# mx.symbol.SoftmaxActivation¶

## Description¶

Applies softmax activation to input. This is intended for internal layers.

This operator has been deprecated, please use softmax.

If mode = instance, this operator will compute a softmax for each instance in the batch. This is the default mode.

If mode = channel, this operator will compute a k-class softmax at each position of each instance, where k = num_channel. This mode can only be used when the input array has at least 3 dimensions. This can be used for fully convolutional network, image segmentation, etc.

Example:

>>> input_array = mx.nd.array([[3., 0.5, -0.5, 2., 7.],
>>>                            [2., -.4, 7.,   3., 0.2]])
>>> softmax_act = mx.nd.SoftmaxActivation(input_array)
>>> print softmax_act.asnumpy()
[[  1.78322066e-02   1.46375655e-03   5.38485940e-04   6.56010211e-03   9.73605454e-01]
[  6.56221947e-03   5.95310994e-04   9.73919690e-01   1.78379621e-02   1.08472735e-03]]


## Usage¶

mx.symbol.SoftmaxActivation(...)


## Arguments¶

Argument

Description

data

NDArray-or-Symbol.

The input array.

mode

{‘channel’, ‘instance’},optional, default=’instance’.

Specifies how to compute the softmax. If set to instance, it computes softmax for each instance. If set to channel, It computes cross channel softmax for each position of each instance.

name

string, optional.

Name of the resulting symbol.

## Value¶

out The result mx.symbol