# Copyright 2020 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Sample `get_config` results for testing backwards compatibility.""" # inputs = tf.keras.Input(10) # x = tf.keras.layers.Dense(10, activation='relu')(inputs) # outputs = tf.keras.layers.Dense(1)(x) # model = tf.keras.Model(inputs, outputs) FUNCTIONAL_DNN = { "input_layers": [["input_1", 0, 0]], "layers": [ { "class_name": "InputLayer", "config": { "batch_input_shape": (None, 10), "dtype": "float32", "name": "input_1", "ragged": False, "sparse": False, }, "inbound_nodes": [], "name": "input_1", }, { "class_name": "Dense", "config": { "activation": "relu", "activity_regularizer": None, "bias_constraint": None, "bias_initializer": {"class_name": "Zeros", "config": {}}, "bias_regularizer": None, "dtype": "float32", "kernel_constraint": None, "kernel_initializer": { "class_name": "GlorotUniform", "config": {"seed": None}, }, "kernel_regularizer": None, "name": "dense", "trainable": True, "units": 10, "use_bias": True, }, "inbound_nodes": [[["input_1", 0, 0, {}]]], "name": "dense", }, { "class_name": "Dense", "config": { "activation": "linear", "activity_regularizer": None, "bias_constraint": None, "bias_initializer": {"class_name": "Zeros", "config": {}}, "bias_regularizer": None, "dtype": "float32", "kernel_constraint": None, "kernel_initializer": { "class_name": "GlorotUniform", "config": {"seed": None}, }, "kernel_regularizer": None, "name": "dense_1", "trainable": True, "units": 1, "use_bias": True, }, "inbound_nodes": [[["dense", 0, 0, {}]]], "name": "dense_1", }, ], "name": "model", "output_layers": [["dense_1", 0, 0]], } # inputs = tf.keras.Input((256, 256, 3)) # x = tf.keras.layers.Conv2D(filters=3, kernel_size=(3, 3))(inputs) # x = tf.keras.layers.Flatten()(x) # outputs = tf.keras.layers.Dense(1)(x) # model = tf.keras.Model(inputs, outputs) FUNCTIONAL_CNN = { "input_layers": [["input_2", 0, 0]], "layers": [ { "class_name": "InputLayer", "config": { "batch_input_shape": (None, 256, 256, 3), "dtype": "float32", "name": "input_2", "ragged": False, "sparse": False, }, "inbound_nodes": [], "name": "input_2", }, { "class_name": "Conv2D", "config": { "activation": "linear", "activity_regularizer": None, "bias_constraint": None, "bias_initializer": {"class_name": "Zeros", "config": {}}, "bias_regularizer": None, "data_format": "channels_last", "dilation_rate": (1, 1), "dtype": "float32", "filters": 3, "kernel_constraint": None, "kernel_initializer": { "class_name": "GlorotUniform", "config": {"seed": None}, }, "kernel_regularizer": None, "kernel_size": (3, 3), "name": "conv2d", "padding": "valid", "strides": (1, 1), "trainable": True, "use_bias": True, }, "inbound_nodes": [[["input_2", 0, 0, {}]]], "name": "conv2d", }, { "class_name": "Flatten", "config": { "data_format": "channels_last", "dtype": "float32", "name": "flatten", "trainable": True, }, "inbound_nodes": [[["conv2d", 0, 0, {}]]], "name": "flatten", }, { "class_name": "Dense", "config": { "activation": "linear", "activity_regularizer": None, "bias_constraint": None, "bias_initializer": {"class_name": "Zeros", "config": {}}, "bias_regularizer": None, "dtype": "float32", "kernel_constraint": None, "kernel_initializer": { "class_name": "GlorotUniform", "config": {"seed": None}, }, "kernel_regularizer": None, "name": "dense_2", "trainable": True, "units": 1, "use_bias": True, }, "inbound_nodes": [[["flatten", 0, 0, {}]]], "name": "dense_2", }, ], "name": "model_1", "output_layers": [["dense_2", 0, 0]], } # inputs = tf.keras.Input((10, 3)) # x = tf.keras.layers.LSTM(10)(inputs) # outputs = tf.keras.layers.Dense(1)(x) # model = tf.keras.Model(inputs, outputs) FUNCTIONAL_LSTM = { "input_layers": [["input_5", 0, 0]], "layers": [ { "class_name": "InputLayer", "config": { "batch_input_shape": (None, 10, 3), "dtype": "float32", "name": "input_5", "ragged": False, "sparse": False, }, "inbound_nodes": [], "name": "input_5", }, { "class_name": "LSTM", "config": { "activation": "tanh", "activity_regularizer": None, "bias_constraint": None, "bias_initializer": {"class_name": "Zeros", "config": {}}, "bias_regularizer": None, "dropout": 0.0, "dtype": "float32", "go_backwards": False, "implementation": 2, "kernel_constraint": None, "kernel_initializer": { "class_name": "GlorotUniform", "config": {"seed": None}, }, "kernel_regularizer": None, "name": "lstm_2", "recurrent_activation": "sigmoid", "recurrent_constraint": None, "recurrent_dropout": 0.0, "recurrent_initializer": { "class_name": "Orthogonal", "config": {"gain": 1.0, "seed": None}, }, "recurrent_regularizer": None, "return_sequences": False, "return_state": False, "stateful": False, "time_major": False, "trainable": True, "unit_forget_bias": True, "units": 10, "unroll": False, "use_bias": True, }, "inbound_nodes": [[["input_5", 0, 0, {}]]], "name": "lstm_2", }, { "class_name": "Dense", "config": { "activation": "linear", "activity_regularizer": None, "bias_constraint": None, "bias_initializer": {"class_name": "Zeros", "config": {}}, "bias_regularizer": None, "dtype": "float32", "kernel_constraint": None, "kernel_initializer": { "class_name": "GlorotUniform", "config": {"seed": None}, }, "kernel_regularizer": None, "name": "dense_4", "trainable": True, "units": 1, "use_bias": True, }, "inbound_nodes": [[["lstm_2", 0, 0, {}]]], "name": "dense_4", }, ], "name": "model_3", "output_layers": [["dense_4", 0, 0]], } # model = tf.keras.Sequential() # model.add(tf.keras.layers.Dense(10)) # model.add(tf.keras.layers.Dense(1)) SEQUENTIAL_DNN = { "layers": [ { "class_name": "Dense", "config": { "activation": "linear", "activity_regularizer": None, "bias_constraint": None, "bias_initializer": {"class_name": "Zeros", "config": {}}, "bias_regularizer": None, "dtype": "float32", "kernel_constraint": None, "kernel_initializer": { "class_name": "GlorotUniform", "config": {"seed": None}, }, "kernel_regularizer": None, "name": "dense_2", "trainable": True, "units": 10, "use_bias": True, }, }, { "class_name": "Dense", "config": { "activation": "linear", "activity_regularizer": None, "bias_constraint": None, "bias_initializer": {"class_name": "Zeros", "config": {}}, "bias_regularizer": None, "dtype": "float32", "kernel_constraint": None, "kernel_initializer": { "class_name": "GlorotUniform", "config": {"seed": None}, }, "kernel_regularizer": None, "name": "dense_3", "trainable": True, "units": 1, "use_bias": True, }, }, ], "name": "sequential_1", } # model = tf.keras.Sequential() # model.add(tf.keras.layers.Conv2D(32, (3, 3))) # model.add(tf.keras.layers.Flatten()) # model.add(tf.keras.layers.Dense(1)) SEQUENTIAL_CNN = { "layers": [ { "class_name": "Conv2D", "config": { "activation": "linear", "activity_regularizer": None, "bias_constraint": None, "bias_initializer": {"class_name": "Zeros", "config": {}}, "bias_regularizer": None, "data_format": "channels_last", "dilation_rate": (1, 1), "dtype": "float32", "filters": 32, "kernel_constraint": None, "kernel_initializer": { "class_name": "GlorotUniform", "config": {"seed": None}, }, "kernel_regularizer": None, "kernel_size": (3, 3), "name": "conv2d_1", "padding": "valid", "strides": (1, 1), "trainable": True, "use_bias": True, }, }, { "class_name": "Flatten", "config": { "data_format": "channels_last", "dtype": "float32", "name": "flatten_1", "trainable": True, }, }, { "class_name": "Dense", "config": { "activation": "linear", "activity_regularizer": None, "bias_constraint": None, "bias_initializer": {"class_name": "Zeros", "config": {}}, "bias_regularizer": None, "dtype": "float32", "kernel_constraint": None, "kernel_initializer": { "class_name": "GlorotUniform", "config": {"seed": None}, }, "kernel_regularizer": None, "name": "dense_6", "trainable": True, "units": 1, "use_bias": True, }, }, ], "name": "sequential_4", } # model = tf.keras.Sequential() # model.add(tf.keras.layers.LSTM(10)) # model.add(tf.keras.layers.Dense(1)) SEQUENTIAL_LSTM = { "layers": [ { "class_name": "LSTM", "config": { "activation": "tanh", "activity_regularizer": None, "bias_constraint": None, "bias_initializer": {"class_name": "Zeros", "config": {}}, "bias_regularizer": None, "dropout": 0.0, "dtype": "float32", "go_backwards": False, "implementation": 2, "kernel_constraint": None, "kernel_initializer": { "class_name": "GlorotUniform", "config": {"seed": None}, }, "kernel_regularizer": None, "name": "lstm", "recurrent_activation": "sigmoid", "recurrent_constraint": None, "recurrent_dropout": 0.0, "recurrent_initializer": { "class_name": "Orthogonal", "config": {"gain": 1.0, "seed": None}, }, "recurrent_regularizer": None, "return_sequences": False, "return_state": False, "stateful": False, "time_major": False, "trainable": True, "unit_forget_bias": True, "units": 10, "unroll": False, "use_bias": True, }, }, { "class_name": "Dense", "config": { "activation": "linear", "activity_regularizer": None, "bias_constraint": None, "bias_initializer": {"class_name": "Zeros", "config": {}}, "bias_regularizer": None, "dtype": "float32", "kernel_constraint": None, "kernel_initializer": { "class_name": "GlorotUniform", "config": {"seed": None}, }, "kernel_regularizer": None, "name": "dense_4", "trainable": True, "units": 1, "use_bias": True, }, }, ], "name": "sequential_2", }