Package apple.metalperformanceshaders

Class MPSRNNSingleGateDescriptor

  • All Implemented Interfaces:
    NSObject
    public class MPSRNNSingleGateDescriptor
extends MPSRNNDescriptor
    MPSRNNSingleGateDescriptor [@dependency] This depends on Metal.framework The MPSRNNSingleGateDescriptor specifies a simple recurrent block/layer descriptor. The RNN layer initialized with a MPSRNNSingleGateDescriptor transforms the input data (image or matrix), and previous output with a set of filters, each producing one feature map in the new output data. The user may provide the RNN unit a single input or a sequence of inputs. Description of operation: Let x_j be the input data (at time index t of sequence, j index containing quadruplet: batch index, x,y and feature index (x=y=0 for matrices)). Let h0_j be the recurrent input (previous output) data from previous time step (at time index t-1 of sequence). Let h1_i be the output data produced at this time step. Let W_ij, U_ij be the weights for input and recurrent input data respectively Let b_i be a bias term Let gi(x) be a neuron activation function Then the new output image h1_i data is computed as follows: h1_i = gi( W_ij * x_j + U_ij * h0_j + b_i ) The '*' stands for convolution (see @ref MPSRNNImageInferenceLayer) or matrix-vector/matrix multiplication (see @ref MPSRNNMatrixInferenceLayer). Summation is over index j (except for the batch index), but there is no summation over repeated index i - the output index. Note that for validity all intermediate images have to be of same size and the U matrix has to be square (ie. outputFeatureChannels == inputFeatureChannels in those). Also the bias terms are scalars wrt. spatial dimensions.

    • Constructor Detail

      • MPSRNNSingleGateDescriptor

        protected MPSRNNSingleGateDescriptor​(org.moe.natj.general.Pointer peer)

    • Method Detail

      • accessInstanceVariablesDirectly

        public static boolean accessInstanceVariablesDirectly()

      • allocWithZone

        public static java.lang.Object allocWithZone​(org.moe.natj.general.ptr.VoidPtr zone)

      • automaticallyNotifiesObserversForKey

        public static boolean automaticallyNotifiesObserversForKey​(java.lang.String key)

      • cancelPreviousPerformRequestsWithTarget

        public static void cancelPreviousPerformRequestsWithTarget​(java.lang.Object aTarget)

      • cancelPreviousPerformRequestsWithTargetSelectorObject

        public static void cancelPreviousPerformRequestsWithTargetSelectorObject​(java.lang.Object aTarget,
                                                                         org.moe.natj.objc.SEL aSelector,
                                                                         java.lang.Object anArgument)

      • classFallbacksForKeyedArchiver

        public static NSArray<java.lang.String> classFallbacksForKeyedArchiver()

      • classForKeyedUnarchiver

        public static org.moe.natj.objc.Class classForKeyedUnarchiver()

      • createRNNSingleGateDescriptorWithInputFeatureChannelsOutputFeatureChannels

        public static java.lang.Object createRNNSingleGateDescriptorWithInputFeatureChannelsOutputFeatureChannels​(long inputFeatureChannels,
                                                                                                          long outputFeatureChannels)
        Creates a MPSRNNSingleGateDescriptor
        Parameters:
        inputFeatureChannels - The number of feature channels in the input image/matrix. Must be >= 1.
        outputFeatureChannels - The number of feature channels in the output image/matrix. Must be >= 1.
        Returns:
        A valid MPSRNNSingleGateDescriptor object or nil, if failure.

      • debugDescription_static

        public static java.lang.String debugDescription_static()

      • description_static

        public static java.lang.String description_static()

      • hash_static

        public static long hash_static()

      • inputWeights

        public MPSCNNConvolutionDataSource inputWeights()
        [@property] inputWeights Contains weights 'W_ij', bias 'b_i' and neuron 'gi' from the simple RNN layer formula. If nil then assumed zero weights, bias and no neuron (identity mapping). Defaults to nil.

      • instanceMethodSignatureForSelector

        public static NSMethodSignature instanceMethodSignatureForSelector​(org.moe.natj.objc.SEL aSelector)

      • instancesRespondToSelector

        public static boolean instancesRespondToSelector​(org.moe.natj.objc.SEL aSelector)

      • isSubclassOfClass

        public static boolean isSubclassOfClass​(org.moe.natj.objc.Class aClass)

      • keyPathsForValuesAffectingValueForKey

        public static NSSet<java.lang.String> keyPathsForValuesAffectingValueForKey​(java.lang.String key)

      • new_objc

        public static java.lang.Object new_objc()

      • recurrentWeights

        public MPSCNNConvolutionDataSource recurrentWeights()
        [@property] recurrentWeights Contains weights 'U_ij' from the simple RNN layer formula. If nil then assumed zero weights. Defaults to nil.

      • resolveClassMethod

        public static boolean resolveClassMethod​(org.moe.natj.objc.SEL sel)

      • resolveInstanceMethod

        public static boolean resolveInstanceMethod​(org.moe.natj.objc.SEL sel)

      • setInputWeights

        public void setInputWeights​(MPSCNNConvolutionDataSource value)
        [@property] inputWeights Contains weights 'W_ij', bias 'b_i' and neuron 'gi' from the simple RNN layer formula. If nil then assumed zero weights, bias and no neuron (identity mapping). Defaults to nil.

      • setRecurrentWeights

        public void setRecurrentWeights​(MPSCNNConvolutionDataSource value)
        [@property] recurrentWeights Contains weights 'U_ij' from the simple RNN layer formula. If nil then assumed zero weights. Defaults to nil.

      • setVersion_static

        public static void setVersion_static​(long aVersion)

      • superclass_static

        public static org.moe.natj.objc.Class superclass_static()

      • version_static

        public static long version_static()