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# 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()
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