A neural transfer function that is useful for classification tasks.


class MPSCNNSoftMax : MPSCNNKernel


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.


Inherits From

Conforms To

See Also

Softmax Layers

class MPSCNNLogSoftMax

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

class MPSCNNLogSoftMaxGradient

A gradient logarithmic softmax filter.

class MPSCNNSoftMaxGradient

A gradient softmax filter.