Fix Typos.

PiperOrigin-RevId: 599005387

site/en/community/contribute/docs_style.md

@@ -63,7 +63,7 @@ repository like this:
 * <code>\[Basics\]\(../../guide/basics.ipynb\)</code> produces
 [Basics](../../guide/basics.ipynb).
 
-This is the prefered approach because this way the links on
+This is the preferred approach because this way the links on
 [tensorflow.org](https://www.tensorflow.org),
 [GitHub](https://github.com/tensorflow/docs){:.external} and
 [Colab](https://github.com/tensorflow/docs/tree/master/site/en/guide/bazics.ipynb){:.external}

site/en/guide/data.ipynb

@@ -1385,7 +1385,7 @@
         "The simplest form of batching stacks `n` consecutive elements of a dataset into\n",
         "a single element. The `Dataset.batch()` transformation does exactly this, with\n",
         "the same constraints as the `tf.stack()` operator, applied to each component\n",
-        "of the elements: i.e. for each component *i*, all elements must have a tensor\n",
+        "of the elements: i.e., for each component *i*, all elements must have a tensor\n",
         "of the exact same shape."
       ]
     },

site/en/guide/dtensor_overview.ipynb

@@ -281,7 +281,7 @@
         "id": "Eyp_qOSyvieo"
       },
       "source": [
-        "<img src=\"https://www.tensorflow.org/images/dtensor/dtensor_layout_rank2.png\" alt=\"A tensorr with it's first axis sharded across mesh dimension 'y' and it's second axis sharded across mesh dimension 'x'\" class=\"no-filter\">\n"
+        "<img src=\"https://www.tensorflow.org/images/dtensor/dtensor_layout_rank2.png\" alt=\"A tensorr with its first axis sharded across mesh dimension 'y' and it's second axis sharded across mesh dimension 'x'\" class=\"no-filter\">\n"
       ]
     },
     {
@@ -303,7 +303,7 @@
       "source": [
         "For the same `mesh_2d`, the layout `Layout([\"x\", dtensor.UNSHARDED], mesh_2d)` is a layout for a rank-2 `Tensor` that is replicated across `\"y\"`, and whose first axis is sharded on mesh dimension `x`.\n",
         "\n",
-        "<img src=\"https://www.tensorflow.org/images/dtensor/dtensor_layout_hybrid.png\" alt=\"A tensor replicated across mesh-dimension y, with it's first axis sharded across mesh dimension 'x'\" class=\"no-filter\">\n"
+        "<img src=\"https://www.tensorflow.org/images/dtensor/dtensor_layout_hybrid.png\" alt=\"A tensor replicated across mesh-dimension y, with its first axis sharded across mesh dimension 'x'\" class=\"no-filter\">\n"
       ]
     },
     {

site/en/guide/migrate/migrating_feature_columns.ipynb

@@ -654,17 +654,17 @@
       "source": [
         "categorical_col = tf1.feature_column.categorical_column_with_identity(\n",
         "    'type', num_buckets=one_hot_dims)\n",
-        "# Convert index to one-hot; e.g. [2] -> [0,0,1].\n",
+        "# Convert index to one-hot; e.g., [2] -> [0,0,1].\n",
         "indicator_col = tf1.feature_column.indicator_column(categorical_col)\n",
         "\n",
-        "# Convert strings to indices; e.g. ['small'] -> [1].\n",
+        "# Convert strings to indices; e.g., ['small'] -> [1].\n",
         "vocab_col = tf1.feature_column.categorical_column_with_vocabulary_list(\n",
         "    'size', vocabulary_list=vocab, num_oov_buckets=1)\n",
         "# Embed the indices.\n",
         "embedding_col = tf1.feature_column.embedding_column(vocab_col, embedding_dims)\n",
         "\n",
         "normalizer_fn = lambda x: (x - weight_mean) / math.sqrt(weight_variance)\n",
-        "# Normalize the numeric inputs; e.g. [2.0] -> [0.0].\n",
+        "# Normalize the numeric inputs; e.g., [2.0] -> [0.0].\n",
         "numeric_col = tf1.feature_column.numeric_column(\n",
         "    'weight', normalizer_fn=normalizer_fn)\n",
         "\n",
@@ -727,12 +727,12 @@
         "  'size': tf.keras.Input(shape=(), dtype='string'),\n",
         "  'weight': tf.keras.Input(shape=(), dtype='float32'),\n",
         "}\n",
-        "# Convert index to one-hot; e.g. [2] -> [0,0,1].\n",
+        "# Convert index to one-hot; e.g., [2] -> [0,0,1].\n",
         "type_output = tf.keras.layers.CategoryEncoding(\n",
         "      one_hot_dims, output_mode='one_hot')(inputs['type'])\n",
-        "# Convert size strings to indices; e.g. ['small'] -> [1].\n",
+        "# Convert size strings to indices; e.g., ['small'] -> [1].\n",
         "size_output = tf.keras.layers.StringLookup(vocabulary=vocab)(inputs['size'])\n",
-        "# Normalize the numeric inputs; e.g. [2.0] -> [0.0].\n",
+        "# Normalize the numeric inputs; e.g., [2.0] -> [0.0].\n",
         "weight_output = tf.keras.layers.Normalization(\n",
         "      axis=None, mean=weight_mean, variance=weight_variance)(inputs['weight'])\n",
         "outputs = {\n",

site/en/guide/migrate/migration_debugging.ipynb

@@ -128,7 +128,7 @@
         "\n",
         "    a. Check training behaviors with TensorBoard\n",
         "\n",
-        "    *   use simple optimizers e.g. SGD and simple distribution strategies e.g.\n",
+        "    *   use simple optimizers e.g., SGD and simple distribution strategies e.g.\n",
         "        `tf.distribute.OneDeviceStrategy` first\n",
         "    *   training metrics\n",
         "    *   evaluation metrics\n",

site/en/guide/profiler.md

@@ -694,7 +694,7 @@ first few batches to avoid inaccuracies due to initialization overhead.
     An example for profiling multiple workers:
 
     ```python
-    # E.g. your worker IP addresses are 10.0.0.2, 10.0.0.3, 10.0.0.4, and you
+    # E.g., your worker IP addresses are 10.0.0.2, 10.0.0.3, 10.0.0.4, and you
     # would like to profile for a duration of 2 seconds.
     tf.profiler.experimental.client.trace(
         'grpc://10.0.0.2:8466,grpc://10.0.0.3:8466,grpc://10.0.0.4:8466',
@@ -845,7 +845,7 @@ more efficient by casting to different data types <b><i>after</i></b> applying
 spatial transformations, such as flipping, cropping, rotating, etc.
 
 Note: Some ops like `tf.image.resize` transparently change the `dtype` to
-`fp32`. Make sure you normalize your data to lie between `0` and `1` if its not
+`fp32`. Make sure you normalize your data to lie between `0` and `1` if it's not
 done automatically. Skipping this step could lead to `NaN` errors if you have
 enabled [AMP](https://developer.nvidia.com/automatic-mixed-precision).
 

site/en/guide/random_numbers.ipynb

@@ -166,7 +166,7 @@
       "source": [
         "See the *Algorithms* section below for more information about it.\n",
         "\n",
-        "Another way to create a generator is with `Generator.from_non_deterministic_state`. A generator created this way will start from a non-deterministic state, depending on e.g. time and OS."
+        "Another way to create a generator is with `Generator.from_non_deterministic_state`. A generator created this way will start from a non-deterministic state, depending on e.g., time and OS."
       ]
     },
     {

site/en/guide/tf_numpy_type_promotion.ipynb

@@ -455,7 +455,7 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "7UmunnJ8Tru3"
+        "id": "7UmunnJ8True3"
       },
       "source": [
         "**First Case**: When `tf.constant` is called with an input with no user-specified dtype."

site/en/hub/common_saved_model_apis/text.md

@@ -132,8 +132,8 @@ preprocessor = hub.load("path/to/preprocessor")  # Must match `encoder`.
 encoder_inputs = preprocessor(text_input)
 
 encoder = hub.load("path/to/encoder")
-enocder_outputs = encoder(encoder_inputs)
-embeddings = enocder_outputs["default"]
+encoder_outputs = encoder(encoder_inputs)
+embeddings = encoder_outputs["default"]
 ```
 
 Recall from the [Reusable SavedModel API](../reusable_saved_models.md) that
@@ -304,8 +304,8 @@ provisions from the [Reusable SavedModel API](../reusable_saved_models.md).
 #### Usage synopsis
 
 ```python
-enocder = hub.load("path/to/encoder")
-enocder_outputs = encoder(encoder_inputs)
+encoder = hub.load("path/to/encoder")
+encoder_outputs = encoder(encoder_inputs)
 ```
 
 or equivalently in Keras:

site/en/hub/tf2_saved_model.md

@@ -82,7 +82,7 @@ and uncompressed SavedModels. For details, see [Caching](caching.md).
 SavedModels can be loaded from a specified `handle`, where the `handle` is a
 filesystem path, valid TFhub.dev model URL (e.g. "https://tfhub.dev/...").
 Kaggle Models URLs mirror TFhub.dev handles in accordance with our Terms and the
-license associated with the model assets, e.g. "https://www.kaggle.com/...".
+license associated with the model assets, e.g., "https://www.kaggle.com/...".
 Handles from Kaggle Models are equivalent to their corresponding TFhub.dev
 handle.
 

site/en/hub/tutorials/action_recognition_with_tf_hub.ipynb

@@ -184,7 +184,7 @@
         "  return list(_VIDEO_LIST)\n",
         "\n",
         "def fetch_ucf_video(video):\n",
-        "  \"\"\"Fetchs a video and cache into local filesystem.\"\"\"\n",
+        "  \"\"\"Fetches a video and cache into local filesystem.\"\"\"\n",
         "  cache_path = os.path.join(_CACHE_DIR, video)\n",
         "  if not os.path.exists(cache_path):\n",
         "    urlpath = request.urljoin(UCF_ROOT, video)\n",

site/en/hub/tutorials/cropnet_cassava.ipynb

@@ -199,7 +199,7 @@
         "id": "QT3XWAtR6BRy"
       },
       "source": [
-        "The *cassava* dataset has images of cassava leaves with 4 distinct diseases as well as healthy cassava leaves. The model can predict all of these classes as well as sixth class for \"unknown\" when the model is not confident in it's prediction."
+        "The *cassava* dataset has images of cassava leaves with 4 distinct diseases as well as healthy cassava leaves. The model can predict all of these classes as well as sixth class for \"unknown\" when the model is not confident in its prediction."
       ]
     },
     {

site/en/hub/tutorials/cross_lingual_similarity_with_tf_hub_multilingual_universal_encoder.ipynb

@@ -271,7 +271,7 @@
         "spanish_sentences = ['perro', 'Los cachorros son agradables.', 'Disfruto de dar largos paseos por la playa con mi perro.']\n",
         "\n",
         "# Multilingual example\n",
-        "multilingual_example = [\"Willkommen zu einfachen, aber\", \"verrassend krachtige\", \"multilingüe\", \"compréhension du langage naturel\", \"модели.\", \"大家是什么意思\" , \"보다 중요한\", \".اللغة التي يتحدثونها\"]\n",
+        "multilingual_example = [\"Willkommen zu einfachen, aber\", \"verrassend krachtige\", \"multilingüe\", \"compréhension du language naturel\", \"модели.\", \"大家是什么意思\" , \"보다 중요한\", \".اللغة التي يتحدثونها\"]\n",
         "multilingual_example_in_en =  [\"Welcome to simple yet\", \"surprisingly powerful\", \"multilingual\", \"natural language understanding\", \"models.\", \"What people mean\", \"matters more than\", \"the language they speak.\"]\n"
       ]
     },
@@ -4174,7 +4174,7 @@
         "id": "Dxu66S8wJIG9"
       },
       "source": [
-        "### Semantic-search crosss-lingual capabilities\n",
+        "### Semantic-search cross-lingual capabilities\n",
         "\n",
         "In this section we show how to retrieve sentences related to a set of sample English sentences. Things to try:\n",
         "\n",

site/en/hub/tutorials/image_enhancing.ipynb

@@ -346,7 +346,7 @@
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "r_dautO6qbTV"
+        "id": "r_defaultO6qbTV"
       },
       "outputs": [],
       "source": [

site/en/hub/tutorials/image_feature_vector.ipynb

@@ -357,7 +357,7 @@
       "source": [
         "## Train the network\n",
         "\n",
-        "Now that our model is built, let's train it and see how it perfoms on our test set."
+        "Now that our model is built, let's train it and see how it performs on our test set."
       ]
     },
     {

site/en/hub/tutorials/movenet.ipynb

@@ -450,7 +450,7 @@
         "id": "ymTVR2I9x22I"
       },
       "source": [
-        "This session demonstrates the minumum working example of running the model on a **single image** to predict the 17 human keypoints."
+        "This session demonstrates the minimum working example of running the model on a **single image** to predict the 17 human keypoints."
       ]
     },
     {
@@ -697,7 +697,7 @@
         "  return output_image\n",
         "\n",
         "def run_inference(movenet, image, crop_region, crop_size):\n",
-        "  \"\"\"Runs model inferece on the cropped region.\n",
+        "  \"\"\"Runs model inference on the cropped region.\n",
         "\n",
         "  The function runs the model inference on the cropped region and updates the\n",
         "  model output to the original image coordinate system.\n",

site/en/hub/tutorials/movinet.ipynb

@@ -890,7 +890,7 @@
         "  steps = video.shape[0]\n",
         "  # estimate duration of the video (in seconds)\n",
         "  duration = steps / video_fps\n",
-        "  # estiamte top_k probabilities and corresponding labels\n",
+        "  # estimate top_k probabilities and corresponding labels\n",
         "  top_probs, top_labels, _ = get_top_k_streaming_labels(probs, k=top_k)\n",
         "\n",
         "  images = []\n",
@@ -950,7 +950,7 @@
         "  logits, states = model({**states, 'image': image})\n",
         "  all_logits.append(logits)\n",
         "\n",
-        "# concatinating all the logits\n",
+        "# concatenating all the logits\n",
         "logits = tf.concat(all_logits, 0)\n",
         "# estimating probabilities\n",
         "probs = tf.nn.softmax(logits, axis=-1)"

site/en/hub/tutorials/senteval_for_universal_sentence_encoder_cmlm.ipynb

@@ -117,7 +117,7 @@
         "id": "7a2ohPn8vMe2"
       },
       "source": [
-        "#Execute a SentEval evaulation task\n",
+        "#Execute a SentEval evaluation task\n",
         "The following code block executes a SentEval task and output the results, choose one of the following tasks to evaluate the USE CMLM model:\n",
         "\n",
         "```\n",

site/en/hub/tutorials/spice.ipynb

@@ -658,7 +658,7 @@
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "eMUTI4L52ZHA"
+        "id": "eMULTI4L52ZHA"
       },
       "outputs": [],
       "source": [

site/en/hub/tutorials/tf2_object_detection.ipynb

@@ -291,7 +291,7 @@
         "id": "yX3pb_pXDjYA"
       },
       "source": [
-        "Intalling the Object Detection API"
+        "Installing the Object Detection API"
       ]
     },
     {
@@ -554,7 +554,7 @@
         "\n",
         "Among the available object detection models there's Mask R-CNN and the output of this model allows instance segmentation.\n",
         "\n",
-        "To visualize it we will use the same method we did before but adding an aditional parameter: `instance_masks=output_dict.get('detection_masks_reframed', None)`\n"
+        "To visualize it we will use the same method we did before but adding an additional parameter: `instance_masks=output_dict.get('detection_masks_reframed', None)`\n"
       ]
     },
     {

site/en/hub/tutorials/tf_hub_generative_image_module.ipynb

@@ -421,7 +421,7 @@
         "If image is from the module space, the descent is quick and converges to a reasonable sample. Try out descending to an image that is **not from the module space**. The descent will only converge if the image is reasonably close to the space of training images.\n",
         "\n",
         "How to make it descend faster and to a more realistic image? One can try:\n",
-        "* using different loss on the image difference, e.g. quadratic,\n",
+        "* using different loss on the image difference, e.g., quadratic,\n",
         "* using different regularizer on the latent vector,\n",
         "* initializing from a random vector in multiple runs,\n",
         "* etc.\n"

site/en/install/source_windows.md

@@ -95,7 +95,7 @@ a release branch that is known to work.
 
 ## Optional: Environmental Variable Set Up
 Run following commands before running build command to avoid issue with package creation:
-(If the below commands were set up while installing the packages, please ignore them). Run `set` check if all the paths were set correctly, run `echo %Environmental Variable%` e.g. `echo %BAZEL_VC%` to check path set up for a specific Environmental Variable
+(If the below commands were set up while installing the packages, please ignore them). Run `set` check if all the paths were set correctly, run `echo %Environmental Variable%` e.g., `echo %BAZEL_VC%` to check path set up for a specific Environmental Variable
 
  Python path set up issue [tensorflow:issue#59943](https://github.com/tensorflow/tensorflow/issues/59943),[tensorflow:issue#9436](https://github.com/tensorflow/tensorflow/issues/9436),[tensorflow:issue#60083](https://github.com/tensorflow/tensorflow/issues/60083)
 
@@ -257,7 +257,7 @@ your platform. Use `pip3 install` to install the package, for example:
 <pre class="devsite-terminal tfo-terminal-windows prettyprint lang-bsh">
 pip3 install C:/tmp/tensorflow_pkg/tensorflow-<var>version</var>-<var>tags</var>.whl
 
-e.g. pip3 install C:/tmp/tensorflow_pkg/tensorflow-2.12.0-cp310-cp310-win_amd64.whl
+e.g., pip3 install C:/tmp/tensorflow_pkg/tensorflow-2.12.0-cp310-cp310-win_amd64.whl
 </pre>
 
 Success: TensorFlow is now installed.

site/en/r1/guide/datasets.md

@@ -437,7 +437,7 @@ dataset = dataset.batch(32)
 iterator = dataset.make_initializable_iterator()
 
 # You can feed the initializer with the appropriate filenames for the current
-# phase of execution, e.g. training vs. validation.
+# phase of execution, e.g., training vs. validation.
 
 # Initialize `iterator` with training data.
 training_filenames = ["/var/data/file1.tfrecord", "/var/data/file2.tfrecord"]
@@ -639,7 +639,7 @@ TODO(mrry): Add this section.
 The simplest form of batching stacks `n` consecutive elements of a dataset into
 a single element. The `Dataset.batch()` transformation does exactly this, with
 the same constraints as the `tf.stack()` operator, applied to each component
-of the elements: i.e. for each component *i*, all elements must have a tensor
+of the elements: i.e., for each component *i*, all elements must have a tensor
 of the exact same shape.
 
 ```python

site/en/r1/guide/distribute_strategy.ipynb

@@ -607,7 +607,7 @@
       },
       "source": [
         "## Using `tf.distribute.Strategy` with custom training loops\n",
-        "As you've seen, using `tf.distrbute.Strategy` with high level APIs is only a couple lines of code change. With a little more effort, `tf.distrbute.Strategy` can also be used by other users who are not using these frameworks.\n",
+        "As you've seen, using `tf.distribute.Strategy` with high level APIs is only a couple lines of code change. With a little more effort, `tf.distribute.Strategy` can also be used by other users who are not using these frameworks.\n",
         "\n",
         "TensorFlow is used for a wide variety of use cases and some users (such as researchers) require more flexibility and control over their training loops. This makes it hard for them to use the high level frameworks such as Estimator or Keras. For instance, someone using a GAN may want to take a different number of generator or discriminator steps each round. Similarly, the high level frameworks are not very suitable for Reinforcement Learning training. So these users will usually write their own training loops.\n",
         "\n",

site/en/r1/guide/graph_viz.md

@@ -251,7 +251,7 @@ is a snippet from the train and test section of a modification of the
 [Estimators MNIST tutorial](../tutorials/estimators/cnn.md), in which we have
 recorded summaries and
 runtime statistics. See the
-[Tensorboard](https://tensorflow.org/tensorboard)
+[TensorBoard](https://tensorflow.org/tensorboard)
 for details on how to record summaries.
 Full source is [here](https://github.com/tensorflow/tensorflow/tree/r1.15/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py).
 

site/en/r1/guide/performance/overview.md

@@ -122,7 +122,7 @@ tf.Session(config=config)
 Intel® has added optimizations to TensorFlow for Intel® Xeon® and Intel® Xeon
 Phi™ through the use of the Intel® Math Kernel Library for Deep Neural Networks
 (Intel® MKL-DNN) optimized primitives. The optimizations also provide speedups
-for the consumer line of processors, e.g. i5 and i7 Intel processors. The Intel
+for the consumer line of processors, e.g., i5 and i7 Intel processors. The Intel
 published paper
 [TensorFlow* Optimizations on Modern Intel® Architecture](https://software.intel.com/en-us/articles/tensorflow-optimizations-on-modern-intel-architecture)
 contains additional details on the implementation.
@@ -255,7 +255,7 @@ bazel build -c opt --copt=-march="broadwell" --config=cuda //tensorflow/tools/pi
   a docker container, the data is not cached and the penalty is paid each time
   TensorFlow starts. The best practice is to include the
   [compute capabilities](http://developer.nvidia.com/cuda-gpus)
-  of the GPUs that will be used, e.g. P100: 6.0, Titan X (Pascal): 6.1,
+  of the GPUs that will be used, e.g., P100: 6.0, Titan X (Pascal): 6.1,
   Titan X (Maxwell): 5.2, and K80: 3.7.
 * Use a version of `gcc` that supports all of the optimizations of the target
   CPU. The recommended minimum gcc version is 4.8.3. On macOS, upgrade to the