from keras.engine import Layer, InputSpec
from keras import initializers, regularizers
from keras import backend as K
class FixedBatchNormalization(Layer):
def __init__(self, epsilon=1e-3, axis=-1,
weights=None, beta_init='zero', gamma_init='one',
gamma_regularizer=None, beta_regularizer=None, **kwargs):
self.supports_masking = True
self.beta_init = initializers.get(beta_init)
self.gamma_init = initializers.get(gamma_init)
self.epsilon = epsilon
self.axis = axis
self.gamma_regularizer = regularizers.get(gamma_regularizer)
self.beta_regularizer = regularizers.get(beta_regularizer)
self.initial_weights = weights
super(FixedBatchNormalization, self).__init__(**kwargs)
def build(self, input_shape):
self.input_spec = [InputSpec(shape=input_shape)]
shape = (input_shape[self.axis],)
self.gamma = self.add_weight(shape,
initializer=self.gamma_init,
regularizer=self.gamma_regularizer,
name='{}_gamma'.format(self.name),
trainable=False)
self.beta = self.add_weight(shape,
initializer=self.beta_init,
regularizer=self.beta_regularizer,
name='{}_beta'.format(self.name),
trainable=False)
self.running_mean = self.add_weight(shape, initializer='zero',
name='{}_running_mean'.format(self.name),
trainable=False)
self.running_std = self.add_weight(shape, initializer='one',
name='{}_running_std'.format(self.name),
trainable=False)
if self.initial_weights is not None:
self.set_weights(self.initial_weights)
del self.initial_weights
self.built = True
def call(self, x, mask=None):
assert self.built, 'Layer must be built before being called'
input_shape = K.int_shape(x)
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]
if sorted(reduction_axes) == range(K.ndim(x))[:-1]:
x_normed = K.batch_normalization(
x, self.running_mean, self.running_std,
self.beta, self.gamma,
epsilon=self.epsilon)
else:
# need broadcasting
broadcast_running_mean = K.reshape(self.running_mean, broadcast_shape)
broadcast_running_std = K.reshape(self.running_std, broadcast_shape)
broadcast_beta = K.reshape(self.beta, broadcast_shape)
broadcast_gamma = K.reshape(self.gamma, broadcast_shape)
x_normed = K.batch_normalization(
x, broadcast_running_mean, broadcast_running_std,
broadcast_beta, broadcast_gamma,
epsilon=self.epsilon)
return x_normed
def get_config(self):
config = {'epsilon': self.epsilon,
'axis': self.axis,
'gamma_regularizer': self.gamma_regularizer.get_config() if self.gamma_regularizer else None,
'beta_regularizer': self.beta_regularizer.get_config() if self.beta_regularizer else None}
base_config = super(FixedBatchNormalization, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
