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Diffstat (limited to 'tensorflow/python/ops/init_ops_test.py')
| -rw-r--r-- | tensorflow/python/ops/init_ops_test.py | 196 |
1 files changed, 196 insertions, 0 deletions
diff --git a/tensorflow/python/ops/init_ops_test.py b/tensorflow/python/ops/init_ops_test.py new file mode 100644 index 0000000000..f6fffa9079 --- /dev/null +++ b/tensorflow/python/ops/init_ops_test.py @@ -0,0 +1,196 @@ +# Copyright 2018 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 initializers in init_ops.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.python.eager import context +from tensorflow.python.framework import ops +from tensorflow.python.ops import init_ops +from tensorflow.python.ops import resource_variable_ops +from tensorflow.python.platform import test + + +class InitializersTest(test.TestCase): + + def _runner(self, + init, + shape, + target_mean=None, + target_std=None, + target_max=None, + target_min=None): + variable = resource_variable_ops.ResourceVariable(init(shape)) + if context.executing_eagerly(): + output = variable.numpy() + else: + sess = ops.get_default_session() + sess.run(variable.initializer) + output = sess.run(variable) + lim = 3e-2 + if target_std is not None: + self.assertGreater(lim, abs(output.std() - target_std)) + if target_mean is not None: + self.assertGreater(lim, abs(output.mean() - target_mean)) + if target_max is not None: + self.assertGreater(lim, abs(output.max() - target_max)) + if target_min is not None: + self.assertGreater(lim, abs(output.min() - target_min)) + + def test_uniform(self): + tensor_shape = (9, 6, 7) + with self.test_session(): + self._runner( + init_ops.RandomUniform(minval=-1, maxval=1, seed=124), + tensor_shape, + target_mean=0., + target_max=1, + target_min=-1) + + def test_normal(self): + tensor_shape = (8, 12, 99) + with self.test_session(): + self._runner( + init_ops.RandomNormal(mean=0, stddev=1, seed=153), + tensor_shape, + target_mean=0., + target_std=1) + + def test_truncated_normal(self): + tensor_shape = (12, 99, 7) + with self.test_session(): + self._runner( + init_ops.TruncatedNormal(mean=0, stddev=1, seed=126), + tensor_shape, + target_mean=0., + target_max=2, + target_min=-2) + + def test_constant(self): + tensor_shape = (5, 6, 4) + with self.test_session(): + self._runner( + init_ops.Constant(2), + tensor_shape, + target_mean=2, + target_max=2, + target_min=2) + + def test_lecun_uniform(self): + tensor_shape = (5, 6, 4, 2) + with self.test_session(): + fan_in, _ = init_ops._compute_fans(tensor_shape) + std = np.sqrt(1. / fan_in) + self._runner( + init_ops.lecun_uniform(seed=123), + tensor_shape, + target_mean=0., + target_std=std) + + def test_glorot_uniform_initializer(self): + tensor_shape = (5, 6, 4, 2) + with self.test_session(): + fan_in, fan_out = init_ops._compute_fans(tensor_shape) + std = np.sqrt(2. / (fan_in + fan_out)) + self._runner( + init_ops.glorot_uniform_initializer(seed=123), + tensor_shape, + target_mean=0., + target_std=std) + + def test_he_uniform(self): + tensor_shape = (5, 6, 4, 2) + with self.test_session(): + fan_in, _ = init_ops._compute_fans(tensor_shape) + std = np.sqrt(2. / fan_in) + self._runner( + init_ops.he_uniform(seed=123), + tensor_shape, + target_mean=0., + target_std=std) + + def test_lecun_normal(self): + tensor_shape = (5, 6, 4, 2) + with self.test_session(): + fan_in, _ = init_ops._compute_fans(tensor_shape) + std = np.sqrt(1. / fan_in) + self._runner( + init_ops.lecun_normal(seed=123), + tensor_shape, + target_mean=0., + target_std=std) + + def test_glorot_normal_initializer(self): + tensor_shape = (5, 6, 4, 2) + with self.test_session(): + fan_in, fan_out = init_ops._compute_fans(tensor_shape) + std = np.sqrt(2. / (fan_in + fan_out)) + self._runner( + init_ops.glorot_normal_initializer(seed=123), + tensor_shape, + target_mean=0., + target_std=std) + + def test_he_normal(self): + tensor_shape = (5, 6, 4, 2) + with self.test_session(): + fan_in, _ = init_ops._compute_fans(tensor_shape) + std = np.sqrt(2. / fan_in) + self._runner( + init_ops.he_normal(seed=123), + tensor_shape, + target_mean=0., + target_std=std) + + def test_Orthogonal(self): + tensor_shape = (20, 20) + with self.test_session(): + self._runner(init_ops.Orthogonal(seed=123), tensor_shape, target_mean=0.) + + def test_Identity(self): + with self.test_session(): + tensor_shape = (3, 4, 5) + with self.assertRaises(ValueError): + self._runner( + init_ops.Identity(), + tensor_shape, + target_mean=1. / tensor_shape[0], + target_max=1.) + + tensor_shape = (3, 3) + self._runner( + init_ops.Identity(), + tensor_shape, + target_mean=1. / tensor_shape[0], + target_max=1.) + + def test_Zeros(self): + tensor_shape = (4, 5) + with self.test_session(): + self._runner( + init_ops.Zeros(), tensor_shape, target_mean=0., target_max=0.) + + def test_Ones(self): + tensor_shape = (4, 5) + with self.test_session(): + self._runner(init_ops.Ones(), tensor_shape, target_mean=1., target_max=1.) + + +if __name__ == '__main__': + test.main() |