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