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(xi)=log(∑jexp(xj)exp(xi))
Input: ( ∗ ) (*) (∗) where * means, any number of additional dimensions
Output: ( ∗ ) (*) (∗), same shape as the input
dim (int) – A dimension along which LogSoftmax will be computed.
a Tensor of the same dimension and shape as the input with values in the range [-inf, 0)
None
Examples:
>>> m = nn.LogSoftmax(dim=1) >>> input = torch.randn(2, 3) >>> output = m(input)
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