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# Copyright 2017 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 computing moving-average statistics."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.contrib.distributions.python.ops import moving_stats
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
rng = np.random.RandomState(0)
class MovingReduceMeanVarianceTest(test.TestCase):
def test_assign_moving_mean_variance(self):
shape = [1, 2]
true_mean = np.array([[0., 3.]])
true_stddev = np.array([[1.1, 0.5]])
with self.cached_session() as sess:
# Start "x" out with this mean.
mean_var = variables.VariableV1(array_ops.zeros_like(true_mean))
variance_var = variables.VariableV1(array_ops.ones_like(true_stddev))
x = random_ops.random_normal(shape, dtype=np.float64, seed=0)
x = true_stddev * x + true_mean
ema, emv = moving_stats.assign_moving_mean_variance(
mean_var, variance_var, x, decay=0.99)
self.assertEqual(ema.dtype.base_dtype, dtypes.float64)
self.assertEqual(emv.dtype.base_dtype, dtypes.float64)
# Run 1000 updates; moving averages should be near the true values.
variables.global_variables_initializer().run()
for _ in range(2000):
sess.run([ema, emv])
[mean_var_, variance_var_, ema_, emv_] = sess.run([
mean_var, variance_var, ema, emv])
# Test that variables are passed-through.
self.assertAllEqual(mean_var_, ema_)
self.assertAllEqual(variance_var_, emv_)
# Test that values are as expected.
self.assertAllClose(true_mean, ema_, rtol=0.005, atol=0.015)
self.assertAllClose(true_stddev**2., emv_, rtol=0.06, atol=0.)
# Change the mean, var then update some more. Moving averages should
# re-converge.
sess.run([
mean_var.assign(np.array([[-1., 2.]])),
variance_var.assign(np.array([[2., 1.]])),
])
for _ in range(2000):
sess.run([ema, emv])
[mean_var_, variance_var_, ema_, emv_] = sess.run([
mean_var, variance_var, ema, emv])
# Test that variables are passed-through.
self.assertAllEqual(mean_var_, ema_)
self.assertAllEqual(variance_var_, emv_)
# Test that values are as expected.
self.assertAllClose(true_mean, ema_, rtol=0.005, atol=0.015)
self.assertAllClose(true_stddev**2., emv_, rtol=0.1, atol=0.)
def test_moving_mean_variance(self):
shape = [1, 2]
true_mean = np.array([[0., 3.]])
true_stddev = np.array([[1.1, 0.5]])
with self.cached_session() as sess:
# Start "x" out with this mean.
x = random_ops.random_normal(shape, dtype=np.float64, seed=0)
x = true_stddev * x + true_mean
ema, emv = moving_stats.moving_mean_variance(
x, decay=0.99)
self.assertEqual(ema.dtype.base_dtype, dtypes.float64)
self.assertEqual(emv.dtype.base_dtype, dtypes.float64)
# Run 1000 updates; moving averages should be near the true values.
variables.global_variables_initializer().run()
for _ in range(2000):
sess.run([ema, emv])
[ema_, emv_] = sess.run([ema, emv])
self.assertAllClose(true_mean, ema_, rtol=0.005, atol=0.015)
self.assertAllClose(true_stddev**2., emv_, rtol=0.06, atol=0.)
class MovingLogExponentialMovingMeanExpTest(test.TestCase):
def test_assign_log_moving_mean_exp(self):
shape = [1, 2]
true_mean = np.array([[0., 3.]])
true_stddev = np.array([[1.1, 0.5]])
decay = 0.99
with self.cached_session() as sess:
# Start "x" out with this mean.
x = random_ops.random_normal(shape, dtype=np.float64, seed=0)
x = true_stddev * x + true_mean
log_mean_exp_var = variables.VariableV1(array_ops.zeros_like(true_mean))
variables.global_variables_initializer().run()
log_mean_exp = moving_stats.assign_log_moving_mean_exp(
log_mean_exp_var, x, decay=decay)
expected_ = np.zeros_like(true_mean)
for _ in range(2000):
x_, log_mean_exp_ = sess.run([x, log_mean_exp])
expected_ = np.log(decay * np.exp(expected_) + (1 - decay) * np.exp(x_))
self.assertAllClose(expected_, log_mean_exp_, rtol=1e-6, atol=1e-9)
if __name__ == "__main__":
test.main()
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