176 lines
6.3 KiB
Python
176 lines
6.3 KiB
Python
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# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Correctness test for tf.keras Embedding models using DistributionStrategy."""
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import numpy as np
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import tensorflow.compat.v2 as tf
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import keras
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from keras.distribute import keras_correctness_test_base
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from keras.optimizers.legacy import gradient_descent as gradient_descent_keras
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class DistributionStrategyEmbeddingModelCorrectnessTest(
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keras_correctness_test_base.TestDistributionStrategyEmbeddingModelCorrectnessBase # noqa: E501
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):
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def get_model(
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self,
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max_words=10,
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initial_weights=None,
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distribution=None,
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input_shapes=None,
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):
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del input_shapes
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with keras_correctness_test_base.MaybeDistributionScope(distribution):
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word_ids = keras.layers.Input(
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shape=(max_words,), dtype=np.int32, name="words"
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)
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word_embed = keras.layers.Embedding(input_dim=20, output_dim=10)(
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word_ids
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)
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if self.use_distributed_dense:
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word_embed = keras.layers.TimeDistributed(
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keras.layers.Dense(4)
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)(word_embed)
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avg = keras.layers.GlobalAveragePooling1D()(word_embed)
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preds = keras.layers.Dense(2, activation="softmax")(avg)
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model = keras.Model(inputs=[word_ids], outputs=[preds])
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if initial_weights:
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model.set_weights(initial_weights)
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model.compile(
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optimizer=gradient_descent_keras.SGD(learning_rate=0.1),
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loss="sparse_categorical_crossentropy",
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metrics=["sparse_categorical_accuracy"],
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)
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return model
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@tf.__internal__.distribute.combinations.generate(
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keras_correctness_test_base.test_combinations_for_embedding_model()
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+ keras_correctness_test_base.multi_worker_mirrored_eager()
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)
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def test_embedding_model_correctness(
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self, distribution, use_numpy, use_validation_data
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):
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self.use_distributed_dense = False
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self.run_correctness_test(distribution, use_numpy, use_validation_data)
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@tf.__internal__.distribute.combinations.generate(
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keras_correctness_test_base.test_combinations_for_embedding_model()
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+ keras_correctness_test_base.multi_worker_mirrored_eager()
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)
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def test_embedding_time_distributed_model_correctness(
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self, distribution, use_numpy, use_validation_data
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):
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self.use_distributed_dense = True
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self.run_correctness_test(distribution, use_numpy, use_validation_data)
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class DistributionStrategySiameseEmbeddingModelCorrectnessTest(
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keras_correctness_test_base.TestDistributionStrategyEmbeddingModelCorrectnessBase # noqa: E501
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):
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def get_model(
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self,
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max_words=10,
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initial_weights=None,
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distribution=None,
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input_shapes=None,
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):
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del input_shapes
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with keras_correctness_test_base.MaybeDistributionScope(distribution):
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word_ids_a = keras.layers.Input(
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shape=(max_words,), dtype=np.int32, name="words_a"
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)
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word_ids_b = keras.layers.Input(
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shape=(max_words,), dtype=np.int32, name="words_b"
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)
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def submodel(embedding, word_ids):
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word_embed = embedding(word_ids)
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rep = keras.layers.GlobalAveragePooling1D()(word_embed)
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return keras.Model(inputs=[word_ids], outputs=[rep])
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word_embed = keras.layers.Embedding(
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input_dim=20,
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output_dim=10,
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input_length=max_words,
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embeddings_initializer=keras.initializers.RandomUniform(0, 1),
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)
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a_rep = submodel(word_embed, word_ids_a).outputs[0]
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b_rep = submodel(word_embed, word_ids_b).outputs[0]
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sim = keras.layers.Dot(axes=1, normalize=True)([a_rep, b_rep])
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model = keras.Model(inputs=[word_ids_a, word_ids_b], outputs=[sim])
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if initial_weights:
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model.set_weights(initial_weights)
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# TODO(b/130808953): Switch back to the V1 optimizer after
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# global_step is made mirrored.
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model.compile(
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optimizer=gradient_descent_keras.SGD(learning_rate=0.1),
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loss="mse",
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metrics=["mse"],
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)
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return model
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def get_data(
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self,
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count=(
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keras_correctness_test_base._GLOBAL_BATCH_SIZE
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* keras_correctness_test_base._EVAL_STEPS
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),
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min_words=5,
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max_words=10,
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max_word_id=19,
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num_classes=2,
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):
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features_a, labels_a, _ = super().get_data(
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count, min_words, max_words, max_word_id, num_classes
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)
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features_b, labels_b, _ = super().get_data(
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count, min_words, max_words, max_word_id, num_classes
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)
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y_train = np.zeros((count, 1), dtype=np.float32)
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y_train[labels_a == labels_b] = 1.0
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y_train[labels_a != labels_b] = -1.0
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# TODO(b/123360757): Add tests for using list as inputs for multi-input
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# models.
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x_train = {
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"words_a": features_a,
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"words_b": features_b,
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}
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x_predict = x_train
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return x_train, y_train, x_predict
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@tf.__internal__.distribute.combinations.generate(
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keras_correctness_test_base.test_combinations_for_embedding_model()
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+ keras_correctness_test_base.multi_worker_mirrored_eager()
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)
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def test_siamese_embedding_model_correctness(
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self, distribution, use_numpy, use_validation_data
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):
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self.run_correctness_test(distribution, use_numpy, use_validation_data)
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if __name__ == "__main__":
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tf.__internal__.distribute.multi_process_runner.test_main()
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