b7014702fd
The experimental_run_tf_function parameter no longer has any effect. I didn't remove the functionality from testing_util.py and keras_parameterized.py to run with experimental_run_tf_function being True and False. I will remove that functionality in a future change. PiperOrigin-RevId: 297674422 Change-Id: I5b1e67f78b4c3b60242241fb4dc2018f0ace6013
106 lines
4.0 KiB
Python
106 lines
4.0 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 advanced activation layers."""
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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 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.platform import test
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@keras_parameterized.run_all_keras_modes
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class AdvancedActivationsTest(keras_parameterized.TestCase):
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def test_leaky_relu(self):
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for alpha in [0., .5, -1.]:
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testing_utils.layer_test(keras.layers.LeakyReLU,
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kwargs={'alpha': alpha},
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input_shape=(2, 3, 4))
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def test_prelu(self):
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testing_utils.layer_test(keras.layers.PReLU, kwargs={},
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input_shape=(2, 3, 4))
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def test_prelu_share(self):
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testing_utils.layer_test(keras.layers.PReLU,
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kwargs={'shared_axes': 1},
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input_shape=(2, 3, 4))
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def test_elu(self):
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for alpha in [0., .5, -1.]:
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testing_utils.layer_test(keras.layers.ELU,
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kwargs={'alpha': alpha},
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input_shape=(2, 3, 4))
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def test_thresholded_relu(self):
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testing_utils.layer_test(keras.layers.ThresholdedReLU,
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kwargs={'theta': 0.5},
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input_shape=(2, 3, 4))
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def test_softmax(self):
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testing_utils.layer_test(keras.layers.Softmax,
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kwargs={'axis': 1},
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input_shape=(2, 3, 4))
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def test_relu(self):
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testing_utils.layer_test(keras.layers.ReLU,
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kwargs={'max_value': 10},
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input_shape=(2, 3, 4))
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x = keras.backend.ones((3, 4))
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if not context.executing_eagerly():
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# Test that we use `leaky_relu` when appropriate in graph mode.
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self.assertTrue(
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'LeakyRelu' in keras.layers.ReLU(negative_slope=0.2)(x).name)
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# Test that we use `relu` when appropriate in graph mode.
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self.assertTrue('Relu' in keras.layers.ReLU()(x).name)
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# Test that we use `relu6` when appropriate in graph mode.
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self.assertTrue('Relu6' in keras.layers.ReLU(max_value=6)(x).name)
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def test_relu_with_invalid_arg(self):
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with self.assertRaisesRegexp(
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ValueError, 'max_value of Relu layer cannot be negative value: -10'):
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testing_utils.layer_test(keras.layers.ReLU,
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kwargs={'max_value': -10},
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input_shape=(2, 3, 4))
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with self.assertRaisesRegexp(
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ValueError,
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'negative_slope of Relu layer cannot be negative value: -2'):
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with self.cached_session():
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testing_utils.layer_test(
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keras.layers.ReLU,
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kwargs={'negative_slope': -2},
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input_shape=(2, 3, 4))
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@keras_parameterized.run_with_all_model_types
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def test_layer_as_activation(self):
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layer = keras.layers.Dense(1, activation=keras.layers.ReLU())
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model = testing_utils.get_model_from_layers([layer], input_shape=(10,))
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model.compile(
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'sgd',
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'mse',
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run_eagerly=testing_utils.should_run_eagerly())
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model.fit(np.ones((10, 10)), np.ones((10, 1)), batch_size=2)
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if __name__ == '__main__':
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test.main()
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