mpscnnlogsoftmax(3) [mojave man page]

MPSCNNLogSoftMax(3)					 MetalPerformanceShaders.framework				       MPSCNNLogSoftMax(3)

NAME

       MPSCNNLogSoftMax

SYNOPSIS

       #import <MPSCNNSoftMax.h>

       Inherits MPSCNNKernel.

   Additional Inherited Members
Detailed Description
       This depends on Metal.framework	The logarithmic softMax filter can be achieved by taking the natural logarithm of the the result of the
       softMax filter. The results are often used to construct a loss function to be minimized when training neural networks. For each feature
       channel per pixel in an image in a feature map, the logarithmic softMax filter computes the following: result channel in pixel =
       pixel(x,y,k)) - ln{sum(exp(pixel(x,y,0)) ... exp(pixel(x,y,N-1))} where N is the number of feature channels and y = ln{x} satisfies e^y =
       x.

Author
       Generated automatically by Doxygen for MetalPerformanceShaders.framework from the source code.

Version MetalPerformanceShaders-100				  Thu Feb 8 2018					       MPSCNNLogSoftMax(3)

MPSCNNSoftMaxGradient(3)				 MetalPerformanceShaders.framework				  MPSCNNSoftMaxGradient(3)

NAME

       MPSCNNSoftMaxGradient

SYNOPSIS

       #import <MPSCNNSoftMax.h>

       Inherits MPSCNNGradientKernel.

   Instance Methods
       (nonnull instancetype) - initWithDevice:
       (nullable instancetype) - initWithCoder:device:

   Additional Inherited Members
Detailed Description
       This depends on Metal.framework	The softMax gradient filter calculates the gradient to be backpropagated. The softMax gradient just as the
       softMax filter, is applied across feature channels and at all spatial locations. It computes the gradient for a given output generated by
       the corresponding softMax (i.e. MPSCNNSoftMax) layer and the gradient computed by the previous layer in the back-propagation pass. For each
       feature channel in an image in a feature map, the softMax gradient filter computes the following: result gradient channel in pixel
       outputGradient(x,y,k) = softMax(x,y,k) * (inputGradient(x,y,k) - sum(inputGradient(x,y,0) * softMax(x,y,0) ... inputGradient(x,y,N-1) *
       softMax(x,y,N-1))) where N is the number of feature channels

       The incoming gradient is the primary source. The original output of corresponding softMax is the secondary source.

Method Documentation
   - (nullable instancetype) initWithCoder: (NSCoder *__nonnull) aDecoder(nonnull id< MTLDevice >) device
       NSSecureCoding compatability  While the standard NSSecureCoding/NSCoding method -initWithCoder: should work, since the file can't know
       which device your data is allocated on, we have to guess and may guess incorrectly. To avoid that problem, use initWithCoder:device
       instead.

       Parameters:
	   aDecoder The NSCoder subclass with your serialized MPSKernel
	   device The MTLDevice on which to make the MPSKernel

       Returns:
	   A new MPSKernel object, or nil if failure.

       Reimplemented from MPSCNNGradientKernel.

   - (nonnull instancetype) initWithDevice: (nonnull id< MTLDevice >) device
       Initializes a MPSCNNSoftMaxGradient function

       Parameters:
	   device The MTLDevice on which this MPSCNNSoftMaxGradient filter will be used

       Returns:
	   A valid MPSCNNSoftMaxGradient object or nil, if failure.

       Reimplemented from MPSCNNGradientKernel.

Author
       Generated automatically by Doxygen for MetalPerformanceShaders.framework from the source code.

Version MetalPerformanceShaders-100				  Thu Feb 8 2018					  MPSCNNSoftMaxGradient(3)