Uses of Class
org.robovm.apple.mlcompute.MLCTensor

Packages that use MLCTensor

Package	Description
org.robovm.apple.mlcompute

Uses of MLCTensor in org.robovm.apple.mlcompute

Methods in org.robovm.apple.mlcompute that return MLCTensor

Modifier and Type	Method	Description
MLCTensor	MLCTrainingGraph.allocateUserGradientForTensor(MLCTensor tensor)
MLCTensor	MLCGraph.concatenate(NSArray<MLCTensor> sources, long dimension)
MLCTensor	MLCGraph.gather(long dimension, MLCTensor source, MLCTensor indices)
MLCTensor	MLCBatchNormalizationLayer.getBeta()
MLCTensor	MLCGroupNormalizationLayer.getBeta()
MLCTensor	MLCInstanceNormalizationLayer.getBeta()
MLCTensor	MLCLayerNormalizationLayer.getBeta()
MLCTensor	MLCConvolutionLayer.getBiases()
MLCTensor	MLCFullyConnectedLayer.getBiases()
MLCTensor	MLCBatchNormalizationLayer.getGamma()
MLCTensor	MLCGroupNormalizationLayer.getGamma()
MLCTensor	MLCInstanceNormalizationLayer.getGamma()
MLCTensor	MLCLayerNormalizationLayer.getGamma()
MLCTensor	MLCBatchNormalizationLayer.getMean()
MLCTensor	MLCInstanceNormalizationLayer.getMean()
MLCTensor	MLCTensorParameter.getTensor()
MLCTensor	MLCBatchNormalizationLayer.getVariance()
MLCTensor	MLCInstanceNormalizationLayer.getVariance()
MLCTensor	MLCConvolutionLayer.getWeights()
MLCTensor	MLCEmbeddingLayer.getWeights()
MLCTensor	MLCFullyConnectedLayer.getWeights()
MLCTensor	MLCLossLayer.getWeights()
MLCTensor	MLCTrainingGraph.gradientTensorForInput(MLCTensor input)
MLCTensor	MLCGraph.node(MLCLayer layer, NSArray<MLCTensor> sources)
MLCTensor	MLCGraph.node(MLCLayer layer, NSArray<MLCTensor> sources, boolean disableUpdate)
MLCTensor	MLCGraph.node(MLCLayer layer, NSArray<MLCTensor> sources, NSArray<MLCTensor> lossLabels)
MLCTensor	MLCGraph.node(MLCLayer layer, MLCTensor source)
MLCTensor	MLCGraph.reshape(NSArray<NSNumber> shape, MLCTensor source)
MLCTensor	MLCGraph.scatter(long dimension, MLCTensor source, MLCTensor indices, MLCTensor copyFrom, MLCReductionType reductionType)
MLCTensor	MLCGraph.select(NSArray<MLCTensor> sources, MLCTensor condition)
MLCTensor	MLCTensor.tensorByDequantizingToType(MLCDataType type, MLCTensor scale, MLCTensor bias)
MLCTensor	MLCTensor.tensorByDequantizingToType(MLCDataType type, MLCTensor scale, MLCTensor bias, long axis)
MLCTensor	MLCTensor.tensorByQuantizingToType(MLCDataType type, float scale, long bias)
MLCTensor	MLCTensor.tensorByQuantizingToType(MLCDataType type, MLCTensor scale, MLCTensor bias, long axis)
MLCTensor	MLCGraph.transpose(NSArray<NSNumber> dimensions, MLCTensor source)

Methods in org.robovm.apple.mlcompute that return types with arguments of type MLCTensor

Modifier and Type	Method	Description
NSArray<MLCTensor>	MLCMultiheadAttentionLayer.getAttentionBiases()
NSArray<MLCTensor>	MLCLSTMLayer.getBiases()
NSArray<MLCTensor>	MLCMultiheadAttentionLayer.getBiases()
NSArray<MLCTensor>	MLCLSTMLayer.getHiddenWeights()
NSArray<MLCTensor>	MLCLSTMLayer.getInputWeights()
NSArray<MLCTensor>	MLCLSTMLayer.getPeepholeWeights()
NSArray<MLCTensor>	MLCMultiheadAttentionLayer.getWeights()
NSArray<MLCTensor>	MLCTrainingGraph.resultGradientTensorsForLayer(MLCLayer layer)
NSArray<MLCTensor>	MLCGraph.resultTensorsForLayer(MLCLayer layer)
NSArray<MLCTensor>	MLCTrainingGraph.sourceGradientTensorsForLayer(MLCLayer layer)
NSArray<MLCTensor>	MLCGraph.sourceTensorsForLayer(MLCLayer layer)
NSArray<MLCTensor>	MLCGraph.split(MLCTensor source, long splitCount, long dimension)
NSArray<MLCTensor>	MLCGraph.split(MLCTensor source, NSArray<NSNumber> splitSectionLengths, long dimension)

Methods in org.robovm.apple.mlcompute with parameters of type MLCTensor

Modifier and Type	Method	Description
MLCTensor	MLCTrainingGraph.allocateUserGradientForTensor(MLCTensor tensor)
boolean	MLCTrainingGraph.bindOptimizerData(NSArray<MLCTensorData> data, NSArray<MLCTensorOptimizerDeviceData> deviceData, MLCTensor tensor)
protected static long	MLCBatchNormalizationLayer.create(long featureChannelCount, MLCTensor mean, MLCTensor variance, MLCTensor beta, MLCTensor gamma, float varianceEpsilon)
protected static long	MLCBatchNormalizationLayer.create(long featureChannelCount, MLCTensor mean, MLCTensor variance, MLCTensor beta, MLCTensor gamma, float varianceEpsilon, float momentum)
protected static long	MLCConvolutionLayer.create(MLCTensor weights, MLCTensor biases, MLCConvolutionDescriptor descriptor)
protected static long	MLCEmbeddingLayer.create(MLCEmbeddingDescriptor descriptor, MLCTensor weights)
protected static long	MLCFullyConnectedLayer.create(MLCTensor weights, MLCTensor biases, MLCConvolutionDescriptor descriptor)
protected static long	MLCGroupNormalizationLayer.create(long featureChannelCount, long groupCount, MLCTensor beta, MLCTensor gamma, float varianceEpsilon)
protected static long	MLCInstanceNormalizationLayer.create(long featureChannelCount, MLCTensor beta, MLCTensor gamma, float varianceEpsilon)
protected static long	MLCInstanceNormalizationLayer.create(long featureChannelCount, MLCTensor beta, MLCTensor gamma, float varianceEpsilon, float momentum)
protected static long	MLCInstanceNormalizationLayer.create(long featureChannelCount, MLCTensor mean, MLCTensor variance, MLCTensor beta, MLCTensor gamma, float varianceEpsilon, float momentum)
protected static long	MLCLayerNormalizationLayer.create(NSArray<NSNumber> normalizedShape, MLCTensor beta, MLCTensor gamma, float varianceEpsilon)
protected static long	MLCLossLayer.create(MLCLossDescriptor lossDescriptor, MLCTensor weights)
protected static long	MLCTensorParameter.create(MLCTensor tensor)
protected static long	MLCTensorParameter.create(MLCTensor tensor, NSArray<MLCTensorData> optimizerData)
static MLCLossLayer	MLCLossLayer.createCategoricalCrossEntropyLoss(MLCReductionType reductionType, float labelSmoothing, long classCount, MLCTensor weights)
static MLCYOLOLossLayer	MLCYOLOLossLayer.createCategoricalCrossEntropyLoss(MLCReductionType reductionType, float labelSmoothing, long classCount, MLCTensor weights)
static MLCLossLayer	MLCLossLayer.createCosineDistanceLoss(MLCReductionType reductionType, MLCTensor weights)
static MLCYOLOLossLayer	MLCYOLOLossLayer.createCosineDistanceLoss(MLCReductionType reductionType, MLCTensor weights)
static MLCLossLayer	MLCLossLayer.createHingeLoss(MLCReductionType reductionType, MLCTensor weights)
static MLCYOLOLossLayer	MLCYOLOLossLayer.createHingeLoss(MLCReductionType reductionType, MLCTensor weights)
static MLCLossLayer	MLCLossLayer.createHuberLoss(MLCReductionType reductionType, float delta, MLCTensor weights)
static MLCYOLOLossLayer	MLCYOLOLossLayer.createHuberLoss(MLCReductionType reductionType, float delta, MLCTensor weights)
static MLCLossLayer	MLCLossLayer.createLogLoss(MLCReductionType reductionType, float epsilon, MLCTensor weights)
static MLCYOLOLossLayer	MLCYOLOLossLayer.createLogLoss(MLCReductionType reductionType, float epsilon, MLCTensor weights)
static MLCLossLayer	MLCLossLayer.createMeanAbsoluteErrorLoss(MLCReductionType reductionType, MLCTensor weights)
static MLCYOLOLossLayer	MLCYOLOLossLayer.createMeanAbsoluteErrorLoss(MLCReductionType reductionType, MLCTensor weights)
static MLCLossLayer	MLCLossLayer.createMeanSquaredErrorLoss(MLCReductionType reductionType, MLCTensor weights)
static MLCYOLOLossLayer	MLCYOLOLossLayer.createMeanSquaredErrorLoss(MLCReductionType reductionType, MLCTensor weights)
static MLCLossLayer	MLCLossLayer.createSigmoidCrossEntropyLoss(MLCReductionType reductionType, float labelSmoothing, MLCTensor weights)
static MLCYOLOLossLayer	MLCYOLOLossLayer.createSigmoidCrossEntropyLoss(MLCReductionType reductionType, float labelSmoothing, MLCTensor weights)
static MLCLossLayer	MLCLossLayer.createSoftmaxCrossEntropyLoss(MLCReductionType reductionType, float labelSmoothing, long classCount, MLCTensor weights)
static MLCYOLOLossLayer	MLCYOLOLossLayer.createSoftmaxCrossEntropyLoss(MLCReductionType reductionType, float labelSmoothing, long classCount, MLCTensor weights)
MLCTensor	MLCGraph.gather(long dimension, MLCTensor source, MLCTensor indices)
NSData	MLCTrainingGraph.gradientData(MLCTensor parameter, MLCLayer layer)
MLCTensor	MLCTrainingGraph.gradientTensorForInput(MLCTensor input)
MLCTensor	MLCGraph.node(MLCLayer layer, MLCTensor source)
MLCTensor	MLCGraph.reshape(NSArray<NSNumber> shape, MLCTensor source)
MLCTensor	MLCGraph.scatter(long dimension, MLCTensor source, MLCTensor indices, MLCTensor copyFrom, MLCReductionType reductionType)
MLCTensor	MLCGraph.select(NSArray<MLCTensor> sources, MLCTensor condition)
NSArray<MLCTensor>	MLCGraph.split(MLCTensor source, long splitCount, long dimension)
NSArray<MLCTensor>	MLCGraph.split(MLCTensor source, NSArray<NSNumber> splitSectionLengths, long dimension)
MLCTensor	MLCTensor.tensorByDequantizingToType(MLCDataType type, MLCTensor scale, MLCTensor bias)
MLCTensor	MLCTensor.tensorByDequantizingToType(MLCDataType type, MLCTensor scale, MLCTensor bias, long axis)
MLCTensor	MLCTensor.tensorByQuantizingToType(MLCDataType type, MLCTensor scale, MLCTensor bias, long axis)
MLCTensor	MLCGraph.transpose(NSArray<NSNumber> dimensions, MLCTensor source)

Method parameters in org.robovm.apple.mlcompute with type arguments of type MLCTensor

Modifier and Type	Method	Description
boolean	MLCInferenceGraph.addInputs(NSDictionary<NSString,MLCTensor> inputs)
boolean	MLCInferenceGraph.addInputs(NSDictionary<NSString,MLCTensor> inputs, NSDictionary<NSString,MLCTensor> lossLabels, NSDictionary<NSString,MLCTensor> lossLabelWeights)
boolean	MLCTrainingGraph.addInputs(NSDictionary<NSString,MLCTensor> inputs, NSDictionary<NSString,MLCTensor> lossLabels)
boolean	MLCTrainingGraph.addInputs(NSDictionary<NSString,MLCTensor> inputs, NSDictionary<NSString,MLCTensor> lossLabels, NSDictionary<NSString,MLCTensor> lossLabelWeights)
boolean	MLCInferenceGraph.addOutputs(NSDictionary<NSString,MLCTensor> outputs)
boolean	MLCTrainingGraph.addOutputs(NSDictionary<NSString,MLCTensor> outputs)
boolean	MLCInferenceGraph.compile(MLCGraphCompilationOptions options, MLCDevice device, NSDictionary<NSString,MLCTensor> inputTensors, NSDictionary<NSString,MLCTensorData> inputTensorsData)
boolean	MLCTrainingGraph.compile(MLCGraphCompilationOptions options, MLCDevice device, NSDictionary<NSString,MLCTensor> inputTensors, NSDictionary<NSString,MLCTensorData> inputTensorsData)
MLCTensor	MLCGraph.concatenate(NSArray<MLCTensor> sources, long dimension)
protected static long	MLCLSTMLayer.create(MLCLSTMDescriptor descriptor, NSArray<MLCTensor> inputWeights, NSArray<MLCTensor> hiddenWeights, NSArray<MLCTensor> biases)
protected static long	MLCLSTMLayer.create(MLCLSTMDescriptor descriptor, NSArray<MLCTensor> inputWeights, NSArray<MLCTensor> hiddenWeights, NSArray<MLCTensor> peepholeWeights, NSArray<MLCTensor> biases)
protected static long	MLCLSTMLayer.create(MLCLSTMDescriptor descriptor, NSArray<MLCTensor> inputWeights, NSArray<MLCTensor> hiddenWeights, NSArray<MLCTensor> peepholeWeights, NSArray<MLCTensor> biases, NSArray<MLCActivationDescriptor> gateActivations, MLCActivationDescriptor outputResultActivation)
protected static long	MLCMultiheadAttentionLayer.create(MLCMultiheadAttentionDescriptor descriptor, NSArray<MLCTensor> weights, NSArray<MLCTensor> biases, NSArray<MLCTensor> attentionBiases)
boolean	MLCInferenceGraph.execute(NSDictionary<NSString,MLCTensorData> inputsData, long batchSize, MLCExecutionOptions options, VoidBlock3<MLCTensor,NSError,Double> completionHandler)
boolean	MLCInferenceGraph.execute(NSDictionary<NSString,MLCTensorData> inputsData, NSDictionary<NSString,MLCTensorData> outputsData, long batchSize, MLCExecutionOptions options, VoidBlock3<MLCTensor,NSError,Double> completionHandler)
boolean	MLCInferenceGraph.execute(NSDictionary<NSString,MLCTensorData> inputsData, NSDictionary<NSString,MLCTensorData> lossLabelsData, NSDictionary<NSString,MLCTensorData> lossLabelWeightsData, long batchSize, MLCExecutionOptions options, VoidBlock3<MLCTensor,NSError,Double> completionHandler)
boolean	MLCInferenceGraph.execute(NSDictionary<NSString,MLCTensorData> inputsData, NSDictionary<NSString,MLCTensorData> lossLabelsData, NSDictionary<NSString,MLCTensorData> lossLabelWeightsData, NSDictionary<NSString,MLCTensorData> outputsData, long batchSize, MLCExecutionOptions options, VoidBlock3<MLCTensor,NSError,Double> completionHandler)
boolean	MLCTrainingGraph.execute(NSDictionary<NSString,MLCTensorData> inputsData, NSDictionary<NSString,MLCTensorData> lossLabelsData, NSDictionary<NSString,MLCTensorData> lossLabelWeightsData, long batchSize, MLCExecutionOptions options, VoidBlock3<MLCTensor,NSError,Double> completionHandler)
boolean	MLCTrainingGraph.execute(NSDictionary<NSString,MLCTensorData> inputsData, NSDictionary<NSString,MLCTensorData> lossLabelsData, NSDictionary<NSString,MLCTensorData> lossLabelWeightsData, NSDictionary<NSString,MLCTensorData> outputsData, long batchSize, MLCExecutionOptions options, VoidBlock3<MLCTensor,NSError,Double> completionHandler)
boolean	MLCTrainingGraph.executeForward(long batchSize, MLCExecutionOptions options, NSDictionary<NSString,MLCTensorData> outputsData, VoidBlock3<MLCTensor,NSError,Double> completionHandler)
boolean	MLCTrainingGraph.executeForward(long batchSize, MLCExecutionOptions options, VoidBlock3<MLCTensor,NSError,Double> completionHandler)
boolean	MLCTrainingGraph.executeGradient(long batchSize, MLCExecutionOptions options, NSDictionary<NSString,MLCTensorData> outputsData, VoidBlock3<MLCTensor,NSError,Double> completionHandler)
boolean	MLCTrainingGraph.executeGradient(long batchSize, MLCExecutionOptions options, VoidBlock3<MLCTensor,NSError,Double> completionHandler)
boolean	MLCTrainingGraph.executeOptimizerUpdate(MLCExecutionOptions options, VoidBlock3<MLCTensor,NSError,Double> completionHandler)
MLCTensor	MLCGraph.node(MLCLayer layer, NSArray<MLCTensor> sources)
MLCTensor	MLCGraph.node(MLCLayer layer, NSArray<MLCTensor> sources, boolean disableUpdate)
MLCTensor	MLCGraph.node(MLCLayer layer, NSArray<MLCTensor> sources, NSArray<MLCTensor> lossLabels)
MLCTensor	MLCGraph.select(NSArray<MLCTensor> sources, MLCTensor condition)
boolean	MLCTrainingGraph.stopGradientForTensors(NSArray<MLCTensor> tensors)

Constructors in org.robovm.apple.mlcompute with parameters of type MLCTensor

Constructor	Description
MLCBatchNormalizationLayer(long featureChannelCount, MLCTensor mean, MLCTensor variance, MLCTensor beta, MLCTensor gamma, float varianceEpsilon)
MLCBatchNormalizationLayer(long featureChannelCount, MLCTensor mean, MLCTensor variance, MLCTensor beta, MLCTensor gamma, float varianceEpsilon, float momentum)
MLCConvolutionLayer(MLCTensor weights, MLCTensor biases, MLCConvolutionDescriptor descriptor)
MLCEmbeddingLayer(MLCEmbeddingDescriptor descriptor, MLCTensor weights)
MLCFullyConnectedLayer(MLCTensor weights, MLCTensor biases, MLCConvolutionDescriptor descriptor)
MLCGroupNormalizationLayer(long featureChannelCount, long groupCount, MLCTensor beta, MLCTensor gamma, float varianceEpsilon)
MLCInstanceNormalizationLayer(long featureChannelCount, MLCTensor beta, MLCTensor gamma, float varianceEpsilon)
MLCInstanceNormalizationLayer(long featureChannelCount, MLCTensor beta, MLCTensor gamma, float varianceEpsilon, float momentum)
MLCInstanceNormalizationLayer(long featureChannelCount, MLCTensor mean, MLCTensor variance, MLCTensor beta, MLCTensor gamma, float varianceEpsilon, float momentum)
MLCLayerNormalizationLayer(NSArray<NSNumber> normalizedShape, MLCTensor beta, MLCTensor gamma, float varianceEpsilon)
MLCLossLayer(MLCLossDescriptor lossDescriptor, MLCTensor weights)
MLCTensorParameter(MLCTensor tensor)
MLCTensorParameter(MLCTensor tensor, NSArray<MLCTensorData> optimizerData)

Constructor parameters in org.robovm.apple.mlcompute with type arguments of type MLCTensor

Constructor	Description
MLCLSTMLayer(MLCLSTMDescriptor descriptor, NSArray<MLCTensor> inputWeights, NSArray<MLCTensor> hiddenWeights, NSArray<MLCTensor> biases)
MLCLSTMLayer(MLCLSTMDescriptor descriptor, NSArray<MLCTensor> inputWeights, NSArray<MLCTensor> hiddenWeights, NSArray<MLCTensor> peepholeWeights, NSArray<MLCTensor> biases)
MLCLSTMLayer(MLCLSTMDescriptor descriptor, NSArray<MLCTensor> inputWeights, NSArray<MLCTensor> hiddenWeights, NSArray<MLCTensor> peepholeWeights, NSArray<MLCTensor> biases, NSArray<MLCActivationDescriptor> gateActivations, MLCActivationDescriptor outputResultActivation)
MLCMultiheadAttentionLayer(MLCMultiheadAttentionDescriptor descriptor, NSArray<MLCTensor> weights, NSArray<MLCTensor> biases, NSArray<MLCTensor> attentionBiases)