def call(self, inputs):
"""如果是条件Layer Norm，则默认以list为输入，第二个是condition
"""
if self.conditional:
inputs, cond = inputs
if self.hidden_units is not None:
cond = self.hidden_dense(cond)
for _ in range(K.ndim(inputs) - K.ndim(cond)):
cond = K.expand_dims(cond, 1)
if self.center:
beta = self.beta_dense(cond) + self.beta
if self.scale:
gamma = self.gamma_dense(cond) + self.gamma
else:
if self.center:
beta = self.beta
if self.scale:
gamma = self.gamma
outputs = inputs
if self.center:
mean = K.mean(outputs, axis=-1, keepdims=True)
outputs = outputs - mean
if self.scale:
variance = K.mean(K.square(outputs), axis=-1, keepdims=True)
std = K.sqrt(variance + self.epsilon)
outputs = outputs / std
outputs = outputs * gamma
if self.center:
outputs = outputs + beta
return outputs
def get_gradients(self, loss, params):
grads = []
for g in super(NewOptimizer, self).get_gradients(loss, params):
if isinstance(g, tf.IndexedSlices):
g = tf.convert_to_tensor(g)
if K.ndim(g) > 1:
g = g - K.mean(g, axis=range(1, K.ndim(g)), keepdims=True)
grads.append(g)
return grads
