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# Copyright 2015 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.
# ==============================================================================
"""Normalization layers.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.keras.python.keras import backend as K
from tensorflow.contrib.keras.python.keras import constraints
from tensorflow.contrib.keras.python.keras import initializers
from tensorflow.contrib.keras.python.keras import regularizers
from tensorflow.contrib.keras.python.keras.engine import InputSpec
from tensorflow.contrib.keras.python.keras.engine import Layer
from tensorflow.python.framework import tensor_shape
class BatchNormalization(Layer):
"""Batch normalization layer (Ioffe and Szegedy, 2014).
Normalize the activations of the previous layer at each batch,
i.e. applies a transformation that maintains the mean activation
close to 0 and the activation standard deviation close to 1.
Arguments:
axis: Integer, the axis that should be normalized
(typically the features axis).
For instance, after a `Conv2D` layer with
`data_format="channels_first"`,
set `axis=1` in `BatchNormalization`.
momentum: Momentum for the moving average.
epsilon: Small float added to variance to avoid dividing by zero.
center: If True, add offset of `beta` to normalized tensor.
If False, `beta` is ignored.
scale: If True, multiply by `gamma`.
If False, `gamma` is not used.
When the next layer is linear (also e.g. `nn.relu`),
this can be disabled since the scaling
will be done by the next layer.
beta_initializer: Initializer for the beta weight.
gamma_initializer: Initializer for the gamma weight.
moving_mean_initializer: Initializer for the moving mean.
moving_variance_initializer: Initializer for the moving variance.
beta_regularizer: Optional regularizer for the beta weight.
gamma_regularizer: Optional regularizer for the gamma weight.
beta_constraint: Optional constraint for the beta weight.
gamma_constraint: Optional constraint for the gamma weight.
Input shape:
Arbitrary. Use the keyword argument `input_shape`
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.
Output shape:
Same shape as input.
References:
- [Batch Normalization: Accelerating Deep Network Training by Reducing
Internal Covariate Shift](https://arxiv.org/abs/1502.03167)
"""
def __init__(self,
axis=-1,
momentum=0.99,
epsilon=1e-3,
center=True,
scale=True,
beta_initializer='zeros',
gamma_initializer='ones',
moving_mean_initializer='zeros',
moving_variance_initializer='ones',
beta_regularizer=None,
gamma_regularizer=None,
beta_constraint=None,
gamma_constraint=None,
**kwargs):
super(BatchNormalization, self).__init__(**kwargs)
self.supports_masking = True
self.axis = axis
self.momentum = momentum
self.epsilon = epsilon
self.center = center
self.scale = scale
self.beta_initializer = initializers.get(beta_initializer)
self.gamma_initializer = initializers.get(gamma_initializer)
self.moving_mean_initializer = initializers.get(moving_mean_initializer)
self.moving_variance_initializer = initializers.get(
moving_variance_initializer)
self.beta_regularizer = regularizers.get(beta_regularizer)
self.gamma_regularizer = regularizers.get(gamma_regularizer)
self.beta_constraint = constraints.get(beta_constraint)
self.gamma_constraint = constraints.get(gamma_constraint)
def build(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape).as_list()
dim = input_shape[self.axis]
if dim is None:
raise ValueError('Axis ' + str(self.axis) + ' of '
'input tensor should have a defined dimension '
'but the layer received an input with shape ' +
str(input_shape) + '.')
self.input_spec = InputSpec(ndim=len(input_shape), axes={self.axis: dim})
shape = (dim,)
if self.scale:
self.gamma = self.add_weight(
shape=shape,
name='gamma',
initializer=self.gamma_initializer,
regularizer=self.gamma_regularizer,
constraint=self.gamma_constraint)
else:
self.gamma = None
if self.center:
self.beta = self.add_weight(
shape=shape,
name='beta',
initializer=self.beta_initializer,
regularizer=self.beta_regularizer,
constraint=self.beta_constraint)
else:
self.beta = None
self.moving_mean = self.add_weight(
shape=shape,
name='moving_mean',
initializer=self.moving_mean_initializer,
trainable=False)
self.moving_variance = self.add_weight(
shape=shape,
name='moving_variance',
initializer=self.moving_variance_initializer,
trainable=False)
self.built = True
def call(self, inputs, training=None):
input_shape = inputs.get_shape().as_list()
# Prepare broadcasting shape.
ndim = len(input_shape)
reduction_axes = list(range(len(input_shape)))
del reduction_axes[self.axis]
broadcast_shape = [1] * len(input_shape)
broadcast_shape[self.axis] = input_shape[self.axis]
# Determines whether broadcasting is needed.
needs_broadcasting = (sorted(reduction_axes) != list(range(ndim))[:-1])
normed, mean, variance = K.normalize_batch_in_training(
inputs, self.gamma, self.beta, reduction_axes, epsilon=self.epsilon)
if training in {0, False}:
return normed
else:
self.add_update([
K.moving_average_update(self.moving_mean, mean, self.momentum),
K.moving_average_update(self.moving_variance, variance, self.momentum)
], inputs)
def normalize_inference():
if needs_broadcasting:
# In this case we must explictly broadcast all parameters.
broadcast_moving_mean = K.reshape(self.moving_mean, broadcast_shape)
broadcast_moving_variance = K.reshape(self.moving_variance,
broadcast_shape)
if self.center:
broadcast_beta = K.reshape(self.beta, broadcast_shape)
else:
broadcast_beta = None
if self.scale:
broadcast_gamma = K.reshape(self.gamma, broadcast_shape)
else:
broadcast_gamma = None
return K.batch_normalization(
inputs,
broadcast_moving_mean,
broadcast_moving_variance,
broadcast_beta,
broadcast_gamma,
epsilon=self.epsilon)
else:
return K.batch_normalization(
inputs,
self.moving_mean,
self.moving_variance,
self.beta,
self.gamma,
epsilon=self.epsilon)
# Pick the normalized form corresponding to the training phase.
return K.in_train_phase(normed, normalize_inference, training=training)
def get_config(self):
config = {
'axis':
self.axis,
'momentum':
self.momentum,
'epsilon':
self.epsilon,
'center':
self.center,
'scale':
self.scale,
'beta_initializer':
initializers.serialize(self.beta_initializer),
'gamma_initializer':
initializers.serialize(self.gamma_initializer),
'moving_mean_initializer':
initializers.serialize(self.moving_mean_initializer),
'moving_variance_initializer':
initializers.serialize(self.moving_variance_initializer),
'beta_regularizer':
regularizers.serialize(self.beta_regularizer),
'gamma_regularizer':
regularizers.serialize(self.gamma_regularizer),
'beta_constraint':
constraints.serialize(self.beta_constraint),
'gamma_constraint':
constraints.serialize(self.gamma_constraint)
}
base_config = super(BatchNormalization, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
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