def construct(self, base, target):
x = F.square(base - target)
return self.get_loss(x)
def construct(self, x):
mean = self.mean(x, -1)
variance = self.mean(F.square(x - mean), -1)
output = (x - mean) / F.sqrt(variance + self.eps)
rescaled_output = output * self.gamma + self.beta
return rescaled_output
def _get_square_sum(grad, value):
norm = P.ReduceSum(False)(F.square(grad), ()) / value
norm = F.expand_dims(F.cast(norm, mstype.float32), 0)
return norm
def construct(self, x):
x = self.sqrt(self.reduce_sum(F.square(x), self.axis))
if not self.keep_dims:
x = self.squeeze(x)
return x
def _get_square_sum(x):
norm = P.ReduceSum(False)(F.square(x), ())
norm = F.expand_dims(F.cast(norm, mstype.float32), 0)
return norm
