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# 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 Keras weights constraints."""
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.platform import test
def get_test_values():
return [0.1, 0.5, 3, 8, 1e-7]
def get_example_array():
np.random.seed(3537)
example_array = np.random.random((100, 100)) * 100. - 50.
example_array[0, 0] = 0. # 0 could possibly cause trouble
return example_array
class KerasConstraintsTest(test.TestCase):
def test_serialization(self):
all_activations = ['max_norm', 'non_neg',
'unit_norm', 'min_max_norm']
for name in all_activations:
fn = keras.constraints.get(name)
ref_fn = getattr(keras.constraints, name)()
assert fn.__class__ == ref_fn.__class__
config = keras.constraints.serialize(fn)
fn = keras.constraints.deserialize(config)
assert fn.__class__ == ref_fn.__class__
def test_max_norm(self):
with self.cached_session():
array = get_example_array()
for m in get_test_values():
norm_instance = keras.constraints.max_norm(m)
normed = norm_instance(keras.backend.variable(array))
assert np.all(keras.backend.eval(normed) < m)
# a more explicit example
norm_instance = keras.constraints.max_norm(2.0)
x = np.array([[0, 0, 0], [1.0, 0, 0], [3, 0, 0], [3, 3, 3]]).T
x_normed_target = np.array([[0, 0, 0], [1.0, 0, 0],
[2.0, 0, 0],
[2. / np.sqrt(3),
2. / np.sqrt(3),
2. / np.sqrt(3)]]).T
x_normed_actual = keras.backend.eval(
norm_instance(keras.backend.variable(x)))
self.assertAllClose(x_normed_actual, x_normed_target, rtol=1e-05)
def test_non_neg(self):
with self.cached_session():
non_neg_instance = keras.constraints.non_neg()
normed = non_neg_instance(keras.backend.variable(get_example_array()))
assert np.all(np.min(keras.backend.eval(normed), axis=1) == 0.)
def test_unit_norm(self):
with self.cached_session():
unit_norm_instance = keras.constraints.unit_norm()
normalized = unit_norm_instance(
keras.backend.variable(get_example_array()))
norm_of_normalized = np.sqrt(
np.sum(keras.backend.eval(normalized) ** 2, axis=0))
# In the unit norm constraint, it should be equal to 1.
difference = norm_of_normalized - 1.
largest_difference = np.max(np.abs(difference))
assert np.abs(largest_difference) < 10e-5
def test_min_max_norm(self):
with self.cached_session():
array = get_example_array()
for m in get_test_values():
norm_instance = keras.constraints.min_max_norm(min_value=m,
max_value=m * 2)
normed = norm_instance(keras.backend.variable(array))
value = keras.backend.eval(normed)
l2 = np.sqrt(np.sum(np.square(value), axis=0))
assert not l2[l2 < m]
assert not l2[l2 > m * 2 + 1e-5]
if __name__ == '__main__':
test.main()
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