axis=list(integer) or integer

name=string

The SoftMax(t,opts) command computes the softmax function of a Tensor,

The SoftMaxCrossEntropyWithLogits(t,labels=x,logits=y) command computes the softmax function with labels x and logits y.

The SoftPlus(t,opts) command computes log(exp(t)+t).

$\mathrm{with}\left(\mathrm{DeepLearning}\right)\:$

$W≔\mathrm{Variable}\left(\left[\left[29.\,93.\,-29.\right]\,\left[-12.\,-80.\,96.\right]\,\left[96.\,-92.\,89.\right]\right]\,\mathrm{datatype}=\mathrm{float}\left[8\right]\right)$

$W≔\left[\begin{array}{c}\mathrm{DeepLearning\; Tensor}\\ \mathrm{Name:\; Variable:0}\\ \mathrm{Shape:\; undefined}\\ \mathrm{Data\; Type:\; float[8]}\end{array}\right]$

$\mathrm{SoftMax}\left(W\right)$

$\left[\begin{array}{c}\mathrm{DeepLearning\; Tensor}\\ \mathrm{Name:\; none}\\ \mathrm{Shape:\; undefined}\\ \mathrm{Data\; Type:\; float[8]}\end{array}\right]$

$\mathrm{SoftPlus}\left(W\,\mathrm{name}=''sp''\right)$

$\left[\begin{array}{c}\mathrm{DeepLearning\; Tensor}\\ \mathrm{Name:\; none}\\ \mathrm{Shape:\; undefined}\\ \mathrm{Data\; Type:\; float[8]}\end{array}\right]$

The SoftMax, SoftMin, SoftPlus and SoftProduct commands were introduced in Maple 2018.

For more information on Maple 2018 changes, see Updates in Maple 2018.