Apply 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(xi)=∑jexp(xj)exp(xi)
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.
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.
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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