pymoose.softmax

pymoose.softmax(x, axis, upmost_index, placement=None)

Softmax function.

\[\mathrm{softmax}(x) = \frac{\exp(x_i)}{\sum_j \exp(x_j)}\]

Parameters

• x – A tensor.

• axis – The dimension along which the softmax divisor’s sum-reduce should be computed.

• upmost_index – The max index that should be used for computing the softmax divisor’s sum-reduce. Generally, this should be the size of the axis dimension of x.

• placement – An optional Placement to pin this operation to.

Returns

Result of applying the softmax function on x along the given axis.