MPSCNNLoss (moe.platform.ios API)

java.lang.Object
- org.moe.natj.general.NativeObject
- - org.moe.natj.objc.ObjCObject
  - - apple.NSObject
    - - apple.metalperformanceshaders.MPSKernel
      - apple.metalperformanceshaders.MPSCNNKernel
        
        apple.metalperformanceshaders.MPSCNNLoss

All Implemented Interfaces:

NSCoding, NSCopying, NSSecureCoding, NSObject
```
public class MPSCNNLoss
extends MPSCNNKernel
```
MPSCNNLoss [@dependency] This depends on Metal.framework.
The MPSCNNLoss filter is only used for training. This filter performs both the forward and backward pass computations. Specifically, it computes the loss between the input (predictions) and target data (labels) and the loss gradient. The loss value can be a 1 x 1 x 1 image containing a scalar loss value or an image (of the same size as the input source image) with per feature channel losses. The loss value is used to determine whether to continue the training operation or to terminate it, once satisfactory results are achieved. The loss gradient is the first gradient computed for the backward pass and serves as input to the next gradient filter (in the backward direction).
The MPSCNNLoss filter is created with a MPSCNNLossDescriptor describing the type of a loss filter and the type of a reduction to use for computing the overall loss.
The MPSCNNLoss filter takes the output of the inference pass (predictions) as input. It also requires the target data (labels) and optionally, weights for the labels. If per-label weights are not supplied, there is an option to use a single weight value by setting the 'weight' properly on the MPSCNNLossDescriptor object. The labels and optional weights need to be supplied by the user using the MPSCNNLossLabels object. The labels and weights are described via the MPSCNNLossDataDescriptor objects, which are in turn used to initialize the MPSCNNLossLabels object.
If the specified reduction operation is MPSCNNReductionTypeNone, the destinationImage should be at least as large as the specified clipRect. The destinationImage will then contain per-element losses. Otherse, a reduction operation will be performed, according to the specified reduction type, and the filter will return a scalar value containing the overall loss. For more information on the available reduction types, see MPSCNNTypes.h. Also see MPSCNNLossDescriptor for the description of optional parameters.
Here is a code example:
// Setup MPSCNNLossDataDescriptor* labelsDescriptor = [MPSCNNLossDataDescriptor cnnLossDataDescriptorWithData: labelsData layout: MPSDataLayoutHeightxWidthxFeatureChannels size: labelsDataSize]; MPSCNNLossLabels* labels = [[MPSCNNLossLabels alloc] initWithDevice: device labelsDescriptor: labelsDescriptor]; MPSCNNLossDescriptor *lossDescriptor = [MPSCNNLossDescriptor cnnLossDescriptorWithType: (MPSCNNLossType)MPSCNNLossTypeMeanAbsoluteError reductionType: (MPSCNNReductionType)MPSCNNReductionTypeSum]; MPSCNNLoss* lossFilter = [[MPSCNNLoss alloc] initWithDevice: device lossDescriptor: lossDescriptor];
// Encode loss filter. // The sourceImage is the output of a previous layer, for example, the SoftMax layer. The lossGradientsImage // is the sourceGradient input image to the first gradient layer (in the backward direction), for example, // the SoftMax gradient filter. [lossFilter encodeToCommandBuffer: commandBuffer sourceImage: sourceImage labels: labels destinationImage: lossGradientsImage];
// In order to guarantee that the loss image data is correctly synchronized for CPU side access, // it is the application's responsibility to call the [labels synchronizeOnCommandBuffer:] // method before accessing the loss image data. [labels synchronizeOnCommandBuffer:commandBuffer]; MPSImage* lossImage = [labels lossImage];

For predictions (y) and labels (t), the available loss filter types are the following:
Mean Absolute Error loss filter. This filter measures the absolute error of the element-wise difference between the predictions and labels. This loss function is computed according to the following formulas: Compute losses: losses = |y - t| Compute weighted losses: weighted_losses = weight(s) * losses Compute overall loss: loss = reduce(weighted_losses, reductionType)
Mean Squared Error loss filter. This filter measures the squared error of the element-wise difference between the predictions and labels. This loss function is computed according to the following formulas: Compute losses: losses = (y - t)^2 Compute weighted losses: weighted_losses = weight(s) * losses Compute overall loss: loss = reduce(weighted_losses, reductionType)
SoftMax Cross Entropy loss filter. This loss filter is applied element-wise. This loss filter combines the LogSoftMax and Negative Log Likelihood operations in a single filter. It is useful for training a classification problem with multiple classes. This loss function is computed according to the following formulas: Compute losses: losses = -t * LogSoftMax(y) Compute weighted losses: weighted_losses = weight(s) * losses Compute overall loss: loss = reduce(weighted_losses, reductionType) If reductionType is MPSCNNReductionTypeMean, the accumulated loss value is divided by width * height instead of width * height * featureChannels.
Sigmoid Cross Entropy loss filter. This loss filter is applied element-wise. This loss function is computed according to the following formulas: Compute losses: losses = max(y, 0) - y * t + log(1 + exp(-|y|)) Compute weighted losses: weighted_losses = weight(s) * losses Compute overall loss: loss = reduce(weighted_losses, reductionType)
Categorical Cross Entropy loss filter. This loss filter is applied element-wise. This loss function is computed according to the following formulas: Compute losses: losses = -t * log(y) Compute weighted losses: weighted_losses = weight(s) * losses Compute overall loss: loss = reduce(weighted_losses, reductionType)
Hinge loss filter. This loss filter is applied element-wise. The labels are expected to be 0.0 or 1.0. This loss function is computed according to the following formulas: Compute losses: losses = max(1 - (t * y), 0.0f) Compute weighted losses: weighted_losses = weight(s) * losses Compute overall loss: loss = reduce(weighted_losses, reductionType)
Huber loss filter. This loss filter is applied element-wise. This loss function is computed according to the following formulas: Compute losses: if (|y - t| <= delta, losses = 0.5 * y^2 if (|y - t| > delta, losses = 0.5 * delta^2 + delta * (|y - t| - delta) Compute weighted losses: weighted_losses = weight(s) * losses Compute overall loss: loss = reduce(weighted_losses, reductionType)
Cosine Distance loss filter. This loss filter is applied element-wise. The only valid reduction type for this loss filter is MPSCNNReductionTypeSum. This loss function is computed according to the following formulas: Compute losses: loss = 1 - reduce_sum(y * t) Compute overall loss: weighted_loss = weight * loss
Log loss filter. This loss filter is applied element-wise. This loss function is computed according to the following formulas: Compute losses: losses = -(t * log(y + epsilon)) - ((1 - t) * log(1 - y + epsilon)) Compute weighted losses: weighted_losses = weight(s) * losses Compute overall loss: loss = reduce(weighted_losses, reductionType)
Kullback-Leibler Divergence loss filter. This loss filter is applied element-wise. The input (predictions) is expected to contain log-probabilities. This loss function is computed according to the following formulas: Compute losses: losses = t * (log(t) - y) Compute weighted losses: weighted_losses = weight(s) * losses Compute overall loss: loss = reduce(weighted_losses, reductionType)

For predictions (y) and labels (t), the loss gradient for each available loss filter type is computed as follows:
Mean Absolute Error loss. The loss gradient is computed according to the following formulas: Compute gradient: d/dy = (y - t) / |y - t| Compute weighted gradient: weighted_gradient = weight(s) * gradient
Mean Squared Error loss. The loss gradient is computed according to the following formulas: Compute gradient: d/dy = 2 * (y - t) Compute weighted gradient: weighted_gradient = weight(s) * gradient
SoftMax Cross Entropy loss. The loss gradient is computed according to the following formulas: First, apply the same label smoothing as in the MPSCNNLoss filter. Compute gradient: d/dy = y - t Compute weighted gradient: weighted_gradient = weight(s) * gradient
Sigmoid Cross Entropy loss. The loss gradient is computed according to the following formulas: First, apply the same label smoothing as in the MPSCNNLoss filter. Compute gradient: d/dy = (1 / (1 + exp(-y)) - t Compute weighted gradient: weighted_gradient = weight(s) * gradient
Categorical Cross Entropy loss. The loss gradient is computed according to the following formulas: Compute gradient: d/dy = -t / y Compute weighted gradient: weighted_gradient = weight(s) * gradient
Hinge loss. The loss gradient is computed according to the following formulas: Compute gradient: d/dy = ((1 + ((1 - (2 * t)) * y)) > 0) ? 1 - (2 * t) : 0 Compute weighted gradient: weighted_gradient = weight(s) * gradient
Huber loss. The loss gradient is computed according to the following formulas: Compute gradient: d/dy = |y - t| > delta ? delta : y - t Compute weighted gradient: weighted_gradient = weight(s) * gradient
Cosine Distance loss. The loss gradient is computed according to the following formulas: Compute gradient: d/dy = -t Compute weighted gradient: weighted_gradient = weight(s) * gradient
Log loss. The loss gradient is computed according to the following formulas: Compute gradient: d/dy = (-2 * epsilon * t - t + y + epsilon) / (y * (1 - y) + epsilon * (epsilon + 1)) Compute weighted gradient: weighted_gradient = weight(s) * gradient
Kullback-Leibler Divergence loss. The loss gradient is computed according to the following formulas: Compute gradient: d/dy = -t / y Compute weighted gradient: weighted_gradient = weight(s) * gradient
The number of output feature channels remains the same as the number of input feature channels.

Nested Class Summary
- Nested classes/interfaces inherited from class apple.NSObject
  NSObject.Function_instanceMethodForSelector_ret, NSObject.Function_methodForSelector_ret

Constructor Summary

Constructors
Modifier Constructor and Description

protected MPSCNNLoss(org.moe.natj.general.Pointer peer)

Constructors
Modifier	Constructor and Description
`protected`	`MPSCNNLoss(org.moe.natj.general.Pointer peer)`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`boolean`	`_supportsSecureCoding()` This property must return YES on all classes that allow secure coding.
`static boolean`	`accessInstanceVariablesDirectly()`
`static MPSCNNLoss`	`alloc()`
`static MPSCNNLoss`	`allocWithZone(org.moe.natj.general.ptr.VoidPtr zone)`
`static boolean`	`automaticallyNotifiesObserversForKey(java.lang.String key)`
`static void`	`cancelPreviousPerformRequestsWithTarget(java.lang.Object aTarget)`
`static void`	`cancelPreviousPerformRequestsWithTargetSelectorObject(java.lang.Object aTarget, org.moe.natj.objc.SEL aSelector, java.lang.Object anArgument)`
`static NSArray<java.lang.String>`	`classFallbacksForKeyedArchiver()`
`static org.moe.natj.objc.Class`	`classForKeyedUnarchiver()`
`static java.lang.String`	`debugDescription_static()`
`float`	`delta()`
`static java.lang.String`	`description_static()`
`MPSImage`	`encodeToCommandBufferSourceImageLabels(MTLCommandBuffer commandBuffer, MPSImage sourceImage, MPSCNNLossLabels labels)` Encode a MPSCNNLoss filter and return a gradient.
`void`	`encodeToCommandBufferSourceImageLabelsDestinationImage(MTLCommandBuffer commandBuffer, MPSImage sourceImage, MPSCNNLossLabels labels, MPSImage destinationImage)` Encode a MPSCNNLoss filter and return a gradient in the destinationImage.
`float`	`epsilon()`
`static long`	`hash_static()`
`MPSCNNLoss`	`init()`
`MPSCNNLoss`	`initWithCoder(NSCoder aDecoder)` NS_DESIGNATED_INITIALIZER
`MPSCNNLoss`	`initWithCoderDevice(NSCoder aDecoder, java.lang.Object device)` support
`MPSCNNLoss`	`initWithDevice(java.lang.Object device)` Standard init with default properties per filter type
`MPSCNNLoss`	`initWithDeviceLossDescriptor(MTLDevice device, MPSCNNLossDescriptor lossDescriptor)` Initialize the loss filter with a loss descriptor.
`static NSObject.Function_instanceMethodForSelector_ret`	`instanceMethodForSelector(org.moe.natj.objc.SEL aSelector)`
`static NSMethodSignature`	`instanceMethodSignatureForSelector(org.moe.natj.objc.SEL aSelector)`
`static boolean`	`instancesRespondToSelector(org.moe.natj.objc.SEL aSelector)`
`static boolean`	`isSubclassOfClass(org.moe.natj.objc.Class aClass)`
`static NSSet<java.lang.String>`	`keyPathsForValuesAffectingValueForKey(java.lang.String key)`
`float`	`labelSmoothing()`
`int`	`lossType()` See MPSCNNLossDescriptor for information about the following properties.
`static MPSCNNLoss`	`new_objc()`
`long`	`numberOfClasses()`
`boolean`	`reduceAcrossBatch()`
`int`	`reductionType()`
`static boolean`	`resolveClassMethod(org.moe.natj.objc.SEL sel)`
`static boolean`	`resolveInstanceMethod(org.moe.natj.objc.SEL sel)`
`static void`	`setVersion_static(long aVersion)`
`static org.moe.natj.objc.Class`	`superclass_static()`
`static boolean`	`supportsSecureCoding()`
`static long`	`version_static()`
`float`	`weight()`

Methods inherited from class apple.metalperformanceshaders.MPSCNNKernel
appendBatchBarrier, clipRect, destinationFeatureChannelOffset, destinationImageAllocator, destinationImageDescriptorForSourceImagesSourceStates, dilationRateX, dilationRateY, edgeMode, encodeToCommandBufferSourceImage, encodeToCommandBufferSourceImageDestinationImage, encodeToCommandBufferSourceImageDestinationStateDestinationImage, encodeToCommandBufferSourceImageDestinationStateDestinationStateIsTemporary, encodingStorageSizeForSourceImageSourceStatesDestinationImage, isBackwards, isResultStateReusedAcrossBatch, isStateModified, kernelHeight, kernelWidth, offset, padding, resultStateForSourceImageSourceStatesDestinationImage, setClipRect, setDestinationFeatureChannelOffset, setDestinationImageAllocator, setEdgeMode, setOffset, setPadding, setSourceFeatureChannelMaxCount, setSourceFeatureChannelOffset, sourceFeatureChannelMaxCount, sourceFeatureChannelOffset, strideInPixelsX, strideInPixelsY, temporaryResultStateForCommandBufferSourceImageSourceStatesDestinationImage

Methods inherited from class apple.metalperformanceshaders.MPSKernel
copyWithZone, copyWithZoneDevice, device, encodeWithCoder, label, options, setLabel, setOptions

Methods inherited from class apple.NSObject
accessibilityActivate, accessibilityActivationPoint, accessibilityAssistiveTechnologyFocusedIdentifiers, accessibilityAttributedHint, accessibilityAttributedLabel, accessibilityAttributedUserInputLabels, accessibilityAttributedValue, accessibilityContainerType, accessibilityCustomActions, accessibilityCustomRotors, accessibilityDecrement, accessibilityDragSourceDescriptors, accessibilityDropPointDescriptors, accessibilityElementAtIndex, accessibilityElementCount, accessibilityElementDidBecomeFocused, accessibilityElementDidLoseFocus, accessibilityElementIsFocused, accessibilityElements, accessibilityElementsHidden, accessibilityFrame, accessibilityHint, accessibilityIncrement, accessibilityLabel, accessibilityLanguage, accessibilityNavigationStyle, accessibilityPath, accessibilityPerformEscape, accessibilityPerformMagicTap, accessibilityRespondsToUserInteraction, accessibilityScroll, accessibilityTextualContext, accessibilityTraits, accessibilityUserInputLabels, accessibilityValue, accessibilityViewIsModal, addObserverForKeyPathOptionsContext, attemptRecoveryFromErrorOptionIndex, attemptRecoveryFromErrorOptionIndexDelegateDidRecoverSelectorContextInfo, autoContentAccessingProxy, awakeAfterUsingCoder, awakeFromNib, class_objc, classForCoder, classForKeyedArchiver, copy, dealloc, debugDescription, description, dictionaryWithValuesForKeys, didChangeValueForKey, didChangeValueForKeyWithSetMutationUsingObjects, didChangeValuesAtIndexesForKey, doesNotRecognizeSelector, fileManagerShouldProceedAfterError, fileManagerWillProcessPath, finalize_objc, forwardingTargetForSelector, forwardInvocation, hash, indexOfAccessibilityElement, isAccessibilityElement, isEqual, isKindOfClass, isMemberOfClass, isProxy, methodForSelector, methodSignatureForSelector, mutableArrayValueForKey, mutableArrayValueForKeyPath, mutableCopy, mutableOrderedSetValueForKey, mutableOrderedSetValueForKeyPath, mutableSetValueForKey, mutableSetValueForKeyPath, observationInfo, observeValueForKeyPathOfObjectChangeContext, performSelector, performSelectorInBackgroundWithObject, performSelectorOnMainThreadWithObjectWaitUntilDone, performSelectorOnMainThreadWithObjectWaitUntilDoneModes, performSelectorOnThreadWithObjectWaitUntilDone, performSelectorOnThreadWithObjectWaitUntilDoneModes, performSelectorWithObject, performSelectorWithObjectAfterDelay, performSelectorWithObjectAfterDelayInModes, performSelectorWithObjectWithObject, prepareForInterfaceBuilder, provideImageDataBytesPerRowOrigin_Size_UserInfo, removeObserverForKeyPath, removeObserverForKeyPathContext, replacementObjectForCoder, replacementObjectForKeyedArchiver, respondsToSelector, self, setAccessibilityActivationPoint, setAccessibilityAttributedHint, setAccessibilityAttributedLabel, setAccessibilityAttributedUserInputLabels, setAccessibilityAttributedValue, setAccessibilityContainerType, setAccessibilityCustomActions, setAccessibilityCustomRotors, setAccessibilityDragSourceDescriptors, setAccessibilityDropPointDescriptors, setAccessibilityElements, setAccessibilityElementsHidden, setAccessibilityFrame, setAccessibilityHint, setAccessibilityLabel, setAccessibilityLanguage, setAccessibilityNavigationStyle, setAccessibilityPath, setAccessibilityRespondsToUserInteraction, setAccessibilityTextualContext, setAccessibilityTraits, setAccessibilityUserInputLabels, setAccessibilityValue, setAccessibilityViewIsModal, setIsAccessibilityElement, setNilValueForKey, setObservationInfo, setShouldGroupAccessibilityChildren, setValueForKey, setValueForKeyPath, setValueForUndefinedKey, setValuesForKeysWithDictionary, shouldGroupAccessibilityChildren, superclass, validateValueForKeyError, validateValueForKeyPathError, valueForKey, valueForKeyPath, valueForUndefinedKey, willChangeValueForKey, willChangeValueForKeyWithSetMutationUsingObjects, willChangeValuesAtIndexesForKey

Methods inherited from class org.moe.natj.objc.ObjCObject
equals, toString

Methods inherited from class org.moe.natj.general.NativeObject
finalize, getPeer, getPeerPointer

Methods inherited from class java.lang.Object
clone, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Constructor Detail
  - MPSCNNLoss
```
protected MPSCNNLoss(org.moe.natj.general.Pointer peer)
```
- Method Detail
  - accessInstanceVariablesDirectly
```
public static boolean accessInstanceVariablesDirectly()
```
  - alloc
```
public static MPSCNNLoss alloc()
```
  - allocWithZone
```
public static MPSCNNLoss 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()
```
  - debugDescription_static
```
public static java.lang.String debugDescription_static()
```
  - delta
```
public float delta()
```
  - description_static
```
public static java.lang.String description_static()
```
  - encodeToCommandBufferSourceImageLabels
```
public MPSImage encodeToCommandBufferSourceImageLabels(MTLCommandBuffer commandBuffer,
                                                       MPSImage sourceImage,
                                                       MPSCNNLossLabels labels)
```
    Encode a MPSCNNLoss filter and return a gradient.
    This -encode call is similar to the encodeToCommandBuffer:sourceImage:labels:destinationImage: above, except that it creates and returns the MPSImage with the loss gradient result.
    
    Parameters:
    
    commandBuffer - The MTLCommandBuffer on which to encode.
    
    sourceImage - The source image from the previous filter in the graph (in the inference direction).
    
    labels - The object containing the target data (labels) and optionally, weights for the labels.
    
    Returns:
    
    The MPSImage containing the gradient result.
  - encodeToCommandBufferSourceImageLabelsDestinationImage
```
public void encodeToCommandBufferSourceImageLabelsDestinationImage(MTLCommandBuffer commandBuffer,
                                                                   MPSImage sourceImage,
                                                                   MPSCNNLossLabels labels,
                                                                   MPSImage destinationImage)
```
    Encode a MPSCNNLoss filter and return a gradient in the destinationImage.
    This filter consumes the output of a previous layer, for example, the SoftMax layer containing predictions, and the MPSCNNLossLabels object containing the target data (labels) and optionally, weights for the labels. The destinationImage contains the computed gradient for the loss layer. It serves as a source gradient input image to the first gradient layer (in the backward direction), in our example, the SoftMax gradient layer.
    
    Parameters:
    
    commandBuffer - The MTLCommandBuffer on which to encode.
    
    sourceImage - The source image from the previous filter in the graph (in the inference direction).
    
    labels - The object containing the target data (labels) and optionally, weights for the labels.
    
    destinationImage - The MPSImage into which to write the gradient result.
  - epsilon
```
public float epsilon()
```
  - hash_static
```
public static long hash_static()
```
  - init
```
public MPSCNNLoss init()
```
    Overrides:
    
    init in class MPSCNNKernel
  - initWithCoder
```
public MPSCNNLoss initWithCoder(NSCoder aDecoder)
```
    Description copied from interface: NSCoding
    
    NS_DESIGNATED_INITIALIZER
    
    Specified by:
    
    initWithCoder in interface NSCoding
    
    Overrides:
    
    initWithCoder in class MPSCNNKernel
  - initWithCoderDevice
```
public MPSCNNLoss initWithCoderDevice(NSCoder aDecoder,
                                      java.lang.Object device)
```
    support
    
    Overrides:
    
    initWithCoderDevice in class MPSCNNKernel
    
    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 MPSCNNLoss initWithDevice(java.lang.Object device)
```
    Description copied from class: MPSCNNKernel
    
    Standard init with default properties per filter type
    
    Overrides:
    
    initWithDevice in class MPSCNNKernel
    
    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.
  - initWithDeviceLossDescriptor
```
public MPSCNNLoss initWithDeviceLossDescriptor(MTLDevice device,
                                               MPSCNNLossDescriptor lossDescriptor)
```
    Initialize the loss filter with a loss descriptor.
    
    Parameters:
    
    device - The device the filter will run on.
    
    lossDescriptor - The loss descriptor.
    
    Returns:
    
    A valid MPSCNNLoss object or nil, if failure.
  - instanceMethodForSelector
```
public static NSObject.Function_instanceMethodForSelector_ret instanceMethodForSelector(org.moe.natj.objc.SEL aSelector)
```
  - 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)
```
  - labelSmoothing
```
public float labelSmoothing()
```
  - lossType
```
public int lossType()
```
    See MPSCNNLossDescriptor for information about the following properties.
  - new_objc
```
public static MPSCNNLoss new_objc()
```
  - numberOfClasses
```
public long numberOfClasses()
```
  - reductionType
```
public int reductionType()
```
  - resolveClassMethod
```
public static boolean resolveClassMethod(org.moe.natj.objc.SEL sel)
```
  - resolveInstanceMethod
```
public static boolean resolveInstanceMethod(org.moe.natj.objc.SEL sel)
```
  - 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 MPSCNNKernel
  - version_static
```
public static long version_static()
```
  - weight
```
public float weight()
```
  - reduceAcrossBatch
```
public boolean reduceAcrossBatch()
```

Class MPSCNNLoss

Nested Class Summary

Nested classes/interfaces inherited from class apple.NSObject

Constructor Summary

Method Summary

Methods inherited from class apple.metalperformanceshaders.MPSCNNKernel

Methods inherited from class apple.metalperformanceshaders.MPSKernel

Methods inherited from class apple.NSObject

Methods inherited from class org.moe.natj.objc.ObjCObject

Methods inherited from class org.moe.natj.general.NativeObject

Methods inherited from class java.lang.Object

Constructor Detail

MPSCNNLoss

Method Detail

accessInstanceVariablesDirectly

alloc

allocWithZone

automaticallyNotifiesObserversForKey

cancelPreviousPerformRequestsWithTarget

cancelPreviousPerformRequestsWithTargetSelectorObject

classFallbacksForKeyedArchiver

classForKeyedUnarchiver

debugDescription_static

delta

description_static

encodeToCommandBufferSourceImageLabels

encodeToCommandBufferSourceImageLabelsDestinationImage

epsilon

hash_static

init

initWithCoder

initWithCoderDevice

initWithDevice

initWithDeviceLossDescriptor

instanceMethodForSelector

instanceMethodSignatureForSelector

instancesRespondToSelector

isSubclassOfClass

keyPathsForValuesAffectingValueForKey

labelSmoothing

lossType

new_objc

numberOfClasses

reductionType

resolveClassMethod

resolveInstanceMethod

setVersion_static

superclass_static

supportsSecureCoding

_supportsSecureCoding

version_static

weight

reduceAcrossBatch