240 lines
8.1 KiB
Python
240 lines
8.1 KiB
Python
# Copyright 2016 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 SimpleRNN layer."""
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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 copy
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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.framework import dtypes
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from tensorflow.python.keras import combinations
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from tensorflow.python.keras import testing_utils
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from tensorflow.python.platform import test
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from tensorflow.python.training import gradient_descent
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@combinations.generate(combinations.keras_mode_combinations())
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class SimpleRNNLayerTest(test.TestCase, parameterized.TestCase):
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def test_return_sequences_SimpleRNN(self):
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num_samples = 2
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timesteps = 3
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embedding_dim = 4
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units = 2
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testing_utils.layer_test(
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keras.layers.SimpleRNN,
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kwargs={'units': units,
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'return_sequences': True},
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input_shape=(num_samples, timesteps, embedding_dim))
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@testing_utils.run_v2_only
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def test_float64_SimpleRNN(self):
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num_samples = 2
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timesteps = 3
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embedding_dim = 4
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units = 2
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testing_utils.layer_test(
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keras.layers.SimpleRNN,
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kwargs={'units': units,
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'return_sequences': True,
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'dtype': 'float64'},
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input_shape=(num_samples, timesteps, embedding_dim),
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input_dtype='float64')
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def test_dynamic_behavior_SimpleRNN(self):
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num_samples = 2
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timesteps = 3
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embedding_dim = 4
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units = 2
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layer = keras.layers.SimpleRNN(units, input_shape=(None, embedding_dim))
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model = keras.models.Sequential()
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model.add(layer)
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model.compile('rmsprop', 'mse')
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x = np.random.random((num_samples, timesteps, embedding_dim))
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y = np.random.random((num_samples, units))
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model.train_on_batch(x, y)
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def test_dropout_SimpleRNN(self):
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num_samples = 2
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timesteps = 3
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embedding_dim = 4
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units = 2
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testing_utils.layer_test(
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keras.layers.SimpleRNN,
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kwargs={'units': units,
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'dropout': 0.1,
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'recurrent_dropout': 0.1},
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input_shape=(num_samples, timesteps, embedding_dim))
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def test_implementation_mode_SimpleRNN(self):
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num_samples = 2
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timesteps = 3
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embedding_dim = 4
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units = 2
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for mode in [0, 1, 2]:
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testing_utils.layer_test(
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keras.layers.SimpleRNN,
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kwargs={'units': units,
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'implementation': mode},
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input_shape=(num_samples, timesteps, embedding_dim))
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def test_constraints_SimpleRNN(self):
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embedding_dim = 4
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layer_class = keras.layers.SimpleRNN
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k_constraint = keras.constraints.max_norm(0.01)
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r_constraint = keras.constraints.max_norm(0.01)
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b_constraint = keras.constraints.max_norm(0.01)
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layer = layer_class(
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5,
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return_sequences=False,
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weights=None,
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input_shape=(None, embedding_dim),
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kernel_constraint=k_constraint,
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recurrent_constraint=r_constraint,
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bias_constraint=b_constraint)
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layer.build((None, None, embedding_dim))
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self.assertEqual(layer.cell.kernel.constraint, k_constraint)
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self.assertEqual(layer.cell.recurrent_kernel.constraint, r_constraint)
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self.assertEqual(layer.cell.bias.constraint, b_constraint)
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def test_with_masking_layer_SimpleRNN(self):
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layer_class = keras.layers.SimpleRNN
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inputs = np.random.random((2, 3, 4))
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targets = np.abs(np.random.random((2, 3, 5)))
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targets /= targets.sum(axis=-1, keepdims=True)
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model = keras.models.Sequential()
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model.add(keras.layers.Masking(input_shape=(3, 4)))
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model.add(layer_class(units=5, return_sequences=True, unroll=False))
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model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
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model.fit(inputs, targets, epochs=1, batch_size=2, verbose=1)
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def test_from_config_SimpleRNN(self):
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layer_class = keras.layers.SimpleRNN
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for stateful in (False, True):
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l1 = layer_class(units=1, stateful=stateful)
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l2 = layer_class.from_config(l1.get_config())
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assert l1.get_config() == l2.get_config()
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def test_deep_copy_SimpleRNN(self):
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cell = keras.layers.SimpleRNNCell(5)
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copied_cell = copy.deepcopy(cell)
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self.assertEqual(copied_cell.units, 5)
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self.assertEqual(cell.get_config(), copied_cell.get_config())
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def test_regularizers_SimpleRNN(self):
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embedding_dim = 4
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layer_class = keras.layers.SimpleRNN
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layer = layer_class(
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5,
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return_sequences=False,
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weights=None,
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input_shape=(None, embedding_dim),
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kernel_regularizer=keras.regularizers.l1(0.01),
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recurrent_regularizer=keras.regularizers.l1(0.01),
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bias_regularizer='l2',
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activity_regularizer='l1')
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layer.build((None, None, 2))
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self.assertLen(layer.losses, 3)
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x = keras.backend.variable(np.ones((2, 3, 2)))
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layer(x)
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if context.executing_eagerly():
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self.assertLen(layer.losses, 4)
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else:
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self.assertLen(layer.get_losses_for(x), 1)
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def test_statefulness_SimpleRNN(self):
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num_samples = 2
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timesteps = 3
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embedding_dim = 4
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units = 2
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layer_class = keras.layers.SimpleRNN
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model = keras.models.Sequential()
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model.add(
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keras.layers.Embedding(
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4,
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embedding_dim,
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mask_zero=True,
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input_length=timesteps,
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batch_input_shape=(num_samples, timesteps)))
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layer = layer_class(
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units, return_sequences=False, stateful=True, weights=None)
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model.add(layer)
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model.compile(
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optimizer=gradient_descent.GradientDescentOptimizer(0.01),
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loss='mse',
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run_eagerly=testing_utils.should_run_eagerly())
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out1 = model.predict(np.ones((num_samples, timesteps)))
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self.assertEqual(out1.shape, (num_samples, units))
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# train once so that the states change
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model.train_on_batch(
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np.ones((num_samples, timesteps)), np.ones((num_samples, units)))
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out2 = model.predict(np.ones((num_samples, timesteps)))
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# if the state is not reset, output should be different
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self.assertNotEqual(out1.max(), out2.max())
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# check that output changes after states are reset
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# (even though the model itself didn't change)
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layer.reset_states()
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out3 = model.predict(np.ones((num_samples, timesteps)))
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self.assertNotEqual(out2.max(), out3.max())
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# check that container-level reset_states() works
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model.reset_states()
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out4 = model.predict(np.ones((num_samples, timesteps)))
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np.testing.assert_allclose(out3, out4, atol=1e-5)
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# check that the call to `predict` updated the states
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out5 = model.predict(np.ones((num_samples, timesteps)))
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self.assertNotEqual(out4.max(), out5.max())
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# Check masking
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layer.reset_states()
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left_padded_input = np.ones((num_samples, timesteps))
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left_padded_input[0, :1] = 0
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left_padded_input[1, :2] = 0
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out6 = model.predict(left_padded_input)
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layer.reset_states()
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right_padded_input = np.ones((num_samples, timesteps))
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right_padded_input[0, -1:] = 0
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right_padded_input[1, -2:] = 0
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out7 = model.predict(right_padded_input)
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np.testing.assert_allclose(out7, out6, atol=1e-5)
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def test_get_initial_states(self):
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batch_size = 4
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cell = keras.layers.SimpleRNNCell(20)
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initial_state = cell.get_initial_state(
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batch_size=batch_size, dtype=dtypes.float32)
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_, state = cell(np.ones((batch_size, 20), dtype=np.float32), initial_state)
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self.assertEqual(state.shape, initial_state.shape)
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if __name__ == '__main__':
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test.main()
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