Softmax - Maple Help
For the best experience, we recommend viewing online help using Google Chrome or Microsoft Edge.

# Online Help

###### All Products    Maple    MapleSim

DeepLearning/Tensor/Softmax

compute softmax of a Tensor

DeepLearning/Tensor/SoftmaxCrossEntropyWithLogits

compute softmax of a Tensor with logits

DeepLearning/Tensor/Softplus

compute softplus of a Tensor

 Calling Sequence Softmax(t,opts) SoftmaxCrossEntropyWithLogits(t,labels=x,logits=y,opts) Softplus(t,opts)

Parameters

 t - Tensor opts - zero or more options as specified below

Options

 • axis=list(integer) or integer

The value of option axis is an integer or list of integers which describes which axis of the input Tensor to reduce across.

 • name=string

The value of option name specifies an optional name for this Tensor, to be displayed in output and when visualizing the dataflow graph.

Description

 • The Softmax(t,opts) command computes the softmax function of a Tensor t,
 • 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) of a Tensor t.

Examples

 > $\mathrm{with}\left(\mathrm{DeepLearning}\right):$
 > $W≔\mathrm{Variable}\left(\left[\left[29.,93.,-29.\right],\left[-12.,-80.,96.\right],\left[96.,-92.,89.\right]\right],\mathrm{datatype}=\mathrm{float}\left[8\right]\right)$
 ${W}{≔}\left[\begin{array}{c}{\mathrm{DeepLearning Variable}}\\ {\mathrm{Name: Variable:0}}\\ {\mathrm{Shape: \left[3, 3\right]}}\\ {\mathrm{Data Type: float\left[8\right]}}\end{array}\right]$ (1)
 > $\mathrm{Softmax}\left(W\right)$
 $\left[\begin{array}{c}{\mathrm{DeepLearning Tensor}}\\ {\mathrm{Shape: \left[3, 3\right]}}\\ {\mathrm{Data Type: float\left[8\right]}}\end{array}\right]$ (2)
 > $\mathrm{Softplus}\left(W\right)$
 $\left[\begin{array}{c}{\mathrm{DeepLearning Tensor}}\\ {\mathrm{Shape: \left[3, 3\right]}}\\ {\mathrm{Data Type: float\left[8\right]}}\end{array}\right]$ (3)

Compatibility

 • The DeepLearning/Tensor/Softmax, DeepLearning/Tensor/SoftmaxCrossEntropyWithLogits and DeepLearning/Tensor/Softplus commands were introduced in Maple 2018.
 • For more information on Maple 2018 changes, see Updates in Maple 2018.

 See Also