def _noisy(self, x, units, kernel_initializer=tf_utils.xavier_initializer(),
name=None, reuse=None, sigma=.4):
name = name if name is not None else 'noisy'
with tf.variable_scope(name, reuse=reuse):
y = self._dense(x, units, kernel_initializer=kernel_initializer, reuse=reuse)
with tf.variable_scope('noisy', reuse=reuse):
# params for the noisy layer
features = x.shape.as_list()[-1]
w_shape = [features, units]
b_shape = [units]
epsilon_w = tf.truncated_normal(w_shape, stddev=sigma, name='epsilon_w')
epsilon_b = tf.truncated_normal(b_shape, stddev=sigma, name='epsilon_b')
noisy_w = tf.get_variable('noisy_w', shape=w_shape,
initializer=kernel_initializer,
regularizer=self.l2_regularizer,
trainable=self.trainable)
noisy_b = tf.get_variable('noisy_b', shape=b_shape,
initializer=tf.constant_initializer(sigma / np.sqrt(units)),
trainable=self.trainable)
# output of the noisy layer
x = tf.matmul(x, noisy_w * epsilon_w) + noisy_b * epsilon_b
x = x + y
if self.trainable:
return x
else:
return y
def _convtrans(self, x, filters, kernel_size, strides=1, padding='same',
kernel_initializer=tf_utils.xavier_initializer(), name=None, reuse=None):
return tf.layers.conv2d_transpose(x, filters, kernel_size,
strides=strides, padding=padding,
kernel_initializer=kernel_initializer,
kernel_regularizer=self.l2_regularizer,
trainable=self.trainable, name=name, reuse=reuse)
def _dense_norm_activation(self, x, units, kernel_initializer=tf_utils.xavier_initializer(),
normalization=None, activation=None, name=None, reuse=None):
x = self._dense(x, units, kernel_initializer=kernel_initializer, name=name, reuse=reuse)
x = tf_utils.norm_activation(x, normalization=normalization, activation=activation,
training=self.training, trainable=self.trainable)
return x
def _convtrans_norm_activation(
self,
x,
filters,
kernel_size,
strides=1,
padding='same',
kernel_initializer=tf_utils.xavier_initializer(),
normalization=None,
activation=None,
trainable=True,
name=None,
reuse=None):
x = self._convtrans(x,
filters,
kernel_size,
strides=strides,
padding=padding,
kernel_initializer=kernel_initializer,
trainable=trainable,
name=name,
reuse=reuse)
x = tf_utils.norm_activation(x,
normalization=normalization,
activation=activation,
training=getattr(self, 'is_training',
False))
return x
def _conv_norm_activation(self,
x,
filters,
kernel_size,
strides=1,
padding='same',
kernel_initializer=tf_utils.xavier_initializer(),
normalization=None,
activation=None,
name=None,
reuse=None):
x = self._conv(x,
filters,
kernel_size,
strides=strides,
padding=padding,
kernel_initializer=kernel_initializer,
name=name,
reuse=reuse)
x = tf_utils.norm_activation(x,
normalization=normalization,
activation=activation,
training=self.training,
trainable=self.trainable)
return x
def _noisy_norm_activation(self, x, units, kernel_initializer=tf_utils.xavier_initializer(),
normalization=None, activation=None,
name=None, reuse=None, sigma=.4):
x = self._noisy(x, units, kernel_initializer=kernel_initializer,
name=name, reuse=reuse, sigma=sigma)
x = tf_utils.norm_activation(x, normalization=normalization, activation=activation,
training=self.training, trainable=self.trainable)
return x
def _dense(self,
x,
units,
kernel_initializer=tf_utils.xavier_initializer(),
name=None,
reuse=None):
return tf.layers.dense(x,
units,
kernel_initializer=kernel_initializer,
kernel_regularizer=self.l2_regularizer,
trainable=self.trainable,
name=name,
reuse=reuse)