| # Copyright 2019 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. |
| # ============================================================================== |
| """Tests for recurrent v2 layers functionality other than GRU, LSTM. |
| |
| See also: lstm_v2_test.py, gru_v2_test.py. |
| """ |
| |
| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
| |
| |
| from absl.testing import parameterized |
| import numpy as np |
| |
| from tensorflow.python import keras |
| from tensorflow.python.framework import test_util |
| from tensorflow.python.keras import keras_parameterized |
| from tensorflow.python.keras import testing_utils |
| from tensorflow.python.keras.layers import recurrent_v2 as rnn_v2 |
| from tensorflow.python.platform import test |
| |
| |
| @keras_parameterized.run_all_keras_modes |
| class RNNV2Test(keras_parameterized.TestCase): |
| |
| @parameterized.parameters([rnn_v2.LSTM, rnn_v2.GRU]) |
| def test_device_placement(self, layer): |
| if not test.is_gpu_available(): |
| self.skipTest('Need GPU for testing.') |
| vocab_size = 20 |
| embedding_dim = 10 |
| batch_size = 8 |
| timestep = 12 |
| units = 5 |
| x = np.random.randint(0, vocab_size, size=(batch_size, timestep)) |
| y = np.random.randint(0, vocab_size, size=(batch_size, timestep)) |
| |
| # Test when GPU is available but not used, the graph should be properly |
| # created with CPU ops. |
| with test_util.device(use_gpu=False): |
| model = keras.Sequential([ |
| keras.layers.Embedding(vocab_size, embedding_dim, |
| batch_input_shape=[batch_size, timestep]), |
| layer(units, return_sequences=True, stateful=True), |
| keras.layers.Dense(vocab_size) |
| ]) |
| model.compile( |
| optimizer='adam', |
| loss='sparse_categorical_crossentropy', |
| run_eagerly=testing_utils.should_run_eagerly(), |
| run_distributed=testing_utils.should_run_distributed()) |
| model.fit(x, y, epochs=1, shuffle=False) |
| |
| @parameterized.parameters([rnn_v2.LSTM, rnn_v2.GRU]) |
| def test_reset_dropout_mask_between_batch(self, layer): |
| # See https://github.com/tensorflow/tensorflow/issues/29187 for more details |
| batch_size = 8 |
| timestep = 12 |
| embedding_dim = 10 |
| units = 5 |
| layer = layer(units, dropout=0.5, recurrent_dropout=0.5) |
| |
| inputs = np.random.random((batch_size, timestep, embedding_dim)).astype( |
| np.float32) |
| previous_dropout, previous_recurrent_dropout = None, None |
| |
| for _ in range(5): |
| layer(inputs, training=True) |
| dropout = layer.cell.get_dropout_mask_for_cell(inputs, training=True) |
| recurrent_dropout = layer.cell.get_recurrent_dropout_mask_for_cell( |
| inputs, training=True) |
| if previous_dropout is not None: |
| self.assertNotAllClose(self.evaluate(previous_dropout), |
| self.evaluate(dropout)) |
| previous_dropout = dropout |
| if previous_recurrent_dropout is not None: |
| self.assertNotAllClose(self.evaluate(previous_recurrent_dropout), |
| self.evaluate(recurrent_dropout)) |
| previous_recurrent_dropout = recurrent_dropout |
| |
| @parameterized.parameters([rnn_v2.LSTM, rnn_v2.GRU]) |
| def test_recurrent_dropout_with_stateful_RNN(self, layer): |
| # See https://github.com/tensorflow/tensorflow/issues/27829 for details. |
| # The issue was caused by using inplace mul for a variable, which was a |
| # warning for RefVariable, but an error for ResourceVariable in 2.0 |
| keras.models.Sequential([ |
| layer(128, stateful=True, return_sequences=True, dropout=0.2, |
| batch_input_shape=[32, None, 5], recurrent_dropout=0.2) |
| ]) |
| |
| |
| if __name__ == '__main__': |
| test.main() |