| # Copyright 2016 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 SimpleRNN layer.""" |
| |
| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
| |
| import numpy as np |
| |
| from tensorflow.python import keras |
| from tensorflow.python.eager import context |
| from tensorflow.python.keras import keras_parameterized |
| from tensorflow.python.keras import testing_utils |
| from tensorflow.python.platform import test |
| from tensorflow.python.training import gradient_descent |
| |
| |
| @keras_parameterized.run_all_keras_modes |
| class SimpleRNNLayerTest(keras_parameterized.TestCase): |
| |
| def test_return_sequences_SimpleRNN(self): |
| num_samples = 2 |
| timesteps = 3 |
| embedding_dim = 4 |
| units = 2 |
| testing_utils.layer_test( |
| keras.layers.SimpleRNN, |
| kwargs={'units': units, |
| 'return_sequences': True}, |
| input_shape=(num_samples, timesteps, embedding_dim)) |
| |
| def test_float64_SimpleRNN(self): |
| num_samples = 2 |
| timesteps = 3 |
| embedding_dim = 4 |
| units = 2 |
| testing_utils.layer_test( |
| keras.layers.SimpleRNN, |
| kwargs={'units': units, |
| 'return_sequences': True, |
| 'dtype': 'float64'}, |
| input_shape=(num_samples, timesteps, embedding_dim), |
| input_dtype='float64') |
| |
| def test_dynamic_behavior_SimpleRNN(self): |
| num_samples = 2 |
| timesteps = 3 |
| embedding_dim = 4 |
| units = 2 |
| layer = keras.layers.SimpleRNN(units, input_shape=(None, embedding_dim)) |
| model = keras.models.Sequential() |
| model.add(layer) |
| model.compile('rmsprop', 'mse') |
| x = np.random.random((num_samples, timesteps, embedding_dim)) |
| y = np.random.random((num_samples, units)) |
| model.train_on_batch(x, y) |
| |
| def test_dropout_SimpleRNN(self): |
| num_samples = 2 |
| timesteps = 3 |
| embedding_dim = 4 |
| units = 2 |
| testing_utils.layer_test( |
| keras.layers.SimpleRNN, |
| kwargs={'units': units, |
| 'dropout': 0.1, |
| 'recurrent_dropout': 0.1}, |
| input_shape=(num_samples, timesteps, embedding_dim)) |
| |
| def test_implementation_mode_SimpleRNN(self): |
| num_samples = 2 |
| timesteps = 3 |
| embedding_dim = 4 |
| units = 2 |
| for mode in [0, 1, 2]: |
| testing_utils.layer_test( |
| keras.layers.SimpleRNN, |
| kwargs={'units': units, |
| 'implementation': mode}, |
| input_shape=(num_samples, timesteps, embedding_dim)) |
| |
| def test_constraints_SimpleRNN(self): |
| embedding_dim = 4 |
| layer_class = keras.layers.SimpleRNN |
| k_constraint = keras.constraints.max_norm(0.01) |
| r_constraint = keras.constraints.max_norm(0.01) |
| b_constraint = keras.constraints.max_norm(0.01) |
| layer = layer_class( |
| 5, |
| return_sequences=False, |
| weights=None, |
| input_shape=(None, embedding_dim), |
| kernel_constraint=k_constraint, |
| recurrent_constraint=r_constraint, |
| bias_constraint=b_constraint) |
| layer.build((None, None, embedding_dim)) |
| self.assertEqual(layer.cell.kernel.constraint, k_constraint) |
| self.assertEqual(layer.cell.recurrent_kernel.constraint, r_constraint) |
| self.assertEqual(layer.cell.bias.constraint, b_constraint) |
| |
| def test_with_masking_layer_SimpleRNN(self): |
| layer_class = keras.layers.SimpleRNN |
| inputs = np.random.random((2, 3, 4)) |
| targets = np.abs(np.random.random((2, 3, 5))) |
| targets /= targets.sum(axis=-1, keepdims=True) |
| model = keras.models.Sequential() |
| model.add(keras.layers.Masking(input_shape=(3, 4))) |
| model.add(layer_class(units=5, return_sequences=True, unroll=False)) |
| model.compile(loss='categorical_crossentropy', optimizer='rmsprop') |
| model.fit(inputs, targets, epochs=1, batch_size=2, verbose=1) |
| |
| def test_from_config_SimpleRNN(self): |
| layer_class = keras.layers.SimpleRNN |
| for stateful in (False, True): |
| l1 = layer_class(units=1, stateful=stateful) |
| l2 = layer_class.from_config(l1.get_config()) |
| assert l1.get_config() == l2.get_config() |
| |
| def test_regularizers_SimpleRNN(self): |
| embedding_dim = 4 |
| layer_class = keras.layers.SimpleRNN |
| layer = layer_class( |
| 5, |
| return_sequences=False, |
| weights=None, |
| input_shape=(None, embedding_dim), |
| kernel_regularizer=keras.regularizers.l1(0.01), |
| recurrent_regularizer=keras.regularizers.l1(0.01), |
| bias_regularizer='l2', |
| activity_regularizer='l1') |
| layer.build((None, None, 2)) |
| self.assertEqual(len(layer.losses), 3) |
| |
| x = keras.backend.variable(np.ones((2, 3, 2))) |
| layer(x) |
| if context.executing_eagerly(): |
| self.assertEqual(len(layer.losses), 4) |
| else: |
| self.assertEqual(len(layer.get_losses_for(x)), 1) |
| |
| def test_statefulness_SimpleRNN(self): |
| num_samples = 2 |
| timesteps = 3 |
| embedding_dim = 4 |
| units = 2 |
| layer_class = keras.layers.SimpleRNN |
| model = keras.models.Sequential() |
| model.add( |
| keras.layers.Embedding( |
| 4, |
| embedding_dim, |
| mask_zero=True, |
| input_length=timesteps, |
| batch_input_shape=(num_samples, timesteps))) |
| layer = layer_class( |
| units, return_sequences=False, stateful=True, weights=None) |
| model.add(layer) |
| model.compile( |
| optimizer=gradient_descent.GradientDescentOptimizer(0.01), |
| loss='mse', |
| run_eagerly=testing_utils.should_run_eagerly(), |
| experimental_run_tf_function=testing_utils.should_run_tf_function()) |
| out1 = model.predict(np.ones((num_samples, timesteps))) |
| self.assertEqual(out1.shape, (num_samples, units)) |
| |
| # train once so that the states change |
| model.train_on_batch( |
| np.ones((num_samples, timesteps)), np.ones((num_samples, units))) |
| out2 = model.predict(np.ones((num_samples, timesteps))) |
| |
| # if the state is not reset, output should be different |
| self.assertNotEqual(out1.max(), out2.max()) |
| |
| # check that output changes after states are reset |
| # (even though the model itself didn't change) |
| layer.reset_states() |
| out3 = model.predict(np.ones((num_samples, timesteps))) |
| self.assertNotEqual(out2.max(), out3.max()) |
| |
| # check that container-level reset_states() works |
| model.reset_states() |
| out4 = model.predict(np.ones((num_samples, timesteps))) |
| np.testing.assert_allclose(out3, out4, atol=1e-5) |
| |
| # check that the call to `predict` updated the states |
| out5 = model.predict(np.ones((num_samples, timesteps))) |
| self.assertNotEqual(out4.max(), out5.max()) |
| |
| # Check masking |
| layer.reset_states() |
| |
| left_padded_input = np.ones((num_samples, timesteps)) |
| left_padded_input[0, :1] = 0 |
| left_padded_input[1, :2] = 0 |
| out6 = model.predict(left_padded_input) |
| |
| layer.reset_states() |
| |
| right_padded_input = np.ones((num_samples, timesteps)) |
| right_padded_input[0, -1:] = 0 |
| right_padded_input[1, -2:] = 0 |
| out7 = model.predict(right_padded_input) |
| |
| np.testing.assert_allclose(out7, out6, atol=1e-5) |
| |
| if __name__ == '__main__': |
| test.main() |