store in ref.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ScatterUpdate'.
+
+
+ Optional argument
+ If True, the assignment will be protected by a lock;
+ otherwise the behavior is undefined, but may exhibit less contention.
+
+
+ = Same as ref. Returned as a convenience for operations that want
+ to use the updated values after the update is done.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation computes
+
+
+ # Scalar indices
+ ref[indices, ...] = updates[...]
+
+ # Vector indices (for each i)
+ ref[indices[i], ...] = updates[i, ...]
+
+ # High rank indices (for each i, ..., j)
+ ref[indices[i, ..., j], ...] = updates[i, ..., j, ...]
+
+
+ This operation outputs ref after the update is done.
+ This makes it easier to chain operations that need to use the reset value.
+
+ If values in ref is to be updated more than once, because there are
+ duplicate entries in indices, the order at which the updates happen
+ for each value is undefined.
+
+ Requires updates.shape = indices.shape + ref.shape[1:] or updates.shape = [].
+
+ <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+ <img style="width:100%" src="https://www.tensorflow.org/images/ScatterUpdate.png" alt>
+ </div>
+
+ See also tf.batch_scatter_update and tf.scatter_nd_update.
+
+
+
+
+ Computes fingerprints of the input strings.
+
+
+ vector of strings to compute fingerprints on.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SdcaFprint'.
+
+
+ a (N,2) shaped matrix where N is the number of elements in the input
+ vector. Each row contains the low and high parts of the fingerprint.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for
+
+
+ a list of vectors which contain example indices.
+
+
+ a list of vectors which contain feature indices.
+
+
+ a list of vectors which contains feature value
+ associated with each feature group.
+
+
+ a list of matrices which contains the dense feature values.
+
+
+ a vector which contains the weight associated with each
+ example.
+
+
+ a vector which contains the label/target associated with each
+ example.
+
+
+ a list of vectors where each value is the indices which has
+ corresponding weights in sparse_weights. This field maybe omitted for the
+ dense approach.
+
+
+ a list of vectors where each value is the weight associated with
+ a sparse feature group.
+
+
+ a list of vectors where the values are the weights associated
+ with a dense feature group.
+
+
+ a list of vectors containing the example state data.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SdcaOptimizer'.
+
+
+ Optional argument
+ Whether to use Adaptive SDCA for the inner loop.
+
+
+ Type of the primal loss. Currently SdcaSolver supports logistic,
+ squared and hinge losses.
+
+
+ Symmetric l1 regularization strength.
+
+
+ Symmetric l2 regularization strength.
+
+
+ Number of partitions of the global loss function.
+
+
+ Number of iterations per mini-batch.
+
+
+ Returns a tuple with multiple values, as follows:
+ out_example_state_data: a list of vectors containing the updated example state
+ data.
+ out_delta_sparse_weights: a list of vectors where each value is the delta
+ weights associated with a sparse feature group.
+ out_delta_dense_weights: a list of vectors where the values are the delta
+ weights associated with a dense feature group.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ linear models with L1 + L2 regularization. As global optimization objective is
+ strongly-convex, the optimizer optimizes the dual objective at each step. The
+ optimizer applies each update one example at a time. Examples are sampled
+ uniformly, and the optimizer is learning rate free and enjoys linear convergence
+ rate.
+
+ [Proximal Stochastic Dual Coordinate Ascent](http://arxiv.org/pdf/1211.2717v1.pdf).<br>
+ Shai Shalev-Shwartz, Tong Zhang. 2012
+
+ $$Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|$$
+
+ [Adding vs. Averaging in Distributed Primal-Dual Optimization](http://arxiv.org/abs/1502.03508).<br>
+ Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan,
+ Peter Richtarik, Martin Takac. 2015
+
+ [Stochastic Dual Coordinate Ascent with Adaptive Probabilities](https://arxiv.org/abs/1502.08053).<br>
+ Dominik Csiba, Zheng Qu, Peter Richtarik. 2015
+
+
+
+
+ Applies L1 regularization shrink step on the parameters.
+
+
+ a list of vectors where each value is the weight associated with a
+ feature group.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SdcaShrinkL1'.
+
+
+ Symmetric l1 regularization strength.
+
+
+ Symmetric l2 regularization strength. Should be a positive float.
+
+
+ Returns the description of the operation
+
+
+
+
+ Computes the maximum along segments of a tensor.
+
+
+
+
+ A 1-D tensor whose size is equal to the size of data's
+ first dimension. Values should be sorted and can be repeated.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SegmentMax'.
+
+
+ Has same shape as data, except for dimension 0 which
+ has size k, the number of segments.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Read
+ [the section on segmentation](https://tensorflow.org/api_guides/python/math_ops#Segmentation)
+ for an explanation of segments.
+
+ Computes a tensor such that
+ \\(output_i = \max_j(data_j)\\) where max is over j such
+ that segment_ids[j] == i.
+
+ If the max is empty for a given segment ID i, output[i] = 0.
+
+ <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+ <img style="width:100%" src="https://www.tensorflow.org/images/SegmentMax.png" alt>
+ </div>
+
+
+
+
+ Computes the mean along segments of a tensor.
+
+
+
+
+ A 1-D tensor whose size is equal to the size of data's
+ first dimension. Values should be sorted and can be repeated.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SegmentMean'.
+
+
+ Has same shape as data, except for dimension 0 which
+ has size k, the number of segments.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Read
+ [the section on segmentation](https://tensorflow.org/api_guides/python/math_ops#Segmentation)
+ for an explanation of segments.
+
+ Computes a tensor such that
+ \\(output_i = \frac{\sum_j data_j}{N}\\) where mean is
+ over j such that segment_ids[j] == i and N is the total number of
+ values summed.
+
+ If the mean is empty for a given segment ID i, output[i] = 0.
+
+ <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+ <img style="width:100%" src="https://www.tensorflow.org/images/SegmentMean.png" alt>
+ </div>
+
+
+
+
+ Computes the minimum along segments of a tensor.
+
+
+
+
+ A 1-D tensor whose size is equal to the size of data's
+ first dimension. Values should be sorted and can be repeated.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SegmentMin'.
+
+
+ Has same shape as data, except for dimension 0 which
+ has size k, the number of segments.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Read
+ [the section on segmentation](https://tensorflow.org/api_guides/python/math_ops#Segmentation)
+ for an explanation of segments.
+
+ Computes a tensor such that
+ \\(output_i = \min_j(data_j)\\) where min is over j such
+ that segment_ids[j] == i.
+
+ If the min is empty for a given segment ID i, output[i] = 0.
+
+ <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+ <img style="width:100%" src="https://www.tensorflow.org/images/SegmentMin.png" alt>
+ </div>
+
+
+
+
+ Computes the product along segments of a tensor.
+
+
+
+
+ A 1-D tensor whose size is equal to the size of data's
+ first dimension. Values should be sorted and can be repeated.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SegmentProd'.
+
+
+ Has same shape as data, except for dimension 0 which
+ has size k, the number of segments.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Read
+ [the section on segmentation](https://tensorflow.org/api_guides/python/math_ops#Segmentation)
+ for an explanation of segments.
+
+ Computes a tensor such that
+ \\(output_i = \prod_j data_j\\) where the product is over j such
+ that segment_ids[j] == i.
+
+ If the product is empty for a given segment ID i, output[i] = 1.
+
+ <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+ <img style="width:100%" src="https://www.tensorflow.org/images/SegmentProd.png" alt>
+ </div>
+
+
+
+
+ Computes the sum along segments of a tensor.
+
+
+
+
+ A 1-D tensor whose size is equal to the size of data's
+ first dimension. Values should be sorted and can be repeated.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SegmentSum'.
+
+
+ Has same shape as data, except for dimension 0 which
+ has size k, the number of segments.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Read
+ [the section on segmentation](https://tensorflow.org/api_guides/python/math_ops#Segmentation)
+ for an explanation of segments.
+
+ Computes a tensor such that
+ \\(output_i = \sum_j data_j\\) where sum is over j such
+ that segment_ids[j] == i.
+
+ If the sum is empty for a given segment ID i, output[i] = 0.
+
+ <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+ <img style="width:100%" src="https://www.tensorflow.org/images/SegmentSum.png" alt>
+ </div>
+
+
+
+
+ Selects elements from x or y, depending on condition.
+
+
+
+
+ = A Tensor which may have the same shape as condition.
+ If condition is rank 1, x may have higher rank,
+ but its first dimension must match the size of condition.
+
+
+ = A Tensor with the same type and shape as x.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Select'.
+
+
+ = A Tensor with the same type and shape as x and y.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The x, and y tensors must all have the same shape, and the
+ output will also have that shape.
+
+ The condition tensor must be a scalar if x and y are scalars.
+ If x and y are vectors or higher rank, then condition must be either a
+ scalar, a vector with size matching the first dimension of x, or must have
+ the same shape as x.
+
+ The condition tensor acts as a mask that chooses, based on the value at each
+ element, whether the corresponding element / row in the output should be
+ taken from x (if true) or y (if false).
+
+ If condition is a vector and x and y are higher rank matrices, then
+ it chooses which row (outer dimension) to copy from x and y.
+ If condition has the same shape as x and y, then it chooses which
+ element to copy from x and y.
+
+ For example:
+
+
+ # 'condition' tensor is [[True, False]
+ # [False, True]]
+ # 't' is [[1, 2],
+ # [3, 4]]
+ # 'e' is [[5, 6],
+ # [7, 8]]
+ select(condition, t, e) # => [[1, 6], [7, 4]]
+
+
+ # 'condition' tensor is [True, False]
+ # 't' is [[1, 2],
+ # [3, 4]]
+ # 'e' is [[5, 6],
+ # [7, 8]]
+ select(condition, t, e) ==> [[1, 2],
+ [7, 8]]
+
+
+
+
+
+
+ Computes the Eigen Decomposition of a batch of square self-adjoint matrices.
+
+
+ Shape is [..., M, M].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SelfAdjointEig'.
+
+
+ Shape is [..., M+1, M].
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The input is a tensor of shape [..., M, M] whose inner-most 2 dimensions
+ form square matrices, with the same constraints as the single matrix
+ SelfAdjointEig.
+
+ The result is a [..., M+1, M] matrix with [..., 0,:] containing the
+ eigenvalues, and subsequent [...,1:, :] containing the eigenvectors. The eigenvalues
+ are sorted in non-decreasing order.
+
+
+
+
+ Computes the eigen decomposition of one or more square self-adjoint matrices.
+
+
+ Tensor input of shape [N, N].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SelfAdjointEigV2'.
+
+
+ Optional argument
+ If True then eigenvectors will be computed and returned in v.
+ Otherwise, only the eigenvalues will be computed.
+
+
+ Returns a tuple with multiple values, as follows:
+ e: Eigenvalues. Shape is [N].
+ v: Eigenvectors. Shape is [N, N].
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Computes the eigenvalues and (optionally) eigenvectors of each inner matrix in
+ input such that input[..., :, :] = v[..., :, :] * diag(e[..., :]). The eigenvalues
+ are sorted in non-decreasing order.
+
+
+ # a is a tensor.
+ # e is a tensor of eigenvalues.
+ # v is a tensor of eigenvectors.
+ e, v = self_adjoint_eig(a)
+ e = self_adjoint_eig(a, compute_v=False)
+
+
+
+
+
+ Computes scaled exponential linear: scale * alpha * (exp(features) - 1)
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Selu'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ if < 0, scale * features otherwise.
+
+ To be used together with
+ initializer = tf.variance_scaling_initializer(factor=1.0, mode='FAN_IN').
+ For correct dropout, use tf.contrib.nn.alpha_dropout.
+
+ See [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)
+
+
+
+
+ Computes gradients for the scaled exponential linear (Selu) operation.
+
+
+ The backpropagated gradients to the corresponding Selu operation.
+
+
+ The outputs of the corresponding Selu operation.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SeluGrad'.
+
+
+ The gradients: gradients * (outputs + scale * alpha)
+ if outputs < 0, scale * gradients otherwise.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ An op that performs gradient updates of embedding tables.
+
+
+ A TensorList of gradients with which to update embedding tables.
+ It contains one tensor per embedding table in the model.
+
+
+ A list of float32 scalars, one for each embedding table,
+ containing the learning rates for each table when dynamic learning rate is
+ enabled through the OptimizationParameters in TPUEmbeddingConfiguration.
+ When the learning rate is constant, the list should be empty.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SendTPUEmbeddingGradients'.
+
+
+ Serialized TPUEmbeddingConfiguration proto.
+
+
+ Returns the description of the operation
+
+
+ The TensorList argument has the same length and shapes as the return value of
+ TPUEmbeddingReceiveActivations, but contains gradients of the model's loss
+ with respect to the embedding activations. The embedding tables are updated
+ from these gradients via the optimizer specified in the configuration given
+ to tpu.initialize_system.
+
+
+
+
+ Converts the given resource_handle representing an iterator to a variant tensor.
+
+
+ A handle to an iterator resource.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SerializeIterator'.
+
+
+ A variant tensor storing the state of the iterator contained in the
+ resource.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Serialize an N-minibatch SparseTensor into an [N, 3] Tensor object.
+
+
+ 2-D. The indices of the minibatch SparseTensor.
+
+
+ 1-D. The values of the minibatch SparseTensor.
+
+
+ 1-D. The shape of the minibatch SparseTensor.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SerializeManySparse'.
+
+
+ Optional argument
+ The dtype to use for serialization; the supported types are string
+ (default) and variant.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The SparseTensor must have rank R greater than 1, and the first dimension
+ is treated as the minibatch dimension. Elements of the SparseTensor
+ must be sorted in increasing order of this first dimension. The serialized
+ SparseTensor objects going into each row of serialized_sparse will have
+ rank R-1.
+
+ The minibatch size N is extracted from sparse_shape[0].
+
+
+
+
+ Serialize a SparseTensor into a [3] Tensor object.
+
+
+ 2-D. The indices of the SparseTensor.
+
+
+ 1-D. The values of the SparseTensor.
+
+
+ 1-D. The shape of the SparseTensor.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SerializeSparse'.
+
+
+ Optional argument
+ The dtype to use for serialization; the supported types are string
+ (default) and variant.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Transforms a Tensor into a serialized TensorProto proto.
+
+
+ A Tensor of type T.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SerializeTensor'.
+
+
+ A serialized TensorProto proto of the input tensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Number of unique elements along last dimension of input set.
+
+
+ 2D Tensor, indices of a SparseTensor.
+
+
+ 1D Tensor, values of a SparseTensor.
+
+
+ 1D Tensor, shape of a SparseTensor.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SetSize'.
+
+
+ Optional argument
+
+
+ For set ranked n, this is a Tensor with rank n-1, and the same 1st
+ n-1 dimensions as set. Each value is the number of unique elements in
+ the corresponding [0...n-1] dimension of set.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Input set is a SparseTensor represented by set_indices, set_values,
+ and set_shape. The last dimension contains values in a set, duplicates are
+ allowed but ignored.
+
+ If validate_indices is True, this op validates the order and range of set
+ indices.
+
+
+
+
+ Returns the shape of a tensor.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Shape'.
+
+
+ Optional argument
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation returns a 1-D integer tensor representing the shape of input.
+
+ For example:
+
+
+ # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]
+ shape(t) ==> [2, 2, 3]
+
+
+
+
+
+ Returns shape of tensors.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ShapeN'.
+
+
+ Optional argument
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation returns N 1-D integer tensors representing shape of input[i]s.
+
+
+
+
+ Generate a sharded filename. The filename is printf formatted as
+
+
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ShardedFilename'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ %s-%05d-of-%05d, basename, shard, num_shards.
+
+
+
+
+ Generate a glob pattern matching all sharded file names.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ShardedFilespec'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Creates a dataset that shuffles and repeats elements from input_dataset
+
+
+
+
+ The number of output elements to buffer in an iterator over
+ this dataset. Compare with the min_after_dequeue attr when creating a
+ RandomShuffleQueue.
+
+
+ A scalar seed for the random number generator. If either seed or
+ seed2 is set to be non-zero, the random number generator is seeded
+ by the given seed. Otherwise, a random seed is used.
+
+
+ A second scalar seed to avoid seed collision.
+
+
+ A scalar representing the number of times the underlying dataset
+ should be repeated. The default is -1, which results in infinite repetition.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ShuffleAndRepeatDataset'.
+
+
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ pseudorandomly.
+
+
+
+
+ Creates a dataset that shuffles elements from input_dataset pseudorandomly.
+
+
+
+
+ The number of output elements to buffer in an iterator over
+ this dataset. Compare with the min_after_dequeue attr when creating a
+ RandomShuffleQueue.
+
+
+ A scalar seed for the random number generator. If either seed or
+ seed2 is set to be non-zero, the random number generator is seeded
+ by the given seed. Otherwise, a random seed is used.
+
+
+ A second scalar seed to avoid seed collision.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ShuffleDataset'.
+
+
+ Optional argument
+ If true, each iterator over this dataset will be given
+ a different pseudorandomly generated seed, based on a sequence seeded by the
+ seed and seed2 inputs. If false, each iterator will be given the same
+ seed, and repeated iteration over this dataset will yield the exact same
+ sequence of results.
+
+
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ An op that shuts down a running distributed TPU system. The Op returns
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ShutdownDistributedTPU'.
+
+
+ Returns the description of the operation
+
+
+ an error if no system is running.
+
+
+
+
+ Computes sigmoid of x element-wise.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Sigmoid'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Specifically, y = 1 / (1 + exp(-x)).
+
+
+
+
+ Computes the gradient of the sigmoid of x wrt its input.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SigmoidGrad'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Specifically, grad = dy * y * (1 - y), where y = sigmoid(x), and
+ dy is the corresponding input gradient.
+
+
+
+
+ Returns an element-wise indication of the sign of a number.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Sign'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ y = sign(x) = -1 if x < 0; 0 if x == 0; 1 if x > 0.
+
+ For complex numbers, y = sign(x) = x / |x| if x != 0, otherwise y = 0.
+
+
+
+
+ Computes sin of x element-wise.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Sin'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes hyperbolic sine of x element-wise.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Sinh'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Returns the size of a tensor.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Size'.
+
+
+ Optional argument
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation returns an integer representing the number of elements in
+ input.
+
+ For example:
+
+
+ # 't' is [[[1, 1,, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]]
+ size(t) ==> 12
+
+
+
+
+
+ Creates a dataset that skips count elements from the input_dataset.
+
+
+
+
+ A scalar representing the number of elements from the input_dataset
+ that should be skipped. If count is -1, skips everything.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SkipDataset'.
+
+
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Parses a text file and creates a batch of examples.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Skipgram'.
+
+
+ Optional argument
+ The number of words to predict to the left and right of the target.
+
+
+ Optional argument
+ The minimum number of word occurrences for it to be included in the
+ vocabulary.
+
+
+ Optional argument
+ Threshold for word occurrence. Words that appear with higher
+ frequency will be randomly down-sampled. Set to 0 to disable.
+
+
+ The corpus's text file name.
+
+
+ The size of produced batch.
+
+
+ Returns a tuple with multiple values, as follows:
+ vocab_word: A vector of words in the corpus.
+ vocab_freq: Frequencies of words. Sorted in the non-ascending order.
+ words_per_epoch: Number of words per epoch in the data file.
+ current_epoch: The current epoch number.
+ total_words_processed: The total number of words processed so far.
+ examples: A vector of word ids.
+ labels: A vector of word ids.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+
+ Return a slice from 'input'.
+
+
+
+
+ begin[i] specifies the offset into the 'i'th dimension of
+ 'input' to slice from.
+
+
+ size[i] specifies the number of elements of the 'i'th dimension
+ of 'input' to slice. If size[i] is -1, all remaining elements in dimension
+ i are included in the slice (i.e. this is equivalent to setting
+ size[i] = input.dim_size(i) - begin[i]).
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Slice'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The output tensor is a tensor with dimensions described by 'size'
+ whose values are extracted from 'input' starting at the offsets in
+ 'begin'.
+
+ *Requirements*:
+ 0 <= begin[i] <= begin[i] + size[i] <= Di for i in [0, n)
+
+
+
+
+ Returns a copy of the input tensor.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Snapshot'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes softmax activations.
+
+
+ 2-D with shape [batch_size, num_classes].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Softmax'.
+
+
+ Same shape as logits.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ For each batch i and class j we have
+
+ $$softmax[i, j] = exp(logits[i, j]) / sum_j(exp(logits[i, j]))$$
+
+
+
+
+ Computes softmax cross entropy cost and gradients to backpropagate.
+
+
+ batch_size x num_classes matrix
+
+
+ batch_size x num_classes matrix
+ The caller must ensure that each batch of labels represents a valid
+ probability distribution.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SoftmaxCrossEntropyWithLogits'.
+
+
+ Returns a tuple with multiple values, as follows:
+ loss: Per example loss (batch_size vector).
+ backprop: backpropagated gradients (batch_size x num_classes matrix).
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Inputs are the logits, not probabilities.
+
+
+
+
+ Computes softplus: log(exp(features) + 1).
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Softplus'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes softplus gradients for a softplus operation.
+
+
+ The backpropagated gradients to the corresponding softplus operation.
+
+
+ The features passed as input to the corresponding softplus operation.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SoftplusGrad'.
+
+
+ The gradients: gradients / (1 + exp(-features)).
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes softsign: features / (abs(features) + 1).
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Softsign'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes softsign gradients for a softsign operation.
+
+
+ The backpropagated gradients to the corresponding softsign operation.
+
+
+ The features passed as input to the corresponding softsign operation.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SoftsignGrad'.
+
+
+ The gradients: gradients / (1 + abs(features)) ** 2.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ SpaceToBatch for 4-D tensors of type T.
+
+
+ 4-D with shape [batch, height, width, depth].
+
+
+ 2-D tensor of non-negative integers with shape [2, 2]. It specifies
+ the padding of the input with zeros across the spatial dimensions as follows:
+
+ paddings = [[pad_top, pad_bottom], [pad_left, pad_right]]
+
+ The effective spatial dimensions of the zero-padded input tensor will be:
+
+ height_pad = pad_top + height + pad_bottom
+ width_pad = pad_left + width + pad_right
+
+ The attr block_size must be greater than one. It indicates the block size.
+
+ * Non-overlapping blocks of size block_size x block size in the height and
+ width dimensions are rearranged into the batch dimension at each location.
+ * The batch of the output tensor is batch * block_size * block_size.
+ * Both height_pad and width_pad must be divisible by block_size.
+
+ The shape of the output will be:
+
+ [batch*block_size*block_size, height_pad/block_size, width_pad/block_size,
+ depth]
+
+ Some examples:
+
+ (1) For the following input of shape [1, 2, 2, 1] and block_size of 2:
+
+
+ x = [[[[1], [2]], [[3], [4]]]]
+
+
+ The output tensor has shape [4, 1, 1, 1] and value:
+
+
+ [[[[1]]], [[[2]]], [[[3]]], [[[4]]]]
+
+
+ (2) For the following input of shape [1, 2, 2, 3] and block_size of 2:
+
+
+ x = [[[[1, 2, 3], [4, 5, 6]],
+ [[7, 8, 9], [10, 11, 12]]]]
+
+
+ The output tensor has shape [4, 1, 1, 3] and value:
+
+
+ [[[1, 2, 3]], [[4, 5, 6]], [[7, 8, 9]], [[10, 11, 12]]]
+
+
+ (3) For the following input of shape [1, 4, 4, 1] and block_size of 2:
+
+
+ x = [[[[1], [2], [3], [4]],
+ [[5], [6], [7], [8]],
+ [[9], [10], [11], [12]],
+ [[13], [14], [15], [16]]]]
+
+
+ The output tensor has shape [4, 2, 2, 1] and value:
+
+
+ x = [[[[1], [3]], [[9], [11]]],
+ [[[2], [4]], [[10], [12]]],
+ [[[5], [7]], [[13], [15]]],
+ [[[6], [8]], [[14], [16]]]]
+
+
+ (4) For the following input of shape [2, 2, 4, 1] and block_size of 2:
+
+
+ x = [[[[1], [2], [3], [4]],
+ [[5], [6], [7], [8]]],
+ [[[9], [10], [11], [12]],
+ [[13], [14], [15], [16]]]]
+
+
+ The output tensor has shape [8, 1, 2, 1] and value:
+
+
+ x = [[[[1], [3]]], [[[9], [11]]], [[[2], [4]]], [[[10], [12]]],
+ [[[5], [7]]], [[[13], [15]]], [[[6], [8]]], [[[14], [16]]]]
+
+
+ Among others, this operation is useful for reducing atrous convolution into
+ regular convolution.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SpaceToBatch'.
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This is a legacy version of the more general SpaceToBatchND.
+
+ Zero-pads and then rearranges (permutes) blocks of spatial data into batch.
+ More specifically, this op outputs a copy of the input tensor where values from
+ the height and width dimensions are moved to the batch dimension. After
+ the zero-padding, both height and width of the input must be divisible by the
+ block size.
+
+
+
+
+ SpaceToBatch for N-D tensors of type T.
+
+
+ N-D with shape input_shape = [batch] + spatial_shape + remaining_shape,
+ where spatial_shape has M dimensions.
+
+
+ 1-D with shape [M], all values must be >= 1.
+
+
+ 2-D with shape [M, 2], all values must be >= 0.
+ paddings[i] = [pad_start, pad_end] specifies the padding for input dimension
+ i + 1, which corresponds to spatial dimension i. It is required that
+ block_shape[i] divides input_shape[i + 1] + pad_start + pad_end.
+
+ This operation is equivalent to the following steps:
+
+ 1. Zero-pad the start and end of dimensions [1, ..., M] of the
+ input according to paddings to produce padded of shape padded_shape.
+
+ 2. Reshape padded to reshaped_padded of shape:
+
+ [batch] +
+ [padded_shape[1] / block_shape[0],
+ block_shape[0],
+ ...,
+ padded_shape[M] / block_shape[M-1],
+ block_shape[M-1]] +
+ remaining_shape
+
+ 3. Permute dimensions of reshaped_padded to produce
+ permuted_reshaped_padded of shape:
+
+ block_shape +
+ [batch] +
+ [padded_shape[1] / block_shape[0],
+ ...,
+ padded_shape[M] / block_shape[M-1]] +
+ remaining_shape
+
+ 4. Reshape permuted_reshaped_padded to flatten block_shape into the batch
+ dimension, producing an output tensor of shape:
+
+ [batch * prod(block_shape)] +
+ [padded_shape[1] / block_shape[0],
+ ...,
+ padded_shape[M] / block_shape[M-1]] +
+ remaining_shape
+
+ Some examples:
+
+ (1) For the following input of shape [1, 2, 2, 1], block_shape = [2, 2], and
+ paddings = [[0, 0], [0, 0]]:
+
+
+ x = [[[[1], [2]], [[3], [4]]]]
+
+
+ The output tensor has shape [4, 1, 1, 1] and value:
+
+
+ [[[[1]]], [[[2]]], [[[3]]], [[[4]]]]
+
+
+ (2) For the following input of shape [1, 2, 2, 3], block_shape = [2, 2], and
+ paddings = [[0, 0], [0, 0]]:
+
+
+ x = [[[[1, 2, 3], [4, 5, 6]],
+ [[7, 8, 9], [10, 11, 12]]]]
+
+
+ The output tensor has shape [4, 1, 1, 3] and value:
+
+
+ [[[1, 2, 3]], [[4, 5, 6]], [[7, 8, 9]], [[10, 11, 12]]]
+
+
+ (3) For the following input of shape [1, 4, 4, 1], block_shape = [2, 2], and
+ paddings = [[0, 0], [0, 0]]:
+
+
+ x = [[[[1], [2], [3], [4]],
+ [[5], [6], [7], [8]],
+ [[9], [10], [11], [12]],
+ [[13], [14], [15], [16]]]]
+
+
+ The output tensor has shape [4, 2, 2, 1] and value:
+
+
+ x = [[[[1], [3]], [[9], [11]]],
+ [[[2], [4]], [[10], [12]]],
+ [[[5], [7]], [[13], [15]]],
+ [[[6], [8]], [[14], [16]]]]
+
+
+ (4) For the following input of shape [2, 2, 4, 1], block_shape = [2, 2], and
+ paddings = [[0, 0], [2, 0]]:
+
+
+ x = [[[[1], [2], [3], [4]],
+ [[5], [6], [7], [8]]],
+ [[[9], [10], [11], [12]],
+ [[13], [14], [15], [16]]]]
+
+
+ The output tensor has shape [8, 1, 3, 1] and value:
+
+
+ x = [[[[0], [1], [3]]], [[[0], [9], [11]]],
+ [[[0], [2], [4]]], [[[0], [10], [12]]],
+ [[[0], [5], [7]]], [[[0], [13], [15]]],
+ [[[0], [6], [8]]], [[[0], [14], [16]]]]
+
+
+ Among others, this operation is useful for reducing atrous convolution into
+ regular convolution.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SpaceToBatchND'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation divides "spatial" dimensions [1, ..., M] of the input into a
+ grid of blocks of shape block_shape, and interleaves these blocks with the
+ "batch" dimension (0) such that in the output, the spatial dimensions
+ [1, ..., M] correspond to the position within the grid, and the batch
+ dimension combines both the position within a spatial block and the original
+ batch position. Prior to division into blocks, the spatial dimensions of the
+ input are optionally zero padded according to paddings. See below for a
+ precise description.
+
+
+
+
+ SpaceToDepth for tensors of type T.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SpaceToDepth'.
+
+
+ Optional argument
+
+
+ The size of the spatial block.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Rearranges blocks of spatial data, into depth. More specifically,
+ this op outputs a copy of the input tensor where values from the height
+ and width dimensions are moved to the depth dimension.
+ The attr block_size indicates the input block size.
+
+ * Non-overlapping blocks of size block_size x block size are rearranged
+ into depth at each location.
+ * The depth of the output tensor is block_size * block_size * input_depth.
+ * The Y, X coordinates within each block of the input become the high order
+ component of the output channel index.
+ * The input tensor's height and width must be divisible by block_size.
+
+ The data_format attr specifies the layout of the input and output tensors
+ with the following options:
+ "NHWC": [ batch, height, width, channels ]
+ "NCHW": [ batch, channels, height, width ]
+ "NCHW_VECT_C":
+ qint8 [ batch, channels / 4, height, width, 4 ]
+
+ It is useful to consider the operation as transforming a 6-D Tensor.
+ e.g. for data_format = NHWC,
+ Each element in the input tensor can be specified via 6 coordinates,
+ ordered by decreasing memory layout significance as:
+ n,oY,bY,oX,bX,iC (where n=batch index, oX, oY means X or Y coordinates
+ within the output image, bX, bY means coordinates
+ within the input block, iC means input channels).
+ The output would be a transpose to the following layout:
+ n,oY,oX,bY,bX,iC
+
+ This operation is useful for resizing the activations between convolutions
+ (but keeping all data), e.g. instead of pooling. It is also useful for training
+ purely convolutional models.
+
+ For example, given an input of shape [1, 2, 2, 1], data_format = "NHWC" and
+ block_size = 2:
+
+
+ x = [[[[1], [2]],
+ [[3], [4]]]]
+
+
+ This operation will output a tensor of shape [1, 1, 1, 4]:
+
+
+ [[[[1, 2, 3, 4]]]]
+
+
+ Here, the input has a batch of 1 and each batch element has shape [2, 2, 1],
+ the corresponding output will have a single element (i.e. width and height are
+ both 1) and will have a depth of 4 channels (1 * block_size * block_size).
+ The output element shape is [1, 1, 4].
+
+ For an input tensor with larger depth, here of shape [1, 2, 2, 3], e.g.
+
+
+ x = [[[[1, 2, 3], [4, 5, 6]],
+ [[7, 8, 9], [10, 11, 12]]]]
+
+
+ This operation, for block_size of 2, will return the following tensor of shape
+ [1, 1, 1, 12]
+
+
+ [[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]]
+
+
+ Similarly, for the following input of shape [1 4 4 1], and a block size of 2:
+
+
+ x = [[[[1], [2], [5], [6]],
+ [[3], [4], [7], [8]],
+ [[9], [10], [13], [14]],
+ [[11], [12], [15], [16]]]]
+
+
+ the operator will return the following tensor of shape [1 2 2 4]:
+
+
+ x = [[[[1, 2, 3, 4],
+ [5, 6, 7, 8]],
+ [[9, 10, 11, 12],
+ [13, 14, 15, 16]]]]
+
+
+
+
+
+ Applies a sparse gradient to a given accumulator.
+
+
+ The handle to a accumulator.
+
+
+ The local_step value at which the sparse gradient was computed.
+
+
+ Indices of the sparse gradient to be accumulated. Must be a
+ vector.
+
+
+ Values are the non-zero slices of the gradient, and must have
+ the same first dimension as indices, i.e., the nnz represented by indices and
+ values must be consistent.
+
+
+ Shape of the sparse gradient to be accumulated.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseAccumulatorApplyGradient'.
+
+
+ Boolean indicating whether gradient_shape is unknown, in which
+ case the input is ignored during validation.
+
+
+ Returns the description of the operation
+
+
+ Does not add if local_step is smaller than the accumulator's
+ global_step.
+
+
+
+
+ Extracts the average sparse gradient in a SparseConditionalAccumulator.
+
+
+ The handle to a SparseConditionalAccumulator.
+
+
+ Number of gradients required before we return an aggregate.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseAccumulatorTakeGradient'.
+
+
+ The data type of accumulated gradients. Needs to correspond to the type
+ of the accumulator.
+
+
+ Returns a tuple with multiple values, as follows:
+ indices: Indices of the average of the accumulated sparse gradients.
+ values: Values of the average of the accumulated sparse gradients.
+ shape: Shape of the average of the accumulated sparse gradients.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ The op will blocks until sufficient (i.e., more than num_required)
+ gradients have been accumulated. If the accumulator has already
+ aggregated more than num_required gradients, it will return its
+ average of the accumulated gradients. Also automatically increments
+ the recorded global_step in the accumulator by 1, and resets the
+ aggregate to 0.
+
+
+
+
+ Adds two SparseTensor objects to produce another SparseTensor.
+
+
+ 2-D. The indices of the first SparseTensor, size [nnz, ndims] Matrix.
+
+
+ 1-D. The values of the first SparseTensor, size [nnz] Vector.
+
+
+ 1-D. The shape of the first SparseTensor, size [ndims] Vector.
+
+
+ 2-D. The indices of the second SparseTensor, size [nnz, ndims] Matrix.
+
+
+ 1-D. The values of the second SparseTensor, size [nnz] Vector.
+
+
+ 1-D. The shape of the second SparseTensor, size [ndims] Vector.
+
+
+ 0-D. The magnitude threshold that determines if an output value/index
+ pair takes space.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseAdd'.
+
+
+ Returns a tuple with multiple values, as follows:
+ sum_indices:
+ sum_values:
+ sum_shape:
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ The input SparseTensor objects' indices are assumed ordered in standard
+ lexicographic order. If this is not the case, before this step run
+ SparseReorder to restore index ordering.
+
+ By default, if two values sum to zero at some index, the output SparseTensor
+ would still include that particular location in its index, storing a zero in the
+ corresponding value slot. To override this, callers can specify thresh,
+ indicating that if the sum has a magnitude strictly smaller than thresh, its
+ corresponding value and index would then not be included. In particular,
+ thresh == 0 (default) means everything is kept and actual thresholding happens
+ only for a positive value.
+
+ In the following shapes, nnz is the count after taking thresh into account.
+
+
+
+
+ The gradient operator for the SparseAdd op.
+
+
+ 1-D with shape [nnz(sum)]. The gradient with respect to
+ the non-empty values of the sum.
+
+
+ 2-D. The indices of the SparseTensor A, size [nnz(A), ndims].
+
+
+ 2-D. The indices of the SparseTensor B, size [nnz(B), ndims].
+
+
+ 2-D. The indices of the sum SparseTensor, size
+ [nnz(sum), ndims].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseAddGrad'.
+
+
+ Returns a tuple with multiple values, as follows:
+ a_val_grad: 1-D with shape [nnz(A)]. The gradient with respect to the
+ non-empty values of A.
+ b_val_grad: 1-D with shape [nnz(B)]. The gradient with respect to the
+ non-empty values of B.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ The SparseAdd op calculates A + B, where A, B, and the sum are all represented
+ as SparseTensor objects. This op takes in the upstream gradient w.r.t.
+ non-empty values of the sum, and outputs the gradients w.r.t. the non-empty
+ values of A and B.
+
+
+
+
+ var: Should be from a Variable().
+
+
+
+
+ Should be from a Variable().
+
+
+ : Should be from a Variable().
+
+
+ Learning rate. Must be a scalar.
+
+
+ Decay factor. Must be a scalar.
+
+
+ Constant factor. Must be a scalar.
+
+
+ The gradient.
+
+
+ A vector of indices into the first dimension of var and accum.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseApplyAdadelta'.
+
+
+ Optional argument
+ If True, updating of the var and accum tensors will be protected by
+ a lock; otherwise the behavior is undefined, but may exhibit less contention.
+
+
+ Same as "var".
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Update relevant entries in '*var' and '*accum' according to the adagrad scheme.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Learning rate. Must be a scalar.
+
+
+ The gradient.
+
+
+ A vector of indices into the first dimension of var and accum.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseApplyAdagrad'.
+
+
+ Optional argument
+ If True, updating of the var and accum tensors will be protected
+ by a lock; otherwise the behavior is undefined, but may exhibit less
+ contention.
+
+
+ Optional argument
+
+
+ Same as "var".
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ That is for rows we have grad for, we update var and accum as follows:
+ $$accum += grad * grad$$
+ $$var -= lr * grad * (1 / sqrt(accum))$$
+
+
+
+
+ Update entries in '*var' and '*accum' according to the proximal adagrad scheme.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ The gradient.
+
+
+ A vector of indices into the first dimension of var and accum.
+
+
+ Learning rate. Must be a scalar.
+
+
+ L1 regularization. Must be a scalar.
+
+
+ L2 regularization. Must be a scalar.
+
+
+ Training step number. Must be a scalar.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseApplyAdagradDA'.
+
+
+ Optional argument
+ If True, updating of the var and accum tensors will be protected by
+ a lock; otherwise the behavior is undefined, but may exhibit less contention.
+
+
+ Same as "var".
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Update '*var' according to the centered RMSProp algorithm.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Scaling factor. Must be a scalar.
+
+
+ Decay rate. Must be a scalar.
+
+
+
+
+ Ridge term. Must be a scalar.
+
+
+ The gradient.
+
+
+ A vector of indices into the first dimension of var, ms and mom.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseApplyCenteredRMSProp'.
+
+
+ Optional argument
+ If True, updating of the var, mg, ms, and mom tensors is
+ protected by a lock; otherwise the behavior is undefined, but may exhibit less
+ contention.
+
+
+ Same as "var".
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The centered RMSProp algorithm uses an estimate of the centered second moment
+ (i.e., the variance) for normalization, as opposed to regular RMSProp, which
+ uses the (uncentered) second moment. This often helps with training, but is
+ slightly more expensive in terms of computation and memory.
+
+ Note that in dense implementation of this algorithm, mg, ms, and mom will
+ update even if the grad is zero, but in this sparse implementation, mg, ms,
+ and mom will not update in iterations during which the grad is zero.
+
+ mean_square = decay * mean_square + (1-decay) * gradient ** 2
+ mean_grad = decay * mean_grad + (1-decay) * gradient
+ Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2)
+
+ $$ms <- rho * ms_{t-1} + (1-rho) * grad * grad$$
+ $$mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon)$$
+ $$var <- var - mom$$
+
+
+
+
+ Update relevant entries in '*var' according to the Ftrl-proximal scheme.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ The gradient.
+
+
+ A vector of indices into the first dimension of var and accum.
+
+
+ Scaling factor. Must be a scalar.
+
+
+ L1 regularization. Must be a scalar.
+
+
+ L2 regularization. Must be a scalar.
+
+
+ Scaling factor. Must be a scalar.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseApplyFtrl'.
+
+
+ Optional argument
+ If True, updating of the var and accum tensors will be protected
+ by a lock; otherwise the behavior is undefined, but may exhibit less
+ contention.
+
+
+ Same as "var".
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ That is for rows we have grad for, we update var, accum and linear as follows:
+ $$accum_new = accum + grad * grad$$
+ $$linear += grad + (accum_{new}^{-lr_{power}} - accum^{-lr_{power}} / lr * var$$
+ $$quadratic = 1.0 / (accum_{new}^{lr_{power}} * lr) + 2 * l2$$
+ $$var = (sign(linear) * l1 - linear) / quadratic\ if\ |linear| > l1\ else\ 0.0$$
+ $$accum = accum_{new}$$
+
+
+
+
+ Update relevant entries in '*var' according to the Ftrl-proximal scheme.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ The gradient.
+
+
+ A vector of indices into the first dimension of var and accum.
+
+
+ Scaling factor. Must be a scalar.
+
+
+ L1 regularization. Must be a scalar.
+
+
+ L2 shrinkage regulariation. Must be a scalar.
+
+
+
+
+ Scaling factor. Must be a scalar.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseApplyFtrlV2'.
+
+
+ Optional argument
+ If True, updating of the var and accum tensors will be protected
+ by a lock; otherwise the behavior is undefined, but may exhibit less
+ contention.
+
+
+ Same as "var".
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ That is for rows we have grad for, we update var, accum and linear as follows:
+ grad_with_shrinkage = grad + 2 * l2_shrinkage * var
+ accum_new = accum + grad_with_shrinkage * grad_with_shrinkage
+ linear += grad_with_shrinkage +
+ (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var
+ quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2
+ var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0
+ accum = accum_new
+
+
+
+
+ Update relevant entries in '*var' and '*accum' according to the momentum scheme.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Learning rate. Must be a scalar.
+
+
+ The gradient.
+
+
+ A vector of indices into the first dimension of var and accum.
+
+
+ Momentum. Must be a scalar.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseApplyMomentum'.
+
+
+ Optional argument
+ If True, updating of the var and accum tensors will be protected
+ by a lock; otherwise the behavior is undefined, but may exhibit less
+ contention.
+
+
+ Optional argument
+ If True, the tensor passed to compute grad will be
+ var - lr * momentum * accum, so in the end, the var you get is actually
+ var - lr * momentum * accum.
+
+
+ Same as "var".
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Set use_nesterov = True if you want to use Nesterov momentum.
+
+ That is for rows we have grad for, we update var and accum as follows:
+
+ $$accum = accum * momentum + grad$$
+ $$var -= lr * accum$$
+
+
+
+
+ Sparse update entries in '*var' and '*accum' according to FOBOS algorithm.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Learning rate. Must be a scalar.
+
+
+ L1 regularization. Must be a scalar.
+
+
+ L2 regularization. Must be a scalar.
+
+
+ The gradient.
+
+
+ A vector of indices into the first dimension of var and accum.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseApplyProximalAdagrad'.
+
+
+ Optional argument
+ If True, updating of the var and accum tensors will be protected by
+ a lock; otherwise the behavior is undefined, but may exhibit less contention.
+
+
+ Same as "var".
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ That is for rows we have grad for, we update var and accum as follows:
+ $$accum += grad * grad$$
+ $$prox_v = var$$
+ $$prox_v -= lr * grad * (1 / sqrt(accum))$$
+ $$var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0}$$
+
+
+
+
+ Sparse update '*var' as FOBOS algorithm with fixed learning rate.
+
+
+ Should be from a Variable().
+
+
+ Scaling factor. Must be a scalar.
+
+
+ L1 regularization. Must be a scalar.
+
+
+ L2 regularization. Must be a scalar.
+
+
+ The gradient.
+
+
+ A vector of indices into the first dimension of var and accum.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseApplyProximalGradientDescent'.
+
+
+ Optional argument
+ If True, the subtraction will be protected by a lock;
+ otherwise the behavior is undefined, but may exhibit less contention.
+
+
+ Same as "var".
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ That is for rows we have grad for, we update var as follows:
+ $$prox_v = var - alpha * grad$$
+ $$var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0}$$
+
+
+
+
+ Update '*var' according to the RMSProp algorithm.
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Should be from a Variable().
+
+
+ Scaling factor. Must be a scalar.
+
+
+ Decay rate. Must be a scalar.
+
+
+
+
+ Ridge term. Must be a scalar.
+
+
+ The gradient.
+
+
+ A vector of indices into the first dimension of var, ms and mom.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseApplyRMSProp'.
+
+
+ Optional argument
+ If True, updating of the var, ms, and mom tensors is protected
+ by a lock; otherwise the behavior is undefined, but may exhibit less
+ contention.
+
+
+ Same as "var".
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Note that in dense implementation of this algorithm, ms and mom will
+ update even if the grad is zero, but in this sparse implementation, ms
+ and mom will not update in iterations during which the grad is zero.
+
+ mean_square = decay * mean_square + (1-decay) * gradient ** 2
+ Delta = learning_rate * gradient / sqrt(mean_square + epsilon)
+
+ $$ms <- rho * ms_{t-1} + (1-rho) * grad * grad$$
+ $$mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon)$$
+ $$var <- var - mom$$
+
+
+
+
+ Concatenates a list of SparseTensor along the specified dimension.
+
+
+ 2-D. Indices of each input SparseTensor.
+
+
+ 1-D. Non-empty values of each SparseTensor.
+
+
+ 1-D. Shapes of each SparseTensor.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseConcat'.
+
+
+ Dimension to concatenate along. Must be in range [-rank, rank),
+ where rank is the number of dimensions in each input SparseTensor.
+
+
+ Returns a tuple with multiple values, as follows:
+ output_indices: 2-D. Indices of the concatenated SparseTensor.
+ output_values: 1-D. Non-empty values of the concatenated SparseTensor.
+ output_shape: 1-D. Shape of the concatenated SparseTensor.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Concatenation is with respect to the dense versions of these sparse tensors.
+ It is assumed that each input is a SparseTensor whose elements are ordered
+ along increasing dimension number.
+
+ All inputs' shapes must match, except for the concat dimension. The
+ indices, values, and shapes lists must have the same length.
+
+ The output shape is identical to the inputs', except along the concat
+ dimension, where it is the sum of the inputs' sizes along that dimension.
+
+ The output elements will be resorted to preserve the sort order along
+ increasing dimension number.
+
+ This op runs in O(M log M) time, where M is the total number of non-empty
+ values across all inputs. This is due to the need for an internal sort in
+ order to concatenate efficiently across an arbitrary dimension.
+
+ For example, if concat_dim = 1 and the inputs are
+
+ sp_inputs[0]: shape = [2, 3]
+ [0, 2]: "a"
+ [1, 0]: "b"
+ [1, 1]: "c"
+
+ sp_inputs[1]: shape = [2, 4]
+ [0, 1]: "d"
+ [0, 2]: "e"
+
+ then the output will be
+
+ shape = [2, 7]
+ [0, 2]: "a"
+ [0, 4]: "d"
+ [0, 5]: "e"
+ [1, 0]: "b"
+ [1, 1]: "c"
+
+ Graphically this is equivalent to doing
+
+ [ a] concat [ d e ] = [ a d e ]
+ [b c ] [ ] [b c ]
+
+
+
+
+ A conditional accumulator for aggregating sparse gradients.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseConditionalAccumulator'.
+
+
+ Optional argument
+ If non-empty, this accumulator is placed in the given container.
+ Otherwise, a default container is used.
+
+
+ Optional argument
+ If non-empty, this accumulator will be shared under the given name
+ across multiple sessions.
+
+
+ Optional argument
+
+
+ The type of the value being accumulated.
+
+
+ The shape of the values.
+
+
+ The handle to the accumulator.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The accumulator accepts gradients marked with local_step greater or
+ equal to the most recent global_step known to the accumulator. The
+ average can be extracted from the accumulator, provided sufficient
+ gradients have been accumulated. Extracting the average automatically
+ resets the aggregate to 0, and increments the global_step recorded by
+ the accumulator.
+
+
+
+
+ Generates sparse cross from a list of sparse and dense tensors.
+
+
+ 2-D. Indices of each input SparseTensor.
+
+
+ 1-D. values of each SparseTensor.
+
+
+ 1-D. Shapes of each SparseTensor.
+
+
+ 2-D. Columns represented by dense Tensor.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseCross'.
+
+
+ If true, returns the hash of the cross instead of the string.
+ This will allow us avoiding string manipulations.
+
+
+ It is used if hashed_output is true.
+ output = hashed_value%num_buckets if num_buckets > 0 else hashed_value.
+
+
+ Specify the hash_key that will be used by the FingerprintCat64
+ function to combine the crosses fingerprints.
+
+
+
+
+
+
+ Returns a tuple with multiple values, as follows:
+ output_indices: 2-D. Indices of the concatenated SparseTensor.
+ output_values: 1-D. Non-empty values of the concatenated or hashed
+ SparseTensor.
+ output_shape: 1-D. Shape of the concatenated SparseTensor.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ The op takes two lists, one of 2D SparseTensor and one of 2D Tensor, each
+ representing features of one feature column. It outputs a 2D SparseTensor with
+ the batchwise crosses of these features.
+
+ For example, if the inputs are
+
+ inputs[0]: SparseTensor with shape = [2, 2]
+ [0, 0]: "a"
+ [1, 0]: "b"
+ [1, 1]: "c"
+
+ inputs[1]: SparseTensor with shape = [2, 1]
+ [0, 0]: "d"
+ [1, 0]: "e"
+
+ inputs[2]: Tensor [["f"], ["g"]]
+
+ then the output will be
+
+ shape = [2, 2]
+ [0, 0]: "a_X_d_X_f"
+ [1, 0]: "b_X_e_X_g"
+ [1, 1]: "c_X_e_X_g"
+
+ if hashed_output=true then the output will be
+
+ shape = [2, 2]
+ [0, 0]: FingerprintCat64(
+ Fingerprint64("f"), FingerprintCat64(
+ Fingerprint64("d"), Fingerprint64("a")))
+ [1, 0]: FingerprintCat64(
+ Fingerprint64("g"), FingerprintCat64(
+ Fingerprint64("e"), Fingerprint64("b")))
+ [1, 1]: FingerprintCat64(
+ Fingerprint64("g"), FingerprintCat64(
+ Fingerprint64("e"), Fingerprint64("c")))
+
+
+
+
+ Adds up a SparseTensor and a dense Tensor, using these special rules:
+
+
+ 2-D. N x R matrix with the indices of non-empty values in a
+ SparseTensor, possibly not in canonical ordering.
+
+
+ 1-D. N non-empty values corresponding to sp_indices.
+
+
+ 1-D. Shape of the input SparseTensor.
+
+
+ R-D. The dense Tensor operand.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseDenseCwiseAdd'.
+
+
+ 1-D. The N values that are operated on.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ (1) Broadcasts the dense side to have the same shape as the sparse side, if
+ eligible;
+ (2) Then, only the dense values pointed to by the indices of the SparseTensor
+ participate in the cwise addition.
+
+ By these rules, the result is a logical SparseTensor with exactly the same
+ indices and shape, but possibly with different non-zero values. The output of
+ this Op is the resultant non-zero values.
+
+
+
+
+ Component-wise divides a SparseTensor by a dense Tensor.
+
+
+ 2-D. N x R matrix with the indices of non-empty values in a
+ SparseTensor, possibly not in canonical ordering.
+
+
+ 1-D. N non-empty values corresponding to sp_indices.
+
+
+ 1-D. Shape of the input SparseTensor.
+
+
+ R-D. The dense Tensor operand.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseDenseCwiseDiv'.
+
+
+ 1-D. The N values that are operated on.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ *Limitation*: this Op only broadcasts the dense side to the sparse side, but not
+ the other direction.
+
+
+
+
+ Component-wise multiplies a SparseTensor by a dense Tensor.
+
+
+ 2-D. N x R matrix with the indices of non-empty values in a
+ SparseTensor, possibly not in canonical ordering.
+
+
+ 1-D. N non-empty values corresponding to sp_indices.
+
+
+ 1-D. Shape of the input SparseTensor.
+
+
+ R-D. The dense Tensor operand.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseDenseCwiseMul'.
+
+
+ 1-D. The N values that are operated on.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The output locations corresponding to the implicitly zero elements in the sparse
+ tensor will be zero (i.e., will not take up storage space), regardless of the
+ contents of the dense tensor (even if it's +/-INF and that INF*0 == NaN).
+
+ *Limitation*: this Op only broadcasts the dense side to the sparse side, but not
+ the other direction.
+
+
+
+
+ Fills empty rows in the input 2-D SparseTensor with a default value.
+
+
+ 2-D. the indices of the sparse tensor.
+
+
+ 1-D. the values of the sparse tensor.
+
+
+ 1-D. the shape of the sparse tensor.
+
+
+ 0-D. default value to insert into location [row, 0, ..., 0]
+ for rows missing from the input sparse tensor.
+ output indices: 2-D. the indices of the filled sparse tensor.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseFillEmptyRows'.
+
+
+ Returns a tuple with multiple values, as follows:
+ output_indices:
+ output_values: 1-D. the values of the filled sparse tensor.
+ empty_row_indicator: 1-D. whether the dense row was missing in the
+ input sparse tensor.
+ reverse_index_map: 1-D. a map from the input indices to the output indices.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ The input SparseTensor is represented via the tuple of inputs
+ (indices, values, dense_shape). The output SparseTensor has the
+ same dense_shape but with indices output_indices and values
+ output_values.
+
+ This op inserts a single entry for every row that doesn't have any values.
+ The index is created as [row, 0, ..., 0] and the inserted value
+ is default_value.
+
+ For example, suppose sp_input has shape [5, 6] and non-empty values:
+
+ [0, 1]: a
+ [0, 3]: b
+ [2, 0]: c
+ [3, 1]: d
+
+ Rows 1 and 4 are empty, so the output will be of shape [5, 6] with values:
+
+ [0, 1]: a
+ [0, 3]: b
+ [1, 0]: default_value
+ [2, 0]: c
+ [3, 1]: d
+ [4, 0]: default_value
+
+ The output SparseTensor will be in row-major order and will have the
+ same shape as the input.
+
+ This op also returns an indicator vector shaped [dense_shape[0]] such that
+
+ empty_row_indicator[i] = True iff row i was an empty row.
+
+ And a reverse index map vector shaped [indices.shape[0]] that is used during
+ backpropagation,
+
+ reverse_index_map[j] = out_j s.t. indices[j, :] == output_indices[out_j, :]
+
+
+
+
+ The gradient of SparseFillEmptyRows.
+
+
+ 1-D. The reverse index map from SparseFillEmptyRows.
+
+
+ 1-D. The gradients from backprop.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseFillEmptyRowsGrad'.
+
+
+ Returns a tuple with multiple values, as follows:
+ d_values: 1-D. The backprop into values.
+ d_default_value: 0-D. The backprop into default_value.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Takes vectors reverse_index_map, shaped [N], and grad_values,
+ shaped [N_full], where N_full >= N and copies data into either
+ d_values or d_default_value. Here d_values is shaped [N] and
+ d_default_value is a scalar.
+
+ d_values[j] = grad_values[reverse_index_map[j]]
+ d_default_value = sum_{k : 0 .. N_full - 1} (
+ grad_values[k] * 1{k not in reverse_index_map})
+
+
+
+
+ Multiply matrix "a" by matrix "b".
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseMatMul'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The inputs must be two-dimensional matrices and the inner dimension of "a" must
+ match the outer dimension of "b". Both "a" and "b" must be Tensors not
+ SparseTensors. This op is optimized for the case where at least one of "a" or
+ "b" is sparse, in the sense that they have a large proportion of zero values.
+ The breakeven for using this versus a dense matrix multiply on one platform was
+ 30% zero values in the sparse matrix.
+
+ The gradient computation of this operation will only take advantage of sparsity
+ in the input gradient when that gradient comes from a Relu.
+
+
+
+
+ Computes the max of elements across dimensions of a SparseTensor.
+
+
+ 2-D. N x R matrix with the indices of non-empty values in a
+ SparseTensor, possibly not in canonical ordering.
+
+
+ 1-D. N non-empty values corresponding to input_indices.
+
+
+ 1-D. Shape of the input SparseTensor.
+
+
+ 1-D. Length-K vector containing the reduction axes.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseReduceMax'.
+
+
+ Optional argument
+ If true, retain reduced dimensions with length 1.
+
+
+ R-K-D. The reduced Tensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This Op takes a SparseTensor and is the sparse counterpart to
+ tf.reduce_max(). In particular, this Op also returns a dense Tensor
+ instead of a sparse one.
+
+ Reduces sp_input along the dimensions given in reduction_axes. Unless
+ keep_dims is true, the rank of the tensor is reduced by 1 for each entry in
+ reduction_axes. If keep_dims is true, the reduced dimensions are retained
+ with length 1.
+
+ If reduction_axes has no entries, all dimensions are reduced, and a tensor
+ with a single element is returned. Additionally, the axes can be negative,
+ which are interpreted according to the indexing rules in Python.
+
+
+
+
+ Computes the max of elements across dimensions of a SparseTensor.
+
+
+ 2-D. N x R matrix with the indices of non-empty values in a
+ SparseTensor, possibly not in canonical ordering.
+
+
+ 1-D. N non-empty values corresponding to input_indices.
+
+
+ 1-D. Shape of the input SparseTensor.
+
+
+ 1-D. Length-K vector containing the reduction axes.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseReduceMaxSparse'.
+
+
+ Optional argument
+ If true, retain reduced dimensions with length 1.
+
+
+ Returns a tuple with multiple values, as follows:
+ output_indices:
+ output_values:
+ output_shape:
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ This Op takes a SparseTensor and is the sparse counterpart to
+ tf.reduce_max(). In contrast to SparseReduceMax, this Op returns a
+ SparseTensor.
+
+ Reduces sp_input along the dimensions given in reduction_axes. Unless
+ keep_dims is true, the rank of the tensor is reduced by 1 for each entry in
+ reduction_axes. If keep_dims is true, the reduced dimensions are retained
+ with length 1.
+
+ If reduction_axes has no entries, all dimensions are reduced, and a tensor
+ with a single element is returned. Additionally, the axes can be negative,
+ which are interpreted according to the indexing rules in Python.
+
+
+
+
+ Computes the sum of elements across dimensions of a SparseTensor.
+
+
+ 2-D. N x R matrix with the indices of non-empty values in a
+ SparseTensor, possibly not in canonical ordering.
+
+
+ 1-D. N non-empty values corresponding to input_indices.
+
+
+ 1-D. Shape of the input SparseTensor.
+
+
+ 1-D. Length-K vector containing the reduction axes.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseReduceSum'.
+
+
+ Optional argument
+ If true, retain reduced dimensions with length 1.
+
+
+ R-K-D. The reduced Tensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This Op takes a SparseTensor and is the sparse counterpart to
+ tf.reduce_sum(). In particular, this Op also returns a dense Tensor
+ instead of a sparse one.
+
+ Reduces sp_input along the dimensions given in reduction_axes. Unless
+ keep_dims is true, the rank of the tensor is reduced by 1 for each entry in
+ reduction_axes. If keep_dims is true, the reduced dimensions are retained
+ with length 1.
+
+ If reduction_axes has no entries, all dimensions are reduced, and a tensor
+ with a single element is returned. Additionally, the axes can be negative,
+ which are interpreted according to the indexing rules in Python.
+
+
+
+
+ Computes the sum of elements across dimensions of a SparseTensor.
+
+
+ 2-D. N x R matrix with the indices of non-empty values in a
+ SparseTensor, possibly not in canonical ordering.
+
+
+ 1-D. N non-empty values corresponding to input_indices.
+
+
+ 1-D. Shape of the input SparseTensor.
+
+
+ 1-D. Length-K vector containing the reduction axes.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseReduceSumSparse'.
+
+
+ Optional argument
+ If true, retain reduced dimensions with length 1.
+
+
+ Returns a tuple with multiple values, as follows:
+ output_indices:
+ output_values:
+ output_shape:
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ This Op takes a SparseTensor and is the sparse counterpart to
+ tf.reduce_sum(). In contrast to SparseReduceSum, this Op returns a
+ SparseTensor.
+
+ Reduces sp_input along the dimensions given in reduction_axes. Unless
+ keep_dims is true, the rank of the tensor is reduced by 1 for each entry in
+ reduction_axes. If keep_dims is true, the reduced dimensions are retained
+ with length 1.
+
+ If reduction_axes has no entries, all dimensions are reduced, and a tensor
+ with a single element is returned. Additionally, the axes can be negative,
+ which are interpreted according to the indexing rules in Python.
+
+
+
+
+ Reorders a SparseTensor into the canonical, row-major ordering.
+
+
+ 2-D. N x R matrix with the indices of non-empty values in a
+ SparseTensor, possibly not in canonical ordering.
+
+
+ 1-D. N non-empty values corresponding to input_indices.
+
+
+ 1-D. Shape of the input SparseTensor.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseReorder'.
+
+
+ Returns a tuple with multiple values, as follows:
+ output_indices: 2-D. N x R matrix with the same indices as input_indices, but
+ in canonical row-major ordering.
+ output_values: 1-D. N non-empty values corresponding to output_indices.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Note that by convention, all sparse ops preserve the canonical ordering along
+ increasing dimension number. The only time ordering can be violated is during
+ manual manipulation of the indices and values vectors to add entries.
+
+ Reordering does not affect the shape of the SparseTensor.
+
+ If the tensor has rank R and N non-empty values, input_indices has
+ shape [N, R], input_values has length N, and input_shape has length R.
+
+
+
+
+ Reshapes a SparseTensor to represent values in a new dense shape.
+
+
+ 2-D. N x R_in matrix with the indices of non-empty values in a
+ SparseTensor.
+
+
+ 1-D. R_in vector with the input SparseTensor's dense shape.
+
+
+ 1-D. R_out vector with the requested new dense shape.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseReshape'.
+
+
+ Returns a tuple with multiple values, as follows:
+ output_indices: 2-D. N x R_out matrix with the updated indices of non-empty
+ values in the output SparseTensor.
+ output_shape: 1-D. R_out vector with the full dense shape of the output
+ SparseTensor. This is the same as new_shape but with any -1 dimensions
+ filled in.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ This operation has the same semantics as reshape on the represented dense
+ tensor. The input_indices are recomputed based on the requested new_shape.
+
+ If one component of new_shape is the special value -1, the size of that
+ dimension is computed so that the total dense size remains constant. At
+ most one component of new_shape can be -1. The number of dense elements
+ implied by new_shape must be the same as the number of dense elements
+ originally implied by input_shape.
+
+ Reshaping does not affect the order of values in the SparseTensor.
+
+ If the input tensor has rank R_in and N non-empty values, and new_shape
+ has length R_out, then input_indices has shape [N, R_in],
+ input_shape has length R_in, output_indices has shape [N, R_out], and
+ output_shape has length R_out.
+
+
+
+
+ Computes the mean along sparse segments of a tensor.
+
+
+
+
+ A 1-D tensor. Has same rank as segment_ids.
+
+
+ A 1-D tensor. Values should be sorted and can be repeated.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseSegmentMean'.
+
+
+ Has same shape as data, except for dimension 0 which
+ has size k, the number of segments.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Read
+ [the section on segmentation](https://tensorflow.org/api_guides/python/math_ops#Segmentation)
+ for an explanation of segments.
+
+ Like SegmentMean, but segment_ids can have rank less than data's first
+ dimension, selecting a subset of dimension 0, specified by indices.
+
+
+
+
+ Computes gradients for SparseSegmentMean.
+
+
+ gradient propagated to the SparseSegmentMean op.
+
+
+ indices passed to the corresponding SparseSegmentMean op.
+
+
+ segment_ids passed to the corresponding SparseSegmentMean op.
+
+
+ dimension 0 of "data" passed to SparseSegmentMean op.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseSegmentMeanGrad'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Returns tensor "output" with same shape as grad, except for dimension 0 whose
+ value is output_dim0.
+
+
+
+
+ Computes the mean along sparse segments of a tensor.
+
+
+
+
+ A 1-D tensor. Has same rank as segment_ids.
+
+
+ A 1-D tensor. Values should be sorted and can be repeated.
+
+
+ Should equal the number of distinct segment IDs.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseSegmentMeanWithNumSegments'.
+
+
+ Has same shape as data, except for dimension 0 which has size
+ num_segments.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Like SparseSegmentMean, but allows missing ids in segment_ids. If an id is
+ misisng, the output tensor at that position will be zeroed.
+
+ Read
+ [the section on segmentation](https://tensorflow.org/api_guides/python/math_ops#Segmentation)
+ for an explanation of segments.
+
+
+
+
+ Computes the sum along sparse segments of a tensor divided by the sqrt of N.
+
+
+
+
+ A 1-D tensor. Has same rank as segment_ids.
+
+
+ A 1-D tensor. Values should be sorted and can be repeated.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseSegmentSqrtN'.
+
+
+ Has same shape as data, except for dimension 0 which
+ has size k, the number of segments.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ N is the size of the segment being reduced.
+
+ Read
+ [the section on segmentation](https://tensorflow.org/api_guides/python/math_ops#Segmentation)
+ for an explanation of segments.
+
+
+
+
+ Computes gradients for SparseSegmentSqrtN.
+
+
+ gradient propagated to the SparseSegmentSqrtN op.
+
+
+ indices passed to the corresponding SparseSegmentSqrtN op.
+
+
+ segment_ids passed to the corresponding SparseSegmentSqrtN op.
+
+
+ dimension 0 of "data" passed to SparseSegmentSqrtN op.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseSegmentSqrtNGrad'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Returns tensor "output" with same shape as grad, except for dimension 0 whose
+ value is output_dim0.
+
+
+
+
+ Computes the sum along sparse segments of a tensor divided by the sqrt of N.
+
+
+
+
+ A 1-D tensor. Has same rank as segment_ids.
+
+
+ A 1-D tensor. Values should be sorted and can be repeated.
+
+
+ Should equal the number of distinct segment IDs.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseSegmentSqrtNWithNumSegments'.
+
+
+ Has same shape as data, except for dimension 0 which
+ has size k, the number of segments.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ N is the size of the segment being reduced.
+
+ Like SparseSegmentSqrtN, but allows missing ids in segment_ids. If an id is
+ misisng, the output tensor at that position will be zeroed.
+
+ Read
+ [the section on segmentation](https://tensorflow.org/api_guides/python/math_ops#Segmentation)
+ for an explanation of segments.
+
+
+
+
+ Computes the sum along sparse segments of a tensor.
+
+
+
+
+ A 1-D tensor. Has same rank as segment_ids.
+
+
+ A 1-D tensor. Values should be sorted and can be repeated.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseSegmentSum'.
+
+
+ Has same shape as data, except for dimension 0 which
+ has size k, the number of segments.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Read
+ [the section on segmentation](https://tensorflow.org/api_guides/python/math_ops#Segmentation)
+ for an explanation of segments.
+
+ Like SegmentSum, but segment_ids can have rank less than data's first
+ dimension, selecting a subset of dimension 0, specified by indices.
+
+ For example:
+
+
+ c = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]])
+
+ # Select two rows, one segment.
+ tf.sparse_segment_sum(c, tf.constant([0, 1]), tf.constant([0, 0]))
+ # => [[0 0 0 0]]
+
+ # Select two rows, two segment.
+ tf.sparse_segment_sum(c, tf.constant([0, 1]), tf.constant([0, 1]))
+ # => [[ 1 2 3 4]
+ # [-1 -2 -3 -4]]
+
+ # Select all rows, two segments.
+ tf.sparse_segment_sum(c, tf.constant([0, 1, 2]), tf.constant([0, 0, 1]))
+ # => [[0 0 0 0]
+ # [5 6 7 8]]
+
+ # Which is equivalent to:
+ tf.segment_sum(c, tf.constant([0, 0, 1]))
+
+
+
+
+
+ Computes the sum along sparse segments of a tensor.
+
+
+
+
+ A 1-D tensor. Has same rank as segment_ids.
+
+
+ A 1-D tensor. Values should be sorted and can be repeated.
+
+
+ Should equal the number of distinct segment IDs.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseSegmentSumWithNumSegments'.
+
+
+ Has same shape as data, except for dimension 0 which
+ has size num_segments.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Like SparseSegmentSum, but allows missing ids in segment_ids. If an id is
+ misisng, the output tensor at that position will be zeroed.
+
+ Read
+ [the section on segmentation](https://tensorflow.org/api_guides/python/math_ops#Segmentation)
+ for an explanation of segments.
+
+ For example:
+
+
+ c = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]])
+
+ tf.sparse_segment_sum_with_num_segments(
+ c, tf.constant([0, 1]), tf.constant([0, 0]), num_segments=3)
+ # => [[0 0 0 0]
+ # [0 0 0 0]
+ # [0 0 0 0]]
+
+ tf.sparse_segment_sum_with_num_segments(c,
+ tf.constant([0, 1]),
+ tf.constant([0, 2],
+ num_segments=4))
+ # => [[ 1 2 3 4]
+ # [ 0 0 0 0]
+ # [-1 -2 -3 -4]
+ # [ 0 0 0 0]]
+
+
+
+
+
+ Slice a SparseTensor based on the start and size.
+
+
+ 2-D tensor represents the indices of the sparse tensor.
+
+
+ 1-D tensor represents the values of the sparse tensor.
+
+
+ 1-D. tensor represents the shape of the sparse tensor.
+
+
+ 1-D. tensor represents the start of the slice.
+
+
+ 1-D. tensor represents the size of the slice.
+ output indices: A list of 1-D tensors represents the indices of the output
+ sparse tensors.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseSlice'.
+
+
+ Returns a tuple with multiple values, as follows:
+ output_indices:
+ output_values: A list of 1-D tensors represents the values of the output sparse
+ tensors.
+ output_shape: A list of 1-D tensors represents the shape of the output sparse
+ tensors.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ For example, if the input is
+
+ input_tensor = shape = [2, 7]
+ [ a d e ]
+ [b c ]
+
+ Graphically the output tensors are:
+
+ sparse_slice([0, 0], [2, 4]) = shape = [2, 4]
+ [ a ]
+ [b c ]
+
+ sparse_slice([0, 4], [2, 3]) = shape = [2, 3]
+ [ d e ]
+ [ ]
+
+
+
+
+ The gradient operator for the SparseSlice op.
+
+
+ 1-D. The gradient with respect to
+ the non-empty values of the sliced SparseTensor.
+
+
+ 2-D. The indices of the input SparseTensor.
+
+
+ 1-D. tensor represents the start of the slice.
+
+
+ 2-D. The indices of the sliced SparseTensor.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseSliceGrad'.
+
+
+ 1-D. The gradient with respect to the non-empty values of input SparseTensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This op takes in the upstream gradient w.r.t. non-empty values of
+ the sliced SparseTensor, and outputs the gradients w.r.t.
+ the non-empty values of input SparseTensor.
+
+
+
+
+ Applies softmax to a batched N-D SparseTensor.
+
+
+ 2-D. NNZ x R matrix with the indices of non-empty values in a
+ SparseTensor, in canonical ordering.
+
+
+ 1-D. NNZ non-empty values corresponding to sp_indices.
+
+
+ 1-D. Shape of the input SparseTensor.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseSoftmax'.
+
+
+ 1-D. The NNZ values for the result SparseTensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The inputs represent an N-D SparseTensor with logical shape [..., B, C]
+ (where N >= 2), and with indices sorted in the canonical lexicographic order.
+
+ This op is equivalent to applying the normal tf.nn.softmax() to each innermost
+ logical submatrix with shape [B, C], but with the catch that *the implicitly
+ zero elements do not participate*. Specifically, the algorithm is equivalent
+ to the following:
+
+ (1) Applies tf.nn.softmax() to a densified view of each innermost submatrix
+ with shape [B, C], along the size-C dimension;
+ (2) Masks out the original implicitly-zero locations;
+ (3) Renormalizes the remaining elements.
+
+ Hence, the SparseTensor result has exactly the same non-zero indices and
+ shape.
+
+
+
+
+ Computes softmax cross entropy cost and gradients to backpropagate.
+
+
+ batch_size x num_classes matrix
+
+
+ batch_size vector with values in [0, num_classes).
+ This is the label for the given minibatch entry.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseSoftmaxCrossEntropyWithLogits'.
+
+
+ Returns a tuple with multiple values, as follows:
+ loss: Per example loss (batch_size vector).
+ backprop: backpropagated gradients (batch_size x num_classes matrix).
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Unlike SoftmaxCrossEntropyWithLogits, this operation does not accept
+ a matrix of label probabilities, but rather a single label per row
+ of features. This label is considered to have probability 1.0 for the
+ given row.
+
+ Inputs are the logits, not probabilities.
+
+
+
+
+ Returns the element-wise max of two SparseTensors.
+
+
+ 2-D. N x R matrix with the indices of non-empty values in a
+ SparseTensor, in the canonical lexicographic ordering.
+
+
+ 1-D. N non-empty values corresponding to a_indices.
+
+
+ 1-D. Shape of the input SparseTensor.
+
+
+ counterpart to a_indices for the other operand.
+
+
+ counterpart to a_values for the other operand; must be of the same dtype.
+
+
+ counterpart to a_shape for the other operand; the two shapes must be equal.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseSparseMaximum'.
+
+
+ Returns a tuple with multiple values, as follows:
+ output_indices: 2-D. The indices of the output SparseTensor.
+ output_values: 1-D. The values of the output SparseTensor.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Assumes the two SparseTensors have the same shape, i.e., no broadcasting.
+
+
+
+
+ Returns the element-wise min of two SparseTensors.
+
+
+ 2-D. N x R matrix with the indices of non-empty values in a
+ SparseTensor, in the canonical lexicographic ordering.
+
+
+ 1-D. N non-empty values corresponding to a_indices.
+
+
+ 1-D. Shape of the input SparseTensor.
+
+
+ counterpart to a_indices for the other operand.
+
+
+ counterpart to a_values for the other operand; must be of the same dtype.
+
+
+ counterpart to a_shape for the other operand; the two shapes must be equal.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseSparseMinimum'.
+
+
+ Returns a tuple with multiple values, as follows:
+ output_indices: 2-D. The indices of the output SparseTensor.
+ output_values: 1-D. The values of the output SparseTensor.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Assumes the two SparseTensors have the same shape, i.e., no broadcasting.
+
+
+
+
+ Split a SparseTensor into num_split tensors along one dimension.
+
+
+ 0-D. The dimension along which to split. Must be in the range
+ [0, rank(shape)).
+
+
+ 2-D tensor represents the indices of the sparse tensor.
+
+
+ 1-D tensor represents the values of the sparse tensor.
+
+
+ 1-D. tensor represents the shape of the sparse tensor.
+ output indices: A list of 1-D tensors represents the indices of the output
+ sparse tensors.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseSplit'.
+
+
+ The number of ways to split.
+
+
+ Returns a tuple with multiple values, as follows:
+ output_indices:
+ output_values: A list of 1-D tensors represents the values of the output sparse
+ tensors.
+ output_shape: A list of 1-D tensors represents the shape of the output sparse
+ tensors.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ If the shape[split_dim] is not an integer multiple of num_split. Slices
+ [0 : shape[split_dim] % num_split] gets one extra dimension.
+ For example, if split_dim = 1 and num_split = 2 and the input is
+
+ input_tensor = shape = [2, 7]
+ [ a d e ]
+ [b c ]
+
+ Graphically the output tensors are:
+
+ output_tensor[0] = shape = [2, 4]
+ [ a ]
+ [b c ]
+
+ output_tensor[1] = shape = [2, 3]
+ [ d e ]
+ [ ]
+
+
+
+
+ Adds up a SparseTensor and a dense Tensor, producing a dense Tensor.
+
+
+ 2-D. The indices of the SparseTensor, with shape [nnz, ndims].
+
+
+ 1-D. The values of the SparseTensor, with shape [nnz].
+
+
+ 1-D. The shape of the SparseTensor, with shape [ndims].
+
+
+ ndims-D Tensor. With shape a_shape.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseTensorDenseAdd'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This Op does not require a_indices be sorted in standard lexicographic order.
+
+
+
+
+ Multiply SparseTensor (of rank 2) "A" by dense matrix "B".
+
+
+ 2-D. The indices of the SparseTensor, size [nnz, 2] Matrix.
+
+
+ 1-D. The values of the SparseTensor, size [nnz] Vector.
+
+
+ 1-D. The shape of the SparseTensor, size [2] Vector.
+
+
+ 2-D. A dense Matrix.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseTensorDenseMatMul'.
+
+
+ Optional argument
+ Use the adjoint of A in the matrix multiply. If A is complex, this
+ is transpose(conj(A)). Otherwise it's transpose(A).
+
+
+ Optional argument
+ Use the adjoint of B in the matrix multiply. If B is complex, this
+ is transpose(conj(B)). Otherwise it's transpose(B).
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ No validity checking is performed on the indices of A. However, the following
+ input format is recommended for optimal behavior:
+
+ if adjoint_a == false:
+ A should be sorted in lexicographically increasing order. Use SparseReorder
+ if you're not sure.
+ if adjoint_a == true:
+ A should be sorted in order of increasing dimension 1 (i.e., "column major"
+ order instead of "row major" order).
+
+
+
+
+ Creates a dataset that splits a SparseTensor into elements row-wise.
+
+
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseTensorSliceDataset'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Converts a sparse representation into a dense tensor.
+
+
+ 0-D, 1-D, or 2-D. sparse_indices[i] contains the complete
+ index where sparse_values[i] will be placed.
+
+
+ 1-D. Shape of the dense output tensor.
+
+
+ 1-D. Values corresponding to each row of sparse_indices,
+ or a scalar value to be used for all sparse indices.
+
+
+ Scalar value to set for indices not specified in
+ sparse_indices.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseToDense'.
+
+
+ Optional argument
+ If true, indices are checked to make sure they are sorted in
+ lexicographic order and that there are no repeats.
+
+
+ Dense output tensor of shape output_shape.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Builds an array dense with shape output_shape such that
+
+
+ # If sparse_indices is scalar
+ dense[i] = (i == sparse_indices ? sparse_values : default_value)
+
+ # If sparse_indices is a vector, then for each i
+ dense[sparse_indices[i]] = sparse_values[i]
+
+ # If sparse_indices is an n by d matrix, then for each i in [0, n)
+ dense[sparse_indices[i][0], ..., sparse_indices[i][d-1]] = sparse_values[i]
+
+
+ All other values in dense are set to default_value. If sparse_values is a
+ scalar, all sparse indices are set to this single value.
+
+ Indices should be sorted in lexicographic order, and indices must not
+ contain any repeats. If validate_indices is true, these properties
+ are checked during execution.
+
+
+
+
+ Applies set operation along last dimension of 2 SparseTensor inputs.
+
+
+ 2D Tensor, indices of a SparseTensor. Must be in row-major
+ order.
+
+
+ 1D Tensor, values of a SparseTensor. Must be in row-major
+ order.
+
+
+ 1D Tensor, shape of a SparseTensor. set1_shape[0...n-1] must
+ be the same as set2_shape[0...n-1], set1_shape[n] is the
+ max set size across 0...n-1 dimensions.
+
+
+ 2D Tensor, indices of a SparseTensor. Must be in row-major
+ order.
+
+
+ 1D Tensor, values of a SparseTensor. Must be in row-major
+ order.
+
+
+ 1D Tensor, shape of a SparseTensor. set2_shape[0...n-1] must
+ be the same as set1_shape[0...n-1], set2_shape[n] is the
+ max set size across 0...n-1 dimensions.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SparseToSparseSetOperation'.
+
+
+ Optional argument
+
+
+
+
+ Returns a tuple with multiple values, as follows:
+ result_indices: 2D indices of a SparseTensor.
+ result_values: 1D values of a SparseTensor.
+ result_shape: 1D Tensor shape of a SparseTensor. result_shape[0...n-1] is
+ the same as the 1st n-1 dimensions of set1 and set2, result_shape[n]
+ is the max result set size across all 0...n-1 dimensions.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ See SetOperationOp::SetOperationFromContext for values of set_operation.
+
+ If validate_indices is True, SparseToSparseSetOperation validates the
+ order and range of set1 and set2 indices.
+
+ Input set1 is a SparseTensor represented by set1_indices, set1_values,
+ and set1_shape. For set1 ranked n, 1st n-1 dimensions must be the same
+ as set2. Dimension n contains values in a set, duplicates are allowed but
+ ignored.
+
+ Input set2 is a SparseTensor represented by set2_indices, set2_values,
+ and set2_shape. For set2 ranked n, 1st n-1 dimensions must be the same
+ as set1. Dimension n contains values in a set, duplicates are allowed but
+ ignored.
+
+ If validate_indices is True, this op validates the order and range of set1
+ and set2 indices.
+
+ Output result is a SparseTensor represented by result_indices,
+ result_values, and result_shape. For set1 and set2 ranked n, this
+ has rank n and the same 1st n-1 dimensions as set1 and set2. The nth
+ dimension contains the result of set_operation applied to the corresponding
+ [0...n-1] dimension of set.
+
+
+
+
+ Splits a tensor into num_split tensors along one dimension.
+
+
+ 0-D. The dimension along which to split. Must be in the range
+ [-rank(value), rank(value)).
+
+
+ The tensor to split.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Split'.
+
+
+ The number of ways to split. Must evenly divide
+ value.shape[split_dim].
+
+
+ They are identically shaped tensors, whose shape matches that of value
+ except along axis, where their sizes are
+ values.shape[split_dim] / num_split.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Splits a tensor into num_split tensors along one dimension.
+
+
+ The tensor to split.
+
+
+ list containing the sizes of each output tensor along the split
+ dimension. Must sum to the dimension of value along split_dim.
+ Can contain one -1 indicating that dimension is to be inferred.
+
+
+ 0-D. The dimension along which to split. Must be in the range
+ [-rank(value), rank(value)).
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SplitV'.
+
+
+
+
+ Tensors whose shape matches that of value
+ except along axis, where their sizes are
+ size_splits[i].
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes square root of x element-wise.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Sqrt'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ I.e., \\(y = \sqrt{x} = x^{1/2}\\).
+
+
+
+
+ Computes the gradient for the sqrt of x wrt its input.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SqrtGrad'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Specifically, grad = dy * 0.5 / y, where y = sqrt(x), and dy
+ is the corresponding input gradient.
+
+
+
+
+ Computes square of x element-wise.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Square'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ I.e., \\(y = x * x = x^2\\).
+
+
+
+
+ Returns (x - y)(x - y) element-wise.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'SquaredDifference'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ *NOTE*: SquaredDifference supports broadcasting. More about broadcasting
+ [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+
+
+
+
+ Removes dimensions of size 1 from the shape of a tensor.
+
+
+ The input to squeeze.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Squeeze'.
+
+
+ Optional argument
+ If specified, only squeezes the dimensions listed. The dimension
+ index starts at 0. It is an error to squeeze a dimension that is not 1. Must
+ be in the range [-rank(input), rank(input)).
+
+
+ Contains the same data as input, but has one or more dimensions of
+ size 1 removed.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Given a tensor input, this operation returns a tensor of the same type with
+ all dimensions of size 1 removed. If you don't want to remove all size 1
+ dimensions, you can remove specific size 1 dimensions by specifying
+ axis.
+
+ For example:
+
+
+ # 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
+ shape(squeeze(t)) ==> [2, 3]
+
+
+ Or, to remove specific size 1 dimensions:
+
+
+ # 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
+ shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1]
+
+
+
+
+
+ Deprecated, use StackV2.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Stack'.
+
+
+ Optional argument
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Deprecated, use StackCloseV2.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'StackClose'.
+
+
+ Returns the description of the operation
+
+
+
+
+ Delete the stack from its resource container.
+
+
+ The handle to a stack.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'StackCloseV2'.
+
+
+ Returns the description of the operation
+
+
+
+
+ Deprecated, use StackPopV2.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'StackPop'.
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Pop the element at the top of the stack.
+
+
+ The handle to a stack.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'StackPopV2'.
+
+
+ The type of the elem that is popped.
+
+
+ The tensor that is popped from the top of the stack.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Deprecated, use StackPushV2.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'StackPush'.
+
+
+ Optional argument
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Push an element onto the stack.
+
+
+ The handle to a stack.
+
+
+ The tensor to be pushed onto the stack.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'StackPushV2'.
+
+
+ Optional argument
+ Swap elem to CPU. Default to false.
+
+
+ The same tensor as the input 'elem'.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ A stack that produces elements in first-in last-out order.
+
+
+ The maximum size of the stack if non-negative. If negative, the stack
+ size is unlimited.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'StackV2'.
+
+
+ Optional argument
+ Overrides the name used for the temporary stack resource. Default
+ value is the name of the 'Stack' op (which is guaranteed unique).
+
+
+ The type of the elements on the stack.
+
+
+ The handle to the stack.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Stage values similar to a lightweight Enqueue.
+
+
+ a list of tensors
+ dtypes A list of data types that inserted values should adhere to.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Stage'.
+
+
+ Optional argument
+ Maximum number of elements in the Staging Area. If > 0, inserts
+ on the container will block when the capacity is reached.
+
+
+ Optional argument
+ The maximum number of bytes allowed for Tensors in the Staging Area.
+ If > 0, inserts will block until sufficient space is available.
+
+
+ Optional argument
+ If non-empty, this queue is placed in the given container. Otherwise,
+ a default container is used.
+
+
+ Optional argument
+ It is necessary to match this name to the matching Unstage Op.
+
+
+ Returns the description of the operation
+
+
+ The basic functionality of this Op is similar to a queue with many
+ fewer capabilities and options. This Op is optimized for performance.
+
+
+
+
+ Op removes all elements in the underlying container.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'StageClear'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+ Returns the description of the operation
+
+
+
+
+ Op peeks at the values at the specified index. If the
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'StagePeek'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ underlying container does not contain sufficient elements
+ this op will block until it does. This Op is optimized for
+ performance.
+
+
+
+
+ Op returns the number of elements in the underlying container.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'StageSize'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Draws samples from a multinomial distribution.
+
+
+ 2-D Tensor with shape [batch_size, num_classes]. Each slice [i, :]
+ represents the unnormalized log probabilities for all classes.
+
+
+ 0-D. Number of independent samples to draw for each row slice.
+
+
+ 2 seeds (shape [2]).
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'StatelessMultinomial'.
+
+
+ Optional argument
+
+
+ 2-D Tensor with shape [batch_size, num_samples]. Each slice [i, :]
+ contains the drawn class labels with range [0, num_classes).
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Outputs deterministic pseudorandom values from a normal distribution.
+
+
+ The shape of the output tensor.
+
+
+ 2 seeds (shape [2]).
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'StatelessRandomNormal'.
+
+
+ Optional argument
+ The type of the output.
+
+
+ Random values with specified shape.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The generated values will have mean 0 and standard deviation 1.
+
+ The outputs are a deterministic function of shape and seed.
+
+
+
+
+ Outputs deterministic pseudorandom random values from a uniform distribution.
+
+
+ The shape of the output tensor.
+
+
+ 2 seeds (shape [2]).
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'StatelessRandomUniform'.
+
+
+ Optional argument
+ The type of the output.
+
+
+ Random values with specified shape.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The generated values follow a uniform distribution in the range [0, 1). The
+ lower bound 0 is included in the range, while the upper bound 1 is excluded.
+
+ The outputs are a deterministic function of shape and seed.
+
+
+
+
+ Outputs deterministic pseudorandom values from a truncated normal distribution.
+
+
+ The shape of the output tensor.
+
+
+ 2 seeds (shape [2]).
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'StatelessTruncatedNormal'.
+
+
+ Optional argument
+ The type of the output.
+
+
+ Random values with specified shape.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The generated values follow a normal distribution with mean 0 and standard
+ deviation 1, except that values whose magnitude is more than 2 standard
+ deviations from the mean are dropped and re-picked.
+
+ The outputs are a deterministic function of shape and seed.
+
+
+
+
+ Check if the input matches the regex pattern.
+
+
+ A string tensor of the text to be processed.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'StaticRegexFullMatch'.
+
+
+ The regular expression to match the input.
+
+
+ A bool tensor with the same shape as input.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The input is a string tensor of any shape. The pattern is the
+ regular expression to be matched with every element of the input tensor.
+ The boolean values (True or False) of the output tensor indicate
+ if the input matches the regex pattern provided.
+
+ The pattern follows the re2 syntax (https://github.com/google/re2/wiki/Syntax)
+
+
+
+
+ Replaces the match of pattern in input with rewrite.
+
+
+ The text to be processed.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'StaticRegexReplace'.
+
+
+ Optional argument
+ If True, the replacement is global, otherwise the replacement
+ is done only on the first match.
+
+
+ The regular expression to match the input.
+
+
+ The rewrite to be applied to the matched expresion.
+
+
+ The text after applying pattern and rewrite.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ It follows the re2 syntax (https://github.com/google/re2/wiki/Syntax)
+
+
+
+
+ Stops gradient computation.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'StopGradient'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ When executed in a graph, this op outputs its input tensor as-is.
+
+ When building ops to compute gradients, this op prevents the contribution of
+ its inputs to be taken into account. Normally, the gradient generator adds ops
+ to a graph to compute the derivatives of a specified 'loss' by recursively
+ finding out inputs that contributed to its computation. If you insert this op
+ in the graph it inputs are masked from the gradient generator. They are not
+ taken into account for computing gradients.
+
+ This is useful any time you want to compute a value with TensorFlow but need
+ to pretend that the value was a constant. Some examples include:
+
+ * The *EM* algorithm where the *M-step* should not involve backpropagation
+ through the output of the *E-step*.
+ * Contrastive divergence training of Boltzmann machines where, when
+ differentiating the energy function, the training must not backpropagate
+ through the graph that generated the samples from the model.
+ * Adversarial training, where no backprop should happen through the adversarial
+ example generation process.
+
+
+
+
+ Return a strided slice from input.
+
+
+
+
+ begin[k] specifies the offset into the kth range specification.
+ The exact dimension this corresponds to will be determined by context.
+ Out-of-bounds values will be silently clamped. If the kth bit of
+ begin_mask then begin[k] is ignored and the full range of the
+ appropriate dimension is used instead. Negative values causes indexing
+ to start from the highest element e.g. If foo==[1,2,3] then foo[-1]==3.
+
+
+ end[i] is like begin with the exception that end_mask is
+ used to determine full ranges.
+
+
+ strides[i] specifies the increment in the ith specification
+ after extracting a given element. Negative indices will reverse
+ the original order. Out or range values are
+ clamped to [0,dim[i]) if slice[i]>0 or [-1,dim[i]-1] if slice[i] < 0
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'StridedSlice'.
+
+
+ Optional argument
+ a bitmask where a bit i being 1 means to ignore the begin
+ value and instead use the largest interval possible. At runtime
+ begin[i] will be replaced with [0, n-1) if stride[i] > 0 or
+ [-1, n-1] if stride[i] < 0
+
+
+ Optional argument
+ analogous to begin_mask
+
+
+ Optional argument
+ a bitmask where bit i being 1 means the ith
+ position is actually an ellipsis. One bit at most can be 1.
+ If ellipsis_mask == 0, then an implicit ellipsis mask of 1 << (m+1)
+ is provided. This means that foo[3:5] == foo[3:5, ...]. An ellipsis
+ implicitly creates as many range specifications as necessary to fully
+ specify the sliced range for every dimension. For example for a 4-dimensional
+ tensor foo the slice foo[2, ..., 5:8] implies foo[2, :, :, 5:8].
+
+
+ Optional argument
+ a bitmask where bit i being 1 means the ith
+ specification creates a new shape 1 dimension. For example
+ foo[:4, tf.newaxis, :2] would produce a shape (4, 1, 2) tensor.
+
+
+ Optional argument
+ a bitmask where bit i implies that the ith
+ specification should shrink the dimensionality. begin and end
+ must imply a slice of size 1 in the dimension. For example in
+ python one might do foo[:, 3, :] which would result in
+ shrink_axis_mask being 2.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Note, most python users will want to use the Python Tensor.__getitem__
+ or Variable.__getitem__ rather than this op directly.
+
+ The goal of this op is to produce a new tensor with a subset of
+ the elements from the n dimensional input tensor. The subset is chosen using
+ a sequence of m sparse range specifications encoded into the arguments
+ of this function. Note, in some cases
+ m could be equal to n, but this need not be the case. Each
+ range specification entry can be one of the following:
+
+ - An ellipsis (...). Ellipses are used to imply zero or more
+ dimensions of full-dimension selection and are produced using
+ ellipsis_mask. For example, foo[...] is the identity slice.
+
+ - A new axis. This is used to insert a new shape=1 dimension and is
+ produced using new_axis_mask. For example, foo[:, ...] where
+ foo is shape (3, 4) produces a (1, 3, 4) tensor.
+
+
+ - A range begin:end:stride. This is used to specify how much to choose from
+ a given dimension. stride can be any integer but 0. begin is an integer
+ which represents the index of the first value to select while end represents
+ the index of the last value to select. The number of values selected in each
+ dimension is end - begin if stride > 0 and begin - end if stride < 0.
+ begin and end can be negative where -1 is the last element, -2 is
+ the second to last. begin_mask controls whether to replace the explicitly
+ given begin with an implicit effective value of 0 if stride > 0 and
+ -1 if stride < 0. end_mask is analogous but produces the number
+ required to create the largest open interval. For example, given a shape
+ (3,) tensor foo[:], the effective begin and end are 0 and 3. Do
+ not assume this is equivalent to foo[0:-1] which has an effective begin
+ and end of 0 and 2. Another example is foo[-2::-1] which reverses the
+ first dimension of a tensor while dropping the last two (in the original
+ order elements). For example foo = [1,2,3,4]; foo[-2::-1] is [4,3].
+
+ - A single index. This is used to keep only elements that have a given
+ index. For example (foo[2, :] on a shape (5,6) tensor produces a
+ shape (6,) tensor. This is encoded in begin and end and
+ shrink_axis_mask.
+
+ Each conceptual range specification is encoded in the op's argument. This
+ encoding is best understand by considering a non-trivial example. In
+ particular,
+ foo[1, 2:4, None, ..., :-3:-1, :] will be encoded as
+
+
+ begin = [1, 2, x, x, 0, x] # x denotes don't care (usually 0)
+ end = [2, 4, x, x, -3, x]
+ strides = [1, 1, x, x, -1, 1]
+ begin_mask = 1<<4 | 1 << 5 = 48
+ end_mask = 1<<5 = 32
+ ellipsis_mask = 1<<3 = 8
+ new_axis_mask = 1<<2 4
+ shrink_axis_mask = 1<<0
+
+
+ In this case if foo.shape is (5, 5, 5, 5, 5, 5) the final shape of
+ the slice becomes (2, 1, 5, 5, 2, 5).
+ Let us walk step by step through each argument specification.
+
+ 1. The first argument in the example slice is turned into begin = 1 and
+ end = begin + 1 = 2. To disambiguate from the original spec 2:4 we
+ also set the appropriate bit in shrink_axis_mask.
+
+ 2. 2:4 is contributes 2, 4, 1 to begin, end, and stride. All masks have
+ zero bits contributed.
+
+ 3. None is a synonym for tf.newaxis. This means insert a dimension of size 1
+ dimension in the final shape. Dummy values are contributed to begin,
+ end and stride, while the new_axis_mask bit is set.
+
+ 4. ... grab the full ranges from as many dimensions as needed to
+ fully specify a slice for every dimension of the input shape.
+
+ 5. :-3:-1 shows the use of negative indices. A negative index i associated
+ with a dimension that has shape s is converted to a positive index
+ s + i. So -1 becomes s-1 (i.e. the last element). This conversion
+ is done internally so begin, end and strides receive x, -3, and -1.
+ The appropriate begin_mask bit is set to indicate the start range is the
+ full range (ignoring the x).
+
+ 6. : indicates that the entire contents of the corresponding dimension
+ is selected. This is equivalent to :: or 0::1. begin, end, and strides
+ receive 0, 0, and 1, respectively. The appropriate bits in begin_mask and
+ end_mask are also set.
+
+ *Requirements*:
+ 0 != strides[i] for i in [0, m)
+ ellipsis_mask must be a power of two (only one ellipsis)
+
+
+
+
+
+ Returns the gradient of StridedSlice.
+
+
+
+
+
+
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'StridedSliceGrad'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Since StridedSlice cuts out pieces of its input which is size
+ shape, its gradient will have the same shape (which is passed here
+ as shape). The gradient will be zero in any element that the slice
+ does not select.
+
+ Arguments are the same as StridedSliceGrad with the exception that
+ dy is the input gradient to be propagated and shape is the
+ shape of StridedSlice's input.
+
+
+
+
+ Formats a string template using a list of tensors.
+
+
+ The list of tensors to format into the placeholder string.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'StringFormat'.
+
+
+ Optional argument
+ A string, the template to format tensor summaries into.
+
+
+ Optional argument
+ A string, at each placeholder in the template a subsequent tensor summary will be inserted.
+
+
+ Optional argument
+ When formatting the tensor summaries print the first and last summarize entries of each tensor dimension.
+
+
+ = The resulting string scalar.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Formats a string template using a list of tensors, pretty-printing tensor summaries.
+
+
+
+
+ Joins the strings in the given list of string tensors into one tensor;
+
+
+ A list of string tensors. The tensors must all have the same shape,
+ or be scalars. Scalars may be mixed in; these will be broadcast to the shape
+ of non-scalar inputs.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'StringJoin'.
+
+
+ Optional argument
+ string, an optional join separator.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ with the given separator (default is an empty separator).
+
+
+
+
+ String lengths of input.
+
+
+ The string for which to compute the length.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'StringLength'.
+
+
+ Optional argument
+ The unit that is counted to compute string length. One of: "BYTE" (for
+ the number of bytes in each string) or "UTF8_CHAR" (for the number of UTF-8
+ encoded Unicode code points in each string). Results are undefined
+ if unit=UTF8_CHAR and the input strings do not contain structurally
+ valid UTF-8.
+
+
+ Integer tensor that has the same shape as input. The output contains the
+ element-wise string lengths of input.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Computes the length of each string given in the input tensor.
+
+
+
+
+ Split elements of input based on delimiter into a SparseTensor.
+
+
+ 1-D. Strings to split.
+
+
+ 0-D. Delimiter characters (bytes), or empty string.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'StringSplit'.
+
+
+ Optional argument
+ A bool. If True, skip the empty strings from the result.
+
+
+ Returns a tuple with multiple values, as follows:
+ indices: A dense matrix of int64 representing the indices of the sparse tensor.
+ values: A vector of strings corresponding to the splited values.
+ shape: a length-2 vector of int64 representing the shape of the sparse
+ tensor, where the first value is N and the second value is the maximum number
+ of tokens in a single input entry.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Let N be the size of source (typically N will be the batch size). Split each
+ element of input based on delimiter and return a SparseTensor
+ containing the splitted tokens. Empty tokens are ignored.
+
+ delimiter can be empty, or a string of split characters. If delimiter is an
+ empty string, each element of input is split into individual single-byte
+ character strings, including splitting of UTF-8 multibyte sequences. Otherwise
+ every character of delimiter is a potential split point.
+
+ For example:
+ N = 2, input[0] is 'hello world' and input[1] is 'a b c', then the output
+ will be
+
+ indices = [0, 0;
+ 0, 1;
+ 1, 0;
+ 1, 1;
+ 1, 2]
+ shape = [2, 3]
+ values = ['hello', 'world', 'a', 'b', 'c']
+
+
+
+
+ Split elements of source based on sep into a SparseTensor.
+
+
+ 1-D string Tensor, the strings to split.
+
+
+ 0-D string Tensor, the delimiter character.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'StringSplitV2'.
+
+
+ Optional argument
+ An int. If maxsplit > 0, limit of the split of the result.
+
+
+ Returns a tuple with multiple values, as follows:
+ indices:
+ values:
+ shape:
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Let N be the size of source (typically N will be the batch size). Split each
+ element of source based on sep and return a SparseTensor
+ containing the split tokens. Empty tokens are ignored.
+
+ For example, N = 2, source[0] is 'hello world' and source[1] is 'a b c',
+ then the output will be
+
+ st.indices = [0, 0;
+ 0, 1;
+ 1, 0;
+ 1, 1;
+ 1, 2]
+ st.shape = [2, 3]
+ st.values = ['hello', 'world', 'a', 'b', 'c']
+
+
+ If sep is given, consecutive delimiters are not grouped together and are
+ deemed to delimit empty strings. For example, source of "1<>2<><>3" and
+ sep of "<>" returns ["1", "2", "", "3"]. If sep is None or an empty
+ string, consecutive whitespace are regarded as a single separator, and the
+ result will contain no empty strings at the startor end if the string has
+ leading or trailing whitespace.
+
+ Note that the above mentioned behavior matches python's str.split.
+
+
+
+
+ Strip leading and trailing whitespaces from the Tensor.
+
+
+ A string Tensor of any shape.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'StringStrip'.
+
+
+ A string Tensor of the same shape as the input.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Converts each string in the input Tensor to its hash mod by a number of buckets.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'StringToHashBucket'.
+
+
+ The number of buckets.
+
+
+ A Tensor of the same shape as the input string_tensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The hash function is deterministic on the content of the string within the
+ process.
+
+ Note that the hash function may change from time to time.
+ This functionality will be deprecated and it's recommended to use
+ tf.string_to_hash_bucket_fast() or tf.string_to_hash_bucket_strong().
+
+
+
+
+ Converts each string in the input Tensor to its hash mod by a number of buckets.
+
+
+ The strings to assign a hash bucket.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'StringToHashBucketFast'.
+
+
+ The number of buckets.
+
+
+ A Tensor of the same shape as the input string_tensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The hash function is deterministic on the content of the string within the
+ process and will never change. However, it is not suitable for cryptography.
+ This function may be used when CPU time is scarce and inputs are trusted or
+ unimportant. There is a risk of adversaries constructing inputs that all hash
+ to the same bucket. To prevent this problem, use a strong hash function with
+ tf.string_to_hash_bucket_strong.
+
+
+
+
+ Converts each string in the input Tensor to its hash mod by a number of buckets.
+
+
+ The strings to assign a hash bucket.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'StringToHashBucketStrong'.
+
+
+ The number of buckets.
+
+
+ The key for the keyed hash function passed as a list of two uint64
+ elements.
+
+
+ A Tensor of the same shape as the input string_tensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The hash function is deterministic on the content of the string within the
+ process. The hash function is a keyed hash function, where attribute key
+ defines the key of the hash function. key is an array of 2 elements.
+
+ A strong hash is important when inputs may be malicious, e.g. URLs with
+ additional components. Adversaries could try to make their inputs hash to the
+ same bucket for a denial-of-service attack or to skew the results. A strong
+ hash prevents this by making it difficult, if not infeasible, to compute inputs
+ that hash to the same bucket. This comes at a cost of roughly 4x higher compute
+ time than tf.string_to_hash_bucket_fast.
+
+
+
+
+ Converts each string in the input Tensor to the specified numeric type.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'StringToNumber'.
+
+
+ Optional argument
+ The numeric type to interpret each string in string_tensor as.
+
+
+ A Tensor of the same shape as the input string_tensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ (Note that int32 overflow results in an error while float overflow
+ results in a rounded value.)
+
+
+
+
+ Returns x - y element-wise.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Sub'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ *NOTE*: Subtract supports broadcasting. More about broadcasting
+ [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+
+
+
+
+ Computes the sum of elements across dimensions of a tensor.
+
+
+ The tensor to reduce.
+
+
+ The dimensions to reduce. Must be in the range
+ [-rank(input), rank(input)).
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Sum'.
+
+
+ Optional argument
+ If true, retain reduced dimensions with length 1.
+
+
+ The reduced tensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Reduces input along the dimensions given in axis. Unless
+ keep_dims is true, the rank of the tensor is reduced by 1 for each entry in
+ axis. If keep_dims is true, the reduced dimensions are
+ retained with length 1.
+
+
+
+
+ Computes the singular value decompositions of one or more matrices.
+
+
+ A tensor of shape [..., M, N] whose inner-most 2 dimensions
+ form matrices of size [M, N]. Let P be the minimum of M and N.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Svd'.
+
+
+ Optional argument
+ If true, left and right singular vectors will be
+ computed and returned in u and v, respectively.
+ If false, u and v are not set and should never referenced.
+
+
+ Optional argument
+ If true, compute full-sized u and v. If false
+ (the default), compute only the leading P singular vectors.
+ Ignored if compute_uv is False.
+
+
+ Returns a tuple with multiple values, as follows:
+ s: Singular values. Shape is [..., P].
+ u: Left singular vectors. If full_matrices is False then shape is
+ [..., M, P]; if full_matrices is True then shape is
+ [..., M, M]. Undefined if compute_uv is False.
+ v: Left singular vectors. If full_matrices is False then shape is
+ [..., N, P]. If full_matrices is True then shape is [..., N, N].
+ Undefined if compute_uv is false.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Computes the SVD of each inner matrix in input such that
+ input[..., :, :] = u[..., :, :] * diag(s[..., :, :]) * transpose(v[..., :, :])
+
+
+ # a is a tensor containing a batch of matrices.
+ # s is a tensor of singular values for each matrix.
+ # u is the tensor containing of left singular vectors for each matrix.
+ # v is the tensor containing of right singular vectors for each matrix.
+ s, u, v = svd(a)
+ s, _, _ = svd(a, compute_uv=False)
+
+
+
+
+
+ Forwards data to the output port determined by pred.
+
+
+ The tensor to be forwarded to the appropriate output.
+
+
+ A scalar that specifies which output port will receive data.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Switch'.
+
+
+ Returns a tuple with multiple values, as follows:
+ output_false: If pred is false, data will be forwarded to this output.
+ output_true: If pred is true, data will be forwarded to this output.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ If pred is true, the data input is forwarded to output_true. Otherwise,
+ the data goes to output_false.
+
+ See also RefSwitch and Merge.
+
+
+
+
+ Creates a dataset that contains count elements from the input_dataset.
+
+
+
+
+ A scalar representing the number of elements from the input_dataset
+ that should be taken. A value of -1 indicates that all of input_dataset
+ is taken.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TakeDataset'.
+
+
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Read SparseTensors from a SparseTensorsMap and concatenate them.
+
+
+ 1-D, The N serialized SparseTensor objects.
+ Shape: [N].
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TakeManySparseFromTensorsMap'.
+
+
+ Optional argument
+ The container name for the SparseTensorsMap read by this op.
+
+
+ Optional argument
+ The shared name for the SparseTensorsMap read by this op.
+ It should not be blank; rather the shared_name or unique Operation name
+ of the Op that created the original SparseTensorsMap should be used.
+
+
+ The dtype of the SparseTensor objects stored in the
+ SparseTensorsMap.
+
+
+ Returns a tuple with multiple values, as follows:
+ sparse_indices: 2-D. The indices of the minibatch SparseTensor.
+ sparse_values: 1-D. The values of the minibatch SparseTensor.
+ sparse_shape: 1-D. The shape of the minibatch SparseTensor.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ The input sparse_handles must be an int64 matrix of shape [N, 1] where
+ N is the minibatch size and the rows correspond to the output handles of
+ AddSparseToTensorsMap or AddManySparseToTensorsMap. The ranks of the
+ original SparseTensor objects that went into the given input ops must all
+ match. When the final SparseTensor is created, it has rank one
+ higher than the ranks of the incoming SparseTensor objects
+ (they have been concatenated along a new row dimension on the left).
+
+ The output SparseTensor object's shape values for all dimensions but the
+ first are the max across the input SparseTensor objects' shape values
+ for the corresponding dimensions. Its first shape value is N, the minibatch
+ size.
+
+ The input SparseTensor objects' indices are assumed ordered in
+ standard lexicographic order. If this is not the case, after this
+ step run SparseReorder to restore index ordering.
+
+ For example, if the handles represent an input, which is a [2, 3] matrix
+ representing two original SparseTensor objects:
+
+
+ index = [ 0]
+ [10]
+ [20]
+ values = [1, 2, 3]
+ shape = [50]
+
+
+ and
+
+
+ index = [ 2]
+ [10]
+ values = [4, 5]
+ shape = [30]
+
+
+ then the final SparseTensor will be:
+
+
+ index = [0 0]
+ [0 10]
+ [0 20]
+ [1 2]
+ [1 10]
+ values = [1, 2, 3, 4, 5]
+ shape = [2 50]
+
+
+
+
+
+ Computes tan of x element-wise.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Tan'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes hyperbolic tangent of x element-wise.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Tanh'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Computes the gradient for the tanh of x wrt its input.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TanhGrad'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Specifically, grad = dy * (1 - y*y), where y = tanh(x), and dy
+ is the corresponding input gradient.
+
+
+
+
+ Returns a tensor that may be mutated, but only persists within a single step.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TemporaryVariable'.
+
+
+ Optional argument
+ Overrides the name used for the temporary variable resource. Default
+ value is the name of the 'TemporaryVariable' op (which is guaranteed unique).
+
+
+ The shape of the variable tensor.
+
+
+ The type of elements in the variable tensor.
+
+
+ A reference to the variable tensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This is an experimental op for internal use only and it is possible to use this
+ op in unsafe ways. DO NOT USE unless you fully understand the risks.
+
+ It is the caller's responsibility to ensure that 'ref' is eventually passed to a
+ matching 'DestroyTemporaryVariable' op after all other uses have completed.
+
+ Outputs a ref to the tensor state so it may be read or modified.
+
+ E.g.
+ var = state_ops._temporary_variable([1, 2], types.float_)
+ var_name = var.op.name
+ var = state_ops.assign(var, [[4.0, 5.0]])
+ var = state_ops.assign_add(var, [[6.0, 7.0]])
+ final = state_ops._destroy_temporary_variable(var, var_name=var_name)
+
+
+
+
+ Deprecated. Use TensorArrayCloseV3
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TensorArrayCloseV2'.
+
+
+ Returns the description of the operation
+
+
+
+
+ Delete the TensorArray from its resource container.
+
+
+ The handle to a TensorArray (output of TensorArray or TensorArrayGrad).
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TensorArrayCloseV3'.
+
+
+ Returns the description of the operation
+
+
+ This enables the user to close and release the resource in the middle
+ of a step/run.
+
+
+
+
+ Deprecated. Use TensorArrayConcatV3
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TensorArrayConcatV2'.
+
+
+ Optional argument
+
+
+
+
+ Returns a tuple with multiple values, as follows:
+ value:
+ lengths:
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+
+
+ Concat the elements from the TensorArray into value value.
+
+
+ The handle to a TensorArray.
+
+
+ A float scalar that enforces proper chaining of operations.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TensorArrayConcatV3'.
+
+
+ Optional argument
+ The expected shape of an element, if known,
+ excluding the first dimension. Used to validate the shapes of
+ TensorArray elements. If this shape is not fully specified, concatenating
+ zero-size TensorArrays is an error.
+
+
+ The type of the elem that is returned.
+
+
+ Returns a tuple with multiple values, as follows:
+ value: All of the elements in the TensorArray, concatenated along the first
+ axis.
+ lengths: A vector of the row sizes of the original T elements in the
+ value output. In the example above, this would be the values:
+ (n1, n2, ..., n(T-1)).
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Takes T elements of shapes
+
+
+ (n0 x d0 x d1 x ...), (n1 x d0 x d1 x ...), ..., (n(T-1) x d0 x d1 x ...)
+
+
+ and concatenates them into a Tensor of shape:
+
+
+ (n0 + n1 + ... + n(T-1) x d0 x d1 x ...)
+
+
+ All elements must have the same shape (excepting the first dimension).
+
+
+
+
+ Deprecated. Use TensorArrayGatherV3
+
+
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TensorArrayGatherV2'.
+
+
+ Optional argument
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Gather specific elements from the TensorArray into output value.
+
+
+ The handle to a TensorArray.
+
+
+ The locations in the TensorArray from which to read tensor elements.
+
+
+ A float scalar that enforces proper chaining of operations.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TensorArrayGatherV3'.
+
+
+ Optional argument
+ The expected shape of an element, if known. Used to
+ validate the shapes of TensorArray elements. If this shape is not
+ fully specified, gathering zero-size TensorArrays is an error.
+
+
+ The type of the elem that is returned.
+
+
+ All of the elements in the TensorArray, concatenated along a new
+ axis (the new dimension 0).
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ All elements selected by indices must have the same shape.
+
+
+
+
+ Deprecated. Use TensorArrayGradV3
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TensorArrayGradV2'.
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Creates a TensorArray for storing the gradients of values in the given handle.
+
+
+ The handle to the forward TensorArray.
+
+
+ A float scalar that enforces proper chaining of operations.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TensorArrayGradV3'.
+
+
+ The gradient source string, used to decide which gradient TensorArray
+ to return.
+
+
+ Returns a tuple with multiple values, as follows:
+ grad_handle:
+ flow_out:
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ If the given TensorArray gradient already exists, returns a reference to it.
+
+ Locks the size of the original TensorArray by disabling its dynamic size flag.
+
+ **A note about the input flow_in:**
+
+ The handle flow_in forces the execution of the gradient lookup to occur
+ only after certain other operations have occurred. For example, when
+ the forward TensorArray is dynamically sized, writes to this TensorArray
+ may resize the object. The gradient TensorArray is statically sized based
+ on the size of the forward TensorArray when this operation executes.
+ Furthermore, the size of the forward TensorArray is frozen by this call.
+ As a result, the flow is used to ensure that the call to generate the gradient
+ TensorArray only happens after all writes are executed.
+
+ In the case of dynamically sized TensorArrays, gradient computation should
+ only be performed on read operations that have themselves been chained via
+ flow to occur only after all writes have executed. That way the final size
+ of the forward TensorArray is known when this operation is called.
+
+ **A note about the source attribute:**
+
+ TensorArray gradient calls use an accumulator TensorArray object. If
+ multiple gradients are calculated and run in the same session, the multiple
+ gradient nodes may accidentally flow through the same accumulator TensorArray.
+ This double counts and generally breaks the TensorArray gradient flow.
+
+ The solution is to identify which gradient call this particular
+ TensorArray gradient is being called in. This is performed by identifying
+ a unique string (e.g. "gradients", "gradients_1", ...) from the input
+ gradient Tensor's name. This string is used as a suffix when creating
+ the TensorArray gradient object here (the attribute source).
+
+ The attribute source is added as a suffix to the forward TensorArray's
+ name when performing the creation / lookup, so that each separate gradient
+ calculation gets its own TensorArray accumulator.
+
+
+
+
+ Creates a TensorArray for storing multiple gradients of values in the given handle.
+
+
+ The handle to the forward TensorArray.
+
+
+ A float scalar that enforces proper chaining of operations.
+
+
+ An int32 vector representing a shape. Elements in the gradient accumulator will
+ have shape which is this shape_to_prepend value concatenated with shape of the
+ elements in the TensorArray corresponding to the input handle.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TensorArrayGradWithShape'.
+
+
+ The gradient source string, used to decide which gradient TensorArray
+ to return.
+
+
+ Returns a tuple with multiple values, as follows:
+ grad_handle:
+ flow_out:
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Similar to TensorArrayGradV3. However it creates an accumulator with an
+ expanded shape compared to the input TensorArray whose gradient is being
+ computed. This enables multiple gradients for the same TensorArray to be
+ calculated using the same accumulator.
+
+
+
+
+ Deprecated. Use TensorArrayReadV3
+
+
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TensorArrayReadV2'.
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Read an element from the TensorArray into output value.
+
+
+ The handle to a TensorArray.
+
+
+
+
+ A float scalar that enforces proper chaining of operations.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TensorArrayReadV3'.
+
+
+ The type of the elem that is returned.
+
+
+ The tensor that is read from the TensorArray.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Deprecated. Use TensorArrayScatterV3
+
+
+
+
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TensorArrayScatterV2'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Scatter the data from the input value into specific TensorArray elements.
+
+
+ The handle to a TensorArray.
+
+
+ The locations at which to write the tensor elements.
+
+
+ The concatenated tensor to write to the TensorArray.
+
+
+ A float scalar that enforces proper chaining of operations.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TensorArrayScatterV3'.
+
+
+ A float scalar that enforces proper chaining of operations.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ indices must be a vector, its length must match the first dim of value.
+
+
+
+
+ Deprecated. Use TensorArraySizeV3
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TensorArraySizeV2'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Get the current size of the TensorArray.
+
+
+ The handle to a TensorArray (output of TensorArray or TensorArrayGrad).
+
+
+ A float scalar that enforces proper chaining of operations.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TensorArraySizeV3'.
+
+
+ The current size of the TensorArray.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Deprecated. Use TensorArraySplitV3
+
+
+
+
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TensorArraySplitV2'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+
+ Deprecated. Use TensorArrayV3
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TensorArrayV2'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ An array of Tensors of given size.
+
+
+ The size of the array.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TensorArrayV3'.
+
+
+ Optional argument
+ The expected shape of an element, if known. Used to
+ validate the shapes of TensorArray elements. If this shape is not
+ fully specified, gathering zero-size TensorArrays is an error.
+
+
+ Optional argument
+ A boolean that determines whether writes to the TensorArray
+ are allowed to grow the size. By default, this is not allowed.
+
+
+ Optional argument
+ If true (default), Tensors in the TensorArray are cleared
+ after being read. This disables multiple read semantics but allows early
+ release of memory.
+
+
+ Optional argument
+ If true (default is false), then all
+ elements in the TensorArray will be expected to have have identical shapes.
+ This allows certain behaviors, like dynamically checking for
+ consistent shapes on write, and being able to fill in properly
+ shaped zero tensors on stack -- even if the element_shape attribute
+ is not fully defined.
+
+
+ Optional argument
+ Overrides the name used for the temporary tensor_array
+ resource. Default value is the name of the 'TensorArray' op (which
+ is guaranteed unique).
+
+
+ The type of the elements on the tensor_array.
+
+
+ Returns a tuple with multiple values, as follows:
+ handle: The handle to the TensorArray.
+ flow: A scalar used to control gradient flow.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Write data via Write and read via Read or Pack.
+
+
+
+
+ Deprecated. Use TensorArrayGradV3
+
+
+
+
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TensorArrayWriteV2'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Push an element onto the tensor_array.
+
+
+ The handle to a TensorArray.
+
+
+ The position to write to inside the TensorArray.
+
+
+ The tensor to write to the TensorArray.
+
+
+ A float scalar that enforces proper chaining of operations.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TensorArrayWriteV3'.
+
+
+ A float scalar that enforces proper chaining of operations.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Creates a dataset that emits components as a tuple of tensors once.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TensorDataset'.
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ The shape of the elements of the given list, as a tensor.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TensorListElementShape'.
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ input_handle: the list
+ element_shape: the shape of elements of the list
+
+
+
+
+ Creates a TensorList which, when stacked, has the value of tensor.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TensorListFromTensor'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Each tensor in the result list corresponds to one row of the input tensor.
+
+ tensor: The input tensor.
+ output_handle: The list.
+
+
+
+
+ Creates a Tensor by indexing into the TensorList.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TensorListGather'.
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Each row in the produced Tensor corresponds to the element in the TensorList
+ specified by the given index (see tf.gather).
+
+ input_handle: The input tensor list.
+ indices: The indices used to index into the list.
+ values: The tensor.
+
+
+
+
+ Returns the item in the list with the given index.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TensorListGetItem'.
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ input_handle: the list
+ index: the position in the list from which an element will be retrieved
+ item: the element at that position
+
+
+
+
+
+
+ Returns the number of tensors in the input tensor list.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TensorListLength'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ input_handle: the input list
+ length: the number of tensors in the list
+
+
+
+
+ Returns the last element of the input list as well as a list with all but that element.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TensorListPopBack'.
+
+
+
+
+ Returns a tuple with multiple values, as follows:
+ output_handle:
+ tensor:
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ Fails if the list is empty.
+
+ input_handle: the input list
+ tensor: the withdrawn last element of the list
+ element_dtype: the type of elements in the list
+ element_shape: the shape of the output tensor
+
+
+
+
+ Returns a list list which has the passed-in Tensor as last element and the other elements of the given list in input_handle.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TensorListPushBack'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ tensor: The tensor to put on the list.
+ input_handle: The old list.
+ output_handle: A list with the elements of the old list followed by tensor.
+ element_dtype: the type of elements in the list.
+ element_shape: a shape compatible with that of elements in the list.
+
+
+
+
+ List of the given size with empty elements.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TensorListReserve'.
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ element_shape: the shape of the future elements of the list
+ num_elements: the number of elements to reserve
+ handle: the output list
+ element_dtype: the desired type of elements in the list.
+
+
+
+
+ Creates a TensorList by indexing into a Tensor.
+
+
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TensorListScatter'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Each member of the TensorList corresponds to one row of the input tensor,
+ specified by the given index (see tf.gather).
+
+ tensor: The input tensor.
+ indices: The indices used to index into the list.
+ element_shape: The shape of the elements in the list (can be less specified than
+ the shape of the tensor).
+ output_handle: The TensorList.
+
+
+
+
+ Sets the index-th position of the list to contain the given tensor.
+
+
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TensorListSetItem'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ input_handle: the list
+ index: the position in the list to which the tensor will be assigned
+ item: the element to be assigned to that position
+ output_handle: the new list, with the element in the proper position
+
+
+
+
+
+ Stacks all tensors in the list.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TensorListStack'.
+
+
+ Optional argument
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Requires that all tensors have the same shape.
+
+ input_handle: the input list
+ tensor: the gathered result
+ num_elements: optional. If not -1, the number of elements in the list.
+
+
+
+
+
+ Creates a dataset that emits each dim-0 slice of components once.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TensorSliceDataset'.
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Outputs a Summary protocol buffer with a tensor.
+
+
+ A tensor to serialize.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TensorSummary'.
+
+
+ Optional argument
+ A json-encoded SummaryDescription proto.
+
+
+ Optional argument
+ An unused list of strings.
+
+
+ Optional argument
+ An unused string.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This op is being phased out in favor of TensorSummaryV2, which lets callers pass
+ a tag as well as a serialized SummaryMetadata proto string that contains
+ plugin-specific data. We will keep this op to maintain backwards compatibility.
+
+
+
+
+ Outputs a Summary protocol buffer with a tensor and per-plugin data.
+
+
+ A string attached to this summary. Used for organization in TensorBoard.
+
+
+ A tensor to serialize.
+
+
+ A serialized SummaryMetadata proto. Contains plugin
+ data.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TensorSummaryV2'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Creates a dataset that emits the lines of one or more text files.
+
+
+ A scalar or a vector containing the name(s) of the file(s) to be
+ read.
+
+
+ A scalar containing either (i) the empty string (no
+ compression), (ii) "ZLIB", or (iii) "GZIP".
+
+
+ A scalar containing the number of bytes to buffer.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TextLineDataset'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ A Reader that outputs the lines of a file delimited by '\n'.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TextLineReader'.
+
+
+ Optional argument
+ Number of lines to skip from the beginning of every file.
+
+
+ Optional argument
+ If non-empty, this reader is placed in the given container.
+ Otherwise, a default container is used.
+
+
+ Optional argument
+ If non-empty, this reader is named in the given bucket
+ with this shared_name. Otherwise, the node name is used instead.
+
+
+ The handle to reference the Reader.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ A Reader that outputs the lines of a file delimited by '\n'.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TextLineReaderV2'.
+
+
+ Optional argument
+ Number of lines to skip from the beginning of every file.
+
+
+ Optional argument
+ If non-empty, this reader is placed in the given container.
+ Otherwise, a default container is used.
+
+
+ Optional argument
+ If non-empty, this reader is named in the given bucket
+ with this shared_name. Otherwise, the node name is used instead.
+
+
+ The handle to reference the Reader.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Creates a dataset that emits the records from one or more TFRecord files.
+
+
+ A scalar or vector containing the name(s) of the file(s) to be
+ read.
+
+
+ A scalar containing either (i) the empty string (no
+ compression), (ii) "ZLIB", or (iii) "GZIP".
+
+
+ A scalar representing the number of bytes to buffer. A value of
+ 0 means no buffering will be performed.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TFRecordDataset'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ A Reader that outputs the records from a TensorFlow Records file.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TFRecordReader'.
+
+
+ Optional argument
+ If non-empty, this reader is placed in the given container.
+ Otherwise, a default container is used.
+
+
+ Optional argument
+ If non-empty, this reader is named in the given bucket
+ with this shared_name. Otherwise, the node name is used instead.
+
+
+ Optional argument
+
+
+ The handle to reference the Reader.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ A Reader that outputs the records from a TensorFlow Records file.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TFRecordReaderV2'.
+
+
+ Optional argument
+ If non-empty, this reader is placed in the given container.
+ Otherwise, a default container is used.
+
+
+ Optional argument
+ If non-empty, this reader is named in the given bucket
+ with this shared_name. Otherwise, the node name is used instead.
+
+
+ Optional argument
+
+
+ The handle to reference the Reader.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Generates labels for candidate sampling with a learned unigram distribution.
+
+
+ A batch_size * num_true matrix, in which each row contains the
+ IDs of the num_true target_classes in the corresponding original label.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ThreadUnsafeUnigramCandidateSampler'.
+
+
+ Optional argument
+ If either seed or seed2 are set to be non-zero, the random number
+ generator is seeded by the given seed. Otherwise, it is seeded by a
+ random seed.
+
+
+ Optional argument
+ An second seed to avoid seed collision.
+
+
+ Number of true labels per context.
+
+
+ Number of candidates to randomly sample.
+
+
+ If unique is true, we sample with rejection, so that all sampled
+ candidates in a batch are unique. This requires some approximation to
+ estimate the post-rejection sampling probabilities.
+
+
+ The sampler will sample integers from the interval [0, range_max).
+
+
+ Returns a tuple with multiple values, as follows:
+ sampled_candidates: A vector of length num_sampled, in which each element is
+ the ID of a sampled candidate.
+ true_expected_count: A batch_size * num_true matrix, representing
+ the number of times each candidate is expected to occur in a batch
+ of sampled candidates. If unique=true, then this is a probability.
+ sampled_expected_count: A vector of length num_sampled, for each sampled
+ candidate representing the number of times the candidate is expected
+ to occur in a batch of sampled candidates. If unique=true, then this is a
+ probability.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ See explanations of candidate sampling and the data formats at
+ go/candidate-sampling.
+
+ For each batch, this op picks a single set of sampled candidate labels.
+
+ The advantages of sampling candidates per-batch are simplicity and the
+ possibility of efficient dense matrix multiplication. The disadvantage is that
+ the sampled candidates must be chosen independently of the context and of the
+ true labels.
+
+
+
+
+ Constructs a tensor by tiling a given tensor.
+
+
+ 1-D or higher.
+
+
+ 1-D. Length must be the same as the number of dimensions in input
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Tile'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation creates a new tensor by replicating input multiples times.
+ The output tensor's i'th dimension has input.dims(i) * multiples[i] elements,
+ and the values of input are replicated multiples[i] times along the 'i'th
+ dimension. For example, tiling [a b c d] by [2] produces
+ [a b c d a b c d].
+
+
+
+
+ Returns the gradient of Tile.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TileGrad'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Since Tile takes an input and repeats the input multiples times
+ along each dimension, TileGrad takes in multiples and aggregates
+ each repeated tile of input into output.
+
+
+
+
+ Provides the time since epoch in seconds.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Timestamp'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Returns the timestamp as a float64 for seconds since the Unix epoch.
+
+ Note: the timestamp is computed when the op is executed, not when it is added
+ to the graph.
+
+
+
+
+ Finds values and indices of the k largest elements for the last dimension.
+
+
+ 1-D or higher with last dimension at least k.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TopK'.
+
+
+ Optional argument
+ If true the resulting k elements will be sorted by the values in
+ descending order.
+
+
+ Number of top elements to look for along the last dimension (along each
+ row for matrices).
+
+
+ Returns a tuple with multiple values, as follows:
+ values: The k largest elements along each last dimensional slice.
+ indices: The indices of values within the last dimension of input.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ If the input is a vector (rank-1), finds the k largest entries in the vector
+ and outputs their values and indices as vectors. Thus values[j] is the
+ j-th largest entry in input, and its index is indices[j].
+
+ For matrices (resp. higher rank input), computes the top k entries in each
+ row (resp. vector along the last dimension). Thus,
+
+ values.shape = indices.shape = input.shape[:-1] + [k]
+
+ If two elements are equal, the lower-index element appears first.
+
+ If k varies dynamically, use TopKV2 below.
+
+
+
+
+ Finds values and indices of the k largest elements for the last dimension.
+
+
+ 1-D or higher with last dimension at least k.
+
+
+ 0-D. Number of top elements to look for along the last dimension (along each
+ row for matrices).
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TopKV2'.
+
+
+ Optional argument
+ If true the resulting k elements will be sorted by the values in
+ descending order.
+
+
+ Returns a tuple with multiple values, as follows:
+ values: The k largest elements along each last dimensional slice.
+ indices: The indices of values within the last dimension of input.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ If the input is a vector (rank-1), finds the k largest entries in the vector
+ and outputs their values and indices as vectors. Thus values[j] is the
+ j-th largest entry in input, and its index is indices[j].
+
+ For matrices (resp. higher rank input), computes the top k entries in each
+ row (resp. vector along the last dimension). Thus,
+
+ values.shape = indices.shape = input.shape[:-1] + [k]
+
+ If two elements are equal, the lower-index element appears first.
+
+
+
+
+ An op enabling differentiation of TPU Embeddings.
+
+
+ A trainable variable, enabling optimizers to find this op.
+
+
+ The embedding activations Tensor to return.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TPUEmbeddingActivations'.
+
+
+ The id of the table in the embedding layer configuration from which
+ these activations were computed.
+
+
+ Identifier of the set of embedding indices which produced these
+ activations.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This op simply returns its first input, which is assumed to have been sliced
+ from the Tensors returned by TPUEmbeddingDequeueActivations. The presence of this
+ op, and its first argument being a trainable Variable, enables automatic
+ differentiation of graphs containing embeddings via the TPU Embedding Python
+ libraries.
+
+
+
+
+ Operator that connects N unreplicated inputs to an N-way replicated TPU computation.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TPUReplicatedInput'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Operator that connects the output of an N-way replicated TPU computation to N separate outputs.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TPUReplicatedOutput'.
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Shuffle dimensions of x according to a permutation.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Transpose'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The output y has the same rank as x. The shapes of x and y satisfy:
+ y.shape[i] == x.shape[perm[i]] for i in [0, 1, ..., rank(x) - 1]
+
+
+
+
+ Returns x / y element-wise for integer types.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TruncateDiv'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Truncation designates that negative numbers will round fractional quantities
+ toward zero. I.e. -7 / 5 = -1. This matches C semantics but it is different
+ than Python semantics. See FloorDiv for a division function that matches
+ Python Semantics.
+
+ *NOTE*: TruncateDiv supports broadcasting. More about broadcasting
+ [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+
+
+
+
+ Outputs random values from a truncated normal distribution.
+
+
+ The shape of the output tensor.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TruncatedNormal'.
+
+
+ Optional argument
+ If either seed or seed2 are set to be non-zero, the random number
+ generator is seeded by the given seed. Otherwise, it is seeded by a
+ random seed.
+
+
+ Optional argument
+ A second seed to avoid seed collision.
+
+
+ The type of the output.
+
+
+ A tensor of the specified shape filled with random truncated normal
+ values.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The generated values follow a normal distribution with mean 0 and standard
+ deviation 1, except that values whose magnitude is more than 2 standard
+ deviations from the mean are dropped and re-picked.
+
+
+
+
+ Returns element-wise remainder of division. This emulates C semantics in that
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TruncateMod'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ the result here is consistent with a truncating divide. E.g. truncate(x / y) *
+ y + truncate_mod(x, y) = x.
+
+ *NOTE*: TruncateMod supports broadcasting. More about broadcasting
+ [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+
+
+
+
+ Perform batches of RPC requests.
+
+
+ 0-D or 1-D. The address (i.e. host_name:port) of the RPC server.
+ If this tensor has more than 1 element, then multiple parallel rpc requests
+ are sent. This argument broadcasts with method and request.
+
+
+ 0-D or 1-D. The method address on the RPC server.
+ If this tensor has more than 1 element, then multiple parallel rpc requests
+ are sent. This argument broadcasts with address and request.
+
+
+ 0-D or 1-D. Serialized proto strings: the rpc request argument.
+ If this tensor has more than 1 element, then multiple parallel rpc requests
+ are sent. This argument broadcasts with address and method.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'TryRpc'.
+
+
+ Optional argument
+ RPC protocol to use. Empty string means use the default protocol.
+ Options include 'grpc'.
+
+
+ Optional argument
+ boolean. If true (default), then failures to connect
+ (i.e., the server does not immediately respond) cause an RPC failure.
+
+
+ Optional argument
+ int. If 0 (default), then the kernel will run the RPC
+ request and only time out if the RPC deadline passes or the session times out.
+ If this value is greater than 0, then the op will raise an exception if
+ the RPC takes longer than timeout_in_ms.
+
+
+ Returns a tuple with multiple values, as follows:
+ response: Same shape as request. Serialized proto strings: the rpc responses.
+ status_code: Same shape as request. Values correspond to tensorflow Status enum codes.
+ status_message: Same shape as request. Values correspond to Status messages
+ returned from the RPC calls.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ This op asynchronously performs either a single RPC request, or a batch
+ of requests. RPC requests are defined by three main parameters:
+
+ - address (the host+port or BNS address of the request)
+ - method (the method name for the request)
+ - request (the serialized proto string, or vector of strings,
+ of the RPC request argument).
+
+ For example, if you have an RPC service running on port localhost:2345,
+ and its interface is configured with the following proto declaration:
+
+
+ service MyService {
+ rpc MyMethod(MyRequestProto) returns (MyResponseProto) {
+ }
+ };
+
+
+ then call this op with arguments:
+
+
+ address = "localhost:2345"
+ method = "MyService/MyMethod"
+
+
+ The request tensor is a string tensor representing serialized MyRequestProto
+ strings; and the output string tensor response will have the same shape
+ and contain (upon successful completion) corresponding serialized
+ MyResponseProto strings.
+
+ For example, to send a single, empty, MyRequestProto, call
+ this op with request = "". To send 5 **parallel** empty requests,
+ call this op with request = ["", "", "", "", ""].
+
+ More generally, one can create a batch of MyRequestProto serialized protos
+ from regular batched tensors using the encode_proto op, and convert
+ the response MyResponseProto serialized protos to batched tensors
+ using the decode_proto op.
+
+ **NOTE** Working with serialized proto strings is faster than instantiating
+ actual proto objects in memory, so no performance degradation is expected
+ compared to writing custom kernels for this workflow.
+
+ Unlike the standard Rpc op, if the connection fails or the remote worker
+ returns an error status, this op does **not** reraise the exception.
+ Instead, the status_code and status_message entry for the corresponding RPC
+ call is set with the error returned from the RPC call. The response tensor
+ will contain valid response values for those minibatch entries whose RPCs did
+ not fail; the rest of the entries will have empty strings.
+
+
+
+
+ Reverses the operation of Batch for a single output Tensor.
+
+
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Unbatch'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ An instance of Unbatch either receives an empty batched_tensor, in which case it
+ asynchronously waits until the values become available from a concurrently
+ running instance of Unbatch with the same container and shared_name, or receives
+ a non-empty batched_tensor in which case it finalizes all other concurrently
+ running instances and outputs its own element from the batch.
+
+ batched_tensor: The possibly transformed output of Batch. The size of the first
+ dimension should remain unchanged by the transformations for the operation to
+ work.
+ batch_index: The matching batch_index obtained from Batch.
+ id: The id scalar emitted by Batch.
+ unbatched_tensor: The Tensor corresponding to this execution.
+ timeout_micros: Maximum amount of time (in microseconds) to wait to receive the
+ batched input tensor associated with a given invocation of the op.
+ container: Container to control resource sharing.
+ shared_name: Instances of Unbatch with the same container and shared_name are
+ assumed to possibly belong to the same batch. If left empty, the op name will
+ be used as the shared name.
+
+
+
+
+ Gradient of Unbatch.
+
+
+
+
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'UnbatchGrad'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Acts like Batch but using the given batch_index index of batching things as they
+ become available. This ensures that the gradients are propagated back in the
+ same session which did the forward pass.
+
+ original_input: The input to the Unbatch operation this is the gradient of.
+ batch_index: The batch_index given to the Unbatch operation this is the gradient
+ of.
+ grad: The downstream gradient.
+ id: The id scalar emitted by Batch.
+ batched_grad: The return value, either an empty tensor or the batched gradient.
+ container: Container to control resource sharing.
+ shared_name: Instances of UnbatchGrad with the same container and shared_name
+ are assumed to possibly belong to the same batch. If left empty, the op name
+ will be used as the shared name.
+
+
+
+
+ Determine the script codes of a given tensor of Unicode integer code points.
+
+
+ A Tensor of int32 Unicode code points.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'UnicodeScript'.
+
+
+ A Tensor of int32 script codes corresponding to each input code point.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation converts Unicode code points to script codes corresponding to
+ each code point. Script codes correspond to International Components for
+ Unicode (ICU) UScriptCode values. See http://icu-project.org/apiref/icu4c/uscript_8h.html.
+ Returns -1 (USCRIPT_INVALID_CODE) for invalid codepoints. Output shape will
+ match input shape.
+
+
+
+
+ Generates labels for candidate sampling with a uniform distribution.
+
+
+ A batch_size * num_true matrix, in which each row contains the
+ IDs of the num_true target_classes in the corresponding original label.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'UniformCandidateSampler'.
+
+
+ Optional argument
+ If either seed or seed2 are set to be non-zero, the random number
+ generator is seeded by the given seed. Otherwise, it is seeded by a
+ random seed.
+
+
+ Optional argument
+ An second seed to avoid seed collision.
+
+
+ Number of true labels per context.
+
+
+ Number of candidates to randomly sample.
+
+
+ If unique is true, we sample with rejection, so that all sampled
+ candidates in a batch are unique. This requires some approximation to
+ estimate the post-rejection sampling probabilities.
+
+
+ The sampler will sample integers from the interval [0, range_max).
+
+
+ Returns a tuple with multiple values, as follows:
+ sampled_candidates: A vector of length num_sampled, in which each element is
+ the ID of a sampled candidate.
+ true_expected_count: A batch_size * num_true matrix, representing
+ the number of times each candidate is expected to occur in a batch
+ of sampled candidates. If unique=true, then this is a probability.
+ sampled_expected_count: A vector of length num_sampled, for each sampled
+ candidate representing the number of times the candidate is expected
+ to occur in a batch of sampled candidates. If unique=true, then this is a
+ probability.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ See explanations of candidate sampling and the data formats at
+ go/candidate-sampling.
+
+ For each batch, this op picks a single set of sampled candidate labels.
+
+ The advantages of sampling candidates per-batch are simplicity and the
+ possibility of efficient dense matrix multiplication. The disadvantage is that
+ the sampled candidates must be chosen independently of the context and of the
+ true labels.
+
+
+
+
+ Finds unique elements in a 1-D tensor.
+
+
+ 1-D.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Unique'.
+
+
+ Optional argument
+
+
+ Returns a tuple with multiple values, as follows:
+ y: 1-D.
+ idx: 1-D.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ This operation returns a tensor y containing all of the unique elements of x
+ sorted in the same order that they occur in x. This operation also returns a
+ tensor idx the same size as x that contains the index of each value of x
+ in the unique output y. In other words:
+
+ y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]
+
+ For example:
+
+
+ # tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8]
+ y, idx = unique(x)
+ y ==> [1, 2, 4, 7, 8]
+ idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]
+
+
+
+
+
+
+ Finds unique elements in a 1-D tensor.
+
+
+ 1-D.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'UniqueWithCounts'.
+
+
+ Optional argument
+
+
+ Returns a tuple with multiple values, as follows:
+ y: 1-D.
+ idx: 1-D.
+ count: 1-D.
+ The TFOperation can be fetched from any of the TFOutputs returned in the tuple values, by fethching the Operation property.
+
+
+ This operation returns a tensor y containing all of the unique elements of x
+ sorted in the same order that they occur in x. This operation also returns a
+ tensor idx the same size as x that contains the index of each value of x
+ in the unique output y. Finally, it returns a third tensor count that
+ contains the count of each element of y in x. In other words:
+
+ y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]
+
+ For example:
+
+
+ # tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8]
+ y, idx, count = unique_with_counts(x)
+ y ==> [1, 2, 4, 7, 8]
+ idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]
+ count ==> [2, 1, 3, 1, 2]
+
+
+
+
+
+
+ Unpacks a given dimension of a rank-R tensor into num rank-(R-1) tensors.
+
+
+ 1-D or higher, with axis dimension size equal to num.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Unpack'.
+
+
+ Optional argument
+ Dimension along which to unpack. Negative values wrap around, so the
+ valid range is [-R, R).
+
+
+
+
+ The list of tensors unpacked from value.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Unpacks num tensors from value by chipping it along the axis dimension.
+ For example, given a tensor of shape (A, B, C, D);
+
+ If axis == 0 then the i'th tensor in output is the slice value[i, :, :, :]
+ and each tensor in output will have shape (B, C, D). (Note that the
+ dimension unpacked along is gone, unlike split).
+
+ If axis == 1 then the i'th tensor in output is the slice value[:, i, :, :]
+ and each tensor in output will have shape (A, C, D).
+ Etc.
+
+ This is the opposite of pack.
+
+
+
+
+ Converts a flat index or array of flat indices into a tuple of
+
+
+ An 0-D or 1-D int Tensor whose elements are indices into the
+ flattened version of an array of dimensions dims.
+
+
+ An 1-D int Tensor. The shape of the array to use for unraveling
+ indices.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'UnravelIndex'.
+
+
+ An 2-D (or 1-D if indices is 0-D) tensor where each row has the
+ same shape as the indices array.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ coordinate arrays.
+
+ @compatibility(numpy)
+ Equivalent to np.unravel_index
+ @end_compatibility
+
+
+
+
+ Computes the maximum along segments of a tensor.
+
+
+
+
+ A tensor whose shape is a prefix of data.shape.END
+ }
+ out_arg {
+ name: "output"
+ description: <<END
+ Has same shape as data, except for the first segment_ids.rank
+ dimensions, which are replaced with a single dimension which has size
+ num_segments.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'UnsortedSegmentMax'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Read
+ [the section on segmentation](https://tensorflow.org/api_guides/python/math_ops#Segmentation)
+ for an explanation of segments.
+
+ This operator is similar to the unsorted segment sum operator found
+ [(here)](../../../api_docs/python/math_ops.md#UnsortedSegmentSum).
+ Instead of computing the sum over segments, it computes the maximum such that:
+
+ \\(output_i = \max_{j...} data[j...]\\) where max is over tuples j... such
+ that segment_ids[j...] == i.
+
+ If the maximum is empty for a given segment ID i, it outputs the smallest
+ possible value for the specific numeric type,
+ output[i] = numeric_limits<T>::lowest().
+
+ If the given segment ID i is negative, then the corresponding value is
+ dropped, and will not be included in the result.
+
+ <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+ <img style="width:100%" src="https://www.tensorflow.org/images/UnsortedSegmentMax.png" alt>
+ </div>
+
+
+
+
+ Computes the minimum along segments of a tensor.
+
+
+
+
+ A tensor whose shape is a prefix of data.shape.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'UnsortedSegmentMin'.
+
+
+ Has same shape as data, except for the first segment_ids.rank
+ dimensions, which are replaced with a single dimension which has size
+ num_segments.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Read
+ [the section on segmentation](https://tensorflow.org/api_guides/python/math_ops#segmentation)
+ for an explanation of segments.
+
+ This operator is similar to the unsorted segment sum operator found
+ [(here)](../../../api_docs/python/math_ops.md#UnsortedSegmentSum).
+ Instead of computing the sum over segments, it computes the minimum such that:
+
+ \\(output_i = \min_{j...} data_[j...]\\) where min is over tuples j... such
+ that segment_ids[j...] == i.
+
+ If the minimum is empty for a given segment ID i, it outputs the largest
+ possible value for the specific numeric type,
+ output[i] = numeric_limits<T>::max().
+
+ If the given segment ID i is negative, then the corresponding value is
+ dropped, and will not be included in the result.
+
+
+
+
+ Computes the product along segments of a tensor.
+
+
+
+
+ A tensor whose shape is a prefix of data.shape.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'UnsortedSegmentProd'.
+
+
+ Has same shape as data, except for the first segment_ids.rank
+ dimensions, which are replaced with a single dimension which has size
+ num_segments.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Read
+ [the section on segmentation](https://tensorflow.org/api_guides/python/math_ops#segmentation)
+ for an explanation of segments.
+
+ This operator is similar to the unsorted segment sum operator found
+ [(here)](../../../api_docs/python/math_ops.md#UnsortedSegmentSum).
+ Instead of computing the sum over segments, it computes the product of all
+ entries belonging to a segment such that:
+
+ \\(output_i = \prod_{j...} data[j...]\\) where the product is over tuples
+ j... such that segment_ids[j...] == i.
+
+ If there is no entry for a given segment ID i, it outputs 1.
+
+ If the given segment ID i is negative, then the corresponding value is
+ dropped, and will not be included in the result.
+
+
+
+
+ Computes the sum along segments of a tensor.
+
+
+
+
+ A tensor whose shape is a prefix of data.shape.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'UnsortedSegmentSum'.
+
+
+ Has same shape as data, except for the first segment_ids.rank
+ dimensions, which are replaced with a single dimension which has size
+ num_segments.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Read
+ [the section on segmentation](https://tensorflow.org/api_guides/python/math_ops#Segmentation)
+ for an explanation of segments.
+
+ Computes a tensor such that
+ \\(output[i] = \sum_{j...} data[j...]\\) where the sum is over tuples j... such
+ that segment_ids[j...] == i. Unlike SegmentSum, segment_ids
+ need not be sorted and need not cover all values in the full
+ range of valid values.
+
+ If the sum is empty for a given segment ID i, output[i] = 0.
+ If the given segment ID i is negative, the value is dropped and will not be
+ added to the sum of the segment.
+
+ num_segments should equal the number of distinct segment IDs.
+
+ <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+ <img style="width:100%" src="https://www.tensorflow.org/images/UnsortedSegmentSum.png" alt>
+ </div>
+
+
+
+
+ Op is similar to a lightweight Dequeue.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Unstage'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The basic functionality is similar to dequeue with many fewer
+ capabilities and options. This Op is optimized for performance.
+
+
+
+
+ Applies upper_bound(sorted_search_values, values) along each row.
+
+
+ 2-D Tensor where each row is ordered.
+
+
+ 2-D Tensor with the same numbers of rows as sorted_search_values. Contains
+ the values that will be searched for in sorted_search_values.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'UpperBound'.
+
+
+ Optional argument
+
+
+ A Tensor with the same shape as values. It contains the last scalar index
+ into the last dimension where values can be inserted without changing the
+ ordered property.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Each set of rows with the same index in (sorted_inputs, values) is treated
+ independently. The resulting row is the equivalent of calling
+ np.searchsorted(sorted_inputs, values, side='right').
+
+ The result is not a global index to the entire
+ Tensor, but rather just the index in the last dimension.
+
+ A 2-D example:
+ sorted_sequence = [[0, 3, 9, 9, 10],
+ [1, 2, 3, 4, 5]]
+ values = [[2, 4, 9],
+ [0, 2, 6]]
+
+ result = UpperBound(sorted_sequence, values)
+
+ result == [[1, 2, 4],
+ [0, 2, 5]]
+
+
+
+
+ Creates a handle to a Variable resource.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'VarHandleOp'.
+
+
+ Optional argument
+ the container this variable is placed in.
+
+
+ Optional argument
+ the name by which this variable is referred to.
+
+
+ the type of this variable. Must agree with the dtypes
+ of all ops using this variable.
+
+
+ The (possibly partially specified) shape of this variable.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Use VariableV2 instead.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Variable'.
+
+
+ Optional argument
+
+
+ Optional argument
+
+
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Returns the shape of the variable pointed to by resource.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'VariableShape'.
+
+
+ Optional argument
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation returns a 1-D integer tensor representing the shape of input.
+
+ For example:
+
+
+ # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]
+ shape(t) ==> [2, 2, 3]
+
+
+
+
+
+ Holds state in the form of a tensor that persists across steps.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'VariableV2'.
+
+
+ Optional argument
+ If non-empty, this variable is placed in the given container.
+ Otherwise, a default container is used.
+
+
+ Optional argument
+ If non-empty, this variable is named in the given bucket
+ with this shared_name. Otherwise, the node name is used instead.
+
+
+ The shape of the variable tensor.
+
+
+ The type of elements in the variable tensor.
+
+
+ A reference to the variable tensor.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Outputs a ref to the tensor state so it may be read or modified.
+ TODO(zhifengc/mrry): Adds a pointer to a more detail document
+ about sharing states in tensorflow.
+
+
+
+
+ Checks whether a resource handle-based variable has been initialized.
+
+
+ the input resource handle.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'VarIsInitializedOp'.
+
+
+ a scalar boolean which is true if the variable has been
+ initialized.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Returns locations of nonzero / true values in a tensor.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Where'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation returns the coordinates of true elements in condition. The
+ coordinates are returned in a 2-D tensor where the first dimension (rows)
+ represents the number of true elements, and the second dimension (columns)
+ represents the coordinates of the true elements. Keep in mind, the shape of
+ the output tensor can vary depending on how many true values there are in
+ condition. Indices are output in row-major order.
+
+ For example:
+
+
+ # 'input' tensor is [[True, False]
+ # [True, False]]
+ # 'input' has two true values, so output has two coordinates.
+ # 'input' has rank of 2, so coordinates have two indices.
+ where(input) ==> [[0, 0],
+ [1, 0]]
+
+ # condition tensor is [[[True, False]
+ # [True, False]]
+ # [[False, True]
+ # [False, True]]
+ # [[False, False]
+ # [False, True]]]
+ # 'input' has 5 true values, so output has 5 coordinates.
+ # 'input' has rank of 3, so coordinates have three indices.
+ where(input) ==> [[0, 0, 0],
+ [0, 1, 0],
+ [1, 0, 1],
+ [1, 1, 1],
+ [2, 1, 1]]
+
+ # condition tensor is [[[1.5, 0.0]
+ # [-0.5, 0.0]]
+ # [[0.0, 0.25]
+ # [0.0, 0.75]]
+ # [[0.0, 0.0]
+ # [0.0, 0.01]]]
+ # 'input' has 5 nonzero values, so output has 5 coordinates.
+ # 'input' has rank of 3, so coordinates have three indices.
+ where(input) ==> [[0, 0, 0],
+ [0, 1, 0],
+ [1, 0, 1],
+ [1, 1, 1],
+ [2, 1, 1]]
+
+ # condition tensor is [[[1.5 + 0.0j, 0.0 + 0.0j]
+ # [0.0 + 0.5j, 0.0 + 0.0j]]
+ # [[0.0 + 0.0j, 0.25 + 1.5j]
+ # [0.0 + 0.0j, 0.75 + 0.0j]]
+ # [[0.0 + 0.0j, 0.0 + 0.0j]
+ # [0.0 + 0.0j, 0.01 + 0.0j]]]
+ # 'input' has 5 nonzero magnitude values, so output has 5 coordinates.
+ # 'input' has rank of 3, so coordinates have three indices.
+ where(input) ==> [[0, 0, 0],
+ [0, 1, 0],
+ [1, 0, 1],
+ [1, 1, 1],
+ [2, 1, 1]]
+
+
+
+
+
+ A Reader that outputs the entire contents of a file as a value.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'WholeFileReader'.
+
+
+ Optional argument
+ If non-empty, this reader is placed in the given container.
+ Otherwise, a default container is used.
+
+
+ Optional argument
+ If non-empty, this reader is named in the given bucket
+ with this shared_name. Otherwise, the node name is used instead.
+
+
+ The handle to reference the Reader.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ To use, enqueue filenames in a Queue. The output of ReaderRead will
+ be a filename (key) and the contents of that file (value).
+
+
+
+
+ A Reader that outputs the entire contents of a file as a value.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'WholeFileReaderV2'.
+
+
+ Optional argument
+ If non-empty, this reader is placed in the given container.
+ Otherwise, a default container is used.
+
+
+ Optional argument
+ If non-empty, this reader is named in the given bucket
+ with this shared_name. Otherwise, the node name is used instead.
+
+
+ The handle to reference the Reader.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ To use, enqueue filenames in a Queue. The output of ReaderRead will
+ be a filename (key) and the contents of that file (value).
+
+
+
+
+ A dataset that creates window datasets from the input dataset.
+
+
+
+
+ A scalar representing the number of elements to accumulate in a window.
+
+
+ A scalar representing the steps moving the sliding window forward in one
+ iteration. It must be positive.
+
+
+ A scalar representing the stride of the input elements of the sliding window.
+ It must be positive.
+
+
+ A scalar representing whether a window should be dropped in case its size is
+ smaller than desired.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'WindowDataset'.
+
+
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Worker heartbeat op.
+
+
+ A string tensor containing a serialized WorkerHeartbeatRequest
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'WorkerHeartbeat'.
+
+
+ A string tensor containing a serialized WorkerHeartbeatResponse
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ Heartbeats may be sent periodically to indicate the coordinator is still active,
+ to retrieve the current worker status and to expedite shutdown when necessary.
+
+
+
+
+ Writes contents to the file at input filename. Creates file and recursively
+
+
+ scalar. The name of the file to which we write the contents.
+
+
+ scalar. The content to be written to the output file.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'WriteFile'.
+
+
+ Returns the description of the operation
+
+
+ creates directory if not existing.
+
+
+
+
+ Returns 0 if x == 0, and x / y otherwise, elementwise.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Xdivy'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Returns 0 if x == 0, and x * log(y) otherwise, elementwise.
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Xlogy'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Returns a tensor of zeros with the same shape and type as x.
+
+
+ a tensor of type T.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ZerosLike'.
+
+
+ a tensor of the same shape and type as x but filled with zeros.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Compute the Hurwitz zeta function \\(\zeta(x, q)\\).
+
+
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Zeta'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The Hurwitz zeta function is defined as:
+
+
+ \\(\zeta(x, q) = \sum_{n=0}^{\infty} (q + n)^{-x}\\)
+
+
+
+
+ Creates a dataset that zips together input_datasets.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'ZipDataset'.
+
+
+
+
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+
+
+ Creates a constant operation from a TFTensor or constant
+
+ Value.
+ Oper name.
+
+ Since TFTensor have implicit conversion operators, you can call this method with
+ a constant like this: graph.Const (23)
+
+
+
+
+ Computes the sum of elements across dimensions of a tensor.
+
+ The reduced tensor.
+ The tensor to reduce. Should have numeric type.
+ The dimensions to reduce. If not se (the default), reduces all dimensions.
+ If set to true retains reduced dimensions with length 1.
+ A name for the operation, optional.
+
+ Reduces input_tensor along the dimensions given in axis.
+ Unless keep_dims is true, the rank of the tensor is reduced by 1 for each
+ entry in axis. If keep_dims is true, the reduced dimensions
+ are retained with length 1.
+
+ If axis has no entries, all dimensions are reduced, and a
+ tensor with a single element is returned.
+
+
+
+
+ Computes the product of elements across dimensions of a tensor.
+
+ The reduced tensor.
+ The tensor to reduce. Should have numeric type.
+ The dimensions to reduce. If not se (the default), reduces all dimensions.
+ If set to true retains reduced dimensions with length 1.
+ A name for the operation, optional.
+
+ Reduces input_tensor along the dimensions given in axis.
+ Unless keep_dims is true, the rank of the tensor is reduced by 1 for each
+ entry in axis. If keep_dims is true, the reduced dimensions
+ are retained with length 1.
+
+ If axis has no entries, all dimensions are reduced, and a
+ tensor with a single element is returned.
+
+
+
+
+ Computes the mean of elements across dimensions of a tensor.
+
+ The reduced tensor.
+ The tensor to reduce. Should have numeric type.
+ The dimensions to reduce. If not set (the default), reduces all dimensions.
+ If set to true retains reduced dimensions with length 1.
+ A name for the operation, optional.
+
+
+ Reduces input_tensor along the dimensions given in axis.
+ Unless keep_dims is true, the rank of the tensor is reduced by 1 for each
+ entry in axis. If keep_dims is true, the reduced dimensions
+ are retained with length 1.
+
+
+ If axis has no entries, all dimensions are reduced, and a
+ tensor with a single element is returned.
+
+
+
+
+ Variable node, with a starting initial value.
+
+ Initial value.
+ Returns the operation that initializes the value of the variable.
+ Returns the value of the variable.
+ If true, this add the variable to the graph's TrainableVariables, this collection is intended to be used by the Optimizer classes.
+ Operation name, optional.
+ The returning Variable contains the variable, with three nodes with the operations making up the variable assignment.
+
+ Variables need to be initialized before the main execution so you will typically want to
+ run the session on the variable
+
+
+
+
+ Registers a specified variable as an initialization variable.
+
+ Variable to register.
+
+
+ This is a convenience method to track the variables that need to be initialized in the graph,
+ you can retrieve the list of all those variables by calling the
+ which will return this list and clear the state at that point.
+
+
+ You typically use this method from helper methods to register all the variables that you want
+ initialized, and a higher level method will retrieve all these variables and initialize them
+ at their convenience.
+
+
+
+
+
+ Gets the list of all registered global variables.
+
+ The array of variables that should be initialized.
+
+ After this method is invoked the list of pending initialization variables
+ is cleared.
+
+
+
+
+ Gets the list of all registered trainable variables.
+
+ The array of variables that should be trained.
+
+
+
+ Variable node, with a starting initial value. Convenience that registers the init variable to a global queue.
+
+ Initial value.
+ Returns the value of the variable.
+ If true, this add the variable to the graph's TrainableVariables, this collection is intended to be used by the Optimizer classes.
+ Operation name, optional.
+ The returning Variable contains the variable, with three nodes with the operations making up the variable assignment.
+
+ Variables need to be initialized before the main execution so you will typically want to
+ run the session on the variable.
+
+ The init sequence for the variable is stored in the graph, you must manually initialize
+ those by running the session on the global variables.
+
+
+
+
+ Variable node, with a starting initial value. Convenience that registers the init variable to a global queue.
+
+ Initial value.
+ If true, this add the variable to the graph's TrainableVariables, this collection is intended to be used by the Optimizer classes.
+ Operation name, optional.
+ The returning Variable contains the variable, with three nodes with the operations making up the variable assignment.
+
+ Variables need to be initialized before the main execution so you will typically want to
+ run the session on the variable.
+
+ The init sequence for the variable is stored in the graph, you must manually initialize
+ those by running the session on the global variables.
+
+
+
+
+ Gets or sets the graph random seed, see remarks for details.
+
+ The seed.
+
+ Operations that rely on a random seed actually derive it from two seeds:
+ the graph-level and operation-level seeds.This sets the graph-level seed.
+
+ Its interactions with operation-level seeds is as follows:
+ 1. If neither the graph-level nor the operation seed is set:
+ A random seed is used for this op.
+ 2. If the graph-level seed is set, but the operation seed is not:
+ The system deterministically picks an operation seed in conjunction
+ with the graph-level seed so that it gets a unique random sequence.
+ 3. If the graph-level seed is not set, but the operation seed is set:
+ A default graph-level seed and the specified operation seed are used to
+ determine the random sequence.
+ 4. If both the graph-level and the operation seed are set:
+ Both seeds are used in conjunction to determine the random sequence.
+
+
+
+
+ Returns the graph and local seeds based on an optionally set incoming seed value.
+
+ The seed value that might be set.
+ Returned graph seed.
+ Returned local seed.
+
+ This helper function returns two seeds derived from graph-level and op-level seeds.
+ Many random operations internally use the two seeds to allow user to change
+ the seed globally for a graph, or for only specific operations.
+
+
+
+
+ Computes dropout.
+
+ A tensor.
+ A scalar Tensor with the same type as x. The probability that each element is kept.
+ A 1-D Tensor of type int32, representing the shape for randomly generated keep/drop flags.
+ Integer seed used for the random distribution, using the TensorFlow SetRandomSeed .
+ Operation name, optional.
+
+ With probability keep_prob, outputs the input element scaled up by 1 / keep_prob,
+ otherwise outputs 0. The scaling is so that the expected sum is unchanged.
+
+
+
+
+ Computes dropout.
+
+ A tensor.
+ A scalar Tensor with the same type as x. The probability that each element is kept.
+ A 1-D Tensor of type int32, representing the shape for randomly generated keep/drop flags.
+ Integer seed used for the random distribution, using the TensorFlow SetRandomSeed .
+ Operation name, optional.
+
+ With probability keep_prob, outputs the input element scaled up by 1 / keep_prob,
+ otherwise outputs 0. The scaling is so that the expected sum is unchanged.
+
+
+
+
+ Clips tensor values to a maximum L2-norm.
+
+
+
+ Given a tensor , and a maximum clip value , this operation normalizes
+ so that its L2-norm is less than or equal to , along the dimensions
+ given in . Specifically, in the default case where all dimensions are used for calculation, if
+ the L2-norm of is already less than or equal to , then
+ is not modified. If the L2-norm is greater than , then this operation returns a tensor of
+ the same type and shape as with its values set to: t* clip_norm / l2norm(t)
+
+ The tensor.
+ The minimum value to clip by. A 0 - D(scalar) tensor, or a tensor with the same shape as .
+ The minimum value to clip by. A 0 - D(scalar) tensor, or a tensor with the same shape as .
+ Operation name, optional.
+ A clipped tensor.
+
+
+
+ Computes the global norm of multiple tensors.
+
+
+
+ Given a tuple or list of tensors , this operation returns the global norm of the elements in all tensors
+ in . The global norm is computed as: global_norm = sqrt(sum([l2norm(t)**2 for t in t_list])). Any
+ entries in that are of type None are ignored.
+
+ The input tensors.
+ Operation name, optional.
+ A clipped tensor.
+
+
+
+ Clips tensor values to a maximum average L2-norm.
+
+
+ Given a tensor , and a maximum clip value , this operation
+ normalizes so that its its average L2-norm is less than or equal to .
+ Specifically, if the average L2-norm is already less than or equal to , then
+ is not modified. If the average L2-norm is greater than , then this operation returns a tensor of the same
+ type and shape as with its values set to: t* clip_norm / l2norm_avg(t). In this case,
+ the average L2-norm of the output tensor is .
+
+ The input tensor.
+ A maximum clipping value.
+ Name of the oper.
+
+
+
+ Computes sigmoid cross entropy given `logits`.
+
+
+
+ Measures the probability error in discrete classification tasks in which each
+ class is independent and not mutually exclusive.For instance, one could
+ perform multilabel classification where a picture can contain both an elephant
+ and a dog at the same time.
+
+
+
+
+
+ Shuffle dimensions of x according to a permutation.
+
+
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'Transpose'.
+
+
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ The output `y` has the same rank as `x`. The shapes of `x` and `y` satisfy:
+ `y.shape[i] == x.shape[perm[i]] for i in [0, 1, ..., rank(x) - 1]`
+
+
+
+
+ Returns if the predicate is true else .
+
+ A scalar determining whether to return the result of true_fn or false_fn.
+ The callable to be performed if pred is true.
+ The callable to be performed if pred is false.
+ Optional name prefix for the returned tensors.
+ TFOutput.
+
+
+
+ Return elements from x or y depending on condition.
+
+
+ LabeledTensor of type `bool`.
+ LabeledTensor for values where condition is true.
+ LabeledTensor for values where condition is false.
+ Optional op name.
+
+ The labeled tensor with values according to condition.
+
+
+
+
+ Stacks a list of rank-`R` tensors into one rank-`(R+1)` tensor.
+
+
+ Packs the list of tensors in into a tensor with rank one higher than
+ each tensor in , by packing them along the dimension.
+ Given a list of length N of tensors of shape (A, B, C): if axis == 0 then the
+ output tensor will have the shape (N, A, B, C); if axis == 1 then the output
+ tensor will have the shape (A, N, B, C); etc.
+
+
+
+
+
+ Creates a sequence of numbers.
+
+
+ Creates a sequence of numbers that begins at `start` and extends by increments of `delta` up to but not including
+ `limit`. The dtype of the resulting tensor is inferred from the inputs unless it is provided explicitly.
+
+ A 0 - D `Tensor` (scalar).Acts as first entry in the range if `limit` is not None; otherwise, acts as range limit and first entry defaults to 0.
+ A 0 - D `Tensor` (scalar).Upper limit of sequence, exclusive. If None, defaults to the value of `start` while the first entry of the range defaults to 0.
+ A 0 - D `Tensor` (scalar).Number that increments `start`. Defaults to 1.
+ The type of the elements of the resulting tensor.
+ A name for the operation.Defaults to "range".
+
+
+
+ Outputs Zero values based on shape of tensor
+
+ Shape of the output tensor
+ Optional Type of the Zero value. Default: Double
+ Operation name, optional.
+
+
+
+
+ Outputs One values based on shape of tensor
+
+ Shape of the output tensor
+ Optional Type of the Zero value. Default: Double
+ Operation name, optional.
+
+
+
+
+ Create a constant tensor based on a shape
+ Used by Zeros and Ones
+
+ Value for tensor
+ Shape of the tensor
+ Optional Type of the Zero value. Default: Double
+ Operation name, optional.
+
+ see https://github.com/tensorflow/tensorflow/blob/r1.1/tensorflow/python/framework/constant_op.py
+
+
+
+ Outputs random values from a normal distribution
+
+ A tensor of the specified shape filled with random normal values.
+ Shape of the output tensor.
+ The mean of the standard distribution.
+ The standard deviation of the normal distribution.
+ Integer seed used for the random distribution, using the TensorFlow SetRandomSeed .
+ Operation name, optional.
+
+
+
+ Randoms the uniform.
+
+ The uniform.
+ Shape.
+ Minval.
+ Maxval.
+ Seed.
+ Oper name.
+
+
+
+ Randoms the uniform.
+
+ The uniform.
+ Shape.
+ Minval.
+ Maxval.
+ Seed.
+ Oper name.
+
+
+
+ Initializes a new instance of the class.
+
+
+
+
+ Sets the tensor shape of the tensor referenced by to the shape described by .
+
+ The tensor on which this method will operate in the graph.
+ The tensor shape, specified as an array of dimensions.
+ Status buffer, if specified a status code will be left here, if not specified, a exception is raised if there is an error.
+
+
+
+ Returns the number of dimensions of the Tensor referenced by output
+
+ The number of dimensions of the tensor.
+ The tensor to probe.
+ Status buffer, if specified a status code will be left here, if not specified, a exception is raised if there is an error.
+
+
+
+ Returns the shape of a tensor specified in .
+
+
+ The tensor shape. If the number of dimensions in the shape is unknown or the shape is, a scalar, the values in the array will be zero. Otherwise, each element of will be set corresponding to the size of the dimension. An unknown dimension is represented by -1.
+ The tensor that you want to look up.
+ Status buffer, if specified a status code will be left here, if not specified, a exception is raised if there is an error.
+
+
+
+ Write out a serialized representation of the graph (as a GraphDef protocol buffer message) into .
+
+ Target buffer where the graphs is serialized into.
+ Status buffer, if specified a status code will be left here, if not specified, a exception is raised if there is an error.
+
+
+
+ Import a serialized graph into this graph, using the specified prefix.
+
+ The import.
+ A buffer containing the serialized graph.
+ A prefix that will be prepended to names of nodes in the when they are imported into the graph.
+ Status buffer, if specified a status code will be left here, if not specified, a exception is raised if there is an error.
+
+
+
+ Import a serialized graph into this graph, using the specified importing options.
+
+ The import.
+ A buffer containing the serialized graph.
+ Importing graph options.
+ Status buffer, if specified a status code will be left here, if not specified, a exception is raised if there is an error.
+
+
+
+ Import a serialized graph held in a byte array into this graph, using the specified prefix.
+
+ The import.
+ A byte array containing the serialized graph.
+ A prefix that will be prepended to names of nodes in the graph when they are imported into the graph.
+ Status buffer, if specified a status code will be left here, if not specified, a exception is raised if there is an error.
+
+
+
+ Import a serialized graph held in a byte array into this graph, using the specified import options.
+
+ The import.
+ A byte array containing the serialized graph.
+ Importing graph options.
+ Status buffer, if specified a status code will be left here, if not specified, a exception is raised if there is an error.
+
+ If you are tryig to load a file stored using the SavedModel file format, you should use the API instead.
+
+
+
+
+ Gets the with the specified name, or null if the named operation does not exist in the graph.
+
+ Name to lookup.
+
+
+
+ Returns the enumerator that returns all the TFOperations in a graph.
+
+ The enumerator.
+
+
+
+ Returns the tensor shape for the specific output pparameters as an array of longs.
+
+ null for single dimension, .
+ The output operation to probe.
+ Status buffer, if specified a status code will be left here, if not specified, a exception is raised if there is an error.
+
+
+
+ Returns the current name scope in use, to change this, use the WithScope method.
+
+ The current name scope.
+
+
+
+ Creates a new namescope by setting the scope to the description provided.
+
+ A new scope that will remain in use until the return TFScope is disposed.
+ The namescope description, if the value is null, this
+ will reset the toplevel namescope to be the empty value.
+
+
+ To more easily name your operations and group then, you can use the
+ WithScope method to set a current name scope that alter the complete name
+ of an operation added to the graph.
+
+
+ The graph starts with a scope set to the empty string, you can introduce new
+ scopes by calling WithScope, and can be conveniently used with the C# using
+ statement, like this:
+
+
+ Assert (graph.CurrentNamescope, "");
+ using (var nested = graph.WithScope ("nested")){
+ Assert (graph.CurrentNameScope, "nested");
+ using (var inner = graph.WithScope ("inner")){
+ Assert (graph.CurrentNameScope, "nested/inner");
+ }
+ }
+
+
+
+
+
+ Set the device to be used for all the operations defined in the using block.
+ Creates and sets to the intended device.
+ If device is already set throws exception.
+
+ Name of the device to be used e.g. "/cpu:0", "/device:GPU:0"
+
+
+
+ Returns the current variable dependencies in use. New tensors and operations will be created
+ with an added input dependency to the operations specified in this property. To change this,
+ use the WithDependencies method.
+
+ The current input dependencies to be used for new tensors and operations.
+
+
+
+ Adds new dependencies for new tensors and operations created while the context is active.
+
+
+
+
+ Imports a graph serialized into the graph
+
+ Serialized graph definition (in protocol buffer format).
+ Import options.
+ Array large enough to contain all the return options.
+ Status buffer, if specified a status code will be left here, if not specified, a exception is raised if there is an error.
+
+ If you are tryig to load a file stored using the SavedModel file format, you should use the API instead.
+
+
+
+
+ Signature of the method that will be invoked by the TFGraph.While method to construct a while loop
+
+
+
+ The method should build up the condition on the conditionGraph and the body of the while
+ loop in the provided bodyGraph. It should set the condOutput to the value used as the
+ condition output and the array of values in bodyOutputs to the final outputs as well as the
+ name to be used, if not set, one will be assigned.
+
+
+ The conditionGraph represents the while condition and the inputs are the current values of the
+ input variables (condInputs). The output should be a scalar boolean.
+
+
+ The loop body graph is in bodyGraph, The inputs are the current values of the loop
+ variables. The outputs are the updated values of the loop variables.
+
+
+ You can use the passed status record problems with it.
+
+
+
+
+
+ Constructs a while loop with the specified inputs and a callback that composes the while loop
+
+ Inputs.
+ Callback method that fills out the various while loop parameters.
+ Status buffer, if specified a status code will be left here, if not specified, a exception is raised if there is an error.
+
+ An array of TFOutputs from creating the While loop, or null if there is an error creating the
+ while loop, or if the constructor raised an exception when it was invoked.
+
+
+
+
+ Adds a gradient: the operations needed to compute the partial derivatives of sum of ` wrt to .
+
+ The partial derivatives, the size of the array is the same as the length of the array.
+ The y elements.
+ The x elements.
+ Initial gradients, which represent the symbolic partial derivatives of some loss function `L` w.r.t. ).
+ If the parameter is null, the implementation will use dx for 'OnesLike' for all shapes in
+ Status buffer, if specified a status code will be left here, if not specified, a exception is raised if there is an error.
+
+ d(y[0] + y[1]+ ...)/dx[0], d(y[0] + y[1] + ...)/dx[1]z...
+
+
+
+
+ Adds a gradient: the operations needed to compute the partial derivatives of sum of ` wrt to .
+
+ The partial derivatives, the size of the array is the same as the length of the array.
+ names the scope into which all gradients operations are being added. This must be unique within
+ the provided graph otherwise this operation will fail. If the value is null, the default prefixing behaviour takes
+ place, see AddGradients for more details.
+
+ The y elements.
+ The x elements.
+ Initial gradients, which represent the symbolic partial derivatives of some loss function `L` w.r.t. ).
+ If the parameter is null, the implementation will use dx for 'OnesLike' for all shapes in
+ Status buffer, if specified a status code will be left here, if not specified, a exception is raised if there is an error.
+
+ d(y[0] + y[1]+ ...)/dx[0], d(y[0] + y[1] + ...)/dx[1]z...
+
+
+
+
+ Creates a TFFunction from a TFGraph
+
+ The function.
+ Name of the new function. Should match the operation name (OpDef.name) regexp [A-Z][A-Za-z0-9_.\\-/]*. If appendHashToFunctioName is false, the name must be unique (at least those registered in graphs where this function will be used).
+ Optional, human readable description of this function.
+ Array of operations to become the body of the function or null.
+ If no array is given , all the
+ operations in function body will become part of the function
+ except operations referenced in inputs. These operations
+ must have a single output (these operations are typically
+ placeholders created for the sole purpose of representing
+ an input).
+
+ If an array is given, all operations
+ in it will become part of the function. In particular, no
+ automatic skipping of dummy input operations is performed.
+
+ Array that specify the inputs to the function, or null. The names used for function inputs are normalized
+ names of the operations (usually placeholders) pointed to by
+ inputs. These operation names should start with a letter.
+ Normalization will convert all letters to lowercase and
+ non-alphanumeric characters to '_' to make resulting names match
+ the "[a-z][a-z0-9_]*" pattern for operation argument names.
+ `inputs` cannot contain the same tensor twice.
+ rray that specify the inputs to the function, or null. This can contain the same tensor twice.
+ The names of the function's outputs. The array either has the same elements of outputs, or be null. Names must match "[a-z][a-z0-9_]*" regexp, if null is passed, the names are generated automatically.
+ If set to true appends hash to functionName, otherwise it will use the specified name in functionName.
+ Status buffer, if specified a status code will be left here, if not specified, a exception is raised if there is an error.
+
+
+ This method converts the graph whose operations (or a subset of its operations) will be converted
+ into a TFFunction.
+
+
+ Note that when the same TF_Output is listed as both an input and an output,
+ the corresponding function's output will equal to this input,
+ instead of the original node's output.
+
+
+ Callers must also satisfy the following constraints:
+
+
+ cannot refer to TFOutputs within a control flow context. For
+ example, one cannot use the output of "switch" node as input.
+
+
+ and cannot have reference types. Reference types are
+ not exposed through C API and are being replaced with Resources. We support
+ reference types inside function's body to support legacy code. Do not
+ use them in new code.
+
+
+ Every node in the function's body must have all of its inputs (including
+ control inputs). In other words, for every node in the body, each input
+ must be either listed in or must come from another node in
+ the body. In particular, it is an error to have a control edge going from
+ a node outside of the body into a node in the body. This applies to control
+ edges going from nodes referenced in to nodes in the body when
+ the former nodes are not in the body (automatically skipped or not
+ included in explicitly specified body).
+
+
+
+
+
+ Returns the serialized VersionDef proto for this graph.
+
+ The versions.
+ The buffer where the serialized protocol buffer will be stored.
+ Status buffer, if specified a status code will be left here, if not specified, a exception is raised if there is an error.
+
+
+
+ Returns the number of TF_Functions registered in this graph.
+
+ The number functions.
+
+
+
+ Returns an the functions that have been defined in the graph.
+
+ The functions.
+
+
+
+ Name of the device to place the operation on.
+
+
+
+
+ Attempts to evaluate the . This is only possible if does not
+ depend on any graph inputs - the function is safe to call if this is not the case though.
+
+ true, if the evaluation is successful, in which case the result is returned in , false otherwise.
+ Output.
+ Tensor.
+
+
+
+ Base class for all the optimizers.
+
+
+
+
+ Varaible to keep track of number of iterations (mini-batch processed)
+
+
+
+
+ Variable to keep track of the learning rate.
+
+
+
+
+ The graph object. It is used for creating Ops through the construction of optimizer.
+
+
+
+
+ Name the optimization operation in the graph.
+ All the operation will be created under this name scope.
+
+
+
+
+ List to hold all the operations which are updated on each iteration of optimizer.
+
+
+
+
+ Construct optimizer.
+
+ The graph object.
+ Name of the operation.
+ The learning rate for the SGD update.
+ Learning rate decay over each update.
+ /// A floating point value. Starting value for the accumulators, must be >=0.
+
+
+
+ Create learning rate time decay operation.
+
+
+
+
+ Initialize the accumulators
+
+
+
+
+ Computes the gradient of the trainable variables in the graph.
+
+ The loss operation to compute the gradient on.
+ list of variable to compute the gradients for.
+ If null the gradient is computed for all the trainable variables in the graph.
+ Place the gradient op on the same device as variable.
+ A list of (gradient, variable) pairs. Variable is always present, but
+ gradient can be `None`.
+
+
+
+ Returns the ops to update the variables in the graph.
+
+ Gradient and Variable tuple.
+
+
+
+ Add operations to minimize `loss` by updating `var_list`.
+
+ This method simply combines calls `compute_gradients()` and
+ `apply_gradients()`. If you want to process the gradient before applying
+ them call `compute_gradients()` and `apply_gradients()` explicitly instead
+ of using this function.
+
+ A `Tensor` containing the value to minimize.
+ list of variable to compute the gradients for.
+ If null the gradient is computed for all the trainable variables in the graph.
+ An Operation that updates the variables.
+
+
+
+ Stochastic gradient descent optimizer.
+ Includes support for momentum, learning rate decay, and Nesterov momentum
+
+
+
+
+ Construct SGD optimizer.
+
+ The graph object.
+ The learning rate for the SGD update.
+ Parameter that accelerates SGD in the relevant direction and dampens oscillations.
+ Learning rate decay over each update.
+ Whether to apply Nesterov momentum.
+ Name the optimizer. All the variable that are created in this class will be created under this scope.
+
+
+
+
+
+
+ The base class for all the adaptive optimizers.
+
+
+
+
+ Constant value used for avoiding division overflow.
+
+
+
+
+ Construct Adagrad optimizer.
+
+ The graph object.
+ The learning rate for the SGD update.
+ Learning rate decay over each update.
+ A floating point value. Starting value for the accumulators, must be positive.
+ Name the optimizer. All the variable that are created in this class will be created under this scope.
+
+
+
+ Adaptive stochastic gradient descent optimizer.
+
+
+
+
+ Construct Adagrad optimizer.
+
+ The graph object.
+ The learning rate for the SGD update.
+ Learning rate decay over each update.
+ A floating point value. Starting value for the accumulators, must be positive.
+ Name the optimizer. All the variable that are created in this class will be created under this scope.
+
+
+
+
+
+
+ RMSProp: Adaptive stochastic gradient descent optimizer.
+
+
+
+
+ Construct Adagrad optimizer.
+
+ The graph object.
+ The learning rate for the SGD update.
+ Factor to compute the moving average over square of gradients.
+ Learning rate decay over each update.
+ A floating point value. Starting value for the accumulators, must be positive.
+ Name the optimizer. All the variable that are created in this class will be created under this scope.
+
+
+
+
+
+
+ Base class for queue implementations.
+ Port of Python implementation https://github.com/tensorflow/tensorflow/blob/r1.3/tensorflow/python/ops/data_flow_ops.py
+
+
+
+
+ A queue is a TensorFlow data structure that stores tensors across
+ multiple steps, and exposes operations that enqueue and dequeue
+ tensors.
+ Each queue element is a tuple of one or more tensors, where each
+ tuple component has a static dtype, and may have a static shape.The
+ queue implementations support versions of enqueue and dequeue that
+ handle single elements, versions that support enqueuing and
+ dequeuing a batch of elements at once.
+
+ Session instance
+
+
+
+ The session that this QueueBased was created for.
+
+ The session.
+
+
+
+ Enqueues a tuple of one or more tensors in this queue.
+
+
+ One or more tensors from which the enqueued tensors should be taken.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QueueEnqueueV2'.
+
+
+ Optional argument
+ If the queue is full, this operation will block for up to
+ timeout_ms milliseconds.
+ Note: This option is not supported yet.
+
+
+ Returns the description of the operation
+
+
+ The components input has k elements, which correspond to the components of
+ tuples stored in the given queue.
+
+
+
+
+ Dequeues a tuple of one or more tensors from this queue.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QueueDequeueV2'.
+
+
+ Optional argument
+ If the queue is empty, this operation will block for up to
+ timeout_ms milliseconds.
+ Note: This option is not supported yet.
+
+
+ One or more tensors that were dequeued as a tuple.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation has k outputs, where k is the number of components
+ in the tuples stored in the given queue, and output i is the ith
+ component of the dequeued tuple.
+
+
+
+
+ Gets the size of this queue.
+
+
+ queue size
+
+
+
+ A FIFOQueue that supports batching variable-sized tensors by padding.
+ Port of Python implementation https://github.com/tensorflow/tensorflow/blob/b46340f40fe5e2ec9bfcd385b07cfb914055fb51/tensorflow/python/ops/data_flow_ops.py#L697
+
+
+
+
+ Creates a queue that dequeues elements in a first-in first-out order.
+ A `PaddingFIFOQueue` has bounded capacity; supports multiple concurrent
+ producers and consumers; and provides exactly-once delivery.
+ A `PaddingFIFOQueue` holds a list of up to `capacity` elements.Each
+ element is a fixed-length tuple of tensors whose dtypes are
+ described by `dtypes`, and whose shapes are described by the `shapes`
+
+
+ The type of each component in a tuple.
+
+ Optional argument
+ The shape of each component in a value. The length of this attr must
+ be either 0 or the same as the length of component_types.
+ Shapes of fixed rank but variable size are allowed by setting
+ any shape dimension to -1. In this case, the inputs' shape may vary along
+ the given dimension, and DequeueMany will pad the given dimension with
+ zeros up to the maximum shape of all elements in the given batch.
+ If the length of this attr is 0, different queue elements may have
+ different ranks and shapes, but only one element may be dequeued at a time.
+ Optional argument. The upper bound on the number of elements in this queue. Negative numbers mean no limit.
+ Optional argument. If non-empty, this queue is placed in the given container. Otherwise, a default container is used.
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'PaddingFIFOQueueV2'.
+
+
+
+ Enqueues a tuple of one or more tensors in this queue.
+
+
+ One or more tensors from which the enqueued tensors should be taken.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QueueEnqueueV2'.
+
+
+ Optional argument
+ If the queue is full, this operation will block for up to
+ timeout_ms milliseconds.
+ Note: This option is not supported yet.
+
+
+ Returns the description of the operation
+
+
+ The components input has k elements, which correspond to the components of
+ tuples stored in the given queue.
+
+ N.B. If the queue is full, this operation will block until the given
+ element has been enqueued (or 'timeout_ms' elapses, if specified).
+
+
+
+
+ Enqueues a tuple of one or more tensors in this queue and runs the session.
+
+
+ One or more tensors from which the enqueued tensors should be taken.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QueueEnqueueV2'.
+
+
+ Values to enqueue
+
+
+ Optional argument
+ If the queue is full, this operation will block for up to
+ timeout_ms milliseconds.
+ Note: This option is not supported yet.
+
+
+ Returns the description of the operation
+
+
+ The components input has k elements, which correspond to the components of
+ tuples stored in the given queue.
+
+
+
+
+ Dequeues a tuple of one or more tensors from the given queue.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QueueDequeueV2'.
+
+
+ Optional argument
+ If the queue is empty, this operation will block for up to
+ timeout_ms milliseconds.
+ Note: This option is not supported yet.
+
+
+ One or more tensors that were dequeued as a tuple.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation has k outputs, where k is the number of components
+ in the tuples stored in the given queue, and output i is the ith
+ component of the dequeued tuple.
+
+
+
+
+ Dequeues a tuple of one or more tensors from this queue and runs the session.
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QueueDequeueV2'.
+
+
+ Optional argument
+ If the queue is empty, this operation will block for up to
+ timeout_ms milliseconds.
+ Note: This option is not supported yet.
+
+
+ One or more tensors that were dequeued as a tuple.
+ The TFOperation can be fetched from the resulting TFOutput, by fethching the Operation property from the result.
+
+
+ This operation has k outputs, where k is the number of components
+ in the tuples stored in the given queue, and output i is the ith
+ component of the dequeued tuple.
+
+
+
+
+ Dequeues elements from this queue and cast all elements to specific T type. It can be use when all elements in the queue of the same T type
+
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QueueDequeueV2'.
+
+
+ Optional argument
+ If the queue is empty, this operation will block for up to
+ timeout_ms milliseconds.
+ Note: This option is not supported yet.
+
+
+
+
+
+
+
+ Gets the size of this queue.
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QueueSizeV2'.
+ queue size
+
+
+
+ Uses provided session instance to obtain the size of this queue
+
+ If specified, the created operation in the graph will be this one, otherwise it will be named 'QueueSizeV2'.
+ number of elements in the queue
+
+
+
+ TFTensor holds a multi-dimensional array of elements of a single data type.
+
+
+
+ You can create tensors with the various constructors in this class, or using
+ the implicit conversions from various data types into a TFTensor, including
+ the creation of tensors from simple constants (returning a tensor that reprensets
+ a scalar, that is, it is a 0D tensor), arrays (returning a tensor of a single
+ dimension, 1D) or arbitrary multidimensional arrays.
+
+
+ Given a tensor, you can retrieve the number of dimensions in it via the
+ NumDims property, or you can retrieve the shape of a tensor, that is how many
+ elements on each dimension the tensor has, by fetching the Shape property.
+
+
+ The implicit conversions for basic types produce tensors of one dimesion with
+ a single element, while the implicit conversion from an array, expects a multi-dimensional
+ array that is converted into a tensor of the right dimensions.
+
+
+ The special "String" tensor data type that you will find in TensorFlow documentation
+ really represents a byte array. You can create string tensors by using the
+ method that takes a byte array buffer as input.
+
+
+
+ TFTensor scalar = 1; // Creates a 0D tensor, for the integer value 1
+ int d = scalar.NumDims; // d will be equal to zero, as it is a 0D tensor
+ long [] shape = scalar.Shape // returns an empty array, as it is a 0D tensor
+
+ TFTensor list = new [] {1,2,3} // Creates a 1D tensor, or vector, for the values 1, 2, 3
+ d = list.NumDims; // d will be one
+ shape = list.Shape; // shape will be an array with a single value 3, representing that the dimension 0 has 3 elements
+
+ // Creates a 3D tensor,
+ TFTensor cube = new [,,] { {{1,2,3},{4,5,6}}}
+ d = cube.NumDims // d will be 3
+ shape = list.Shape // shape will be [1,2,3] which is the shape of the above 3D array
+
+
+
+
+
+
+ Signature that methods must conform to to be used to release memory that was passed to a manually allocated TFTensor
+
+
+
+
+ Creates a new tensor from a portion of an array of sbytes
+
+ Represents the tensor shape.
+ The linear array of data, the data is shuffled to fit in the tensor with the specified dimensions.
+ The offset into the provided data array where the data resides.
+ The number of bytes to copy from count into the tensor.
+
+ Use the FromBuffer method to create a tensor that has the specified dimensions
+ and is initialized with data from the data array. The data is copied starting
+ at the start offset, for count bytes and is laid out into the tensor following the
+ specified dimensions.
+
+
+
+
+ Creates a new tensor from a portion of an array of bytes
+
+ Represents the tensor shape.
+ The linear array of data, the data is shuffled to fit in the tensor with the specified dimensions.
+ The offset into the provided data array where the data resides.
+ The number of bytes to copy from count into the tensor.
+
+ Use the FromBuffer method to create a tensor that has the specified dimensions
+ and is initialized with data from the data array. The data is copied starting
+ at the start offset, for count bytes and is laid out into the tensor following the
+ specified dimensions.
+
+
+
+
+ Creates a new tensor from a portion of an array of shorts
+
+ Represents the tensor shape.
+ The linear array of data, the data is shuffled to fit in the tensor with the specified dimensions.
+ The offset into the provided data array where the data resides.
+ The number of bytes to copy from count into the tensor.
+
+ Use the FromBuffer method to create a tensor that has the specified dimensions
+ and is initialized with data from the data array. The data is copied starting
+ at the start offset, for count bytes and is laid out into the tensor following the
+ specified dimensions.
+
+
+
+
+ Creates a new tensor from a portion of an array of ushorts
+
+ Represents the tensor shape.
+ The linear array of data, the data is shuffled to fit in the tensor with the specified dimensions.
+ The offset into the provided data array where the data resides.
+ The number of bytes to copy from count into the tensor.
+
+ Use the FromBuffer method to create a tensor that has the specified dimensions
+ and is initialized with data from the data array. The data is copied starting
+ at the start offset, for count bytes and is laid out into the tensor following the
+ specified dimensions.
+
+
+
+
+ Creates a new tensor from a portion of an array of ints
+
+ Represents the tensor shape.
+ The linear array of data, the data is shuffled to fit in the tensor with the specified dimensions.
+ The offset into the provided data array where the data resides.
+ The number of bytes to copy from count into the tensor.
+
+ Use the FromBuffer method to create a tensor that has the specified dimensions
+ and is initialized with data from the data array. The data is copied starting
+ at the start offset, for count bytes and is laid out into the tensor following the
+ specified dimensions.
+
+
+
+
+ Creates a new tensor from a portion of an array of floats
+
+ Represents the tensor shape.
+ The linear array of data, the data is shuffled to fit in the tensor with the specified dimensions.
+ The offset into the provided data array where the data resides.
+ The number of bytes to copy from count into the tensor.
+
+ Use the FromBuffer method to create a tensor that has the specified dimensions
+ and is initialized with data from the data array. The data is copied starting
+ at the start offset, for count bytes and is laid out into the tensor following the
+ specified dimensions.
+
+
+
+
+ Creates a new tensor from a portion of an array of doubles
+
+ Represents the tensor shape.
+ The linear array of data, the data is shuffled to fit in the tensor with the specified dimensions.
+ The offset into the provided data array where the data resides.
+ The number of bytes to copy from count into the tensor.
+
+ Use the FromBuffer method to create a tensor that has the specified dimensions
+ and is initialized with data from the data array. The data is copied starting
+ at the start offset, for count bytes and is laid out into the tensor following the
+ specified dimensions.
+
+
+
+
+ Creates a new tensor from a portion of an array of longs
+
+ Represents the tensor shape.
+ The linear array of data, the data is shuffled to fit in the tensor with the specified dimensions.
+ The offset into the provided data array where the data resides.
+ The number of bytes to copy from count into the tensor.
+
+ Use the FromBuffer method to create a tensor that has the specified dimensions
+ and is initialized with data from the data array. The data is copied starting
+ at the start offset, for count bytes and is laid out into the tensor following the
+ specified dimensions.
+
+
+
+
+ Creates a new tensor from a portion of an array of Complex numbers
+
+ Represents the tensor shape.
+ The linear array of data, the data is shuffled to fit in the tensor with the specified dimensions.
+ The offset into the provided data array where the data resides.
+ The number of bytes to copy from count into the tensor.
+
+ Use the FromBuffer method to create a tensor that has the specified dimensions
+ and is initialized with data from the data array. The data is copied starting
+ at the start offset, for count bytes and is laid out into the tensor following the
+ specified dimensions.
+
+
+
+
+ Creates a constant tensor from an array, the shape reflects the shape of the C# array and the underlying type reflects the C# type.
+
+
+
+
+ Creates a constant tensor with a single dimension from an integer value.
+
+
+
+
+ Creates a constant tensor with a single dimension from a boolean value.
+
+
+
+
+ Creates a constant tensor with a single dimension from an sbyte value.
+
+
+
+
+ Creates a constant tensor with a single dimension from a short value.
+
+
+
+
+ Creates a constant tensor with a single dimension from an ushort value.
+
+
+
+
+ Creates a constant tensor with a single dimension from an byte value.
+
+
+
+
+ Creates a constant tensor with a single dimension from a Complex value.
+
+
+
+
+ Creates a constant tensor with a single dimension from a float value.
+
+
+
+
+ Creates a constant tensor with a single dimension from a double value.
+
+
+
+
+ Creates a constant tensor with a single dimension from a long value.
+
+
+
+
+ Creates a 1 dimensional tensor from an array of booleans.
+
+ Data.
+
+
+
+ Creates a 1 dimensional tensor from an array of sbytes.
+
+ Data.
+
+
+
+ Creates a 1 dimensional tensor from an array of bytes.
+
+ Data.
+
+
+
+ Creates a 1 dimensional tensor from an array of shorts.
+
+ Data.
+
+
+
+ Creates a 1 dimensional tensor from an array of ushorts
+
+ Data.
+
+
+
+ Creates a 1 dimensional tensor from an array of ints.
+
+ Data.
+
+
+
+ Creates a 1 dimensional tensor from an array of floats.
+
+ Data.
+
+
+
+ Creates a 1 dimensional tensor from an array of doubles.
+
+ Data.
+
+
+
+ Creates a 1 dimensional tensor from an array of longs.
+
+ Data.
+
+
+
+ Creates a 1 dimensional tensor from an array of complex numbers.
+
+ Data.
+
+
+
+ Creates a single-dimension tensor from a byte buffer. This is different than creating a tensor from a byte array that produces a tensor with as many elements as the byte array.
+
+
+
+
+ Converts a single-dimension tensor into a byte buffer. The byte array can be further decoded into strings using appropriate encoding scheme e.g. "UTF8"
+
+
+
+
+ Creates a multi-dimension tensor from an array of byte buffer. The bytes for string[i] are represented as buffer[i][:].
+
+
+
+
+ Converts a multi-dimension tensor into a byte buffer array. The byte array can be further decoded into strings using appropriate encoding scheme e.g. "UTF8"
+
+
+
+
+ Converts an integer into a 1-dimensional, 1-valued tensor.
+
+ The tensor representing the integer value.
+ Value to initialize the tensor with.
+
+
+
+ Converts a boolean into a 1-dimensional, 1-valued tensor.
+
+ The tensor representing the integer value.
+ Value to initialize the tensor with.
+
+
+
+ Converts a long into a 1-dimensional, 1-valued tensor.
+
+ The tensor representing the long value.
+ Value to initialize the tensor with.
+
+
+
+ Converts a double into a 1-dimensional, 1-valued tensor.
+
+ The tensor representing the double value.
+ Value to initialize the tensor with.
+
+
+
+ Converts a float into a 1-dimensional, 1-valued tensor.
+
+ The tensor representing the float value.
+ Value to initialize the tensor with.
+
+
+
+ Converts a Complex number into a 1-dimensional, 1-valued tensor.
+
+ The tensor representing the complex value.
+ Value to initialize the tensor with.
+
+
+
+ Converts a byte into a 1-dimensional, 1-valued tensor.
+
+ The tensor representing the byte value.
+ Value to initialize the tensor with.
+
+
+
+ Converts a C# array into a tensor.
+
+ The tensor containing the data.
+ single dimension, or multi-dimensional array.
+
+ This implicit conversion can convert single or multidimensional arrays of
+ booleans, sbytes, byte, shorts, ushorts, ints, longs, doubles, floats and
+ complex numbers into a tensor with the same dimensional shape as the provided
+ array.
+
+
+
+
+ Low-level tensor constructor that creates a tensor from a buffer pointed to by an IntPtr.
+
+ Specifies the data type held by the tensor, as well as how to interpret the provided data.
+ Describes the tensor shape, an array that indicates .
+ Pointer to the raw data that will be used to initialize the tensor.
+ The size of the data being passed in.
+ Deallocator method, it is invoked when the tensor is destroyed to release the data pointed to by . On platforms like iOS (or other static compilation platforms), yiou must annotate the method specified in the deallocator with a .
+ An optional argument of data that is passed to the deallocator method when the tensor is destroyed, you can use this to pass context information.
+
+
+
+ Low-level: Creates an empty tensor of the specified type and shape, with the specified number of elements
+
+ Data type.
+ Tensor shape.
+ Size in bytes of the tensor, this will be the actual memory allocated.
+
+ It is the responsibility of the caller to ensure that the size is correct given the data type size
+ and the tensor dimension specified in dims.
+
+
+
+
+ Returns the data type for the tensor.
+
+ The type of the tensor.
+
+
+
+ Returns the number of dimensions in the tensor.
+
+
+ For single-dimension tensors the return is 1, 2 dimensions is 2 and so on.
+
+
+
+
+ Returns the number of elements on a specific dimension in the tensor.
+
+ The tensor dimension.
+ Dimension that you are querying.
+
+ If you have a tensor of 3 elements by 5, represented by [3 5],
+ the GetTensorDimension(0) will return 3, the GetTensorDimension(1)
+ will return 5.
+
+
+
+
+ Returns a pointer to the raw data in the tensor.
+
+
+ The contents of the Data must be interpreted according to the type of the
+ data as described by the DataType property. The amount of data
+ is given by the the TensorByteSize property.
+
+
+
+
+ Returns the tensor shape, this is an array whose size determines the number of dimensions on the tensor, and each element is the size of the dimension
+
+
+ An array of size 0 is used for constants, an array of size 1 is used
+ for single-dimension arrays, where the dimension is the value of the
+ first element. And so on.
+
+
+
+
+ Converts a to a system type.
+
+ The to be converted.
+ The system type corresponding to the given .
+
+
+
+ Converts a system type to a .
+
+ The system type to be converted.
+ The corresponding to the given type.
+
+
+
+ Returns the value of the Tensor as a C# type if possible, or null if the data type can not be represented in C#
+
+
+ The default is set to false, which returns .NET multi-dimensional arrays for multi-dimensional
+ tensors. This is useful to feed the data back as a TFTensor created from an array. Set to
+ true if you want to get arrays pointing to arrays, which are slightly more convenient to work
+ with from C#
+
+
+ Jagged arrays create various intermediate arrays, while multi-dimensional arrays are more
+ efficient memory-wise.
+
+ The value encodes the contents of the tensor, and could include simple values, arrays and multi-dimensional values.
+
+
+
+ Returns a that represents the current .
+
+ A that represents the current .
+
+
+
+ Contains TensorFlow fundamental methods and utility functions.
+
+
+
+
+ Returns the version of the TensorFlow runtime in use.
+
+ The version.
+
+
+
+ Gets the size in bytes of the specified TensorFlow data type.
+
+ The data type size.
+ Dt.
+
+
+
+ Retrieves the ProtocolBuffer describing all of the available operations in
+ the TensorFlow library in current use.
+
+ The buffer contains a ProtocolBuffer encoded payload, you need a ProtocolBuffer reader to process the contents.
+
+
+
+ Returns a serialized KernelList protocol buffer containing KernelDefs for all registered kernels
+
+ Status buffer, if specified a status code will be left here, if not specified, a exception is raised if there is an error.
+ The all registered kernels.
+
+
+
+ Returns a serialized KernelList protocol buffer containing KernelDefs for all
+ kernels registered for the operation specified.
+
+ The operation to look up.
+ Status buffer, if specified a status code will be left here, if not specified, a exception is raised if there is an error.
+ The registered kernels for the specified operation.
+
+
+
+ Base class for many TensorFlow data types that provides a common idiom to dispose and
+ release resources associated with the native data types. Generally, you do not need to use this.
+
+
+
+ This implements the Dispose pattern in a reusable form for TensorFlow types.
+
+
+ Subclasses invoke the constructor with the handle that this will wrap, and must
+ override the NativeDispose method (internal) to release the associated resource.
+
+
+
+
+
+ Returns the opaque handle to the object that this TFDisposable owns.
+
+ The handle.
+
+
+
+ Initializes a new instance of the class.
+
+
+
+
+ Initializes a new instance of the class
+ from the handle that it will wrap.
+
+
+
+
+ Releases all resource used by the object.
+
+ Call Dispose when you are finished using the . The
+ Dispose method leaves the in an unusable state. After
+ calling Dispose, you must release all references to the so
+ the garbage collector can reclaim the memory that the was occupying.
+
+
+
+ Dispose the specified object
+
+ If set to true it means that this method was called from Dispose, otherwise from the finalizer.
+
+
+
+ ase class for many TensorFlow data types that provides a common idiom to dispose and
+ release resources associated with the native data types and whose unmanaged resource
+ disposing can be called from a background thread (the finalizer). Users do not
+ need to deal with this class.
+
+
+ Some object deletion APIs in TensorFlow can be invoked from a background thread,
+ so the release methods are suitable to be invoked from the Finalizer thread, in
+ those scenarios, subclass from this class rather than the TFDisposable class.
+
+
+
+
+ Initializes a new instance of the class
+ from the handle that it will wrap.
+
+
+
+
+ Initializes a new instance of the class.
+
+
+
+
+ Dispose the object, unlike the default implementat in TFDisposable,
+ this will release the unmanaged resources from a background thread.
+
+ If set to true disposing.
+
+
+
+ TensorFlow Exception
+
+
+
+
+ Initializes a new instance of the class with a message.
+
+ Message.
+
+
+
+ Used to track the result of TensorFlow operations.
+
+
+
+ TFStatus is used to track the status of a call to some TensorFlow
+ operations. Instances of this object are passed to various
+ TensorFlow operations and you can use the
+ to quickly check if the operation succeeded, or get more detail from the
+ and a human-readable text
+ using the property.
+
+
+ The convenience can be used
+ to raise a if the status of the
+ operation did not succeed.
+
+
+
+
+
+ Per-thread global status that you can use if you do not need to create a new instance of this object.
+
+
+ This is provided as a convenience for APIs that take a TFStatus. While the TFStatus is usually an
+ optional parameter, when it is made optional, API calls that fail raise an exception. Use this
+ property to pass a TFStatus without having to allocate a new one. The problem with this of course
+ is that you risk having multiple parts of your code override this thread-global variable.
+
+
+
+
+ Initializes a new instance of the class.
+
+
+
+
+ Sets the status code on this TFStatus.
+
+ Code.
+ Message.
+
+
+
+ Gets the status code for the status code.
+
+ The status code as an enumeration.
+
+
+
+ Gets a human-readable status message.
+
+ The status message.
+
+
+
+ Returns a that represents the current .
+
+ A that represents the current .
+
+
+
+ Gets a value indicating whether this state has been set to ok.
+
+ true if ok; otherwise, false.
+
+
+
+ Gets a value indicating whether this state has been set to an error.
+
+ true if error; otherwise, false.
+
+
+
+ Convenience method that raises an exception if the current status is an error.
+
+
+ You can use this method as a convenience to raise an exception after you
+ invoke an operation if the operation did not succeed.
+
+
+
+
+ The session options object holds configuration options that you want to use during your session, like the TensorFlow target or the configuration.
+
+
+
+
+ Initializes a new instance of the class.
+
+
+
+
+ Sets the target in options.
+
+ target can be empty, a single entry, or a comma separated list of entries.
+ Each entry is in one of the following formats: "local", ip:port, host:port.
+
+
+
+
+ Sets the configuration information for the session.
+
+ Serialized protocol buffer for the tensorflow.ConfigProto message.
+ Length of the buffer.
+ If config was not parsed successfully as a ConfigProto, the error is recorded here.
+
+ The configuration option is a Protocol Buffer representing the tensorflow.ConfigProto
+
+
+
+
+ Class to unset device name in the graph within using block.
+
+
+
+
+ Pops the device name back to previous device name in use.
+
+ Call when you are finished using the
+ to restore to the default device to be used in the .
+
+
+
+
+ A grouping of operations with defined inputs and outputs.
+ Once created and added to graphs, functions can be invoked by creating an
+ operation whose operation type matches the function name.
+
+
+
+
+ Write out a serialized representation of the function as a FunctionDef protocol message to the provided
+
+ An allocated buffer where the function will be serialized.
+ Status buffer, if specified a status code will be left here, if not specified, a exception is raised if there is an error.
+
+
+
+ Construct and return the function whose FunctionDef representation is
+ serialized in proto
+
+ The function definition, or null on failure.
+ Array containing the serialized FunctionDef in a protocol buffer.
+ Status buffer, if specified a status code will be left here, if not specified, a exception is raised if there is an error.
+
+
+
+ TFGraph variable dependencies handle.
+
+
+ Instances of this class, when disposed, restore
+ to the value it had before the method
+ was called.
+
+
+
+
+
+ Pops the variable dependencies to the previous dependencies in use.
+
+ Call when you are finished using the
+ to restore the previous variable dependencies in use in the .
+
+
+
+
+ TFGraph name scope handle
+
+
+ Instances of this class when disposed restore the CurrentNameScope to the
+ value they had when the TFGraph.WithScope method was called.
+
+
+
+
+ Pops the name space to the previous namescope in use.
+
+ Call when you are finished using the
+ to restore the previous name scope in use in the .
+
+
+
+
+ Low-level TensorFlow operation builder
+
+
+ This is the low-level API that is used to create operations by manually specificying all
+ the parameters of an operation (inputs, outputs, attribute descriptions) that can then
+ be attached into a graph.
+
+
+ Generally, you will instead be using the methods surfaced in
+ that surfaces a C# high-level API that has already been bound to the built-in TensorFlow
+ nodes.
+
+
+ You create instances bound to a graph, add inputs, attributes and so on, and when you are done
+ you can call the method that will turn this TFOperationDesc
+ into a .
+
+
+
+
+
+ Specifies the device for the operation, if one is not provided, the operation is unconstrained.
+
+ This instance, allows for chaining operation invocations.
+ The device to constraint to in this operation.
+
+
+
+ Adds the specified input to the operation
+
+ The input.
+ Input.
+
+
+
+ Adds a series of inputs to the operation.
+
+ Inputs, this is a params array for your convenience.
+
+
+
+ Ensure that the operation does not execute before the control operation does.
+
+ Operation that must be executed before running this operation.
+
+
+ A control input is an Operation that must be executed before running the operation
+ currently being built.
+
+
+ For example, an Assert operation may be added as a control input for this operation.
+ The Assert now behaves as a pre-condition that will always verify itself before
+ running the operation.
+
+
+
+
+
+ Turns the operation description into an actual operation in the graph.
+
+ The operation on success, or null on error.
+ Optional status, on failure the operation is not added to the graph. If you pass null (the default), this operation throws on error conditions.
+
+
+
+ Sets an attribute on the function to the specified value.
+
+ The attribute name.
+ The value for the attribute.
+
+
+
+ Represents a computation node in the graph. Tensorflow operations are attached to a .
+
+
+ TFOperations are usually created by invoking one of the methods in
+ , but they can also be constructed
+ manually using the low-level API.
+
+
+
+
+ Gets the handle to the unmanaged TF_Operation object.
+
+ The handle.
+
+
+
+ The name for this operation/
+
+ The name.
+
+
+
+ Gets the number of outputs on this operation.
+
+ The number outputs.
+
+
+
+ Gets the number of inputs for this operation.
+
+ The number inputs.
+
+
+
+ Gets the number of control inputs oto an operation
+
+ The number control inputs.
+
+
+
+ Gets the number of operations that have this operation as a control input.
+
+
+
+
+ Get the list of operations that have this operation as a control input.
+
+ The control outputs.
+
+
+
+ Query the operation for a type attribute
+
+ The value (type) of the attribute.
+
+
+
+ Query the operation for a shape attribute
+
+ Shape array for the attribute
+ Name of the attribute to query
+ Number of dimensions as specified by the attribute metadata
+ Status of the operations
+
+
+
+ Encodes the TFOperation as a protocol buffer payload
+
+ The buffer with the encoded operation in the protocol buffer format.
+ Status buffer, if specified a status code will be left here, if not specified, a exception is raised if there is an error.
+
+
+
+
+
+ Returns the handle to the idx-th output of the operation.
+
+ Index of the output in the operation.
+
+
+
+ Device type
+
+
+
+
+ The device is the Central Processing Unit (CPU)
+
+
+
+
+ The device is a Graphics Processing Unit (GPU)
+
+
+
+
+ The device is a Tensor Processing Unit (TPU)
+
+
+
+
+ Describes the device attributes
+
+
+
+
+ The full name of the device (e.g. /job:worker/replica:0/...)
+
+
+
+
+ Gets the type of the device.
+
+ The type of the device.
+
+
+
+ The amount of memory associated with a given device.
+
+ The memory limit bytes.
+
+
+
+ Contains options that are used to control how graph importing works.
+
+
+
+
+ Adds an input mapping from a source name and index to a destination output
+
+ Source name.
+ Source index (in the source).
+ Replacement value for the srcName:srcIndex.
+
+ Set any imported nodes with input `src_name:src_index` to have that input
+ replaced with `dst`. `src_name` refers to a node in the graph to be imported,
+ `dst` references a node already existing in the graph being imported into.
+
+
+
+
+ Cause the imported graph to have a control dependency on the provided operation.
+
+ This operation should exist in the graph being imported to.
+
+
+
+ Add an output in the graph definition to be returned via the return outputs parameter.
+
+ Operation name.
+ Operation index.
+
+ If the output is remapped via an input
+ mapping, the corresponding existing tensor in graph will be returned.
+
+
+
+
+ Gets the number return outputs added via AddReturnOutput.
+
+ The number return outputs.
+
+
+
+ Sets any imported nodes with a given control input to have it replaced with an operation
+
+ Node in the graph to be imported.
+ References an operation that already exists in the graph being imported.
+
+ Set any imported nodes with control input to have that input
+ replaced with .
+
+
+
+
+ Set whether to uniquify imported operation names.
+
+ If set to true imported operation names will be modified if their name already exists in the graph.
+ If set to false conflicting names will be treated as an error.
+
+
+ Note that this option has no effect if a prefix is set, since the prefix will guarantee all names are
+ Defaults to false.
+
+
+
+
+ Sets the uniquify prefix. This option has no effect if no prefix is specified.
+
+ If set to true the specified prefix will be modified if it already exists as an
+ operation name or prefix in the graph.
+ If set to false a conflicting prefix will be treated as an error.
+
+
+
+
+ Drives the execution of a graph
+
+
+
+ This creates a new context to execute a TFGraph. You can use the
+ constructor to create an empty session, or you can load an existing
+ model using the static method in this class.
+
+
+ To execute operations with the graph, call the method
+ which returns an object that you can use to build the operation by providing
+ the inputs, requesting the operations that you want to execute and the desired outputs.
+
+
+ The method is a high-level helper function that wraps a
+ call to the method which just takes too many parameters that must
+ be kept in sync.
+
+
+
+
+
+ Gets the graph associated with this TensorFlow session.
+
+ The graph.
+
+
+
+ Creates a new execution session associated with the specified session graph with some configuration options.
+
+ The Graph to which this session is associated.
+ Session options.
+ Status buffer, if specified a status code will be left here, if not specified, a exception is raised if there is an error.
+
+
+
+ Creates a new execution session associated with the specified session graph.
+
+ The Graph to which this session is associated.
+ Status buffer, if specified a status code will be left here, if not specified, a exception is raised if there is an error.
+
+
+
+ Creates a new execution session with an empty graph
+
+ Status buffer, if specified a status code will be left here, if not specified, a exception is raised if there is an error.
+
+ The created graph can be retrieved using the Graph property on the session.
+
+
+
+
+ Lists available devices in this session.
+
+ Status buffer, if specified a status code will be left here, if not specified, a exception is raised if there is an error.
+
+
+
+ Creates a session and graph from a model stored in the SavedModel file format.
+
+ On success, this populates the provided with the contents of the graph stored in the specified model and with the MetaGraphDef of the loaded model.
+ Session options to use for the new session.
+ Options to use to initialize the state (can be null).
+ must be set to the path of the exported SavedModel.
+ must include the set of tags used to identify one MetaGraphDef in the SavedModel.
+ This must be a newly created graph.
+ On success, this will be populated on return with the contents of the MetaGraphDef (can be null).
+ Status buffer, if specified a status code will be left here, if not specified, a exception is raised if there is an error.
+
+
+ This function creates a new session using the specified and then initializes
+ the state (restoring tensors and other assets) using .
+
+
+ This function loads the data that was saved using the SavedModel file format, as described
+ here: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md
+
+
+
+
+
+ Closes the session. Contacts any other processes associated with the session, if applicable.
+
+ Status buffer, if specified a status code will be left here, if not specified, a exception is raised if there is an error.
+
+ Can not be called after calling DeleteSession.
+
+
+
+
+ Deletes the session.
+
+ Status.
+
+
+
+ Use the runner class to easily configure inputs, outputs and targets to be passed to the session runner.
+
+
+
+ The runner has a simple API that allows developers to call the AddTarget, AddInput, AddOutput and Fetch
+ to construct the parameters that will be passed to the TFSession.Run method.
+
+
+ Instances of this class are created by calling the GetRunner method on the TFSession.
+
+
+ The various methods in this class return an instance to the Runner itsel, to allow
+ to easily construct chains of execution like this:
+
+
+ var result = session.GetRunner ().AddINput (myInput).Fetch (MyOutput).Run ();
+
+
+ You do not need to chain the operations, this works just the same:
+
+
+ runner = session.GetRunner ();
+ runner.AddInput(myInput);
+ runner.Fetch(myOutput);
+ var results = runner.Run();
+
+
+
+
+
+ Adds an input to the session
+
+ An instance to the runner, so you can easily chain the operations together.
+ Incoming port.
+ Value to assing to the incoming port.
+
+
+
+ Adds an input to the session specified by name, with an optional index in the operation (separated by a colon).
+
+ An instance to the runner, so you can easily chain the operations together.
+ Incoming port, with an optional index separated by a colon.
+ Value to assing to the incoming port.
+
+
+
+ Adds the specified operations as the ones to be retrieved.
+
+ An instance to the runner, so you can easily chain the operations together.
+ One or more targets.
+
+
+
+ Adds the specified operation names as the ones to be retrieved.
+
+ An instance to the runner, so you can easily chain the operations together.
+ One or more target names.
+
+
+
+ Makes the Run method return the index-th output of the tensor referenced by operation.
+
+ The instance of runner, to allow chaining operations.
+ The name of the operation in the graph.
+ The index of the output in the operation.
+
+
+
+ Makes the Run method return the output of the tensor referenced by operation, the operation string can contain the output index.
+
+ The instance of runner, to allow chaining operations.
+ The name of the operation in the graph, which might be a simple name, or it might be name:index,
+ where the index is the .
+
+
+
+ Makes the Run method return the output of the tensor referenced by output
+
+ The instance of runner, to allow chaining operations.
+ The output referencing a specified tensor.
+
+
+
+ Makes the Run method return the output of all the tensor referenced by outputs.
+
+ The instance of runner, to allow chaining operations.
+ The outputs referencing a specified tensor.
+
+
+
+ Makes the Run method return the output of all the tensor referenced by outputs.
+
+ The instance of runner, to allow chaining operations.
+ The output sreferencing a specified tensor.
+
+
+
+ Protocol buffer encoded block containing the metadata passed to the method.
+
+
+
+
+ Protocol buffer encoded block containing the run options passed to the method.
+
+
+
+
+ Execute the graph fragments necessary to compute all requested fetches.
+
+ One TFTensor for each call to Fetch that you made, in the order that you made them.
+ Status buffer, if specified a status code will be left here, if not specified, a exception is raised if there is an error.
+
+
+
+ Run the specified operation, by adding it implicity to the output, single return value
+
+ The output of the operation.
+ Status buffer, if specified a status code will be left here, if not specified, a exception is raised if there is an error.
+
+ This method is a convenience method, and when you call it, it will clear any
+ calls that you might have done to Fetch() and use the specified operation to Fetch
+ instead.
+
+
+
+
+ Gets a new runner, this provides a simpler API to prepare the inputs to run on a session
+
+ The runner.
+
+ The runner has a simple API that allows developers to call the AddTarget, AddInput, AddOutput and Fetch
+ to construct the parameters that will be passed to the TFSession.Run method.
+
+ The Run method will return an array of TFTensor values, one for each invocation to the Fetch method.
+
+
+
+
+ Executes a pipeline given the specified inputs, inputValues, outputs, targetOpers, runMetadata and runOptions.
+ A simpler API is available by calling the method which performs all the bookkeeping
+ necessary.
+
+ An array of tensors fetched from the requested outputs.
+ Inputs nodes.
+ Input values.
+ Output nodes.
+ Target operations to execute.
+ Run metadata, a buffer containing the protocol buffer encoded value for https://github.com/tensorflow/tensorflow/blob/r1.9/tensorflow/core/protobuf/config.proto.
+ Run options, a buffer containing the protocol buffer encoded value for https://github.com/tensorflow/tensorflow/blob/r1.9/tensorflow/core/protobuf/config.proto.
+ Status buffer, if specified a status code will be left here, if not specified, a exception is raised if there is an error.
+
+
+
+ Token returned from using one of the Partial Run Setup methods from ,
+ and use this token subsequently for other invocations.
+
+
+ Calling Dispose on this object will release the resources associated with setting up
+ a partial run.
+
+
+
+
+ Prepares the session for a partial run.
+
+ A token that can be used to call repeatedly. To complete your partial run, you should call Dispose on the resulting method.
+ Inputs.
+ Outputs.
+ Target operations to run.
+ Status buffer, if specified a status code will be left here, if not specified, a exception is raised if there is an error.
+
+
+
+ Restores a tensor from a serialized tensorflor file.
+
+ The deserialized tensor from the file.
+ File containing your saved tensors.
+ The name that was used to save the tensor.
+ The data type for the tensor.
+
+ using (var session = new TFSession()){
+ var a = session.Graph.Const(30, "a");
+ var b = session.Graph.Const(12, "b");
+ var multiplyResults = session.GetRunner().Run(session.Graph.Add(a, b));
+ var multiplyResultValue = multiplyResults.GetValue();
+ Console.WriteLine("a*b={0}", multiplyResultValue);
+ session.SaveTensors($"saved.tsf", ("a", a), ("b", b));
+ }
+
+
+
+
+ Saves the tensors in the session to a file.
+
+ The tensors.
+ File to store the tensors in (for example: tensors.tsf).
+ An array of tuples that include the name you want to give the tensor on output, and the tensor you want to save.
+
+
+ Tensors saved with this method can be loaded by calling .
+
+
+ using (var session = new TFSession ()) {
+ var a = session.Graph.Const(30, "a");
+ var b = session.Graph.Const(12, "b");
+ var multiplyResults = session.GetRunner().Run(session.Graph.Add(a, b));
+ var multiplyResultValue = multiplyResults.GetValue();
+ Console.WriteLine("a*b={0}", multiplyResultValue);
+ session.SaveTensors($"saved.tsf", ("a", a), ("b", b));
+ }
+
+
+
+
+
+ Represents a dynamically loaded library of TensorFlow operations, use to load and consume TensorFlow operations from an external library.
+
+
+ Use the static method to load a dynamic library.
+ Once that function returns
+
+
+
+
+ Load the library specified by and register the operations and
+ kernels present in that library.
+
+ Handle to the loaded library.
+ Name of the library to load, this is a platform specific name.
+ Status buffer, if specified a status code will be left here, if not specified, a exception is raised if there is an error.
+
+ The provided is passed to the operating system dynamic loader
+ and it will load the library using the operating system defined search paths and rules to load this.
+
+
+
+
+ Retrieves the ProtocolBuffer describing the available operations in
+ the loaded TensorFlow library.
+
+ The buffer contains a ProtocolBuffer encoded payload, you need a ProtocolBuffer reader to process the contents.
+
+
+
+ The data type for a specific tensor.
+
+
+ Tensors have uniform data types, all the elements of the tensor are of this
+ type and they dictate how TensorFlow will treat the data stored.
+
+
+
+
+ The TFDataType has not been set
+
+
+
+
+ Single precission floatint point, 32-bits (C# float)
+
+
+
+
+ Double precission floatint point, 64-bits (C# double)
+
+
+
+
+ 32-bit signed integers (C# int)
+
+
+
+
+ 8 bit unsigned integers (C# byte)
+
+
+
+
+ 16-bit signed integers (C# short)
+
+
+
+
+ 8-bit signed integers (C# sbyte)
+
+
+
+
+ Binary blob
+
+
+
+
+ Single precission complex numbers (32-bit floats)
+
+
+
+
+ 32-bit float based complex numbers
+
+
+
+
+ 64-bit signed integers (C# long)
+
+
+
+
+ 8-bit boolean (C# bool)
+
+
+
+
+ Quantized 8-bit signed integer
+
+
+
+
+ Quantized 8-bit unsigned integer
+
+
+
+
+ Quantized 32-bit signed integer
+
+
+
+
+ Float32 truncated to 16 bits. Only for cast operations.
+
+
+
+
+ Quantized 16-bit signed integer
+
+
+
+
+ Quantized 16-bit unsigned integer
+
+
+
+
+ 16-bit unsigned integers (C# long)
+
+
+
+
+ Double precission complex numbers (32-bit floats)
+
+
+
+
+ Half floats - 16-bit half precision floating point.
+
+
+
+
+ Handle to a mutable resource.
+
+
+
+
+ Variant data type
+
+
+
+
+ 32-bit unsigned integers
+
+
+
+
+ 64-bit unsigned integers
+
+
+
+
+ Status code for invoking a tensorflow operation.
+
+
+
+
+ Not an error; returned on success
+
+
+
+
+ The operation was cancelled (typically by the caller).
+
+
+
+
+ Unknown error. An example of where this error may be returned is
+ if a Status value received from another address space belongs to
+ an error-space that is not known in this address space. Also
+ errors raised by APIs that do not return enough error information
+ may be converted to this error.
+
+
+
+
+ Client specified an invalid argument. Note that this differs
+ from FailedPrecondition. InvalidArgumentindicates arguments
+ that are problematic regardless of the state of the system
+ (e.g., a malformed file name).
+
+
+
+
+ Deadline expired before operation could complete. For operations
+ that change the state of the system, this error may be returned
+ even if the operation has completed successfully. For example, a
+ successful response from a server could have been delayed long
+ enough for the deadline to expire.
+
+
+
+
+ Some requested entity (e.g., file or directory) was not found.
+ For privacy reasons, this code may be returned when the client
+ does not have the access right to the entity.
+
+
+
+
+ Some entity that we attempted to create (e.g., file or directory) already exists.
+
+
+
+
+ The caller does not have permission to execute the specified
+ operation. PermissionDenied must not be used for rejections
+ caused by exhausting some resource (use ResourceExhausted
+ instead for those errors). PermissionDeniedmust not be
+ used if the caller can not be identified (use Unauthenticated
+ instead for those errors).
+
+
+
+
+ The request does not have valid authentication credentials for the
+ operation.
+
+
+
+
+ Some resource has been exhausted, perhaps a per-user quota, or
+ perhaps the entire file system is out of space.
+
+
+
+
+ Operation was rejected because the system is not in a state
+ required for the operation's execution. For example, directory
+ to be deleted may be non-empty, an rmdir operation is applied to
+ a non-directory, etc.
+
+ A litmus test that may help a service implementor in deciding
+ between FailedPrecondition, Aborted, and Unavailable:
+
+ (a) Use Unavailableif the client can retry just the failing call.
+ (b) Use Aborted if the client should retry at a higher-level
+ (e.g., restarting a read-modify-write sequence).
+ (c) Use FailedPrecondition if the client should not retry until
+ the system state has been explicitly fixed. E.g., if an "rmdir"
+ fails because the directory is non-empty, FailedPrecondition
+ should be returned since the client should not retry unless
+ they have first fixed up the directory by deleting files from it.
+ (d) Use FailedPrecondition if the client performs conditional
+ REST Get/Update/Delete on a resource and the resource on the
+ server does not match the condition. E.g., conflicting
+ read-modify-write on the same resource.
+
+
+
+
+ The operation was aborted, typically due to a concurrency issue
+ like sequencer check failures, transaction aborts, etc.
+
+ See litmus test above for deciding between FailedPrecondition,
+ Aborted and Unavailable
+
+
+
+
+ Operation tried to iterate past the valid input range. E.g., seeking or
+ reading past end of file.
+
+ Unlike InvalidArgument, this error indicates a problem that may
+ be fixed if the system state changes. For example, a 32-bit file
+ system will generate InvalidArgument if asked to read at an
+ offset that is not in the range [0,2^32-1], but it will generate
+ OutOfRange if asked to read from an offset past the current
+ file size.
+
+ There is a fair bit of overlap between FailedPrecondition and
+ OutOfRange. We recommend using OutOfRane (the more specific
+ error) when it applies so that callers who are iterating through
+ a space can easily look for an OutOfRange error to detect when
+ they are done.
+
+
+
+
+ Operation is not implemented or not supported/enabled in this service.
+
+
+
+
+ Internal errors. Means some invariants expected by underlying
+ system has been broken. If you see one of these errors,
+ something is very broken.
+
+
+
+
+ The service is currently unavailable. This is a most likely a
+ transient condition and may be corrected by retrying with
+ a backoff.
+
+ See litmus test above for deciding between FailedPrecondition,
+ Aborted, and Unavailable.
+
+
+
+
+ Unrecoverable data loss or corruption.
+
+
+
+
+ Represents a specific input of an operation.
+
+
+
+
+ The operation that this input is for
+
+
+
+
+ The index of the output within the Operation
+
+
+
+
+ Represents a specific output of an operation on a tensor.
+
+
+
+ TFOutput objects represent one of the outputs of an operation in the graph
+ (TFGraph). Outputs have a data type, and eventually a shape that you can
+ retrieve by calling the method.
+
+
+ These can be passed as an input argument to a function for adding operations
+ to a graph, or to the TFSession's Run and GetRunner method as values to be
+ fetched.
+
+
+
+
+
+ The index of the output within the operation.
+
+
+
+
+ Gets the number consumers.
+
+ The number consumers.
+
+ This number can change when new operations are added to the graph.
+
+
+
+
+ Gets the type of the output.
+
+ The type of the output.
+
+
+
+ Initializes a new TFOutput instance.
+
+ The operation to which to attach the output.
+ The index of the output within the operation, if not specified, it defaults to zero.
+
+
+
+ Initializes a new TFOutput instance from another TFOutput
+
+ The other TFOutput that is having its operation attached.
+ The index of the output within the operation, if not specified, it defaults to zero.
+
+
+
+ Get list of all current consumers of a specific output of an operation
+
+ The output consumers.
+
+ A concurrent modification of the graph can increase the number of consumers of
+ an operation.
+ This can return null if the TFOutput does not point to a valid object.
+
+
+
+
+ The associated operation.
+
+ The operation.
+
+
+
+ Returns a that represents the current .
+
+ A that represents the current .
+
+
+
+ Low-level: Enumeration describing the types of a metadata attribute
+
+
+
+
+ The type of the attribute is a string
+
+
+
+
+ The type of the attribute is an int.
+
+
+
+
+ The type of the attribute is a float
+
+
+
+
+ The type of the attribute is a bool.
+
+
+
+
+ The type of the attribute is a type.
+
+
+
+
+ The type of the attribute is a tensor shape
+
+
+
+
+ The type of the attribute is a tensor
+
+
+
+
+ The type of the attribute is a placeholder
+
+
+
+
+ The type of the attribute is a function
+
+
+
+
+ Low-level: this describes the tensorflow type information for an attribute in the low-level attributes used by operations.
+
+
+ This is a low-level operation returned by the .
+ This is included for completeness, but is not generally used from C#, as you have access to the high-level
+ bindings in the type.
+
+
+
+
+ Returns a that represents the current .
+
+ A that represents the current .
+
+
+
+ Represents the shape of a tensor, it describes how many dimensions the tensor has in a given axis
+
+
+
+ The shapes can be created by calling the constructor with the number of dimensions
+ in the shape. The null value is used to specify that the shape is unknown,
+ an empty array is used to create a scalar, and other values are used to specify
+ the number of dimensions.
+
+
+ For the Unknown case, you can use , for
+ scalars, you can use the shape.
+
+
+ To create a 2-element vector, use:
+ new TFShape (2)
+
+
+ To create a 2x3 matrix, use:
+ new TFShape (2, 3)
+
+
+ To create a shape with an unknown number of elements, you can pass the value
+ -1. This is typically used to indicate the shape of tensors that represent a
+ variable-sized batch of values.
+
+
+ To create a matrix with 4 columns and an unknown number of rows:
+ var batch = new TFShape (-1, 4)
+
+
+
+
+
+ Represents an unknown number of dimensions in the tensor.
+
+ The unknown.
+
+
+
+ This shape is used to represent scalar values.
+
+ The scalar.
+
+
+
+ Initializes a new instance of the class.
+
+ This is a params argument, so you can provide multiple values to it.
+ A null value means that this is an unknown shape, a single value is used to create a vector,
+ two values are used to create a 2-D matrix and so on.
+
+
+
+
+
+
+
+ Gets the length of the specified dimension in the tensor
+
+ The length, -1 for shapes that have an unknown dimension.
+ Dimension.
+
+
+
+ Number of dimensions represented by this shape.
+
+ The number dimensions, -1 if the number of dimensions is unknown, 0 if the shape represent a scalar, 1 for a vector, 2 for a matrix and so on..
+
+
+
+ Gets a value indicating whether all the dimensions in the are fully specified.
+
+ true if is fully specified; otherwise, false.
+
+
+
+ Returns the shape as an array
+
+ null if the shape represents an unknown shape, otherwise an array with N elements, one per dimension, and each element can be either -1 (if the dimension size is unspecified) or the size of the dimension.
+
+
+
+ Returns the shape as an array
+
+ null if the shape represents an unknown shape, otherwise an array with N elements, one per dimension, and each element can be either -1 (if the dimension size is unspecified) or the size of the dimension.
+
+
+
+ Gets a value indicating whether one of the dimensions in the shape is larger than Int32.MaxValue.
+
+ true if is long array; otherwise, false.
+
+
+
+ Returns a that represents the current .
+
+ A that represents the current .
+
+
+
+ Gets the dimensions for the specified index.
+
+ Index.
+
+
+
+ Returns the shape as a 1-dimensional tensor with each element corresponding to the specified shape dimension.
+
+ The tensor.
+
+
+
+ Adds a to a , yielding a shape made up of the concatenation of the first and the second shapes.
+
+ The first to add.
+ The second to add.
+ The that is the sum of the values of left and right.
+
+
+
+ Performs an implicit conversion from to .
+
+ The shape.
+ The result of the conversion.
+
+
+
+ The Variable class holds the TFOutput nodes that are used to initialize, read and assign a value to a variable.
+
+
+ A variable maintains state in the graph across calls to `run()`. You add a
+ variable to the graph by constructing an instance of the class `Variable`.
+
+ The `Variable()` constructor requires an initial value for the variable,
+ which can be a `Tensor` of any type and shape. The initial value defines the
+ type and shape of the variable. After construction, the type and shape of
+ the variable are fixed. The value can be changed using one of the assign
+ methods.
+
+ When a variable is created a VarHandleOp is created which is returned as
+ the VariableOp property, an assign operation is created that can be accessed
+ using the assignHandle and you can read the value of the variable using the
+ ReadHandle.
+
+ When you launch the graph, variables have to be explicitly initialized before
+ you can run Ops that use their value. You can initialize a variable by
+ running its *initializer op*, restoring the variable from a save file, or
+ simply running an `assign` Op that assigns a value to the variable. In fact,
+ the variable *initializer op* is just an `assign` Op that assigns the
+ variable's initial value to the variable itself.
+
+ There is an implicit conversion from the Variable into the VarHandleOp if
+ used.
+
+
+
+
+ Returns the ReadVariableOp that is used to fetch the value of the variable from the graph.
+
+ The read op.
+
+
+
+ Returns the AssignVariableOp that is used to assign the initial value to the variable from the graph.
+
+ The assign op.
+
+
+
+ Returns the VarHandleOp that was created using the shape of the initial value.
+
+ The variable op.
+
+
+
+ Returns the VarHandleOp (the VariableOp property).
+
+ The variable handle created for the variable.
+ Variable reference.
+
+ This implicit operator exists to preserve the compatibility with code that
+ created Variables and expected the result to be the VariableOp.
+
+
+
+
diff --git a/Assets/TensorFlowSharp/TensorFlowSharp.xml.meta b/Assets/TensorFlowSharp/TensorFlowSharp.xml.meta
new file mode 100644
index 0000000..66cdca4
--- /dev/null
+++ b/Assets/TensorFlowSharp/TensorFlowSharp.xml.meta
@@ -0,0 +1,7 @@
+fileFormatVersion: 2
+guid: 148b5c3182c39294fad1c24bb143575d
+TextScriptImporter:
+ externalObjects: {}
+ userData:
+ assetBundleName:
+ assetBundleVariant:
diff --git a/Assets/TensorFlowSharp/libtensorflow.dll b/Assets/TensorFlowSharp/libtensorflow.dll
new file mode 100644
index 0000000..d667cff
Binary files /dev/null and b/Assets/TensorFlowSharp/libtensorflow.dll differ
diff --git a/Assets/TensorFlowSharp/libtensorflow.dll.meta b/Assets/TensorFlowSharp/libtensorflow.dll.meta
new file mode 100644
index 0000000..e20a4be
--- /dev/null
+++ b/Assets/TensorFlowSharp/libtensorflow.dll.meta
@@ -0,0 +1,27 @@
+fileFormatVersion: 2
+guid: 8053694f739d0c64989ce8809adfbf9c
+PluginImporter:
+ externalObjects: {}
+ serializedVersion: 2
+ iconMap: {}
+ executionOrder: {}
+ defineConstraints: []
+ isPreloaded: 0
+ isOverridable: 0
+ isExplicitlyReferenced: 0
+ validateReferences: 1
+ platformData:
+ - first:
+ Any:
+ second:
+ enabled: 1
+ settings: {}
+ - first:
+ Editor: Editor
+ second:
+ enabled: 0
+ settings:
+ DefaultValueInitialized: true
+ userData:
+ assetBundleName:
+ assetBundleVariant:
diff --git a/Assets/TilemapTools/Brushes/Coordinate Brush/Scripts/Editor/CoordinateBrush.cs b/Assets/TilemapTools/Brushes/Coordinate Brush/Scripts/Editor/CoordinateBrush.cs
index 15e3ba7..6d4df00 100755
--- a/Assets/TilemapTools/Brushes/Coordinate Brush/Scripts/Editor/CoordinateBrush.cs
+++ b/Assets/TilemapTools/Brushes/Coordinate Brush/Scripts/Editor/CoordinateBrush.cs
@@ -5,7 +5,7 @@
namespace UnityEditor
{
[CustomGridBrush(true, false, false, "Coordinate Brush")]
- public class CoordinateBrush : GridBrush {
+ public class CoordinateBrush : UnityEditor.Tilemaps.GridBrush {
public int z = 0;
public override void Paint(GridLayout grid, GameObject brushTarget, Vector3Int position)
@@ -39,7 +39,7 @@ public static void CreateBrush()
}
[CustomEditor(typeof(CoordinateBrush))]
- public class CoordinateBrushEditor : GridBrushEditor
+ public class CoordinateBrushEditor : UnityEditor.Tilemaps.GridBrushEditor
{
private CoordinateBrush coordinateBrush { get { return target as CoordinateBrush; } }
diff --git a/Assets/TilemapTools/Brushes/GameObject Brush/Scripts/Editor/GameObjectBrush.cs b/Assets/TilemapTools/Brushes/GameObject Brush/Scripts/Editor/GameObjectBrush.cs
index 259ae53..c432cb4 100755
--- a/Assets/TilemapTools/Brushes/GameObject Brush/Scripts/Editor/GameObjectBrush.cs
+++ b/Assets/TilemapTools/Brushes/GameObject Brush/Scripts/Editor/GameObjectBrush.cs
@@ -466,7 +466,7 @@ public override int GetHashCode()
}
[CustomEditor(typeof(GameObjectBrush))]
- public class GameObjectBrushEditor : GridBrushEditorBase
+ public class GameObjectBrushEditor : UnityEditor.Tilemaps.GridBrushEditorBase
{
public GameObjectBrush brush { get { return target as GameObjectBrush; } }
diff --git a/Assets/TilemapTools/Brushes/Line Brush/Scripts/Editor/LineBrush.cs b/Assets/TilemapTools/Brushes/Line Brush/Scripts/Editor/LineBrush.cs
index 26d3402..441b865 100755
--- a/Assets/TilemapTools/Brushes/Line Brush/Scripts/Editor/LineBrush.cs
+++ b/Assets/TilemapTools/Brushes/Line Brush/Scripts/Editor/LineBrush.cs
@@ -8,7 +8,7 @@
namespace UnityEditor
{
[CustomGridBrush(true, false, false, "Line Brush")]
- public class LineBrush : GridBrush
+ public class LineBrush : UnityEditor.Tilemaps.GridBrush
{
public bool lineStartActive = false;
public bool fillGaps = false;
@@ -159,7 +159,7 @@ public static IEnumerable GetPointsOnLine(Vector2Int p1, Vector2Int
}
[CustomEditor(typeof(LineBrush))]
- public class LineBrushEditor : GridBrushEditor
+ public class LineBrushEditor : UnityEditor.Tilemaps.GridBrushEditor
{
private LineBrush lineBrush { get { return target as LineBrush; } }
diff --git a/Assets/TilemapTools/Brushes/Prefab Brush/Scripts/Editor/PrefabBrush.cs b/Assets/TilemapTools/Brushes/Prefab Brush/Scripts/Editor/PrefabBrush.cs
index 8a3aab3..d4268ed 100755
--- a/Assets/TilemapTools/Brushes/Prefab Brush/Scripts/Editor/PrefabBrush.cs
+++ b/Assets/TilemapTools/Brushes/Prefab Brush/Scripts/Editor/PrefabBrush.cs
@@ -65,7 +65,7 @@ private static float GetPerlinValue(Vector3Int position, float scale, float offs
}
[CustomEditor(typeof(PrefabBrush))]
- public class PrefabBrushEditor : GridBrushEditorBase
+ public class PrefabBrushEditor : UnityEditor.Tilemaps.GridBrushEditorBase
{
private PrefabBrush prefabBrush { get { return target as PrefabBrush; } }
diff --git a/Assets/TilemapTools/Brushes/Random Brush/Scripts/Editor/RandomBrush.cs b/Assets/TilemapTools/Brushes/Random Brush/Scripts/Editor/RandomBrush.cs
index a7720a0..a88f25f 100755
--- a/Assets/TilemapTools/Brushes/Random Brush/Scripts/Editor/RandomBrush.cs
+++ b/Assets/TilemapTools/Brushes/Random Brush/Scripts/Editor/RandomBrush.cs
@@ -7,7 +7,7 @@
namespace UnityEditor
{
[CustomGridBrush(false, true, false, "Random Brush")]
- public class RandomBrush : GridBrush
+ public class RandomBrush : UnityEditor.Tilemaps.GridBrush
{
public TileBase[] randomTiles;
@@ -49,7 +49,7 @@ public static void CreateBrush()
}
[CustomEditor(typeof(RandomBrush))]
- public class RandomBrushEditor : GridBrushEditor
+ public class RandomBrushEditor : UnityEditor.Tilemaps.GridBrushEditor
{
private RandomBrush randomBrush { get { return target as RandomBrush; } }
private GameObject lastBrushTarget;
diff --git a/Assets/TilemapTools/Brushes/Tint Brush Smooth/Scripts/Editor/TintBrushSmooth.cs b/Assets/TilemapTools/Brushes/Tint Brush Smooth/Scripts/Editor/TintBrushSmooth.cs
index cd2962b..5e4ef20 100755
--- a/Assets/TilemapTools/Brushes/Tint Brush Smooth/Scripts/Editor/TintBrushSmooth.cs
+++ b/Assets/TilemapTools/Brushes/Tint Brush Smooth/Scripts/Editor/TintBrushSmooth.cs
@@ -81,7 +81,7 @@ private TintTextureGenerator GetGenerator(GridLayout grid)
}
[CustomEditor(typeof(TintBrushSmooth))]
- public class TintBrushSmoothEditor : GridBrushEditorBase
+ public class TintBrushSmoothEditor : UnityEditor.Tilemaps.GridBrushEditorBase
{
public override GameObject[] validTargets
{
diff --git a/Assets/TilemapTools/Brushes/Tint Brush/Scripts/Editor/TintBrush.cs b/Assets/TilemapTools/Brushes/Tint Brush/Scripts/Editor/TintBrush.cs
index fbc2c8f..a0545d5 100755
--- a/Assets/TilemapTools/Brushes/Tint Brush/Scripts/Editor/TintBrush.cs
+++ b/Assets/TilemapTools/Brushes/Tint Brush/Scripts/Editor/TintBrush.cs
@@ -61,7 +61,7 @@ private static void SetColor(Tilemap tilemap, Vector3Int position, Color color)
[CustomEditor(typeof(TintBrush))]
- public class TintBrushEditor : GridBrushEditorBase
+ public class TintBrushEditor : UnityEditor.Tilemaps.GridBrushEditorBase
{
public override GameObject[] validTargets
{
diff --git a/Logs/Packages-Update.log b/Logs/Packages-Update.log
new file mode 100644
index 0000000..e062025
--- /dev/null
+++ b/Logs/Packages-Update.log
@@ -0,0 +1,54 @@
+
+=== Wed Aug 21 00:40:26 2019
+
+Packages were changed.
+Update Mode: updateDependencies
+
+The following packages were added:
+ com.unity.2d.tilemap@1.0.0
+ com.unity.analytics@3.3.2
+ com.unity.purchasing@2.0.6
+ com.unity.ads@2.0.8
+ com.unity.textmeshpro@2.0.1
+ com.unity.package-manager-ui@2.2.0
+ com.unity.collab-proxy@1.2.16
+ com.unity.ext.nunit@1.0.0
+ com.unity.test-framework@1.0.13
+ com.unity.timeline@1.1.0
+ com.unity.2d.sprite@1.0.0
+ com.unity.ide.vscode@1.0.7
+ com.unity.ide.rider@1.0.8
+ com.unity.ugui@1.0.0
+ com.unity.modules.ai@1.0.0
+ com.unity.modules.animation@1.0.0
+ com.unity.modules.androidjni@1.0.0
+ com.unity.modules.assetbundle@1.0.0
+ com.unity.modules.audio@1.0.0
+ com.unity.modules.cloth@1.0.0
+ com.unity.modules.director@1.0.0
+ com.unity.modules.imageconversion@1.0.0
+ com.unity.modules.imgui@1.0.0
+ com.unity.modules.jsonserialize@1.0.0
+ com.unity.modules.particlesystem@1.0.0
+ com.unity.modules.physics@1.0.0
+ com.unity.modules.physics2d@1.0.0
+ com.unity.modules.screencapture@1.0.0
+ com.unity.modules.terrain@1.0.0
+ com.unity.modules.terrainphysics@1.0.0
+ com.unity.modules.tilemap@1.0.0
+ com.unity.modules.ui@1.0.0
+ com.unity.modules.uielements@1.0.0
+ com.unity.modules.umbra@1.0.0
+ com.unity.modules.unityanalytics@1.0.0
+ com.unity.modules.unitywebrequest@1.0.0
+ com.unity.modules.unitywebrequestassetbundle@1.0.0
+ com.unity.modules.unitywebrequestaudio@1.0.0
+ com.unity.modules.unitywebrequesttexture@1.0.0
+ com.unity.modules.unitywebrequestwww@1.0.0
+ com.unity.modules.vehicles@1.0.0
+ com.unity.modules.video@1.0.0
+ com.unity.modules.vr@1.0.0
+ com.unity.modules.wind@1.0.0
+ com.unity.modules.xr@1.0.0
+ com.unity.multiplayer-hlapi@1.0.2
+ com.unity.xr.legacyinputhelpers@2.0.2
diff --git a/Packages/manifest.json b/Packages/manifest.json
new file mode 100644
index 0000000..bc19e66
--- /dev/null
+++ b/Packages/manifest.json
@@ -0,0 +1,51 @@
+{
+ "dependencies": {
+ "com.unity.2d.sprite": "1.0.0",
+ "com.unity.2d.tilemap": "1.0.0",
+ "com.unity.ads": "2.0.8",
+ "com.unity.analytics": "3.3.2",
+ "com.unity.collab-proxy": "1.2.16",
+ "com.unity.ext.nunit": "1.0.0",
+ "com.unity.ide.rider": "1.0.8",
+ "com.unity.ide.vscode": "1.0.7",
+ "com.unity.multiplayer-hlapi": "1.0.2",
+ "com.unity.package-manager-ui": "2.2.0",
+ "com.unity.purchasing": "2.0.6",
+ "com.unity.test-framework": "1.0.13",
+ "com.unity.textmeshpro": "2.0.1",
+ "com.unity.timeline": "1.1.0",
+ "com.unity.ugui": "1.0.0",
+ "com.unity.xr.legacyinputhelpers": "2.0.2",
+ "com.unity.modules.ai": "1.0.0",
+ "com.unity.modules.androidjni": "1.0.0",
+ "com.unity.modules.animation": "1.0.0",
+ "com.unity.modules.assetbundle": "1.0.0",
+ "com.unity.modules.audio": "1.0.0",
+ "com.unity.modules.cloth": "1.0.0",
+ "com.unity.modules.director": "1.0.0",
+ "com.unity.modules.imageconversion": "1.0.0",
+ "com.unity.modules.imgui": "1.0.0",
+ "com.unity.modules.jsonserialize": "1.0.0",
+ "com.unity.modules.particlesystem": "1.0.0",
+ "com.unity.modules.physics": "1.0.0",
+ "com.unity.modules.physics2d": "1.0.0",
+ "com.unity.modules.screencapture": "1.0.0",
+ "com.unity.modules.terrain": "1.0.0",
+ "com.unity.modules.terrainphysics": "1.0.0",
+ "com.unity.modules.tilemap": "1.0.0",
+ "com.unity.modules.ui": "1.0.0",
+ "com.unity.modules.uielements": "1.0.0",
+ "com.unity.modules.umbra": "1.0.0",
+ "com.unity.modules.unityanalytics": "1.0.0",
+ "com.unity.modules.unitywebrequest": "1.0.0",
+ "com.unity.modules.unitywebrequestassetbundle": "1.0.0",
+ "com.unity.modules.unitywebrequestaudio": "1.0.0",
+ "com.unity.modules.unitywebrequesttexture": "1.0.0",
+ "com.unity.modules.unitywebrequestwww": "1.0.0",
+ "com.unity.modules.vehicles": "1.0.0",
+ "com.unity.modules.video": "1.0.0",
+ "com.unity.modules.vr": "1.0.0",
+ "com.unity.modules.wind": "1.0.0",
+ "com.unity.modules.xr": "1.0.0"
+ }
+}
diff --git a/ProjectSettings/PresetManager.asset b/ProjectSettings/PresetManager.asset
new file mode 100644
index 0000000..1850551
Binary files /dev/null and b/ProjectSettings/PresetManager.asset differ
diff --git a/ProjectSettings/ProjectVersion.txt b/ProjectSettings/ProjectVersion.txt
index b33736c..6b67648 100644
--- a/ProjectSettings/ProjectVersion.txt
+++ b/ProjectSettings/ProjectVersion.txt
@@ -1 +1,2 @@
-m_EditorVersion: 2017.2.0p2
+m_EditorVersion: 2019.2.1f1
+m_EditorVersionWithRevision: 2019.2.1f1 (ca4d5af0be6f)
diff --git a/ProjectSettings/VFXManager.asset b/ProjectSettings/VFXManager.asset
new file mode 100644
index 0000000..11bb426
Binary files /dev/null and b/ProjectSettings/VFXManager.asset differ
diff --git a/ProjectSettings/XRSettings.asset b/ProjectSettings/XRSettings.asset
new file mode 100644
index 0000000..482590c
--- /dev/null
+++ b/ProjectSettings/XRSettings.asset
@@ -0,0 +1,10 @@
+{
+ "m_SettingKeys": [
+ "VR Device Disabled",
+ "VR Device User Alert"
+ ],
+ "m_SettingValues": [
+ "False",
+ "False"
+ ]
+}
\ No newline at end of file
diff --git a/UnityPackageManager/manifest.json b/UnityPackageManager/manifest.json
deleted file mode 100644
index 526aca6..0000000
--- a/UnityPackageManager/manifest.json
+++ /dev/null
@@ -1,4 +0,0 @@
-{
- "dependencies": {
- }
-}