Update TensorFlow to version 2.3 (#238)

* Update TensorFlow to version 2.2.0 * tf 2.3 now matches f-d on stateful tests * Update TensorFlow from 2.2 to 2.3 * fixed gru issue in generate_test_models.py * Reformat code * Fix layer-type name for nested models. Model -> Functional * Fix docstring Co-authored-by: Dobiasd <[email protected]>
Dobiasd · Aug 13, 2020 · ae6bc8b · ae6bc8b
1 parent 74eb39a
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diff --git a/.travis.yml b/.travis.yml
@@ -33,7 +33,7 @@ install:
   - sudo apt-get install libblas-dev liblapack-dev libatlas-base-dev gfortran
   # python libs
   - sudo pip3 install --upgrade pip
-  - sudo pip3 install numpy scipy h5py "tensorflow==2.1.1"
+  - sudo pip3 install numpy scipy h5py "tensorflow==2.3.0"
   - echo "Version numbers of TensorFlow and Keras:"
   - python3 -c "import tensorflow as tf; import tensorflow.keras; print(tf.__version__); print(tensorflow.keras.__version__)"
   # FunctionalPlus

diff --git a/README.md b/README.md
@@ -174,7 +174,7 @@ Requirements and Installation
 
 - A **C++14**-compatible compiler: Compilers from these versions on are fine: GCC 4.9, Clang 3.7 (libc++ 3.7) and Visual C++ 2015
 - Python 3.7 or higher
-- TensorFlow 2.1.1
+- TensorFlow 2.3.0
 
 Guides for different ways to install frugally-deep can be found in [`INSTALL.md`](INSTALL.md).
 

diff --git a/include/fdeep/import_model.hpp b/include/fdeep/import_model.hpp
@@ -1106,6 +1106,7 @@ inline layer_ptr create_layer(const get_param_f& get_param,
 
     const wrapper_layer_creators wrapper_creators = {
             {"Model", create_model_layer},
+            {"Functional", create_model_layer},
             {"TimeDistributed", create_time_distributed_layer},
     };
 

diff --git a/keras_export/convert_model.py b/keras_export/convert_model.py
@@ -126,7 +126,7 @@ def embedding_layer_names(model):
             if isinstance(layer, Embedding):
                 result.add(layer.name)
         layer_type = type(layer).__name__
-        if layer_type in ['Model', 'Sequential']:
+        if layer_type in ['Model', 'Sequential', 'Functional']:
             result.union(embedding_layer_names(layer))
         return result
 
@@ -602,7 +602,7 @@ def get_all_weights(model):
     assert K.image_data_format() == 'channels_last'
     for layer in layers:
         layer_type = type(layer).__name__
-        if layer_type in ['Model', 'Sequential']:
+        if layer_type in ['Model', 'Sequential', 'Functional']:
             result = merge_two_disjunct_dicts(result, get_all_weights(layer))
         else:
             if hasattr(layer, 'data_format'):
@@ -664,7 +664,8 @@ def convert_sequential_to_model(model):
             model.inbound_nodes = inbound_nodes
     assert model.layers
     for i in range(len(model.layers)):
-        if type(model.layers[i]).__name__ in ['Model', 'Sequential']:
+        layer_type = type(model.layers[i]).__name__
+        if layer_type in ['Model', 'Sequential', 'Functional']:
             # "model.layers[i] = ..." would not overwrite the layer.
             model._layers[i] = convert_sequential_to_model(model.layers[i])
     return model

diff --git a/keras_export/generate_test_models.py b/keras_export/generate_test_models.py
@@ -643,8 +643,9 @@ def get_test_model_gru_stateful_optional(stateful):
 
     model = Model(inputs=inputs, outputs=outputs, name='test_model_gru')
     model.compile(loss='mse', optimizer='nadam')
+
     # fit to dummy data
-    training_data_size = 2
+    training_data_size = stateful_batch_size
     data_in = generate_input_data(training_data_size, input_shapes)
     initial_data_out = model.predict(data_in)
     data_out = generate_output_data(training_data_size, initial_data_out)
@@ -653,7 +654,7 @@ def get_test_model_gru_stateful_optional(stateful):
 
 
 def get_test_model_variable():
-    """Returns a exhaustive model for variably shaped input tensors."""
+    """Returns a model with variably shaped input tensors."""
 
     input_shapes = [
         (None, None, 1),

diff --git a/test/Dockerfile b/test/Dockerfile
@@ -12,12 +12,12 @@ RUN curl https://bazel.build/bazel-release.pub.gpg | apt-key add -
 RUN echo "deb [arch=amd64] https://storage.googleapis.com/bazel-apt stable jdk1.8" | tee /etc/apt/sources.list.d/bazel.list
 RUN apt update && apt install bazel-2.0.0
 
-RUN git clone -b 'r2.1' --single-branch --depth 1 https://github.com/tensorflow/tensorflow.git
+RUN git clone -b 'r2.3' --single-branch --depth 1 https://github.com/tensorflow/tensorflow.git
 WORKDIR /tensorflow
 RUN ./configure
 RUN bazel-2.0.0 build -c opt //tensorflow/tools/pip_package:build_pip_package
 RUN ./bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
-RUN pip install /tmp/tensorflow_pkg/tensorflow-2.1.1-cp37-cp37m-linux_x86_64.whl
+RUN pip install /tmp/tensorflow_pkg/tensorflow-2.3.0-cp37-cp37m-linux_x86_64.whl
 WORKDIR /
 
 RUN git clone -b 'v0.2.9-p0' --single-branch --depth 1 https://github.com/Dobiasd/FunctionalPlus && cd FunctionalPlus && mkdir -p build && cd build && cmake .. && make && make install

diff --git a/test/stateful_test/stateful_recurrent_tests.cpp b/test/stateful_test/stateful_recurrent_tests.cpp
@@ -168,16 +168,16 @@ int main()
     vec_append(all_results, *result[0].as_vector());
 
     // ************************* BIDIRECTIONAL TESTS ************************* //
-    #define TF_BIDI_STATE_RESET_WORKS false
+
     // *********** TEST 9: "bidi-GRU_nonstateful_no_init_state.json" ***********
     model = load_model("./models/bidi-GRU_nonstateful_no_init_state.json");
     /// state_reset = true
     result = model.predict({test_in_0});
     vec_append(all_results, *result[0].as_vector());
-    if(TF_BIDI_STATE_RESET_WORKS) model.reset_states();
+    model.reset_states();
     result = model.predict({test_in_1});
     vec_append(all_results, *result[0].as_vector());
-    if(TF_BIDI_STATE_RESET_WORKS) model.reset_states();
+    model.reset_states();
     /// state_reset = false
     result = model.predict({test_in_0});
     vec_append(all_results, *result[0].as_vector());
@@ -189,10 +189,10 @@ int main()
     /// state_reset = true
     result = model.predict({test_in_0, test_state_0, test_state_1});
     vec_append(all_results, *result[0].as_vector());
-    if(TF_BIDI_STATE_RESET_WORKS) model.reset_states();
+    model.reset_states();
     result = model.predict({test_in_1, test_state_0, test_state_1});
     vec_append(all_results, *result[0].as_vector());
-    if(TF_BIDI_STATE_RESET_WORKS) model.reset_states();
+    model.reset_states();
     /// state_reset = false
     result = model.predict({test_in_0, test_state_0, test_state_1});
     vec_append(all_results, *result[0].as_vector());
@@ -204,10 +204,10 @@ int main()
     /// state_reset = true
     result = model.predict_stateful({test_in_0});
     vec_append(all_results, *result[0].as_vector());
-    if(TF_BIDI_STATE_RESET_WORKS)  model.reset_states();
+    model.reset_states();
     result = model.predict_stateful({test_in_1});
     vec_append(all_results, *result[0].as_vector());
-    if(TF_BIDI_STATE_RESET_WORKS) model.reset_states();
+    model.reset_states();
     /// state_reset = false
     result = model.predict_stateful({test_in_0});
     vec_append(all_results, *result[0].as_vector());
@@ -219,10 +219,10 @@ int main()
     /// state_reset = true
     result = model.predict_stateful({test_in_0, test_state_0, test_state_1});
     vec_append(all_results, *result[0].as_vector());
-    if(TF_BIDI_STATE_RESET_WORKS)  model.reset_states();
+    model.reset_states();
     result = model.predict_stateful({test_in_1, test_state_0, test_state_1});
     vec_append(all_results, *result[0].as_vector());
-    if(TF_BIDI_STATE_RESET_WORKS) model.reset_states();
+    model.reset_states();
     /// state_reset = false
     result = model.predict_stateful({test_in_0, test_state_0, test_state_1});
     vec_append(all_results, *result[0].as_vector());
@@ -234,10 +234,10 @@ int main()
     /// state_reset = true
     result = model.predict({test_in_0});
     vec_append(all_results, *result[0].as_vector());
-    if(TF_BIDI_STATE_RESET_WORKS) model.reset_states();
+    model.reset_states();
     result = model.predict({test_in_1});
     vec_append(all_results, *result[0].as_vector());
-    if(TF_BIDI_STATE_RESET_WORKS) model.reset_states();
+    model.reset_states();
     /// state_reset = false
     result = model.predict({test_in_0});
     vec_append(all_results, *result[0].as_vector());
@@ -249,10 +249,10 @@ int main()
     /// state_reset = true
     result = model.predict({test_in_0, test_state_0, test_state_1, test_state_2, test_state_3});
     vec_append(all_results, *result[0].as_vector());
-    if(TF_BIDI_STATE_RESET_WORKS) model.reset_states();
+    model.reset_states();
     result = model.predict({test_in_1, test_state_0, test_state_1, test_state_2, test_state_3});
     vec_append(all_results, *result[0].as_vector());
-    if(TF_BIDI_STATE_RESET_WORKS) model.reset_states();
+    model.reset_states();
     /// state_reset = false
     result = model.predict({test_in_0, test_state_0, test_state_1, test_state_2, test_state_3});
     vec_append(all_results, *result[0].as_vector());
@@ -264,10 +264,10 @@ int main()
     /// state_reset = true
     result = model.predict_stateful({test_in_0});
     vec_append(all_results, *result[0].as_vector());
-    if(TF_BIDI_STATE_RESET_WORKS) model.reset_states();
+    model.reset_states();
     result = model.predict_stateful({test_in_1});
     vec_append(all_results, *result[0].as_vector());
-    if(TF_BIDI_STATE_RESET_WORKS) model.reset_states();
+    model.reset_states();
     /// state_reset = false
     result = model.predict_stateful({test_in_0});
     vec_append(all_results, *result[0].as_vector());
@@ -279,18 +279,16 @@ int main()
     /// state_reset = true
     result = model.predict_stateful({test_in_0, test_state_0, test_state_1, test_state_2, test_state_3});
     vec_append(all_results, *result[0].as_vector());
-    if(TF_BIDI_STATE_RESET_WORKS) model.reset_states();
+    model.reset_states();
     result = model.predict_stateful({test_in_1, test_state_0, test_state_1, test_state_2, test_state_3});
     vec_append(all_results, *result[0].as_vector());
-    if(TF_BIDI_STATE_RESET_WORKS) model.reset_states();
+    model.reset_states();
     /// state_reset = false
     result = model.predict_stateful({test_in_0, test_state_0, test_state_1, test_state_2, test_state_3});
     vec_append(all_results, *result[0].as_vector());
     result = model.predict_stateful({test_in_1, test_state_0, test_state_1, test_state_2, test_state_3});
     vec_append(all_results, *result[0].as_vector());
 
-    #undef TF_BIDI_STATE_RESET_WORKS
-
     if(verbose){
         std::cout << "\n\nOUTPUT ***" << std::endl;
             for(size_t idx = 0; idx < all_results.size(); ++ idx){