Softmax#

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

Applies the Softmax 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.

Softmax is defined as:

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

When the input Tensor is a sparse tensor then the unspecifiedvalues are treated as-inf.

Shape:

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

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

Returns

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

Parameters

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

Return type

None

Note

This module doesn’t work directly with NLLLoss,which expects the Log to be computed between the Softmax and itself.UseLogSoftmax instead (it’s faster and has better numerical properties).

Examples:

>>>m=nn.Softmax(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