c8823160e0
1. Change all test_util.run_all_in_graph_and_eager_modes to combination. 2. Replace import tensorflow.python.keras with explicit module import. 3. Update BUILD file to not rely on the overall Keras target. PiperOrigin-RevId: 299403937 Change-Id: Ic798fd0cc2602b6447aaf28922a4a418514d7131
115 lines
4.1 KiB
Python
115 lines
4.1 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 Keras weights constraints."""
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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 math
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import numpy as np
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from tensorflow.python.keras import backend
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from tensorflow.python.keras import combinations
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from tensorflow.python.keras import constraints
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from tensorflow.python.platform import test
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def get_test_values():
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return [0.1, 0.5, 3, 8, 1e-7]
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def get_example_array():
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np.random.seed(3537)
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example_array = np.random.random((100, 100)) * 100. - 50.
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example_array[0, 0] = 0. # 0 could possibly cause trouble
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return example_array
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def get_example_kernel(width):
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np.random.seed(3537)
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example_array = np.random.rand(width, width, 2, 2)
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return example_array
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@combinations.generate(combinations.combine(mode=['graph', 'eager']))
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class KerasConstraintsTest(test.TestCase):
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def test_serialization(self):
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all_activations = ['max_norm', 'non_neg',
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'unit_norm', 'min_max_norm']
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for name in all_activations:
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fn = constraints.get(name)
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ref_fn = getattr(constraints, name)()
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assert fn.__class__ == ref_fn.__class__
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config = constraints.serialize(fn)
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fn = constraints.deserialize(config)
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assert fn.__class__ == ref_fn.__class__
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def test_max_norm(self):
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array = get_example_array()
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for m in get_test_values():
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norm_instance = constraints.max_norm(m)
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normed = norm_instance(backend.variable(array))
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assert np.all(backend.eval(normed) < m)
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# a more explicit example
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norm_instance = constraints.max_norm(2.0)
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x = np.array([[0, 0, 0], [1.0, 0, 0], [3, 0, 0], [3, 3, 3]]).T
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x_normed_target = np.array(
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[[0, 0, 0], [1.0, 0, 0], [2.0, 0, 0],
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[2. / np.sqrt(3), 2. / np.sqrt(3), 2. / np.sqrt(3)]]).T
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x_normed_actual = backend.eval(norm_instance(backend.variable(x)))
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self.assertAllClose(x_normed_actual, x_normed_target, rtol=1e-05)
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def test_non_neg(self):
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non_neg_instance = constraints.non_neg()
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normed = non_neg_instance(backend.variable(get_example_array()))
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assert np.all(np.min(backend.eval(normed), axis=1) == 0.)
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def test_unit_norm(self):
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unit_norm_instance = constraints.unit_norm()
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normalized = unit_norm_instance(backend.variable(get_example_array()))
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norm_of_normalized = np.sqrt(np.sum(backend.eval(normalized)**2, axis=0))
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# In the unit norm constraint, it should be equal to 1.
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difference = norm_of_normalized - 1.
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largest_difference = np.max(np.abs(difference))
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assert np.abs(largest_difference) < 10e-5
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def test_min_max_norm(self):
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array = get_example_array()
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for m in get_test_values():
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norm_instance = constraints.min_max_norm(min_value=m, max_value=m * 2)
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normed = norm_instance(backend.variable(array))
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value = backend.eval(normed)
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l2 = np.sqrt(np.sum(np.square(value), axis=0))
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assert not l2[l2 < m]
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assert not l2[l2 > m * 2 + 1e-5]
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def test_conv2d_radial_constraint(self):
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for width in (3, 4, 5, 6):
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array = get_example_kernel(width)
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norm_instance = constraints.radial_constraint()
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normed = norm_instance(backend.variable(array))
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value = backend.eval(normed)
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assert np.all(value.shape == array.shape)
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assert np.all(value[0:, 0, 0, 0] == value[-1:, 0, 0, 0])
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assert len(set(value[..., 0, 0].flatten())) == math.ceil(float(width) / 2)
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if __name__ == '__main__':
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test.main()
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