Class

MPSCNNSoftMax

A neural transfer function that is useful for classification tasks.

Declaration

@interface MPSCNNSoftMax : MPSCNNKernel

Overview

The softmax filter is applied across feature channels in a convolutional manner at all spatial locations. The softmax filter can be seen as the combination of an activation function (exponential) and a normalization operator.

For each feature channel per pixel in an image in a feature map, the softmax filter computes the following:

pixel = exp(pixel(x,y,k))/sum(exp(pixel(x,y,0)) ... exp(pixel(x,y,N-1))

Where R is the result channel in the pixel and N is the number of feature channels.

Relationships

Inherits From

See Also

Softmax Layers

MPSCNNLogSoftMax

A neural transfer function that is useful for constructing a loss function to be minimized when training neural networks.

MPSCNNLogSoftMaxGradient

A gradient logarithmic softmax filter.

MPSCNNSoftMaxGradient

A gradient softmax filter.

Beta Software

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