Package apple.metalperformanceshaders

Class MPSNNOptimizerAdam

  • All Implemented Interfaces:
    NSCoding, NSCopying, NSSecureCoding, NSObject
    public class MPSNNOptimizerAdam
extends MPSNNOptimizer
    MPSNNOptimizerAdam The MPSNNOptimizerAdam performs an Adam Update Initialization time m[0] = 0 (Initialize initial 1st moment vector aka momentum, user is responsible for this) v[0] = 0 (Initialize initial 2nd moment vector aka velocity, user is responsible for this) t = 0 (Initialize timestep) https://arxiv.org/abs/1412.6980 At update time: t = t + 1 lr[t] = learningRate * sqrt(1 - beta2^t) / (1 - beta1^t) m[t] = beta1 * m[t-1] + (1 - beta1) * g v[t] = beta2 * v[t-1] + (1 - beta2) * (g ^ 2) variable = variable - lr[t] * m[t] / (sqrt(v[t]) + epsilon) where, g is gradient of error wrt variable v[t] is velocity m[t] is momentum

    • Constructor Detail

      • MPSNNOptimizerAdam

        protected MPSNNOptimizerAdam​(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)

      • beta1

        public double beta1()
        [@property] beta1 The beta1 at which we update values Default value is 0.9

      • beta2

        public double beta2()
        [@property] beta2 The beta2 at which we update values Default value is 0.999

      • 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()

      • debugDescription_static

        public static java.lang.String debugDescription_static()

      • description_static

        public static java.lang.String description_static()

      • encodeToCommandBufferBatchNormalizationGradientStateBatchNormalizationSourceStateInputMomentumVectorsInputVelocityVectorsMaximumVelocityVectorsResultState

        public void encodeToCommandBufferBatchNormalizationGradientStateBatchNormalizationSourceStateInputMomentumVectorsInputVelocityVectorsMaximumVelocityVectorsResultState​(MTLCommandBuffer commandBuffer,
                                                                                                                                                                       MPSCNNBatchNormalizationState batchNormalizationGradientState,
                                                                                                                                                                       MPSCNNBatchNormalizationState batchNormalizationSourceState,
                                                                                                                                                                       NSArray<? extends MPSVector> inputMomentumVectors,
                                                                                                                                                                       NSArray<? extends MPSVector> inputVelocityVectors,
                                                                                                                                                                       NSArray<? extends MPSVector> maximumVelocityVectors,
                                                                                                                                                                       MPSCNNNormalizationGammaAndBetaState resultState)
        Encode an AMSGrad variant of MPSNNOptimizerAdam object to a command buffer to perform out of place update The following operations would be applied At update time: t = t + 1 lr[t] = learningRate * sqrt(1 - beta2^t) / (1 - beta1^t) m[t] = beta1 * m[t-1] + (1 - beta1) * g v[t] = beta2 * v[t-1] + (1 - beta2) * (g ^ 2) maxVel[t] = max(maxVel[t-1],v[t]) variable = variable - lr[t] * m[t] / (sqrt(maxVel[t]) + epsilon)
        Parameters:
        commandBuffer - A valid MTLCommandBuffer to receive the encoded kernel.
        batchNormalizationGradientState - A valid MPSCNNBatchNormalizationState object which specifies the input state with gradients for this update.
        batchNormalizationSourceState - A valid MPSCNNBatchNormalizationState object which specifies the input state with original gamma/beta for this update.
        inputMomentumVectors - An array MPSVector object which specifies the gradient momentum vectors which will be updated and overwritten. The index 0 corresponds to gamma, index 1 corresponds to beta, array can be of size 1 in which case beta won't be updated
        inputVelocityVectors - An array MPSVector object which specifies the gradient velocity vectors which will be updated and overwritten. The index 0 corresponds to gamma, index 1 corresponds to beta, array can be of size 1 in which case beta won't be updated
        maximumVelocityVectors - An array MPSVector object which specifies the maximum velocity vectors which will be updated and overwritten. The index 0 corresponds to weights, index 1 corresponds to biases, array can be of size 1 in which case biases won't be updated. May be nil, if nil then normal Adam optimizer behaviour is followed.
        resultState - A valid MPSCNNNormalizationGammaAndBetaState object which specifies the resultValues state which will be updated and overwritten.

      • encodeToCommandBufferBatchNormalizationGradientStateBatchNormalizationSourceStateInputMomentumVectorsInputVelocityVectorsResultState

        public void encodeToCommandBufferBatchNormalizationGradientStateBatchNormalizationSourceStateInputMomentumVectorsInputVelocityVectorsResultState​(MTLCommandBuffer commandBuffer,
                                                                                                                                                 MPSCNNBatchNormalizationState batchNormalizationGradientState,
                                                                                                                                                 MPSCNNBatchNormalizationState batchNormalizationSourceState,
                                                                                                                                                 NSArray<? extends MPSVector> inputMomentumVectors,
                                                                                                                                                 NSArray<? extends MPSVector> inputVelocityVectors,
                                                                                                                                                 MPSCNNNormalizationGammaAndBetaState resultState)
        Encode an MPSNNOptimizerAdam object to a command buffer to perform out of place update The following operations would be applied t = t + 1 lr[t] = learningRate * sqrt(1 - beta2^t) / (1 - beta1^t) m[t] = beta1 * m[t-1] + (1 - beta1) * g v[t] = beta2 * v[t-1] + (1 - beta2) * (g ^ 2) variable = variable - lr[t] * m[t] / (sqrt(v[t]) + epsilon)
        Parameters:
        commandBuffer - A valid MTLCommandBuffer to receive the encoded kernel.
        batchNormalizationGradientState - A valid MPSCNNBatchNormalizationState object which specifies the input state with gradients for this update.
        batchNormalizationSourceState - A valid MPSCNNBatchNormalizationState object which specifies the input state with original gamma/beta for this update.
        inputMomentumVectors - An array MPSVector object which specifies the gradient momentum vectors which will be updated and overwritten. The index 0 corresponds to gamma, index 1 corresponds to beta, array can be of size 1 in which case beta won't be updated
        inputVelocityVectors - An array MPSVector object which specifies the gradient velocity vectors which will be updated and overwritten. The index 0 corresponds to gamma, index 1 corresponds to beta, array can be of size 1 in which case beta won't be updated
        resultState - A valid MPSCNNNormalizationGammaAndBetaState object which specifies the resultValues state which will be updated and overwritten.

      • encodeToCommandBufferBatchNormalizationStateInputMomentumVectorsInputVelocityVectorsMaximumVelocityVectorsResultState

        public void encodeToCommandBufferBatchNormalizationStateInputMomentumVectorsInputVelocityVectorsMaximumVelocityVectorsResultState​(MTLCommandBuffer commandBuffer,
                                                                                                                                  MPSCNNBatchNormalizationState batchNormalizationState,
                                                                                                                                  NSArray<? extends MPSVector> inputMomentumVectors,
                                                                                                                                  NSArray<? extends MPSVector> inputVelocityVectors,
                                                                                                                                  NSArray<? extends MPSVector> maximumVelocityVectors,
                                                                                                                                  MPSCNNNormalizationGammaAndBetaState resultState)
        Encode an AMSGrad variant of MPSNNOptimizerAdam object to a command buffer to perform out of place update The following operations would be applied At update time: t = t + 1 lr[t] = learningRate * sqrt(1 - beta2^t) / (1 - beta1^t) m[t] = beta1 * m[t-1] + (1 - beta1) * g v[t] = beta2 * v[t-1] + (1 - beta2) * (g ^ 2) maxVel[t] = max(maxVel[t-1],v[t]) variable = variable - lr[t] * m[t] / (sqrt(maxVel[t]) + epsilon)
        Parameters:
        commandBuffer - A valid MTLCommandBuffer to receive the encoded kernel.
        batchNormalizationState - A valid MPSCNNBatchNormalizationState object which specifies the input state with gradients and original gamma/beta for this update.
        inputMomentumVectors - An array MPSVector object which specifies the gradient momentum vectors which will be updated and overwritten. The index 0 corresponds to gamma, index 1 corresponds to beta, array can be of size 1 in which case beta won't be updated
        inputVelocityVectors - An array MPSVector object which specifies the gradient velocity vectors which will be updated and overwritten. The index 0 corresponds to gamma, index 1 corresponds to beta, array can be of size 1 in which case beta won't be updated
        maximumVelocityVectors - An array MPSVector object which specifies the maximum velocity vectors which will be updated and overwritten. The index 0 corresponds to weights, index 1 corresponds to biases, array can be of size 1 in which case biases won't be updated. May be nil, if nil then normal Adam optimizer behaviour is followed.
        resultState - A valid MPSCNNNormalizationGammaAndBetaState object which specifies the resultValues state which will be updated and overwritten.

      • encodeToCommandBufferBatchNormalizationStateInputMomentumVectorsInputVelocityVectorsResultState

        public void encodeToCommandBufferBatchNormalizationStateInputMomentumVectorsInputVelocityVectorsResultState​(MTLCommandBuffer commandBuffer,
                                                                                                            MPSCNNBatchNormalizationState batchNormalizationState,
                                                                                                            NSArray<? extends MPSVector> inputMomentumVectors,
                                                                                                            NSArray<? extends MPSVector> inputVelocityVectors,
                                                                                                            MPSCNNNormalizationGammaAndBetaState resultState)
        Encode an MPSNNOptimizerAdam object to a command buffer to perform out of place update The following operations would be applied t = t + 1 lr[t] = learningRate * sqrt(1 - beta2^t) / (1 - beta1^t) m[t] = beta1 * m[t-1] + (1 - beta1) * g v[t] = beta2 * v[t-1] + (1 - beta2) * (g ^ 2) variable = variable - lr[t] * m[t] / (sqrt(v[t]) + epsilon)
        Parameters:
        commandBuffer - A valid MTLCommandBuffer to receive the encoded kernel.
        batchNormalizationState - A valid MPSCNNBatchNormalizationState object which specifies the input state with gradients and original gamma/beta for this update.
        inputMomentumVectors - An array MPSVector object which specifies the gradient momentum vectors which will be updated and overwritten. The index 0 corresponds to gamma, index 1 corresponds to beta, array can be of size 1 in which case beta won't be updated
        inputVelocityVectors - An array MPSVector object which specifies the gradient velocity vectors which will be updated and overwritten. The index 0 corresponds to gamma, index 1 corresponds to beta, array can be of size 1 in which case beta won't be updated
        resultState - A valid MPSCNNNormalizationGammaAndBetaState object which specifies the resultValues state which will be updated and overwritten.

      • encodeToCommandBufferConvolutionGradientStateConvolutionSourceStateInputMomentumVectorsInputVelocityVectorsMaximumVelocityVectorsResultState

        public void encodeToCommandBufferConvolutionGradientStateConvolutionSourceStateInputMomentumVectorsInputVelocityVectorsMaximumVelocityVectorsResultState​(MTLCommandBuffer commandBuffer,
                                                                                                                                                         MPSCNNConvolutionGradientState convolutionGradientState,
                                                                                                                                                         MPSCNNConvolutionWeightsAndBiasesState convolutionSourceState,
                                                                                                                                                         NSArray<? extends MPSVector> inputMomentumVectors,
                                                                                                                                                         NSArray<? extends MPSVector> inputVelocityVectors,
                                                                                                                                                         NSArray<? extends MPSVector> maximumVelocityVectors,
                                                                                                                                                         MPSCNNConvolutionWeightsAndBiasesState resultState)
        Encode an AMSGrad variant of MPSNNOptimizerAdam object to a command buffer to perform out of place update The following operations would be applied At update time: t = t + 1 lr[t] = learningRate * sqrt(1 - beta2^t) / (1 - beta1^t) m[t] = beta1 * m[t-1] + (1 - beta1) * g v[t] = beta2 * v[t-1] + (1 - beta2) * (g ^ 2) maxVel[t] = max(maxVel[t-1],v[t]) variable = variable - lr[t] * m[t] / (sqrt(maxVel[t]) + epsilon)
        Parameters:
        commandBuffer - A valid MTLCommandBuffer to receive the encoded kernel.
        convolutionGradientState - A valid MPSCNNConvolutionGradientState object which specifies the input state with gradients for this update.
        convolutionSourceState - A valid MPSCNNConvolutionWeightsAndBiasesState object which specifies the input state with values to be updated.
        inputMomentumVectors - An array MPSVector object which specifies the gradient momentum vectors which will be updated and overwritten. The index 0 corresponds to weights, index 1 corresponds to biases, array can be of size 1 in which case biases won't be updated
        inputVelocityVectors - An array MPSVector object which specifies the gradient velocity vectors which will be updated and overwritten. The index 0 corresponds to weights, index 1 corresponds to biases, array can be of size 1 in which case biases won't be updated
        maximumVelocityVectors - An array MPSVector object which specifies the maximum velocity vectors which will be updated and overwritten. The index 0 corresponds to weights, index 1 corresponds to biases, array can be of size 1 in which case biases won't be updated. May be nil, if nil then normal Adam optimizer behaviour is followed.
        resultState - A valid MPSCNNConvolutionWeightsAndBiasesState object which specifies the resultValues state which will be updated and overwritten.

      • encodeToCommandBufferConvolutionGradientStateConvolutionSourceStateInputMomentumVectorsInputVelocityVectorsResultState

        public void encodeToCommandBufferConvolutionGradientStateConvolutionSourceStateInputMomentumVectorsInputVelocityVectorsResultState​(MTLCommandBuffer commandBuffer,
                                                                                                                                   MPSCNNConvolutionGradientState convolutionGradientState,
                                                                                                                                   MPSCNNConvolutionWeightsAndBiasesState convolutionSourceState,
                                                                                                                                   NSArray<? extends MPSVector> inputMomentumVectors,
                                                                                                                                   NSArray<? extends MPSVector> inputVelocityVectors,
                                                                                                                                   MPSCNNConvolutionWeightsAndBiasesState resultState)
        Encode an MPSNNOptimizerAdam object to a command buffer to perform out of place update The following operations would be applied t = t + 1 lr[t] = learningRate * sqrt(1 - beta2^t) / (1 - beta1^t) m[t] = beta1 * m[t-1] + (1 - beta1) * g v[t] = beta2 * v[t-1] + (1 - beta2) * (g ^ 2) variable = variable - lr[t] * m[t] / (sqrt(v[t]) + epsilon)
        Parameters:
        commandBuffer - A valid MTLCommandBuffer to receive the encoded kernel.
        convolutionGradientState - A valid MPSCNNConvolutionGradientState object which specifies the input state with gradients for this update.
        convolutionSourceState - A valid MPSCNNConvolutionWeightsAndBiasesState object which specifies the input state with values to be updated.
        inputMomentumVectors - An array MPSVector object which specifies the gradient momentum vectors which will be updated and overwritten. The index 0 corresponds to weights, index 1 corresponds to biases, array can be of size 1 in which case biases won't be updated
        inputVelocityVectors - An array MPSVector object which specifies the gradient velocity vectors which will be updated and overwritten. The index 0 corresponds to weights, index 1 corresponds to biases, array can be of size 1 in which case biases won't be updated
        resultState - A valid MPSCNNConvolutionWeightsAndBiasesState object which specifies the resultValues state which will be updated and overwritten.

      • encodeToCommandBufferInputGradientMatrixInputValuesMatrixInputMomentumMatrixInputVelocityMatrixMaximumVelocityMatrixResultValuesMatrix

        public void encodeToCommandBufferInputGradientMatrixInputValuesMatrixInputMomentumMatrixInputVelocityMatrixMaximumVelocityMatrixResultValuesMatrix​(MTLCommandBuffer commandBuffer,
                                                                                                                                                   MPSMatrix inputGradientMatrix,
                                                                                                                                                   MPSMatrix inputValuesMatrix,
                                                                                                                                                   MPSMatrix inputMomentumMatrix,
                                                                                                                                                   MPSMatrix inputVelocityMatrix,
                                                                                                                                                   MPSMatrix maximumVelocityMatrix,
                                                                                                                                                   MPSMatrix resultValuesMatrix)

      • encodeToCommandBufferInputGradientMatrixInputValuesMatrixInputMomentumMatrixInputVelocityMatrixResultValuesMatrix

        public void encodeToCommandBufferInputGradientMatrixInputValuesMatrixInputMomentumMatrixInputVelocityMatrixResultValuesMatrix​(MTLCommandBuffer commandBuffer,
                                                                                                                              MPSMatrix inputGradientMatrix,
                                                                                                                              MPSMatrix inputValuesMatrix,
                                                                                                                              MPSMatrix inputMomentumMatrix,
                                                                                                                              MPSMatrix inputVelocityMatrix,
                                                                                                                              MPSMatrix resultValuesMatrix)

      • encodeToCommandBufferInputGradientVectorInputValuesVectorInputMomentumVectorInputVelocityVectorMaximumVelocityVectorResultValuesVector

        public void encodeToCommandBufferInputGradientVectorInputValuesVectorInputMomentumVectorInputVelocityVectorMaximumVelocityVectorResultValuesVector​(MTLCommandBuffer commandBuffer,
                                                                                                                                                   MPSVector inputGradientVector,
                                                                                                                                                   MPSVector inputValuesVector,
                                                                                                                                                   MPSVector inputMomentumVector,
                                                                                                                                                   MPSVector inputVelocityVector,
                                                                                                                                                   MPSVector maximumVelocityVector,
                                                                                                                                                   MPSVector resultValuesVector)
        Encode an AMSGrad variant of MPSNNOptimizerAdam object to a command buffer to perform out of place update The following operations would be applied At update time: t = t + 1 lr[t] = learningRate * sqrt(1 - beta2^t) / (1 - beta1^t) m[t] = beta1 * m[t-1] + (1 - beta1) * g v[t] = beta2 * v[t-1] + (1 - beta2) * (g ^ 2) maxVel[t] = max(maxVel[t-1],v[t]) variable = variable - lr[t] * m[t] / (sqrt(maxVel[t]) + epsilon)
        Parameters:
        commandBuffer - A valid MTLCommandBuffer to receive the encoded kernel.
        inputGradientVector - A valid MPSVector object which specifies the input vector of gradients for this update.
        inputValuesVector - A valid MPSVector object which specifies the input vector of values to be updated.
        inputMomentumVector - A valid MPSVector object which specifies the gradient momentum vector which will be updated and overwritten.
        inputVelocityVector - A valid MPSVector object which specifies the gradient velocity vector which will be updated and overwritten.
        maximumVelocityVector - A valid MPSVector object which specifies the maximum velocity vector which will be updated and overwritten. May be nil, if nil then normal Adam optimizer behaviour is followed.
        resultValuesVector - A valid MPSCNNConvolutionWeightsAndBiasesState object which specifies the resultValues state which will be updated and overwritten.

      • encodeToCommandBufferInputGradientVectorInputValuesVectorInputMomentumVectorInputVelocityVectorResultValuesVector

        public void encodeToCommandBufferInputGradientVectorInputValuesVectorInputMomentumVectorInputVelocityVectorResultValuesVector​(MTLCommandBuffer commandBuffer,
                                                                                                                              MPSVector inputGradientVector,
                                                                                                                              MPSVector inputValuesVector,
                                                                                                                              MPSVector inputMomentumVector,
                                                                                                                              MPSVector inputVelocityVector,
                                                                                                                              MPSVector resultValuesVector)
        Encode an MPSNNOptimizerAdam object to a command buffer to perform out of place update The following operations would be applied t = t + 1 lr[t] = learningRate * sqrt(1 - beta2^t) / (1 - beta1^t) m[t] = beta1 * m[t-1] + (1 - beta1) * g v[t] = beta2 * v[t-1] + (1 - beta2) * (g ^ 2) variable = variable - lr[t] * m[t] / (sqrt(v[t]) + epsilon)
        Parameters:
        commandBuffer - A valid MTLCommandBuffer to receive the encoded kernel.
        inputGradientVector - A valid MPSVector object which specifies the input vector of gradients for this update.
        inputValuesVector - A valid MPSVector object which specifies the input vector of values to be updated.
        inputMomentumVector - A valid MPSVector object which specifies the gradient momentum vector which will be updated and overwritten.
        inputVelocityVector - A valid MPSVector object which specifies the gradient velocity vector which will be updated and overwritten.
        resultValuesVector - A valid MPSVector object which specifies the resultValues vector which will be updated and overwritten.

      • epsilon

        public float epsilon()
        [@property] epsilon The epsilon at which we update values This value is usually used to ensure to avoid divide by 0, default value is 1e-8

      • hash_static

        public static long hash_static()

      • initWithCoderDevice

        public MPSNNOptimizerAdam initWithCoderDevice​(NSCoder aDecoder,
                                              java.lang.Object device)
        Description copied from class: MPSKernel
        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.
        Overrides:
        initWithCoderDevice in class MPSNNOptimizer
        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.

      • initWithDevice

        public MPSNNOptimizerAdam initWithDevice​(java.lang.Object device)
        Description copied from class: MPSKernel
        Standard init with default properties per filter type
        Overrides:
        initWithDevice in class MPSNNOptimizer
        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.

      • initWithDeviceBeta1Beta2EpsilonTimeStepOptimizerDescriptor

        public MPSNNOptimizerAdam initWithDeviceBeta1Beta2EpsilonTimeStepOptimizerDescriptor​(MTLDevice device,
                                                                                     double beta1,
                                                                                     double beta2,
                                                                                     float epsilon,
                                                                                     long timeStep,
                                                                                     MPSNNOptimizerDescriptor optimizerDescriptor)
        Full initialization for the adam update
        Parameters:
        device - The device on which the kernel will execute.
        beta1 - The beta1 to update values
        beta2 - The beta2 to update values
        epsilon - The epsilon at which we update values
        timeStep - The timeStep at which values will start updating
        optimizerDescriptor - The optimizerDescriptor which will have a bunch of properties to be applied
        Returns:
        A valid MPSNNOptimizerAdam object or nil, if failure.

      • initWithDeviceLearningRate

        public MPSNNOptimizerAdam initWithDeviceLearningRate​(MTLDevice device,
                                                     float learningRate)
        Convenience initialization for the adam update
        Parameters:
        device - The device on which the kernel will execute.
        learningRate - The learningRate at which we will update values
        Returns:
        A valid MPSNNOptimizerAdam object or nil, if failure.

      • 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()

      • resolveClassMethod

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

      • resolveInstanceMethod

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

      • setTimeStep

        public void setTimeStep​(long value)
        [@property] timeStep Current timeStep for the update, number of times update has occurred

      • setVersion_static

        public static void setVersion_static​(long aVersion)

      • superclass_static

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

      • supportsSecureCoding

        public static boolean supportsSecureCoding()

      • _supportsSecureCoding

        public boolean _supportsSecureCoding()
        Description copied from interface: NSSecureCoding
        This property must return YES on all classes that allow secure coding. Subclasses of classes that adopt NSSecureCoding and override initWithCoder: must also override this method and return YES. The Secure Coding Guide should be consulted when writing methods that decode data.
        Specified by:
        _supportsSecureCoding in interface NSSecureCoding
        Overrides:
        _supportsSecureCoding in class MPSNNOptimizer

      • timeStep

        public long timeStep()
        [@property] timeStep Current timeStep for the update, number of times update has occurred

      • version_static

        public static long version_static()