MPSCNNNeuronGradient(3)

NAME
MPSCNNNeuronGradient

SYNOPSIS

#import <MPSCNNNeuron.h>

Inherits MPSCNNGradientKernel.

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

  Properties
MPSCNNNeuronType neuronType
float a
float b
float c
NSData * data

  Additional Inherited Members

Detailed Description

This depends on Metal.framework This filter is a backward filter for the neuron activation function filter.

The following filter types are supported: MPSCNNNeuronTypeNone ///< df/dx = 1 MPSCNNNeuronTypeLinear ///< df/dx = a MPSCNNNeuronTypeReLU ///< df/dx = [ 1, if x >= 0 [ a, if x < 0 MPSCNNNeuronTypeSigmoid ///< df/dx = e^x / (e^x + 1)^2 MPSCNNNeuronTypeHardSigmoid ///< df/dx = [ a, if (x >= 0) and (x <= 1) [ 0, otherwise MPSCNNNeuronTypeTanH ///< df/dx = a * b * (1 - tanh^2(b * x)) MPSCNNNeuronTypeAbsolute ///< df/dx = sign(x) MPSCNNNeuronTypeSoftPlus ///< df/dx = (a * b * exp(b * x)) / (exp(b * x) + 1) MPSCNNNeuronTypeSoftSign ///< df/dx = 1 / (|x| + 1)^2 MPSCNNNeuronTypeELU ///< df/dx = [ a * exp(x), x < 0 [ 1, x >= 0 MPSCNNNeuronTypePReLU ///< df/dx = [ 1, if x >= 0 [ aV, if x < 0 MPSCNNNeuronTypeReLUN ///< df/dx = [ 1, if x >= 0 [ a, if x < 0 [ b, if x >= b MPSCNNNeuronTypePower ///< df/dx = a * c * (a * x + b)^(c - 1) MPSCNNNeuronTypeExponential ///< df/dx = [ a * exp(a * x + b), if c == -1 [ a * log(c) * c^(a * x + b), if c != -1 MPSCNNNeuronTypeLogarithm ///< df/dx = [ a / (a * in + b), if c == -1 [ a / (log(c) * (a * in + b)), if c != -1

The result of the above operation is multiplied with the gradient, computed by the preceeding filter (going backwards).

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
Standard init with default properties per filter type

Parameters:
    device The device that the filter will be used on. May not be NULL.

Returns:
    A pointer to the newly initialized object. This will fail, returning nil if the device is not supported. Devices must be MTLFeatureSet_iOS_GPUFamily2_v1 or later.

Reimplemented from MPSCNNGradientKernel.

  - (nonnull instancetype) initWithDevice: (nonnull id< MTLDevice >) device(MPSNNNeuronDescriptor *_Nonnull) neuronDescriptor
Initialize the neuron gradient filter with a neuron descriptor.

Parameters:
    device The device the filter will run on.
    neuronDescriptor The neuron descriptor. For the neuron of type MPSCNNNeuronTypePReLU, the neuron descriptor references an NSData object containing a float array with the per feature channel value of PReLu parameter and, in this case, the MPSCNNNeuronGradient retains the NSData object.

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

Property Documentation

  - (float) a [read], [nonatomic], [assign]

  - (float) b [read], [nonatomic], [assign]

  - (float) c [read], [nonatomic], [assign]

  - (NSData*) data [read], [nonatomic], [retain]

  - (MPSCNNNeuronType) neuronType [read], [nonatomic], [assign]

Author

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