def mul_mean(first_input,
second_input,
axis=None,
keepdims=False,
target="cce"):
temp = mul(first_input, second_input, target=target)
output, _ = mean(temp, axis, keepdims)
return output
def mean_square(inputs, axis=None, keepdims=False, target="cce"):
"""Mean of square value of a tensor, alongside the specified axis.
Arguments:
input: A tensor.
axis: A list of integer. Axes to compute the mean.
keepdims: A boolean, whether to keep the dimensions or not.
If `keepdims` is `False`, the rank of the tensor is reduced
by 1 for each entry in `axis`. If `keepdims` is `True`,
the reduced dimensions are retained with length 1.
Returns:
A tensor with the mean of element-wise square value of `input`.
Notice: There is some precision problem for the operator and remain to solve
"""
inputs_square = square(inputs)
return mean(inputs_square, axis, keepdims, target=target)
def simple_mean(x, target=utils.CCE):
"""SimpleMean"""
return math.mean(x, axis=[2, 3], keepdims=True, target=target)
def reduce_mean(x, axis, keepdims, target=utils.CCE):
"""ReduceMean"""
return math.mean(x, axis=axis, keepdims=keepdims, target=target)