MPSCNNBatchNormalizationDataSource (moe.platform.ios API)

All Superinterfaces:

NSCopying
```
public interface MPSCNNBatchNormalizationDataSource
extends NSCopying
```
[@protocol] MPSCNNBatchNormalizationDataSource
The MPSCNNBatchNormalizationDataSource protocol declares the methods that an instance of MPSCNNBatchNormalizationState uses to initialize the scale factors, bias terms, and batch statistics.

Method Summary

All Methods Instance Methods Abstract Methods Default Methods
Modifier and Type	Method and Description
`default boolean`	`_supportsSecureCoding()` NSSecureCoding compatibility.
`org.moe.natj.general.ptr.FloatPtr`	`beta()` Returns a pointer to the bias terms for the batch normalization.
`default MPSCNNBatchNormalizationDataSource`	`copyWithZoneDevice(org.moe.natj.general.ptr.VoidPtr zone, MTLDevice device)` Optional copy method to create a copy of the data source for use with a new device.
`default void`	`encodeWithCoder(NSCoder aCoder)` NSSecureCoding compatibility.
`default float`	`epsilon()` An optional tiny number to use to maintain numerical stability.
`org.moe.natj.general.ptr.FloatPtr`	`gamma()` Returns a pointer to the scale factors for the batch normalization.
`default MPSCNNBatchNormalizationDataSource`	`initWithCoder(NSCoder aDecoder)` NSSecureCoding compatibility.
`java.lang.String`	`label()` A label that is transferred to the batch normalization filter at init time
`boolean`	`load_objc()` Alerts the data source that the data will be needed soon
`org.moe.natj.general.ptr.FloatPtr`	`mean()` Returns a pointer to batch mean values with which to initialize the state for a subsequent batch normalization.
`long`	`numberOfFeatureChannels()` Returns the number of feature channels within images to be normalized using the supplied parameters.
`void`	`purge()` Alerts the data source that the data is no longer needed
`default boolean`	`updateGammaAndBetaWithBatchNormalizationState(MPSCNNBatchNormalizationState batchNormalizationState)` Compute new gamma and beta values using current values and gradients contained within a MPSCNNBatchNormalizationState.
`default MPSCNNNormalizationGammaAndBetaState`	`updateGammaAndBetaWithCommandBufferBatchNormalizationState(MTLCommandBuffer commandBuffer, MPSCNNBatchNormalizationState batchNormalizationState)` Compute new gamma and beta values using current values and gradients contained within a MPSCNNBatchNormalizationState.
`default boolean`	`updateMeanAndVarianceWithBatchNormalizationState(MPSCNNBatchNormalizationState batchNormalizationState)` Compute new mean and variance values using current batch statistics contained within a MPSCNNBatchNormalizationState.
`default MPSCNNNormalizationMeanAndVarianceState`	`updateMeanAndVarianceWithCommandBufferBatchNormalizationState(MTLCommandBuffer commandBuffer, MPSCNNBatchNormalizationState batchNormalizationState)` Compute new mean and variance values using current batch statistics contained within a MPSCNNBatchNormalizationState.
`org.moe.natj.general.ptr.FloatPtr`	`variance()` Returns a pointer to batch variance values with which to initialize the state for a subsequent batch normalization.

Methods inherited from interface apple.foundation.protocol.NSCopying
copyWithZone

- Method Detail
  - beta
```
org.moe.natj.general.ptr.FloatPtr beta()
```
    Returns a pointer to the bias terms for the batch normalization. If NULL then no bias is to be applied.
  - copyWithZoneDevice
```
default MPSCNNBatchNormalizationDataSource copyWithZoneDevice(org.moe.natj.general.ptr.VoidPtr zone,
                                                              MTLDevice device)
```
    Optional copy method to create a copy of the data source for use with a new device.
    
    Parameters:
    
    zone - The NSZone on which to allocate.
    
    device - The device where the kernel which uses this data source will be used.
    
    Returns:
    
    A pointer to a copy of this data source.
  - encodeWithCoder
```
default void encodeWithCoder(NSCoder aCoder)
```
    NSSecureCoding compatibility.
  - epsilon
```
default float epsilon()
```
    An optional tiny number to use to maintain numerical stability.
    output_image = (input_image - mean[c]) * gamma[c] / sqrt(variance[c] + epsilon) + beta[c]; Defalt value if method unavailable: FLT_MIN
  - gamma
```
org.moe.natj.general.ptr.FloatPtr gamma()
```
    Returns a pointer to the scale factors for the batch normalization.
  - initWithCoder
```
default MPSCNNBatchNormalizationDataSource initWithCoder(NSCoder aDecoder)
```
    NSSecureCoding compatibility.
  - label
```
java.lang.String label()
```
    A label that is transferred to the batch normalization filter at init time
    Overridden by a MPSCNNBatchNormalizationNode.label if it is non-nil.
  - load_objc
```
boolean load_objc()
```
    Alerts the data source that the data will be needed soon
    Each load alert will be balanced by a purge later, when MPS no longer needs the data from this object. Load will always be called atleast once after initial construction or each purge of the object before anything else is called.
    
    Returns:
    
    Returns YES on success. If NO is returned, expect MPS object construction to fail.
  - mean
```
org.moe.natj.general.ptr.FloatPtr mean()
```
    Returns a pointer to batch mean values with which to initialize the state for a subsequent batch normalization.
  - numberOfFeatureChannels
```
long numberOfFeatureChannels()
```
    Returns the number of feature channels within images to be normalized using the supplied parameters.
  - purge
```
void purge()
```
    Alerts the data source that the data is no longer needed
    Each load alert will be balanced by a purge later, when MPS no longer needs the data from this object.
  - _supportsSecureCoding
```
default boolean _supportsSecureCoding()
```
    NSSecureCoding compatibility.
  - updateGammaAndBetaWithBatchNormalizationState
```
default boolean updateGammaAndBetaWithBatchNormalizationState(MPSCNNBatchNormalizationState batchNormalizationState)
```
    Compute new gamma and beta values using current values and gradients contained within a MPSCNNBatchNormalizationState. Perform the update using the CPU.
    
    Parameters:
    
    batchNormalizationState - The MPSCNNBatchNormalizationState object containing the current gamma and beta values and the gradient values.
    
    Returns:
    
    A boolean value indicating if the update was performed.
  - updateGammaAndBetaWithCommandBufferBatchNormalizationState
```
default MPSCNNNormalizationGammaAndBetaState updateGammaAndBetaWithCommandBufferBatchNormalizationState(MTLCommandBuffer commandBuffer,
                                                                                                        MPSCNNBatchNormalizationState batchNormalizationState)
```
    Compute new gamma and beta values using current values and gradients contained within a MPSCNNBatchNormalizationState. Perform the update using a GPU.
    This operation is expected to also decrement the read count of batchNormalizationState by 1.
    
    Parameters:
    
    commandBuffer - The command buffer on which to encode the update.
    
    batchNormalizationState - The MPSCNNBatchNormalizationState object containing the current gamma and beta values and the gradient values.
    
    Returns:
    
    A MPSCNNNormalizationMeanAndVarianceState object containing updated mean and variance values. If NULL, the MPSNNGraph batch normalization filter gamma and beta values will remain unmodified.
  - updateMeanAndVarianceWithBatchNormalizationState
```
default boolean updateMeanAndVarianceWithBatchNormalizationState(MPSCNNBatchNormalizationState batchNormalizationState)
```
    Compute new mean and variance values using current batch statistics contained within a MPSCNNBatchNormalizationState. Perform the update using the CPU.
    
    Parameters:
    
    batchNormalizationState - The MPSCNNBatchNormalizationState object containing the current batch statistics.
    
    Returns:
    
    A boolean value indicating if the update was performed.
  - updateMeanAndVarianceWithCommandBufferBatchNormalizationState
```
default MPSCNNNormalizationMeanAndVarianceState updateMeanAndVarianceWithCommandBufferBatchNormalizationState(MTLCommandBuffer commandBuffer,
                                                                                                              MPSCNNBatchNormalizationState batchNormalizationState)
```
    Compute new mean and variance values using current batch statistics contained within a MPSCNNBatchNormalizationState. Perform the update using a GPU.
    This operation is expected to also decrement the read count of batchNormalizationState by 1.
    
    Parameters:
    
    commandBuffer - The command buffer on which to encode the update.
    
    batchNormalizationState - The MPSCNNBatchNormalizationState object containing the current batch statistics.
    
    Returns:
    
    A MPSCNNNormalizationMeanAndVarianceState object containing updated mean and variance values. If NULL, the MPSNNGraph batch normalization filter mean and variance values will remain unmodified.
  - variance
```
org.moe.natj.general.ptr.FloatPtr variance()
```
    Returns a pointer to batch variance values with which to initialize the state for a subsequent batch normalization.

Interface MPSCNNBatchNormalizationDataSource

Method Summary

Methods inherited from interface apple.foundation.protocol.NSCopying

Method Detail

beta

copyWithZoneDevice

encodeWithCoder

epsilon

gamma

initWithCoder

label

load_objc

mean

numberOfFeatureChannels

purge

_supportsSecureCoding

updateGammaAndBetaWithBatchNormalizationState

updateGammaAndBetaWithCommandBufferBatchNormalizationState

updateMeanAndVarianceWithBatchNormalizationState

updateMeanAndVarianceWithCommandBufferBatchNormalizationState

variance