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# torch.nn.functional.softmax

torch.nn.functional.softmax(input, dim=None, _stacklevel=3, dtype=None)

  Applies a softmax function.

  Softmax is defined as:

  Softmax(x i)=exp⁡(x i)∑j exp⁡(x j)\text{Softmax}(x_{i}) = \frac{\exp(x_i)}{\sum_j \exp(x_j)}Softmax(x i​)=∑j​e x p(x j​)e x p(x i​)​

  It is applied to all slices along dim, and will re-scale them so that the elements lie in the range [0, 1] and sum to 1.

  See `Softmax` for more details.

  Parameters:

    - input (Tensor) – input

    - dim (int) – A dimension along which softmax will be computed.

    - dtype (`torch.dtype`, optional) – the desired data type of returned tensor. If specified, the input tensor is casted to `dtype` before the operation is performed. This is useful for preventing data type overflows. Default: None.

  Return type:

    Tensor

  Note

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

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