nn.Softmax

nn.Softmax(dim=None)

• dim: 计算的维度 A dimension along which Softmax will be computed (so every slice along dim will sum to 1).

用softmax函数将N维输入进行归一化，归一化之后每个输出的Tensor范围在[0, 1]，并且归一化的那一维和为1
Applies the Softmax function to an n-dimensional input Tensor rescaling them so that the elements of the n-dimensional output Tensor lie in the range [0,1] and sum to 1.

实例：从下面的例子可以看出，Softmax维度为0时对最后一维即列进行归一化，因此，维度为1时对行进行归一化

input = torch.Tensor([[1,2,3], [4,5,6], [7,8,9]])
m0 = nn.Softmax(dim=0)
m1 = nn.Softmax(dim=1)
output0 = m0(input)
output1 = m1(input)

print("input: ", input)
print("output0: ", output0)
print("output1: ", output1)
'''
input:  tensor([[1., 2., 3.],
        [4., 5., 6.],
        [7., 8., 9.]])
output0:  tensor([[0.0024, 0.0024, 0.0024],
        [0.0473, 0.0473, 0.0473],
        [0.9503, 0.9503, 0.9503]])
output1:  tensor([[0.0900, 0.2447, 0.6652],
        [0.0900, 0.2447, 0.6652],
        [0.0900, 0.2447, 0.6652]])'''