"""Batch Normalization for TensorFlow.
"""
"""
Copyright 2017 Parag K. Mital. See also NOTICE.md.
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.
"""
import tensorflow as tf
from tensorflow.python.ops import control_flow_ops
[docs]def batch_norm(x, phase_train, name='bn', decay=0.9, reuse=None, affine=True):
"""
Batch normalization on convolutional maps.
from: https://stackoverflow.com/questions/33949786/how-could-i-
use-batch-normalization-in-tensorflow
Only modified to infer shape from input tensor x.
[DEPRECATED] Use tflearn or slim batch normalization instead.
Parameters
----------
x
Tensor, 4D BHWD input maps
phase_train
boolean tf.Variable, true indicates training phase
name
string, variable name
decay : float, optional
Description
reuse : None, optional
Description
affine
whether to affine-transform outputs
Return
------
normed
batch-normalized maps
"""
with tf.variable_scope(name, reuse=reuse):
shape = x.get_shape().as_list()
beta = tf.get_variable(
name='beta',
shape=[shape[-1]],
initializer=tf.constant_initializer(0.0),
trainable=True)
gamma = tf.get_variable(
name='gamma',
shape=[shape[-1]],
initializer=tf.constant_initializer(1.0),
trainable=affine)
if len(shape) == 4:
batch_mean, batch_var = tf.nn.moments(x, [0, 1, 2], name='moments')
else:
batch_mean, batch_var = tf.nn.moments(x, [0], name='moments')
ema = tf.train.ExponentialMovingAverage(decay=decay)
ema_apply_op = ema.apply([batch_mean, batch_var])
ema_mean, ema_var = ema.average(batch_mean), ema.average(batch_var)
def mean_var_with_update():
with tf.control_dependencies([ema_apply_op]):
return tf.identity(batch_mean), tf.identity(batch_var)
mean, var = control_flow_ops.cond(phase_train, mean_var_with_update,
lambda: (ema_mean, ema_var))
# tf.nn.batch_normalization
normed = tf.nn.batch_norm_with_global_normalization(
x, mean, var, beta, gamma, 1e-6, affine)
return normed