Softmin#

classtorch.nn.modules.activation.Softmin(dim=None)[source]#

Applies the Softmin function to an n-dimensional input Tensor.

Rescales them so that the elements of the n-dimensional output Tensorlie in the range[0, 1] and sum to 1.

Softmin is defined as:

$$\text{Softmin}(x_i)=\frac{\exp (-x_i)}{\sum _j\exp (-x_j)}$$Softmin(xi​)=∑j​exp(−xj​)exp(−xi​)​

Shape:

• Input:$(\ast )$(∗) where* means, any number of additionaldimensions

• Output:$(\ast )$(∗), same shape as the input

Parameters

dim (int) – A dimension along which Softmin will be computed (so every slicealong dim will sum to 1).

Returns

a Tensor of the same dimension and shape as the input, withvalues in the range [0, 1]

Return type

None

Examples:

>>>m=nn.Softmin(dim=1)>>>input=torch.randn(2,3)>>>output=m(input)

extra_repr()[source]#

Return the extra representation of the module.

Return type

str

forward(input)[source]#

Runs the forward pass.

Return type

Tensor