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# LogSoftmax

class torch.nn.LogSoftmax(dim=None)

  Applies the log⁡(Softmax(x))\log(\text{Softmax}(x))log(Softmax(x)) function to an n-dimensional input Tensor. The LogSoftmax formulation can be simplified as:

  LogSoftmax(x i)=log⁡(exp⁡(x i)∑j exp⁡(x j))\text{LogSoftmax}(x_{i}) = \log\left(\frac{\exp(x_i) }{ \sum_j \exp(x_j)} \right) LogSoftmax(x i​)=log(∑j​exp(x j​)exp(x i​)​)
  Shape:

    - Input: (∗)(*)(∗) where * means, any number of additional dimensions

    - Output: (∗)(*)(∗), same shape as the input

  Parameters:

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

  Returns:

    a Tensor of the same dimension and shape as the input with values in the range [-inf, 0)

  Return type:

    None

  Examples:

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

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