Applies a softmax function.
Softmax is defined as:
Softmax ( x i ) = e x p ( x i ) ∑ j e x p ( x j ) \text{Softmax}(x_{i}) = \frac{exp(x_i)}{\sum_j exp(x_j)} Softmax(xi)=∑jexp(xj)exp(xi)
where i , j i, j i,j run over sparse tensor indices and unspecified entries are ignores. This is equivalent to defining unspecified entries as negative infinity so that e x p ( x k ) = 0 exp(x_k) = 0 exp(xk)=0 when the entry with index k k k has not specified.
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.
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
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