130 lines
5.1 KiB
Python
130 lines
5.1 KiB
Python
# 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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"""Tests for recurrent v2 layers functionality other than GRU, LSTM.
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See also: lstm_v2_test.py, gru_v2_test.py.
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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from absl.testing import parameterized
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import numpy as np
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from tensorflow.python import keras
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from tensorflow.python.eager import context
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from tensorflow.python.keras import keras_parameterized
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from tensorflow.python.keras import testing_utils
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from tensorflow.python.keras.layers import embeddings
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from tensorflow.python.keras.layers import recurrent_v2 as rnn_v2
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from tensorflow.python.ops.ragged import ragged_factory_ops
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from tensorflow.python.platform import test
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@keras_parameterized.run_all_keras_modes
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class RNNV2Test(keras_parameterized.TestCase):
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@parameterized.parameters([rnn_v2.LSTM, rnn_v2.GRU])
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def test_device_placement(self, layer):
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if not test.is_gpu_available():
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self.skipTest('Need GPU for testing.')
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vocab_size = 20
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embedding_dim = 10
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batch_size = 8
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timestep = 12
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units = 5
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x = np.random.randint(0, vocab_size, size=(batch_size, timestep))
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y = np.random.randint(0, vocab_size, size=(batch_size, timestep))
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# Test when GPU is available but not used, the graph should be properly
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# created with CPU ops.
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with testing_utils.device(should_use_gpu=False):
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model = keras.Sequential([
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keras.layers.Embedding(vocab_size, embedding_dim,
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batch_input_shape=[batch_size, timestep]),
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layer(units, return_sequences=True, stateful=True),
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keras.layers.Dense(vocab_size)
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])
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model.compile(
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optimizer='adam',
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loss='sparse_categorical_crossentropy',
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run_eagerly=testing_utils.should_run_eagerly())
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model.fit(x, y, epochs=1, shuffle=False)
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@parameterized.parameters([rnn_v2.LSTM, rnn_v2.GRU])
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def test_reset_dropout_mask_between_batch(self, layer):
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# See https://github.com/tensorflow/tensorflow/issues/29187 for more details
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batch_size = 8
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timestep = 12
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embedding_dim = 10
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units = 5
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layer = layer(units, dropout=0.5, recurrent_dropout=0.5)
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inputs = np.random.random((batch_size, timestep, embedding_dim)).astype(
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np.float32)
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previous_dropout, previous_recurrent_dropout = None, None
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for _ in range(5):
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layer(inputs, training=True)
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dropout = layer.cell.get_dropout_mask_for_cell(inputs, training=True)
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recurrent_dropout = layer.cell.get_recurrent_dropout_mask_for_cell(
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inputs, training=True)
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if previous_dropout is not None:
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self.assertNotAllClose(self.evaluate(previous_dropout),
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self.evaluate(dropout))
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previous_dropout = dropout
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if previous_recurrent_dropout is not None:
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self.assertNotAllClose(self.evaluate(previous_recurrent_dropout),
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self.evaluate(recurrent_dropout))
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previous_recurrent_dropout = recurrent_dropout
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@parameterized.parameters([rnn_v2.LSTM, rnn_v2.GRU])
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def test_recurrent_dropout_with_stateful_RNN(self, layer):
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# See https://github.com/tensorflow/tensorflow/issues/27829 for details.
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# The issue was caused by using inplace mul for a variable, which was a
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# warning for RefVariable, but an error for ResourceVariable in 2.0
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keras.models.Sequential([
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layer(128, stateful=True, return_sequences=True, dropout=0.2,
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batch_input_shape=[32, None, 5], recurrent_dropout=0.2)
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])
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def test_recurrent_dropout_saved_model(self):
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if not context.executing_eagerly():
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self.skipTest('v2-only test')
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inputs = keras.Input(shape=(784, 3), name='digits')
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x = keras.layers.GRU(64, activation='relu', name='GRU', dropout=0.1)(inputs)
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x = keras.layers.Dense(64, activation='relu', name='dense')(x)
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outputs = keras.layers.Dense(
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10, activation='softmax', name='predictions')(
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x)
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model = keras.Model(inputs=inputs, outputs=outputs, name='3_layer')
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model.save(os.path.join(self.get_temp_dir(), 'model'), save_format='tf')
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@parameterized.parameters([rnn_v2.LSTM, rnn_v2.GRU])
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def test_ragged(self, layer):
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vocab_size = 100
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inputs = ragged_factory_ops.constant(
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np.random.RandomState(0).randint(0, vocab_size, [128, 25]))
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embedder = embeddings.Embedding(input_dim=vocab_size, output_dim=16)
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embedded_inputs = embedder(inputs)
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lstm = layer(32)
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lstm(embedded_inputs)
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if __name__ == '__main__':
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test.main()
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