mlcompute

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 Go to latest Published: Jun 25, 2026 License: MIT Imports: 10 Imported by: 0

Documentation

Rendered for darwin/amd64

Overview

Package mlcompute provides purego-based Go bindings for the macOS MLCompute framework.

Apple documentation: https://developer.apple.com/documentation/mlcompute

Index

Constants

This section is empty.

Variables

This section is empty.

Functions

func MLCActivationTypeDebugDescription 

func MLCActivationTypeDebugDescription(activationType MLCActivationType) *foundation.NSString

@abstract Returns a textual description of the activation type, suitable for debugging.

func MLCArithmeticOperationDebugDescription 

func MLCArithmeticOperationDebugDescription(operation MLCArithmeticOperation) *foundation.NSString

@abstract Returns a textual description of the arithmetic operation, suitable for debugging.

func MLCComparisonOperationDebugDescription 

func MLCComparisonOperationDebugDescription(operation MLCComparisonOperation) *foundation.NSString

@abstract Returns a textual description of the comparison operation, suitable for debugging.

func MLCConvolutionTypeDebugDescription 

func MLCConvolutionTypeDebugDescription(convolutionType MLCConvolutionType) *foundation.NSString

@abstract Returns a textual description of the convolution type, suitable for debugging.

func MLCGradientClippingTypeDebugDescription 

func MLCGradientClippingTypeDebugDescription(gradientClippingType MLCGradientClippingType) *foundation.NSString

@abstract Returns a textual description of the gradient clipping type, suitable for debugging.

func MLCLSTMResultModeDebugDescription 

func MLCLSTMResultModeDebugDescription(mode MLCLSTMResultMode) *foundation.NSString

@abstract Returns a textual description of the LSTM result mode, suitable for debugging.

func MLCLayerSupportsDataTypeOnDevice 

func MLCLayerSupportsDataTypeOnDevice(dataType MLCDataType, device *MLCDevice) bool

Returns a Boolean that indicates whether instances of this layer accept source tensors for the data type and device that you specify.

func MLCLossTypeDebugDescription 

func MLCLossTypeDebugDescription(lossType MLCLossType) *foundation.NSString

@abstract Returns a textual description of the loss type, suitable for debugging.

func MLCPaddingPolicyDebugDescription 

func MLCPaddingPolicyDebugDescription(paddingPolicy MLCPaddingPolicy) *foundation.NSString

@abstract Returns a textual description of the padding policy, suitable for debugging.

func MLCPaddingTypeDebugDescription 

func MLCPaddingTypeDebugDescription(paddingType MLCPaddingType) *foundation.NSString

@abstract Returns a textual description of the padding type, suitable for debugging.

func MLCPlatformGetRNGseed 

func MLCPlatformGetRNGseed() *foundation.NSNumber

Returns the global random number generator seed value.

func MLCPlatformSetRNGSeedTo 

func MLCPlatformSetRNGSeedTo(seed *foundation.NSNumber)

Sets the global random number generator seed value.

func MLCPoolingTypeDebugDescription 

func MLCPoolingTypeDebugDescription(poolingType MLCPoolingType) *foundation.NSString

@abstract Returns a textual description of the pooling type, suitable for debugging.

func MLCReductionTypeDebugDescription 

func MLCReductionTypeDebugDescription(reductionType MLCReductionType) *foundation.NSString

@abstract Returns a textual description of the reduction type, suitable for debugging.

func MLCSampleModeDebugDescription 

func MLCSampleModeDebugDescription(mode MLCSampleMode) *foundation.NSString

@abstract Returns a textual description of the sample mode, suitable for debugging.

func MLCSoftmaxOperationDebugDescription 

func MLCSoftmaxOperationDebugDescription(operation MLCSoftmaxOperation) *foundation.NSString

@abstract Returns a textual description of the softmax operation, suitable for debugging.

func MLCTensorDescriptorMaxTensorDimensions 

func MLCTensorDescriptorMaxTensorDimensions() uint

@property maxTensorDimensions @abstract The maximum number of tensor dimensions supported

func SymbolAvailable 

func SymbolAvailable(symbol string) bool

SymbolAvailable reports whether the named C symbol was bound when the library loaded. Calling a generated wrapper whose symbol is unavailable dereferences a nil function variable and panics.

Types

type Acl_entry_id_t 

type Acl_entry_id_t int64
const (
	ACL_FIRST_ENTRY Acl_entry_id_t = 0
	ACL_NEXT_ENTRY  Acl_entry_id_t = -1
	ACL_LAST_ENTRY  Acl_entry_id_t = -2
)

func (Acl_entry_id_t) String 

func (e Acl_entry_id_t) String() string

type Acl_flag_t 

type Acl_flag_t int64
const (
	ACL_FLAG_DEFER_INHERIT      Acl_flag_t = 1
	ACL_FLAG_NO_INHERIT         Acl_flag_t = 131072
	ACL_ENTRY_INHERITED         Acl_flag_t = 16
	ACL_ENTRY_FILE_INHERIT      Acl_flag_t = 32
	ACL_ENTRY_DIRECTORY_INHERIT Acl_flag_t = 64
	ACL_ENTRY_LIMIT_INHERIT     Acl_flag_t = 128
	ACL_ENTRY_ONLY_INHERIT      Acl_flag_t = 256
)

func (Acl_flag_t) String 

func (e Acl_flag_t) String() string

type Acl_perm_t 

type Acl_perm_t int64
const (
	ACL_READ_DATA           Acl_perm_t = 2
	ACL_LIST_DIRECTORY      Acl_perm_t = 2
	ACL_WRITE_DATA          Acl_perm_t = 4
	ACL_ADD_FILE            Acl_perm_t = 4
	ACL_EXECUTE             Acl_perm_t = 8
	ACL_SEARCH              Acl_perm_t = 8
	ACL_DELETE              Acl_perm_t = 16
	ACL_APPEND_DATA         Acl_perm_t = 32
	ACL_ADD_SUBDIRECTORY    Acl_perm_t = 32
	ACL_DELETE_CHILD        Acl_perm_t = 64
	ACL_READ_ATTRIBUTES     Acl_perm_t = 128
	ACL_WRITE_ATTRIBUTES    Acl_perm_t = 256
	ACL_READ_EXTATTRIBUTES  Acl_perm_t = 512
	ACL_WRITE_EXTATTRIBUTES Acl_perm_t = 1024
	ACL_READ_SECURITY       Acl_perm_t = 2048
	ACL_WRITE_SECURITY      Acl_perm_t = 4096
	ACL_CHANGE_OWNER        Acl_perm_t = 8192
	ACL_SYNCHRONIZE         Acl_perm_t = 1048576
)

func (Acl_perm_t) String 

func (e Acl_perm_t) String() string

type Acl_tag_t 

type Acl_tag_t int64
const (
	ACL_UNDEFINED_TAG  Acl_tag_t = 0
	ACL_EXTENDED_ALLOW Acl_tag_t = 1
	ACL_EXTENDED_DENY  Acl_tag_t = 2
)

func (Acl_tag_t) String 

func (e Acl_tag_t) String() string

type Acl_type_t 

type Acl_type_t int64
const (
	ACL_TYPE_EXTENDED Acl_type_t = 256
	ACL_TYPE_ACCESS   Acl_type_t = 0
	ACL_TYPE_DEFAULT  Acl_type_t = 1
	ACL_TYPE_AFS      Acl_type_t = 2
	ACL_TYPE_CODA     Acl_type_t = 3
	ACL_TYPE_NTFS     Acl_type_t = 4
	ACL_TYPE_NWFS     Acl_type_t = 5
)

func (Acl_type_t) String 

func (e Acl_type_t) String() string

type Clockid_t 

type Clockid_t int64

func (Clockid_t) String 

func (e Clockid_t) String() string

type Dispatch_autorelease_frequency_t 

type Dispatch_autorelease_frequency_t uint64
const (
	DISPATCH_AUTORELEASE_FREQUENCY_INHERIT   Dispatch_autorelease_frequency_t = 0
	DISPATCH_AUTORELEASE_FREQUENCY_WORK_ITEM Dispatch_autorelease_frequency_t = 1
	DISPATCH_AUTORELEASE_FREQUENCY_NEVER     Dispatch_autorelease_frequency_t = 2
)

func (Dispatch_autorelease_frequency_t) String 

func (e Dispatch_autorelease_frequency_t) String() string

type Dispatch_block_flags_t 

type Dispatch_block_flags_t uint64
const (
	DISPATCH_BLOCK_BARRIER           Dispatch_block_flags_t = 1
	DISPATCH_BLOCK_DETACHED          Dispatch_block_flags_t = 2
	DISPATCH_BLOCK_ASSIGN_CURRENT    Dispatch_block_flags_t = 4
	DISPATCH_BLOCK_NO_QOS_CLASS      Dispatch_block_flags_t = 8
	DISPATCH_BLOCK_INHERIT_QOS_CLASS Dispatch_block_flags_t = 16
	DISPATCH_BLOCK_ENFORCE_QOS_CLASS Dispatch_block_flags_t = 32
)

func (Dispatch_block_flags_t) String 

func (e Dispatch_block_flags_t) String() string

type Filesec_property_t 

type Filesec_property_t int64
const (
	FILESEC_OWNER         Filesec_property_t = 1
	FILESEC_GROUP         Filesec_property_t = 2
	FILESEC_UUID          Filesec_property_t = 3
	FILESEC_MODE          Filesec_property_t = 4
	FILESEC_ACL           Filesec_property_t = 5
	FILESEC_GRPUUID       Filesec_property_t = 6
	FILESEC_ACL_RAW       Filesec_property_t = 100
	FILESEC_ACL_ALLOCSIZE Filesec_property_t = 101
)

func (Filesec_property_t) String 

func (e Filesec_property_t) String() string

type Idtype_t 

type Idtype_t int64
const (
	P_ALL  Idtype_t = 0
	P_PID  Idtype_t = 1
	P_PGID Idtype_t = 2
)

func (Idtype_t) String 

func (e Idtype_t) String() string

type Ipc_info_object_type_t 

type Ipc_info_object_type_t int64
const (
	IPC_OTYPE_NONE                 Ipc_info_object_type_t = 0
	IPC_OTYPE_THREAD_CONTROL       Ipc_info_object_type_t = 1
	IPC_OTYPE_TASK_CONTROL         Ipc_info_object_type_t = 2
	IPC_OTYPE_HOST                 Ipc_info_object_type_t = 3
	IPC_OTYPE_HOST_PRIV            Ipc_info_object_type_t = 4
	IPC_OTYPE_PROCESSOR            Ipc_info_object_type_t = 5
	IPC_OTYPE_PROCESSOR_SET        Ipc_info_object_type_t = 6
	IPC_OTYPE_PROCESSOR_SET_NAME   Ipc_info_object_type_t = 7
	IPC_OTYPE_TIMER                Ipc_info_object_type_t = 8
	IPC_OTYPE_PORT_SUBST_ONCE      Ipc_info_object_type_t = 9
	IPC_OTYPE_MIG                  Ipc_info_object_type_t = 10
	IPC_OTYPE_MEMORY_OBJECT        Ipc_info_object_type_t = 11
	IPC_OTYPE_XMM_PAGER            Ipc_info_object_type_t = 12
	IPC_OTYPE_XMM_KERNEL           Ipc_info_object_type_t = 13
	IPC_OTYPE_XMM_REPLY            Ipc_info_object_type_t = 14
	IPC_OTYPE_UND_REPLY            Ipc_info_object_type_t = 15
	IPC_OTYPE_HOST_NOTIFY          Ipc_info_object_type_t = 16
	IPC_OTYPE_HOST_SECURITY        Ipc_info_object_type_t = 17
	IPC_OTYPE_LEDGER               Ipc_info_object_type_t = 18
	IPC_OTYPE_MAIN_DEVICE          Ipc_info_object_type_t = 19
	IPC_OTYPE_TASK_NAME            Ipc_info_object_type_t = 20
	IPC_OTYPE_SUBSYSTEM            Ipc_info_object_type_t = 21
	IPC_OTYPE_IO_DONE_QUEUE        Ipc_info_object_type_t = 22
	IPC_OTYPE_SEMAPHORE            Ipc_info_object_type_t = 23
	IPC_OTYPE_LOCK_SET             Ipc_info_object_type_t = 24
	IPC_OTYPE_CLOCK                Ipc_info_object_type_t = 25
	IPC_OTYPE_CLOCK_CTRL           Ipc_info_object_type_t = 26
	IPC_OTYPE_IOKIT_IDENT          Ipc_info_object_type_t = 27
	IPC_OTYPE_NAMED_ENTRY          Ipc_info_object_type_t = 28
	IPC_OTYPE_IOKIT_CONNECT        Ipc_info_object_type_t = 29
	IPC_OTYPE_IOKIT_OBJECT         Ipc_info_object_type_t = 30
	IPC_OTYPE_UPL                  Ipc_info_object_type_t = 31
	IPC_OTYPE_MEM_OBJ_CONTROL      Ipc_info_object_type_t = 32
	IPC_OTYPE_AU_SESSIONPORT       Ipc_info_object_type_t = 33
	IPC_OTYPE_FILEPORT             Ipc_info_object_type_t = 34
	IPC_OTYPE_LABELH               Ipc_info_object_type_t = 35
	IPC_OTYPE_TASK_RESUME          Ipc_info_object_type_t = 36
	IPC_OTYPE_VOUCHER              Ipc_info_object_type_t = 37
	IPC_OTYPE_VOUCHER_ATTR_CONTROL Ipc_info_object_type_t = 38
	IPC_OTYPE_WORK_INTERVAL        Ipc_info_object_type_t = 39
	IPC_OTYPE_UX_HANDLER           Ipc_info_object_type_t = 40
	IPC_OTYPE_UEXT_OBJECT          Ipc_info_object_type_t = 41
	IPC_OTYPE_ARCADE_REG           Ipc_info_object_type_t = 42
	IPC_OTYPE_EVENTLINK            Ipc_info_object_type_t = 43
	IPC_OTYPE_TASK_INSPECT         Ipc_info_object_type_t = 44
	IPC_OTYPE_TASK_READ            Ipc_info_object_type_t = 45
	IPC_OTYPE_THREAD_INSPECT       Ipc_info_object_type_t = 46
	IPC_OTYPE_THREAD_READ          Ipc_info_object_type_t = 47
	IPC_OTYPE_SUID_CRED            Ipc_info_object_type_t = 48
	IPC_OTYPE_HYPERVISOR           Ipc_info_object_type_t = 49
	IPC_OTYPE_TASK_ID_TOKEN        Ipc_info_object_type_t = 50
	IPC_OTYPE_TASK_FATAL           Ipc_info_object_type_t = 51
	IPC_OTYPE_KCDATA               Ipc_info_object_type_t = 52
	IPC_OTYPE_EXCLAVES_RESOURCE    Ipc_info_object_type_t = 53
	IPC_OTYPE_THREAD_RESUME        Ipc_info_object_type_t = 54
	IPC_OTYPE_UNKNOWN              Ipc_info_object_type_t = 4294967295
)

func (Ipc_info_object_type_t) String 

func (e Ipc_info_object_type_t) String() string

type Launch_data_type_t 

type Launch_data_type_t int64
const (
	LAUNCH_DATA_DICTIONARY Launch_data_type_t = 1
	LAUNCH_DATA_ARRAY      Launch_data_type_t = 2
	LAUNCH_DATA_FD         Launch_data_type_t = 3
	LAUNCH_DATA_INTEGER    Launch_data_type_t = 4
	LAUNCH_DATA_REAL       Launch_data_type_t = 5
	LAUNCH_DATA_BOOL       Launch_data_type_t = 6
	LAUNCH_DATA_STRING     Launch_data_type_t = 7
	LAUNCH_DATA_OPAQUE     Launch_data_type_t = 8
	LAUNCH_DATA_ERRNO      Launch_data_type_t = 9
	LAUNCH_DATA_MACHPORT   Launch_data_type_t = 10
)

func (Launch_data_type_t) String 

func (e Launch_data_type_t) String() string

type MDLabelDomain 

type MDLabelDomain int64

@typedef MDLabelDomain @abstract These constants are used to specify a domain to MDLabelCreate(). 

const (
	KMDLabelUserDomain  MDLabelDomain = 0
	KMDLabelLocalDomain MDLabelDomain = 1
)

func (MDLabelDomain) String 

func (e MDLabelDomain) String() string

type MDQueryOptionFlags 

type MDQueryOptionFlags int64
const (
	KMDQuerySynchronous        MDQueryOptionFlags = 1
	KMDQueryWantsUpdates       MDQueryOptionFlags = 4
	KMDQueryAllowFSTranslation MDQueryOptionFlags = 8
)

func (MDQueryOptionFlags) String 

func (e MDQueryOptionFlags) String() string

type MDQuerySortOptionFlags 

type MDQuerySortOptionFlags int64

@enum MDQuerySortOptionFlags @constant kMDQueryReverseSortOrderFlag Sort the attribute in reverse order. 

const (
	KMDQueryReverseSortOrderFlag MDQuerySortOptionFlags = 1
)

func (MDQuerySortOptionFlags) String 

func (e MDQuerySortOptionFlags) String() string

type MLCActivationDescriptor 

type MLCActivationDescriptor struct {
	foundation.NSObject
}

A configuration object you use to create an activation layer.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcactivationdescriptor

func MLCActivationDescriptorDescriptorWithType 

func MLCActivationDescriptorDescriptorWithType(activationType MLCActivationType) *MLCActivationDescriptor

Creates an activation descriptor with the activation type you specify.

func MLCActivationDescriptorDescriptorWithTypeA 

func MLCActivationDescriptorDescriptorWithTypeA(activationType MLCActivationType, a float32) *MLCActivationDescriptor

Creates an activation descriptor with the activation type and parameter a that you specify.

func MLCActivationDescriptorDescriptorWithTypeAB 

func MLCActivationDescriptorDescriptorWithTypeAB(activationType MLCActivationType, a float32, b float32) *MLCActivationDescriptor

Creates an activation descriptor with the activation type and parameters a and b that you specify.

func MLCActivationDescriptorDescriptorWithTypeABC 

func MLCActivationDescriptorDescriptorWithTypeABC(activationType MLCActivationType, a float32, b float32, c float32) *MLCActivationDescriptor

Creates an activation descriptor with the activation type and parameters a, b, and c that you specify.

func MLCActivationDescriptorFromID 

func MLCActivationDescriptorFromID(id objc.ID) *MLCActivationDescriptor

func (*MLCActivationDescriptor) A 

func (o *MLCActivationDescriptor) A() float32

@property a @abstract Parameter to the activation function

func (*MLCActivationDescriptor) ActivationType 

func (o *MLCActivationDescriptor) ActivationType() MLCActivationType

@property activationType @abstract The type of activation function

func (*MLCActivationDescriptor) B 

func (o *MLCActivationDescriptor) B() float32

@property b @abstract Parameter to the activation function

func (*MLCActivationDescriptor) C 

func (o *MLCActivationDescriptor) C() float32

@property c @abstract Parameter to the activation function

type MLCActivationLayer 

type MLCActivationLayer struct {
	MLCLayer
}

A layer that applies an activation function to the source tensor and produces an output.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcactivationlayer

func MLCActivationLayerAbsoluteLayer 

func MLCActivationLayerAbsoluteLayer() *MLCActivationLayer

@abstract Create an absolute activation layer @return A new activation layer

func MLCActivationLayerCeluLayer 

func MLCActivationLayerCeluLayer() *MLCActivationLayer

@abstract Create a CELU activation layer @return A new activation layer

func MLCActivationLayerCeluLayerWithA 

func MLCActivationLayerCeluLayerWithA(a float32) *MLCActivationLayer

@abstract Create a CELU activation layer @param a The \p a value for the CELU formation @return A new activation layer

func MLCActivationLayerClampLayerWithMinValueMaxValue 

func MLCActivationLayerClampLayerWithMinValueMaxValue(minValue float32, maxValue float32) *MLCActivationLayer

@abstract Create a clamp activation layer @param minValue The minimum range used by clamp @param maxValue The maximum range used by clamp @return A new activation layer

func MLCActivationLayerEluLayer 

func MLCActivationLayerEluLayer() *MLCActivationLayer

@abstract Create an ELU activation layer @return A new activation layer

func MLCActivationLayerEluLayerWithA 

func MLCActivationLayerEluLayerWithA(a float32) *MLCActivationLayer

@abstract Create an ELU activation layer @param a The \p a value for the ELU formation @return A new activation layer

func MLCActivationLayerFromID 

func MLCActivationLayerFromID(id objc.ID) *MLCActivationLayer

func MLCActivationLayerGeluLayer 

func MLCActivationLayerGeluLayer() *MLCActivationLayer

@abstract Create a GELU activation layer @return A new activation layer

func MLCActivationLayerHardShrinkLayer 

func MLCActivationLayerHardShrinkLayer() *MLCActivationLayer

@abstract Create a hard shrink activation layer @return A new activation layer

func MLCActivationLayerHardShrinkLayerWithA 

func MLCActivationLayerHardShrinkLayerWithA(a float32) *MLCActivationLayer

@abstract Create a hard shrink activation layer @param a The \p a value for the hard shrink formation @return A new activation layer

func MLCActivationLayerHardSigmoidLayer 

func MLCActivationLayerHardSigmoidLayer() *MLCActivationLayer

@abstract Create a hard sigmoid activation layer @return A new activation layer

func MLCActivationLayerHardSwishLayer 

func MLCActivationLayerHardSwishLayer() *MLCActivationLayer

@abstract Create a hardswish activation layer @return A new activation layer

func MLCActivationLayerLayerWithDescriptor 

func MLCActivationLayerLayerWithDescriptor(descriptor *MLCActivationDescriptor) *MLCActivationLayer

Creates an activation layer with the descriptor you specify.

func MLCActivationLayerLeakyReLULayer 

func MLCActivationLayerLeakyReLULayer() *MLCActivationLayer

@abstract Create a leaky ReLU activation layer @return A new activation layer

func MLCActivationLayerLeakyReLULayerWithNegativeSlope 

func MLCActivationLayerLeakyReLULayerWithNegativeSlope(negativeSlope float32) *MLCActivationLayer

@abstract Create a leaky ReLU activation layer @param negativeSlope Controls the angle of the negative slope @return A new activation layer

func MLCActivationLayerLinearLayerWithScaleBias 

func MLCActivationLayerLinearLayerWithScaleBias(scale float32, bias float32) *MLCActivationLayer

@abstract Create a linear activation layer @param scale The scale factor @param bias The bias value @return A new activation layer

func MLCActivationLayerLogSigmoidLayer 

func MLCActivationLayerLogSigmoidLayer() *MLCActivationLayer

@abstract Create a log sigmoid activation layer @return A new activation layer

func MLCActivationLayerRelu6Layer 

func MLCActivationLayerRelu6Layer() *MLCActivationLayer

@abstract Create a ReLU6 activation layer @return A new activation layer

func MLCActivationLayerReluLayer 

func MLCActivationLayerReluLayer() *MLCActivationLayer

@abstract Create a ReLU activation layer @return A new activation layer

func MLCActivationLayerRelunLayerWithAB 

func MLCActivationLayerRelunLayerWithAB(a float32, b float32) *MLCActivationLayer

@abstract Create a ReLUN activation layer @discussion This can be used to implement layers such as ReLU6 for example. @param a The \p a value @param b The \p b value @return A new activation layer

func MLCActivationLayerSeluLayer 

func MLCActivationLayerSeluLayer() *MLCActivationLayer

@abstract Create a SELU activation layer @return A new activation layer

func MLCActivationLayerSigmoidLayer 

func MLCActivationLayerSigmoidLayer() *MLCActivationLayer

@abstract Create a sigmoid activation layer @return A new activation layer

func MLCActivationLayerSoftPlusLayer 

func MLCActivationLayerSoftPlusLayer() *MLCActivationLayer

@abstract Create a soft plus activation layer @return A new activation layer

func MLCActivationLayerSoftPlusLayerWithBeta 

func MLCActivationLayerSoftPlusLayerWithBeta(beta float32) *MLCActivationLayer

@abstract Create a soft plus activation layer @param beta The beta value for the softplus formation @return A new activation layer

func MLCActivationLayerSoftShrinkLayer 

func MLCActivationLayerSoftShrinkLayer() *MLCActivationLayer

@abstract Create a soft shrink activation layer @return A new activation layer

func MLCActivationLayerSoftShrinkLayerWithA 

func MLCActivationLayerSoftShrinkLayerWithA(a float32) *MLCActivationLayer

@abstract Create a soft shrink activation layer @param a The \p a value for the soft shrink formation @return A new activation layer

func MLCActivationLayerSoftSignLayer 

func MLCActivationLayerSoftSignLayer() *MLCActivationLayer

@abstract Create a soft sign activation layer @return A new activation layer

func MLCActivationLayerTanhLayer 

func MLCActivationLayerTanhLayer() *MLCActivationLayer

@abstract Create a tanh activation layer @return A new activation layer

func MLCActivationLayerTanhShrinkLayer 

func MLCActivationLayerTanhShrinkLayer() *MLCActivationLayer

@abstract Create a TanhShrink activation layer @return A new activation layer

func MLCActivationLayerThresholdLayerWithThresholdReplacement 

func MLCActivationLayerThresholdLayerWithThresholdReplacement(threshold float32, replacement float32) *MLCActivationLayer

@abstract Create a threshold activation layer @param threshold The value to threshold at @param replacement The value to replace with @return A new activation layer

func (*MLCActivationLayer) Descriptor 

func (o *MLCActivationLayer) Descriptor() *MLCActivationDescriptor

@property descriptor @abstract The activation descriptor

type MLCActivationType 

type MLCActivationType int64

An activation type that you specify for an activation descriptor. 

const (
	// An activation type that implements the identity function.
	MLCActivationTypeNone MLCActivationType = 0
	// An activation type that implements the rectified linear unit activation function.
	MLCActivationTypeReLU MLCActivationType = 1
	// An activation type that implements the linear activation function.
	MLCActivationTypeLinear MLCActivationType = 2
	// An activation type that implements the sigmoid activation function.
	MLCActivationTypeSigmoid MLCActivationType = 3
	// An activation type that implements the hard sigmoid activation function.
	MLCActivationTypeHardSigmoid MLCActivationType = 4
	// An activation type that implements the hyperbolic tangent activation function.
	MLCActivationTypeTanh MLCActivationType = 5
	// An activation type that implements the absolute activation function.
	MLCActivationTypeAbsolute MLCActivationType = 6
	// An activation type that implements the soft plus activation function.
	MLCActivationTypeSoftPlus MLCActivationType = 7
	// An activation type that implements the parametric soft sign activation function.
	MLCActivationTypeSoftSign MLCActivationType = 8
	// An activation type that implements the exponential linear unit activation function.
	MLCActivationTypeELU MLCActivationType = 9
	// An activation type that implements the ReLUN activation function.
	MLCActivationTypeReLUN MLCActivationType = 10
	// An activation type that implements the log sigmoid activation function.
	MLCActivationTypeLogSigmoid MLCActivationType = 11
	// An activation type that implements the scaled exponential linear unit activation function.
	MLCActivationTypeSELU MLCActivationType = 12
	// An activation type that implements the CELU activation function.
	MLCActivationTypeCELU MLCActivationType = 13
	// An activation type that implements the hard shrink activation function.
	MLCActivationTypeHardShrink MLCActivationType = 14
	// An activation type that implements the soft shrink activation function.
	MLCActivationTypeSoftShrink MLCActivationType = 15
	// An activation type that implements the hyperbolic tangent shrink activation function.
	MLCActivationTypeTanhShrink MLCActivationType = 16
	// An activation type that implements the threshold activation function.
	MLCActivationTypeThreshold MLCActivationType = 17
	// An activation type that implements the gaussian error linear unit activation function.
	MLCActivationTypeGELU MLCActivationType = 18
	// An activation type that implements the hard swish activation function.
	MLCActivationTypeHardSwish MLCActivationType = 19
	// An activation type that implements the clamp activation function.
	MLCActivationTypeClamp MLCActivationType = 20
	// The count of activation types.
	MLCActivationTypeCount MLCActivationType = 21
)

func (MLCActivationType) String 

func (e MLCActivationType) String() string

type MLCAdamOptimizer 

type MLCAdamOptimizer struct {
	MLCOptimizer
}

An optimizer that represents the adaptive moment estimation algorithm.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcadamoptimizer

func MLCAdamOptimizerFromID 

func MLCAdamOptimizerFromID(id objc.ID) *MLCAdamOptimizer

func MLCAdamOptimizerOptimizerWithDescriptor 

func MLCAdamOptimizerOptimizerWithDescriptor(optimizerDescriptor *MLCOptimizerDescriptor) *MLCAdamOptimizer

Creates an Adam optimizer with the descriptor you specify.

func MLCAdamOptimizerOptimizerWithDescriptorBeta1Beta2EpsilonTimeStep 

func MLCAdamOptimizerOptimizerWithDescriptorBeta1Beta2EpsilonTimeStep(optimizerDescriptor *MLCOptimizerDescriptor, beta1 float32, beta2 float32, epsilon float32, timeStep uint) *MLCAdamOptimizer

Creates an Adam optimizer with the values you specify.

func MLCAdamOptimizerOptimizerWithDescriptorBeta1Beta2EpsilonUsesAMSGradTimeStep 

func MLCAdamOptimizerOptimizerWithDescriptorBeta1Beta2EpsilonUsesAMSGradTimeStep(optimizerDescriptor *MLCOptimizerDescriptor, beta1 float32, beta2 float32, epsilon float32, usesAMSGrad bool, timeStep uint) *MLCAdamOptimizer

Creates an Adam optimizer with the values you specify.

func (*MLCAdamOptimizer) Beta1 

func (o *MLCAdamOptimizer) Beta1() float32

@property beta1 @abstract Coefficent used for computing running averages of gradient. @discussion The default is 0.9.

func (*MLCAdamOptimizer) Beta2 

func (o *MLCAdamOptimizer) Beta2() float32

@property beta2 @abstract Coefficent used for computing running averages of square of gradient. @discussion The default is 0.999.

func (*MLCAdamOptimizer) Epsilon 

func (o *MLCAdamOptimizer) Epsilon() float32

func (*MLCAdamOptimizer) TimeStep 

func (o *MLCAdamOptimizer) TimeStep() uint

@property timeStep @abstract The current timestep used for the update. @discussion The default is 1.

func (*MLCAdamOptimizer) UsesAMSGrad 

func (o *MLCAdamOptimizer) UsesAMSGrad() bool

@property usesAMSGrad @abstract Whether to use the AMSGrad variant of this algorithm @discussion The default is false

type MLCAdamWOptimizer 

type MLCAdamWOptimizer struct {
	MLCOptimizer
}

An optimizer that represents the Adam algorithm with weight decay.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcadamwoptimizer

func MLCAdamWOptimizerFromID 

func MLCAdamWOptimizerFromID(id objc.ID) *MLCAdamWOptimizer

func MLCAdamWOptimizerOptimizerWithDescriptor 

func MLCAdamWOptimizerOptimizerWithDescriptor(optimizerDescriptor *MLCOptimizerDescriptor) *MLCAdamWOptimizer

Creates a default optimizer with the descriptor you specify.

func MLCAdamWOptimizerOptimizerWithDescriptorBeta1Beta2EpsilonUsesAMSGradTimeStep 

func MLCAdamWOptimizerOptimizerWithDescriptorBeta1Beta2EpsilonUsesAMSGradTimeStep(optimizerDescriptor *MLCOptimizerDescriptor, beta1 float32, beta2 float32, epsilon float32, usesAMSGrad bool, timeStep uint) *MLCAdamWOptimizer

Creates an AdamW optimizer with the values you specify.

func (*MLCAdamWOptimizer) Beta1 

func (o *MLCAdamWOptimizer) Beta1() float32

@property beta1 @abstract Coefficent used for computing running averages of gradient. @discussion The default is 0.9.

func (*MLCAdamWOptimizer) Beta2 

func (o *MLCAdamWOptimizer) Beta2() float32

@property beta2 @abstract Coefficent used for computing running averages of square of gradient. @discussion The default is 0.999.

func (*MLCAdamWOptimizer) Epsilon 

func (o *MLCAdamWOptimizer) Epsilon() float32

@property epsilon @abstract A term added to improve numerical stability. @discussion The default is 1e-8.

func (*MLCAdamWOptimizer) TimeStep 

func (o *MLCAdamWOptimizer) TimeStep() uint

@property timeStep @abstract The current timestep used for the update. @discussion The default is 1.

func (*MLCAdamWOptimizer) UsesAMSGrad 

func (o *MLCAdamWOptimizer) UsesAMSGrad() bool

@property usesAMSGrad @abstract Whether to use the AMSGrad variant of this algorithm @discussion The default is false

type MLCArithmeticLayer 

type MLCArithmeticLayer struct {
	MLCLayer
}

A layer that performs an arithmetic operation.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcarithmeticlayer

func MLCArithmeticLayerFromID 

func MLCArithmeticLayerFromID(id objc.ID) *MLCArithmeticLayer

func MLCArithmeticLayerLayerWithOperation 

func MLCArithmeticLayerLayerWithOperation(operation MLCArithmeticOperation) *MLCArithmeticLayer

Creates an arithmetic layer with the operation you specify.

func (*MLCArithmeticLayer) Operation 

func (o *MLCArithmeticLayer) Operation() MLCArithmeticOperation

@property operation @abstract The arithmetic operation.

type MLCArithmeticOperation 

type MLCArithmeticOperation int64

Constants that describe an arithmetic operation. 

const (
	// Calculates the element-wise sum of the inputs.
	MLCArithmeticOperationAdd MLCArithmeticOperation = 0
	// Calculates the element-wise difference between the inputs.
	MLCArithmeticOperationSubtract MLCArithmeticOperation = 1
	// Calculates the element-wise product of the inputs.
	MLCArithmeticOperationMultiply MLCArithmeticOperation = 2
	// Calculates the element-wise division of the inputs.
	MLCArithmeticOperationDivide MLCArithmeticOperation = 3
	// Calculates the element-wise floor of the inputs.
	MLCArithmeticOperationFloor MLCArithmeticOperation = 4
	// Calculates the element-wise rounding of the inputs.
	MLCArithmeticOperationRound MLCArithmeticOperation = 5
	// Calculates the element-wise ceiling of the inputs.
	MLCArithmeticOperationCeil MLCArithmeticOperation = 6
	// Calculates the element-wise square root of the input.
	MLCArithmeticOperationSqrt MLCArithmeticOperation = 7
	// Calculates the element-wise reciprocal of the square root of the input.
	MLCArithmeticOperationRsqrt MLCArithmeticOperation = 8
	// Calculates the element-wise sine of the input.
	MLCArithmeticOperationSin MLCArithmeticOperation = 9
	// Calculates the element-wise cosine of the input.
	MLCArithmeticOperationCos MLCArithmeticOperation = 10
	// Calculates the element-wise tangent of the input.
	MLCArithmeticOperationTan MLCArithmeticOperation = 11
	// Calculates the element-wise inverse sine of the input.
	MLCArithmeticOperationAsin MLCArithmeticOperation = 12
	// Calculates the element-wise inverse cosine of the input.
	MLCArithmeticOperationAcos MLCArithmeticOperation = 13
	// Calculates the element-wise inverse tangent of the input.
	MLCArithmeticOperationAtan MLCArithmeticOperation = 14
	// Calculates the element-wise hyperbolic sine of the input.
	MLCArithmeticOperationSinh MLCArithmeticOperation = 15
	// Calculates the element-wise hyperbolic cosine of the input.
	MLCArithmeticOperationCosh MLCArithmeticOperation = 16
	// Calculates the element-wise hyperbolic tangent of the input.
	MLCArithmeticOperationTanh MLCArithmeticOperation = 17
	// Calculates the element-wise inverse hyperbolic sine of the input.
	MLCArithmeticOperationAsinh MLCArithmeticOperation = 18
	// Calculates the element-wise inverse hyperbolic cosine of the input.
	MLCArithmeticOperationAcosh MLCArithmeticOperation = 19
	// Calculates the element-wise inverse hyperbolic tangent of the input.
	MLCArithmeticOperationAtanh MLCArithmeticOperation = 20
	// Calculates the element-wise first input raised to the power of the second input.
	MLCArithmeticOperationPow MLCArithmeticOperation = 21
	// Calculates the element-wise result of the exponent raised to the power of the input.
	MLCArithmeticOperationExp MLCArithmeticOperation = 22
	// Calculates the element-wise result of the number 2 raised to the power of the input.
	MLCArithmeticOperationExp2 MLCArithmeticOperation = 23
	// Calculates the element-wise natural logarithm of the input.
	MLCArithmeticOperationLog MLCArithmeticOperation = 24
	// Calculates the element-wise base 2 logarithm of the input.
	MLCArithmeticOperationLog2 MLCArithmeticOperation = 25
	// Calculates the element-wise product of the inputs, and returns 0 when the result isn’t a number or infinity.
	MLCArithmeticOperationMultiplyNoNaN MLCArithmeticOperation = 26
	// Calculates the element-wise division of the inputs, and returns 0 if the denominator is 0.
	MLCArithmeticOperationDivideNoNaN MLCArithmeticOperation = 27
	// Calculates the element-wise minimum of the inputs.
	MLCArithmeticOperationMin MLCArithmeticOperation = 28
	// Calculates the element-wise maximum the inputs.
	MLCArithmeticOperationMax MLCArithmeticOperation = 29
	// The total number of arithmetic operations.
	MLCArithmeticOperationCount MLCArithmeticOperation = 30
)

func (MLCArithmeticOperation) String 

func (e MLCArithmeticOperation) String() string

type MLCBatchNormalizationLayer 

type MLCBatchNormalizationLayer struct {
	MLCLayer
}

A layer that normalizes a batch of inputs.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcbatchnormalizationlayer

func MLCBatchNormalizationLayerFromID 

func MLCBatchNormalizationLayerFromID(id objc.ID) *MLCBatchNormalizationLayer

func MLCBatchNormalizationLayerLayerWithFeatureChannelCountMeanVarianceBetaGammaVarianceEpsilon 

func MLCBatchNormalizationLayerLayerWithFeatureChannelCountMeanVarianceBetaGammaVarianceEpsilon(featureChannelCount uint, mean *MLCTensor, variance *MLCTensor, beta *MLCTensor, gamma *MLCTensor, varianceEpsilon float32) *MLCBatchNormalizationLayer

Creates a batch normalization layer with the number of feature channels, tensors, and variance epsilon you specify.

func MLCBatchNormalizationLayerLayerWithFeatureChannelCountMeanVarianceBetaGammaVarianceEpsilonMomentum 

func MLCBatchNormalizationLayerLayerWithFeatureChannelCountMeanVarianceBetaGammaVarianceEpsilonMomentum(featureChannelCount uint, mean *MLCTensor, variance *MLCTensor, beta *MLCTensor, gamma *MLCTensor, varianceEpsilon float32, momentum float32) *MLCBatchNormalizationLayer

Creates a batch normalization layer with the number of feature channels, tensors, variance epsilon, and momentum you specify.

func (*MLCBatchNormalizationLayer) Beta 

func (o *MLCBatchNormalizationLayer) Beta() *MLCTensor

@property beta @abstract The beta tensor

func (*MLCBatchNormalizationLayer) BetaParameter 

func (o *MLCBatchNormalizationLayer) BetaParameter() *MLCTensorParameter

@property betaParameter @abstract The beta tensor parameter used for optimizer update

func (*MLCBatchNormalizationLayer) FeatureChannelCount 

func (o *MLCBatchNormalizationLayer) FeatureChannelCount() uint

@property featureChannelCount @abstract The number of feature channels

func (*MLCBatchNormalizationLayer) Gamma 

func (o *MLCBatchNormalizationLayer) Gamma() *MLCTensor

@property gamma @abstract The gamma tensor

func (*MLCBatchNormalizationLayer) GammaParameter 

func (o *MLCBatchNormalizationLayer) GammaParameter() *MLCTensorParameter

@property gammaParameter @abstract The gamma tensor parameter used for optimizer update

func (*MLCBatchNormalizationLayer) Mean 

func (o *MLCBatchNormalizationLayer) Mean() *MLCTensor

@property mean @abstract The mean tensor

func (*MLCBatchNormalizationLayer) Momentum 

func (o *MLCBatchNormalizationLayer) Momentum() float32

@property momentum @abstract The value used for the running mean and variance computation @discussion The default is 0.99f.

func (*MLCBatchNormalizationLayer) Variance 

func (o *MLCBatchNormalizationLayer) Variance() *MLCTensor

@property variance @abstract The variance tensor

func (*MLCBatchNormalizationLayer) VarianceEpsilon 

func (o *MLCBatchNormalizationLayer) VarianceEpsilon() float32

@property varianceEpsilon @abstract A value used for numerical stability

type MLCComparisonLayer 

type MLCComparisonLayer struct {
	MLCLayer
}

A layer that performs elementwise comparison of two tensors.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlccomparisonlayer

func MLCComparisonLayerFromID 

func MLCComparisonLayerFromID(id objc.ID) *MLCComparisonLayer

func MLCComparisonLayerLayerWithOperation 

func MLCComparisonLayerLayerWithOperation(operation MLCComparisonOperation) *MLCComparisonLayer

Creates a comparison layer with the operation you specify.

func (*MLCComparisonLayer) Operation 

func (o *MLCComparisonLayer) Operation() MLCComparisonOperation

type MLCComparisonOperation 

type MLCComparisonOperation int64

A comparison operation. 

const (
	MLCComparisonOperationEqual          MLCComparisonOperation = 0
	MLCComparisonOperationNotEqual       MLCComparisonOperation = 1
	MLCComparisonOperationLess           MLCComparisonOperation = 2
	MLCComparisonOperationGreater        MLCComparisonOperation = 3
	MLCComparisonOperationLessOrEqual    MLCComparisonOperation = 4
	MLCComparisonOperationGreaterOrEqual MLCComparisonOperation = 5
	MLCComparisonOperationLogicalAND     MLCComparisonOperation = 6
	MLCComparisonOperationLogicalOR      MLCComparisonOperation = 7
	MLCComparisonOperationLogicalNOT     MLCComparisonOperation = 8
	MLCComparisonOperationLogicalNAND    MLCComparisonOperation = 9
	MLCComparisonOperationLogicalNOR     MLCComparisonOperation = 10
	MLCComparisonOperationLogicalXOR     MLCComparisonOperation = 11
	// A number that represents the operation count.
	MLCComparisonOperationCount MLCComparisonOperation = 12
)

func (MLCComparisonOperation) String 

func (e MLCComparisonOperation) String() string

type MLCConcatenationLayer 

type MLCConcatenationLayer struct {
	MLCLayer
}

A layer that combines tensors into a single tensor.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcconcatenationlayer

func MLCConcatenationLayerFromID 

func MLCConcatenationLayerFromID(id objc.ID) *MLCConcatenationLayer

func MLCConcatenationLayerLayer 

func MLCConcatenationLayerLayer() *MLCConcatenationLayer

Creates a concatenation layer with a dimension value of 1, which typically represents feature channels.

func MLCConcatenationLayerLayerWithDimension 

func MLCConcatenationLayerLayerWithDimension(dimension uint) *MLCConcatenationLayer

Creates a concatenation layer with the dimension you specify.

func (*MLCConcatenationLayer) Dimension 

func (o *MLCConcatenationLayer) Dimension() uint

@property dimension @abstract The dimension (or axis) along which to concatenate tensors @discussion The default value is 1 (which typically represents features channels)

type MLCConvolutionDescriptor 

type MLCConvolutionDescriptor struct {
	foundation.NSObject
}

A configuration object you use to create a convolution or fully connected layer.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcconvolutiondescriptor

func MLCConvolutionDescriptorConvolutionTransposeDescriptorWithKernelSizesInputFeatureChannelCountOutputFeatureChannelCountGroupCountStridesDilationRatesPaddingPolicyPaddingSizes 

func MLCConvolutionDescriptorConvolutionTransposeDescriptorWithKernelSizesInputFeatureChannelCountOutputFeatureChannelCountGroupCountStridesDilationRatesPaddingPolicyPaddingSizes(kernelSizes *foundation.NSArray[*foundation.NSNumber], inputFeatureChannelCount uint, outputFeatureChannelCount uint, groupCount uint, strides *foundation.NSArray[*foundation.NSNumber], dilationRates *foundation.NSArray[*foundation.NSNumber], paddingPolicy MLCPaddingPolicy, paddingSizes *foundation.NSArray[*foundation.NSNumber]) *MLCConvolutionDescriptor

Creates a convolution transpose descriptor with the kernel and padding options, number of feature channels and groups, and dilation rates you specify.

func MLCConvolutionDescriptorConvolutionTransposeDescriptorWithKernelSizesInputFeatureChannelCountOutputFeatureChannelCountStridesPaddingPolicyPaddingSizes 

func MLCConvolutionDescriptorConvolutionTransposeDescriptorWithKernelSizesInputFeatureChannelCountOutputFeatureChannelCountStridesPaddingPolicyPaddingSizes(kernelSizes *foundation.NSArray[*foundation.NSNumber], inputFeatureChannelCount uint, outputFeatureChannelCount uint, strides *foundation.NSArray[*foundation.NSNumber], paddingPolicy MLCPaddingPolicy, paddingSizes *foundation.NSArray[*foundation.NSNumber]) *MLCConvolutionDescriptor

Creates a convolution transpose descriptor with the kernel sizes, number of feature channels, strides, and padding options you specify.

func MLCConvolutionDescriptorConvolutionTransposeDescriptorWithKernelWidthKernelHeightInputFeatureChannelCountOutputFeatureChannelCount 

func MLCConvolutionDescriptorConvolutionTransposeDescriptorWithKernelWidthKernelHeightInputFeatureChannelCountOutputFeatureChannelCount(kernelWidth uint, kernelHeight uint, inputFeatureChannelCount uint, outputFeatureChannelCount uint) *MLCConvolutionDescriptor

Creates a descriptor for convolution transpose with the kernel sizes and number of feature channels you specify.

func MLCConvolutionDescriptorDepthwiseConvolutionDescriptorWithKernelSizesInputFeatureChannelCountChannelMultiplierStridesDilationRatesPaddingPolicyPaddingSizes 

func MLCConvolutionDescriptorDepthwiseConvolutionDescriptorWithKernelSizesInputFeatureChannelCountChannelMultiplierStridesDilationRatesPaddingPolicyPaddingSizes(kernelSizes *foundation.NSArray[*foundation.NSNumber], inputFeatureChannelCount uint, channelMultiplier uint, strides *foundation.NSArray[*foundation.NSNumber], dilationRates *foundation.NSArray[*foundation.NSNumber], paddingPolicy MLCPaddingPolicy, paddingSizes *foundation.NSArray[*foundation.NSNumber]) *MLCConvolutionDescriptor

Creates a convolution descriptor with the kernel and padding options, number of input channels, channel multiplier, and dilation rates you specify.

func MLCConvolutionDescriptorDepthwiseConvolutionDescriptorWithKernelSizesInputFeatureChannelCountChannelMultiplierStridesPaddingPolicyPaddingSizes 

func MLCConvolutionDescriptorDepthwiseConvolutionDescriptorWithKernelSizesInputFeatureChannelCountChannelMultiplierStridesPaddingPolicyPaddingSizes(kernelSizes *foundation.NSArray[*foundation.NSNumber], inputFeatureChannelCount uint, channelMultiplier uint, strides *foundation.NSArray[*foundation.NSNumber], paddingPolicy MLCPaddingPolicy, paddingSizes *foundation.NSArray[*foundation.NSNumber]) *MLCConvolutionDescriptor

Creates a depthwise convolution descriptor with the kernel and padding options, number of input feature channels, and channel multiplier you specify.

func MLCConvolutionDescriptorDepthwiseConvolutionDescriptorWithKernelWidthKernelHeightInputFeatureChannelCountChannelMultiplier 

func MLCConvolutionDescriptorDepthwiseConvolutionDescriptorWithKernelWidthKernelHeightInputFeatureChannelCountChannelMultiplier(kernelWidth uint, kernelHeight uint, inputFeatureChannelCount uint, channelMultiplier uint) *MLCConvolutionDescriptor

Creates a descriptor for depthwise convolution with the kernel sizes, number of input feature channels, and channel multiplier you specify.

func MLCConvolutionDescriptorDescriptorWithKernelSizesInputFeatureChannelCountOutputFeatureChannelCountGroupCountStridesDilationRatesPaddingPolicyPaddingSizes 

func MLCConvolutionDescriptorDescriptorWithKernelSizesInputFeatureChannelCountOutputFeatureChannelCountGroupCountStridesDilationRatesPaddingPolicyPaddingSizes(kernelSizes *foundation.NSArray[*foundation.NSNumber], inputFeatureChannelCount uint, outputFeatureChannelCount uint, groupCount uint, strides *foundation.NSArray[*foundation.NSNumber], dilationRates *foundation.NSArray[*foundation.NSNumber], paddingPolicy MLCPaddingPolicy, paddingSizes *foundation.NSArray[*foundation.NSNumber]) *MLCConvolutionDescriptor

Creates a convolution descriptor with the kernel and padding options, number of feature channels and groups, and dilation rates you specify.

func MLCConvolutionDescriptorDescriptorWithKernelSizesInputFeatureChannelCountOutputFeatureChannelCountStridesPaddingPolicyPaddingSizes 

func MLCConvolutionDescriptorDescriptorWithKernelSizesInputFeatureChannelCountOutputFeatureChannelCountStridesPaddingPolicyPaddingSizes(kernelSizes *foundation.NSArray[*foundation.NSNumber], inputFeatureChannelCount uint, outputFeatureChannelCount uint, strides *foundation.NSArray[*foundation.NSNumber], paddingPolicy MLCPaddingPolicy, paddingSizes *foundation.NSArray[*foundation.NSNumber]) *MLCConvolutionDescriptor

Creates a convolution descriptor with the kernel sizes, number of feature channels, strides, padding policy, and padding sizes you specify.

func MLCConvolutionDescriptorDescriptorWithKernelWidthKernelHeightInputFeatureChannelCountOutputFeatureChannelCount 

func MLCConvolutionDescriptorDescriptorWithKernelWidthKernelHeightInputFeatureChannelCountOutputFeatureChannelCount(kernelWidth uint, kernelHeight uint, inputFeatureChannelCount uint, outputFeatureChannelCount uint) *MLCConvolutionDescriptor

Creates a convolution descriptor with the kernel sizes and number of feature channels you specify.

func MLCConvolutionDescriptorDescriptorWithTypeKernelSizesInputFeatureChannelCountOutputFeatureChannelCountGroupCountStridesDilationRatesPaddingPolicyPaddingSizes 

func MLCConvolutionDescriptorDescriptorWithTypeKernelSizesInputFeatureChannelCountOutputFeatureChannelCountGroupCountStridesDilationRatesPaddingPolicyPaddingSizes(convolutionType MLCConvolutionType, kernelSizes *foundation.NSArray[*foundation.NSNumber], inputFeatureChannelCount uint, outputFeatureChannelCount uint, groupCount uint, strides *foundation.NSArray[*foundation.NSNumber], dilationRates *foundation.NSArray[*foundation.NSNumber], paddingPolicy MLCPaddingPolicy, paddingSizes *foundation.NSArray[*foundation.NSNumber]) *MLCConvolutionDescriptor

Creates a descriptor with the type, kernel sizes, number of feature channels and groups, strides, dilation rates, and padding policy you specify.

func MLCConvolutionDescriptorFromID 

func MLCConvolutionDescriptorFromID(id objc.ID) *MLCConvolutionDescriptor

func (*MLCConvolutionDescriptor) ConvolutionType 

func (o *MLCConvolutionDescriptor) ConvolutionType() MLCConvolutionType

@property convolutionType @abstract The type of convolution.

func (*MLCConvolutionDescriptor) DilationRateInX 

func (o *MLCConvolutionDescriptor) DilationRateInX() uint

@property dilationRateInX @abstract The dilation rate i.e. stride of elements in the kernel in x.

func (*MLCConvolutionDescriptor) DilationRateInY 

func (o *MLCConvolutionDescriptor) DilationRateInY() uint

@property dilationRateInY @abstract The dilation rate i.e. stride of elements in the kernel in y.

func (*MLCConvolutionDescriptor) GroupCount 

func (o *MLCConvolutionDescriptor) GroupCount() uint

@property groupCount @abstract Number of blocked connections from input channels to output channels

func (*MLCConvolutionDescriptor) InputFeatureChannelCount 

func (o *MLCConvolutionDescriptor) InputFeatureChannelCount() uint

@property inputFeatureChannelCount @abstract Number of channels in the input tensor

func (*MLCConvolutionDescriptor) IsConvolutionTranspose 

func (o *MLCConvolutionDescriptor) IsConvolutionTranspose() bool

@property isConvolutionTranspose @abstract A flag to indicate if this is a convolution transpose

func (*MLCConvolutionDescriptor) KernelHeight 

func (o *MLCConvolutionDescriptor) KernelHeight() uint

@property kernelHeight @abstract The convolution kernel size in y.

func (*MLCConvolutionDescriptor) KernelWidth 

func (o *MLCConvolutionDescriptor) KernelWidth() uint

@property kernelWidth @abstract The convolution kernel size in x.

func (*MLCConvolutionDescriptor) OutputFeatureChannelCount 

func (o *MLCConvolutionDescriptor) OutputFeatureChannelCount() uint

@property outputFeatureChannelCount @abstract Number of channels in the output tensor

func (*MLCConvolutionDescriptor) PaddingPolicy 

func (o *MLCConvolutionDescriptor) PaddingPolicy() MLCPaddingPolicy

@property paddingPolicy @abstract The padding policy to use.

func (*MLCConvolutionDescriptor) PaddingSizeInX 

func (o *MLCConvolutionDescriptor) PaddingSizeInX() uint

@property paddingSizeInX @abstract The pooling size in x (left and right) to use if paddingPolicy is MLCPaddingPolicyUsePaddingSize

func (*MLCConvolutionDescriptor) PaddingSizeInY 

func (o *MLCConvolutionDescriptor) PaddingSizeInY() uint

@property paddingSizeInY @abstract The pooling size in y (top and bottom) to use if paddingPolicy is MLCPaddingPolicyUsePaddingSize

func (*MLCConvolutionDescriptor) StrideInX 

func (o *MLCConvolutionDescriptor) StrideInX() uint

@property strideInX @abstract The stride of the kernel in x.

func (*MLCConvolutionDescriptor) StrideInY 

func (o *MLCConvolutionDescriptor) StrideInY() uint

@property strideInY @abstract The stride of the kernel in y.

func (*MLCConvolutionDescriptor) UsesDepthwiseConvolution 

func (o *MLCConvolutionDescriptor) UsesDepthwiseConvolution() bool

@property usesDepthwiseConvolution @abstract A flag to indicate depthwise convolution

type MLCConvolutionLayer 

type MLCConvolutionLayer struct {
	MLCLayer
}

A layer that applies a convolution over a signal.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcconvolutionlayer

func MLCConvolutionLayerFromID 

func MLCConvolutionLayerFromID(id objc.ID) *MLCConvolutionLayer

func MLCConvolutionLayerLayerWithWeightsBiasesDescriptor 

func MLCConvolutionLayerLayerWithWeightsBiasesDescriptor(weights *MLCTensor, biases *MLCTensor, descriptor *MLCConvolutionDescriptor) *MLCConvolutionLayer

Creates a convolution layer with the weights, biases, and descriptor you specify.

func (*MLCConvolutionLayer) Biases 

func (o *MLCConvolutionLayer) Biases() *MLCTensor

@property biases @abstract The bias tensor used by the convolution layer

func (*MLCConvolutionLayer) BiasesParameter 

func (o *MLCConvolutionLayer) BiasesParameter() *MLCTensorParameter

@property biasesParameter @abstract The bias tensor parameter used for optimizer update

func (*MLCConvolutionLayer) Descriptor 

func (o *MLCConvolutionLayer) Descriptor() *MLCConvolutionDescriptor

@property descriptor @abstract The convolution descriptor

func (*MLCConvolutionLayer) Weights 

func (o *MLCConvolutionLayer) Weights() *MLCTensor

@property weights @abstract The weights tensor used by the convolution layer

func (*MLCConvolutionLayer) WeightsParameter 

func (o *MLCConvolutionLayer) WeightsParameter() *MLCTensorParameter

@property weightsParameter @abstract The weights tensor parameter used for optimizer update

type MLCConvolutionType 

type MLCConvolutionType int64

The convolution type specified for a convolution layer. 

const (
	// The standard convolution type.
	MLCConvolutionTypeStandard MLCConvolutionType = 0
	// The transposed convolution type.
	MLCConvolutionTypeTransposed MLCConvolutionType = 1
	// The depthwise convolution type.
	MLCConvolutionTypeDepthwise MLCConvolutionType = 2
)

func (MLCConvolutionType) String 

func (e MLCConvolutionType) String() string

type MLCDataType 

type MLCDataType int64

A tensor data type. 

const (
	MLCDataTypeInvalid MLCDataType = 0
	// The 32-bit floating-point data type.
	MLCDataTypeFloat32 MLCDataType = 1
	// The 16-bit floating-point data type.
	MLCDataTypeFloat16 MLCDataType = 3
	// The Boolean data type.
	MLCDataTypeBoolean MLCDataType = 4
	// The 64-bit integer data type.
	MLCDataTypeInt64 MLCDataType = 5
	// The 32-bit integer data type.
	MLCDataTypeInt32 MLCDataType = 7
	// The 8-bit integer data type.
	MLCDataTypeInt8 MLCDataType = 8
	// The 8-bit unsigned integer data type.
	MLCDataTypeUInt8 MLCDataType = 9
	MLCDataTypeCount MLCDataType = 10
)

func (MLCDataType) String 

func (e MLCDataType) String() string

type MLCDevice 

type MLCDevice struct {
	foundation.NSObject
}

An object that represents the CPU or one or more GPUs the framework uses to execute a neural network.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcdevice

func MLCDeviceAneDevice 

func MLCDeviceAneDevice() *MLCDevice

Creates a device that uses the Apple Neural Engine, if one exists.

func MLCDeviceCpuDevice 

func MLCDeviceCpuDevice() *MLCDevice

Creates a device that uses the CPU.

func MLCDeviceDeviceWithGPUDevices 

func MLCDeviceDeviceWithGPUDevices(gpus *foundation.NSArray[metal.MTLDevice]) *MLCDevice

Creates a device using the GPUs you specify.

func MLCDeviceDeviceWithType 

func MLCDeviceDeviceWithType(type_ MLCDeviceType) *MLCDevice

Creates a device of the type you specify.

func MLCDeviceDeviceWithTypeSelectsMultipleComputeDevices 

func MLCDeviceDeviceWithTypeSelectsMultipleComputeDevices(type_ MLCDeviceType, selectsMultipleComputeDevices bool) *MLCDevice

Creates a device that you can configure to use multiple compute devices.

func MLCDeviceFromID 

func MLCDeviceFromID(id objc.ID) *MLCDevice

func MLCDeviceGpuDevice 

func MLCDeviceGpuDevice() *MLCDevice

Creates a device that uses a GPU, if one exists.

func (*MLCDevice) ActualDeviceType 

func (o *MLCDevice) ActualDeviceType() MLCDeviceType

@property actualDeviceType @abstract The specific device selected. @discussion This can be CPU, GPU or ANE. If type is MLCDeviceTypeAny, this property can be used to find out the specific device type that is selected.

func (*MLCDevice) GpuDevices 

func (o *MLCDevice) GpuDevices() *foundation.NSArray[metal.MTLDevice]

func (*MLCDevice) Type 

func (o *MLCDevice) Type() MLCDeviceType

@property type @abstract The type specified when the device is created @discussion Recommend that developers use MLCDeviceTypeAny as the device type. This will ensure that MLCompute will select the best device to execute the neural network. If developers want to be able to control device selection, they can select CPU or GPU and for the GPU, they can also select a specific Metal device.

type MLCDeviceType 

type MLCDeviceType int64

A device type for execution of a neural network. 

const (
	// A device type that represents the CPU.
	MLCDeviceTypeCPU MLCDeviceType = 0
	// A device type that represents the GPU.
	MLCDeviceTypeGPU MLCDeviceType = 1
	// A device type that represents either the CPU or GPU.
	MLCDeviceTypeAny MLCDeviceType = 2
	// A device type that represents the Apple Neural Engine.
	MLCDeviceTypeANE MLCDeviceType = 3
	// A number that represents the number of device types.
	MLCDeviceTypeCount MLCDeviceType = 4
)

func (MLCDeviceType) String 

func (e MLCDeviceType) String() string

type MLCDropoutLayer 

type MLCDropoutLayer struct {
	MLCLayer
}

A layer that deactivates neurons randomly to avoid overfitting.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcdropoutlayer

func MLCDropoutLayerFromID 

func MLCDropoutLayerFromID(id objc.ID) *MLCDropoutLayer

func MLCDropoutLayerLayerWithRateSeed 

func MLCDropoutLayerLayerWithRateSeed(rate float32, seed uint) *MLCDropoutLayer

Creates a dropout layer with the probability rate and random number generator seed you specify.

func (*MLCDropoutLayer) Rate 

func (o *MLCDropoutLayer) Rate() float32

@property rate @abstract The probability that each element is dropped

func (*MLCDropoutLayer) Seed 

func (o *MLCDropoutLayer) Seed() uint

@property seed @abstract The initial seed used to generate random numbers

type MLCEmbeddingDescriptor 

type MLCEmbeddingDescriptor struct {
	foundation.NSObject
}

A configuration object you use to create an embedding layer.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcembeddingdescriptor

func MLCEmbeddingDescriptorDescriptorWithEmbeddingCountEmbeddingDimension 

func MLCEmbeddingDescriptorDescriptorWithEmbeddingCountEmbeddingDimension(embeddingCount *foundation.NSNumber, embeddingDimension *foundation.NSNumber) *MLCEmbeddingDescriptor

Creates an embedding descriptor with the size of the dictionary and dimension of embedding vectors you specify.

func MLCEmbeddingDescriptorDescriptorWithEmbeddingCountEmbeddingDimensionPaddingIndexMaximumNormPNormScalesGradientByFrequency 

func MLCEmbeddingDescriptorDescriptorWithEmbeddingCountEmbeddingDimensionPaddingIndexMaximumNormPNormScalesGradientByFrequency(embeddingCount *foundation.NSNumber, embeddingDimension *foundation.NSNumber, paddingIndex *foundation.NSNumber, maximumNorm *foundation.NSNumber, pNorm *foundation.NSNumber, scalesGradientByFrequency bool) *MLCEmbeddingDescriptor

Creates a new embedding descriptor with the size and dimension of embedding vectors, padding index, and norm and scaling options that you specify.

func MLCEmbeddingDescriptorFromID 

func MLCEmbeddingDescriptorFromID(id objc.ID) *MLCEmbeddingDescriptor

func (*MLCEmbeddingDescriptor) EmbeddingCount 

func (o *MLCEmbeddingDescriptor) EmbeddingCount() *foundation.NSNumber

@property embeddingCount @abstract The size of the dictionary

func (*MLCEmbeddingDescriptor) EmbeddingDimension 

func (o *MLCEmbeddingDescriptor) EmbeddingDimension() *foundation.NSNumber

@property embeddingDimension @abstract The dimension of embedding vectors

func (*MLCEmbeddingDescriptor) MaximumNorm 

func (o *MLCEmbeddingDescriptor) MaximumNorm() *foundation.NSNumber

@property maximumNorm @abstract A float, if set, in the forward pass only, the selected embedding vectors will be re-normalized to have an Lp norm of less than maximumNorm in the dictionary, Default=nil

func (*MLCEmbeddingDescriptor) PNorm 

func (o *MLCEmbeddingDescriptor) PNorm() *foundation.NSNumber

@property pNorm @abstract A float, the p of the Lp norm, can be set to infinity norm by [NSNumber numberWithFloat:INFINITY]. Default=2.0

func (*MLCEmbeddingDescriptor) PaddingIndex 

func (o *MLCEmbeddingDescriptor) PaddingIndex() *foundation.NSNumber

@property paddingIndex @abstract If set, the embedding vector at paddingIndex is initialized with zero and will not be updated in gradient pass, Default=nil

func (*MLCEmbeddingDescriptor) ScalesGradientByFrequency 

func (o *MLCEmbeddingDescriptor) ScalesGradientByFrequency() bool

@property scalesGradientByFrequency @abstract If set, the gradients are scaled by the inverse of the frequency of the words in batch before the weight update. Default=NO

type MLCEmbeddingLayer 

type MLCEmbeddingLayer struct {
	MLCLayer
}

A layer that stores a word embedding.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcembeddinglayer

func MLCEmbeddingLayerFromID 

func MLCEmbeddingLayerFromID(id objc.ID) *MLCEmbeddingLayer

func MLCEmbeddingLayerLayerWithDescriptorWeights 

func MLCEmbeddingLayerLayerWithDescriptorWeights(descriptor *MLCEmbeddingDescriptor, weights *MLCTensor) *MLCEmbeddingLayer

Creates an embedding layer with the descriptor and word embedding weights tensor you specify.

func (*MLCEmbeddingLayer) Descriptor 

func (o *MLCEmbeddingLayer) Descriptor() *MLCEmbeddingDescriptor

func (*MLCEmbeddingLayer) Weights 

func (o *MLCEmbeddingLayer) Weights() *MLCTensor

@property weights @abstract The array of word embeddings

func (*MLCEmbeddingLayer) WeightsParameter 

func (o *MLCEmbeddingLayer) WeightsParameter() *MLCTensorParameter

@property weightsParameter @abstract The weights tensor parameter used for optimizer update

type MLCExecutionOptions 

type MLCExecutionOptions int64

A bitmask that specifies the options you use when executing a graph. 

const (
	// The option to execute the graph in the most efficient way possible.
	MLCExecutionOptionsNone MLCExecutionOptions = 0
	// The option to skip writing input data to device memory.
	MLCExecutionOptionsSkipWritingInputDataToDevice MLCExecutionOptions = 1
	// The option to execute the graph synchronously.
	MLCExecutionOptionsSynchronous MLCExecutionOptions = 2
	// The option to return profiling information in the callback before returning from execution.
	MLCExecutionOptionsProfiling MLCExecutionOptions = 4
	// The option to execute the forward pass for inference only.
	MLCExecutionOptionsForwardForInference MLCExecutionOptions = 8
	// The option to enable additional per-layer profiling information using signposts.
	MLCExecutionOptionsPerLayerProfiling MLCExecutionOptions = 16
)

func (MLCExecutionOptions) String 

func (e MLCExecutionOptions) String() string

type MLCFullyConnectedLayer 

type MLCFullyConnectedLayer struct {
	MLCLayer
}

A layer that connects each input to each output within its layer.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcfullyconnectedlayer

func MLCFullyConnectedLayerFromID 

func MLCFullyConnectedLayerFromID(id objc.ID) *MLCFullyConnectedLayer

func MLCFullyConnectedLayerLayerWithWeightsBiasesDescriptor 

func MLCFullyConnectedLayerLayerWithWeightsBiasesDescriptor(weights *MLCTensor, biases *MLCTensor, descriptor *MLCConvolutionDescriptor) *MLCFullyConnectedLayer

Creates a fully connected layer with the weights, biases, and convolution descriptor you specify.

func (*MLCFullyConnectedLayer) Biases 

func (o *MLCFullyConnectedLayer) Biases() *MLCTensor

@property biases @abstract The bias tensor used by the convolution layer

func (*MLCFullyConnectedLayer) BiasesParameter 

func (o *MLCFullyConnectedLayer) BiasesParameter() *MLCTensorParameter

@property biasesParameter @abstract The bias tensor parameter used for optimizer update

func (*MLCFullyConnectedLayer) Descriptor 

func (o *MLCFullyConnectedLayer) Descriptor() *MLCConvolutionDescriptor

@property descriptor @abstract The convolution descriptor

func (*MLCFullyConnectedLayer) Weights 

func (o *MLCFullyConnectedLayer) Weights() *MLCTensor

@property weights @abstract The weights tensor used by the convolution layer

func (*MLCFullyConnectedLayer) WeightsParameter 

func (o *MLCFullyConnectedLayer) WeightsParameter() *MLCTensorParameter

@property weightsParameter @abstract The weights tensor parameter used for optimizer update

type MLCGatherLayer 

type MLCGatherLayer struct {
	MLCLayer
}

A layer that fetches data at the locations you specify.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcgatherlayer

func MLCGatherLayerFromID 

func MLCGatherLayerFromID(id objc.ID) *MLCGatherLayer

func MLCGatherLayerLayerWithDimension 

func MLCGatherLayerLayerWithDimension(dimension uint) *MLCGatherLayer

Creates a gather layer with the dimension you specify.

func (*MLCGatherLayer) Dimension 

func (o *MLCGatherLayer) Dimension() uint

@property dimension @abstract The dimension along which to index

type MLCGradientClippingType 

type MLCGradientClippingType int64

A clipping type the system applies to a gradient. 

const (
	// An option that clips by value.
	MLCGradientClippingTypeByValue MLCGradientClippingType = 0
	// An option that clips by norm.
	MLCGradientClippingTypeByNorm MLCGradientClippingType = 1
	// An option that clips by global norm.
	MLCGradientClippingTypeByGlobalNorm MLCGradientClippingType = 2
)

func (MLCGradientClippingType) String 

func (e MLCGradientClippingType) String() string

type MLCGramMatrixLayer 

type MLCGramMatrixLayer struct {
	MLCLayer
}

A layer that computes the uncentered cross-correlation values between the spacial planes of each feature channel of a tensor.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcgrammatrixlayer

func MLCGramMatrixLayerFromID 

func MLCGramMatrixLayerFromID(id objc.ID) *MLCGramMatrixLayer

func MLCGramMatrixLayerLayerWithScale 

func MLCGramMatrixLayerLayerWithScale(scale float32) *MLCGramMatrixLayer

Creates a gram matrix layer with the scaling factor you specify.

func (*MLCGramMatrixLayer) Scale 

func (o *MLCGramMatrixLayer) Scale() float32

@property scale @abstract The scale factor

type MLCGraph 

type MLCGraph struct {
	foundation.NSObject
}

A graph of layers you use to build a training or inference graph.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcgraph

func MLCGraphFromID 

func MLCGraphFromID(id objc.ID) *MLCGraph

func MLCGraphGraph 

func MLCGraphGraph() *MLCGraph

Creates a new graph.

func (*MLCGraph) BindAndWriteDataForInputsToDeviceBatchSizeSynchronous 

func (o *MLCGraph) BindAndWriteDataForInputsToDeviceBatchSizeSynchronous(inputsData *foundation.NSDictionary[*foundation.NSString, *MLCTensorData], inputTensors *foundation.NSDictionary[*foundation.NSString, *MLCTensor], device *MLCDevice, batchSize uint, synchronous bool) bool

Associates the given data with the input tensors, and if the device is a GPU, also copies the data to the device memory.

func (*MLCGraph) BindAndWriteDataForInputsToDeviceSynchronous 

func (o *MLCGraph) BindAndWriteDataForInputsToDeviceSynchronous(inputsData *foundation.NSDictionary[*foundation.NSString, *MLCTensorData], inputTensors *foundation.NSDictionary[*foundation.NSString, *MLCTensor], device *MLCDevice, synchronous bool) bool

Associates the given data with the input tensors, and if the device is a GPU, also copies the data to the device memory.

func (*MLCGraph) ConcatenateWithSourcesDimension 

func (o *MLCGraph) ConcatenateWithSourcesDimension(sources *foundation.NSArray[*MLCTensor], dimension uint) *MLCTensor

Adds a new concatenation layer to the graph using the source tensors and concatenation dimension you specify.

func (*MLCGraph) Device 

func (o *MLCGraph) Device() *MLCDevice

@abstract The device to be used when compiling and executing a graph

func (*MLCGraph) GatherWithDimensionSourceIndices 

func (o *MLCGraph) GatherWithDimensionSourceIndices(dimension uint, source *MLCTensor, indices *MLCTensor) *MLCTensor

Adds a gather layer to the graph using the source tensor, dimension along which to index, and the indices you specify.

func (*MLCGraph) Layers 

func (o *MLCGraph) Layers() *foundation.NSArray[*MLCLayer]

@abstract Layers in the graph

func (*MLCGraph) NodeWithLayerSource 

func (o *MLCGraph) NodeWithLayerSource(layer *MLCLayer, source *MLCTensor) *MLCTensor

Adds the layer and source tensor that you specify to the graph.

func (*MLCGraph) NodeWithLayerSources 

func (o *MLCGraph) NodeWithLayerSources(layer *MLCLayer, sources *foundation.NSArray[*MLCTensor]) *MLCTensor

Adds the layer and source tensors that you specify to the graph.

func (*MLCGraph) NodeWithLayerSourcesDisableUpdate 

func (o *MLCGraph) NodeWithLayerSourcesDisableUpdate(layer *MLCLayer, sources *foundation.NSArray[*MLCTensor], disableUpdate bool) *MLCTensor

Adds the layer, source tensors, and option to disable optimizer updates that you specify to the graph.

func (*MLCGraph) NodeWithLayerSourcesLossLabels 

func (o *MLCGraph) NodeWithLayerSourcesLossLabels(layer *MLCLayer, sources *foundation.NSArray[*MLCTensor], lossLabels *foundation.NSArray[*MLCTensor]) *MLCTensor

Adds the layer, sources, and loss labels tensors that you specify to the graph.

func (*MLCGraph) ReshapeWithShapeSource 

func (o *MLCGraph) ReshapeWithShapeSource(shape *foundation.NSArray[*foundation.NSNumber], source *MLCTensor) *MLCTensor

Adds a new reshape layer to the graph using the shape and source tensor you specify.

func (*MLCGraph) ResultTensorsForLayer 

func (o *MLCGraph) ResultTensorsForLayer(layer *MLCLayer) *foundation.NSArray[*MLCTensor]

Gets the result tensors for a layer in the training graph.

func (*MLCGraph) ScatterWithDimensionSourceIndicesCopyFromReductionType 

func (o *MLCGraph) ScatterWithDimensionSourceIndicesCopyFromReductionType(dimension uint, source *MLCTensor, indices *MLCTensor, copyFrom *MLCTensor, reductionType MLCReductionType) *MLCTensor

Adds a scatter layer to the graph.

func (*MLCGraph) SelectWithSourcesCondition 

func (o *MLCGraph) SelectWithSourcesCondition(sources *foundation.NSArray[*MLCTensor], condition *MLCTensor) *MLCTensor

Adds a select layer to the graph using the condition mask and source tensors you specify.

func (*MLCGraph) SourceTensorsForLayer 

func (o *MLCGraph) SourceTensorsForLayer(layer *MLCLayer) *foundation.NSArray[*MLCTensor]

Gets the source tensors for a layer in the training graph.

func (*MLCGraph) SplitWithSourceSplitCountDimension 

func (o *MLCGraph) SplitWithSourceSplitCountDimension(source *MLCTensor, splitCount uint, dimension uint) *foundation.NSArray[*MLCTensor]

Adds a new split layer to the graph using the source tensor, number of splits, and dimension to split the source tensor that you specify.

func (*MLCGraph) SplitWithSourceSplitSectionLengthsDimension 

func (o *MLCGraph) SplitWithSourceSplitSectionLengthsDimension(source *MLCTensor, splitSectionLengths *foundation.NSArray[*foundation.NSNumber], dimension uint) *foundation.NSArray[*MLCTensor]

Adds a new split layer to the graph using the source tensor, lengths of each split section, and dimension to split the source tensor that you specify.

func (*MLCGraph) SummarizedDOTDescription 

func (o *MLCGraph) SummarizedDOTDescription() *foundation.NSString

@abstract A DOT representation of the graph. @discussion For more info on the DOT language, refer to https://en.wikipedia.org/wiki/DOT_(graph_description_language). Edges that have a dashed lines are those that have stop gradients, while those with solid lines don't.

func (*MLCGraph) TransposeWithDimensionsSource 

func (o *MLCGraph) TransposeWithDimensionsSource(dimensions *foundation.NSArray[*foundation.NSNumber], source *MLCTensor) *MLCTensor

Adds a new transpose layer to the graph using the dimensions and source tensor you specify.

type MLCGraphCompilationOptions 

type MLCGraphCompilationOptions int64

A bitmask that specifies the options you use when compiling a graph. 

const (
	// The default option for graph compilation.
	MLCGraphCompilationOptionsNone MLCGraphCompilationOptions = 0
	// The option to debug layers during graph compilation.
	MLCGraphCompilationOptionsDebugLayers MLCGraphCompilationOptions = 1
	// The option to disable layer fusion during graph compilation.
	MLCGraphCompilationOptionsDisableLayerFusion MLCGraphCompilationOptions = 2
	// The option to link graphs during graph compilation.
	MLCGraphCompilationOptionsLinkGraphs MLCGraphCompilationOptions = 4
	// The option to compute all gradients during graph compilation.
	MLCGraphCompilationOptionsComputeAllGradients MLCGraphCompilationOptions = 8
)

func (MLCGraphCompilationOptions) String 

func (e MLCGraphCompilationOptions) String() string

type MLCGroupNormalizationLayer 

type MLCGroupNormalizationLayer struct {
	MLCLayer
}

A layer that divides the channels into groups for normalization.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcgroupnormalizationlayer

func MLCGroupNormalizationLayerFromID 

func MLCGroupNormalizationLayerFromID(id objc.ID) *MLCGroupNormalizationLayer

func MLCGroupNormalizationLayerLayerWithFeatureChannelCountGroupCountBetaGammaVarianceEpsilon 

func MLCGroupNormalizationLayerLayerWithFeatureChannelCountGroupCountBetaGammaVarianceEpsilon(featureChannelCount uint, groupCount uint, beta *MLCTensor, gamma *MLCTensor, varianceEpsilon float32) *MLCGroupNormalizationLayer

Creates a group normalization layer with the number of feature channels and groups, beta and gamma tensors, and variance epsilon you specify.

func (*MLCGroupNormalizationLayer) Beta 

func (o *MLCGroupNormalizationLayer) Beta() *MLCTensor

@property beta @abstract The beta tensor

func (*MLCGroupNormalizationLayer) BetaParameter 

func (o *MLCGroupNormalizationLayer) BetaParameter() *MLCTensorParameter

@property betaParameter @abstract The beta tensor parameter used for optimizer update

func (*MLCGroupNormalizationLayer) FeatureChannelCount 

func (o *MLCGroupNormalizationLayer) FeatureChannelCount() uint

@property featureChannelCount @abstract The number of feature channels

func (*MLCGroupNormalizationLayer) Gamma 

func (o *MLCGroupNormalizationLayer) Gamma() *MLCTensor

@property gamma @abstract The gamma tensor

func (*MLCGroupNormalizationLayer) GammaParameter 

func (o *MLCGroupNormalizationLayer) GammaParameter() *MLCTensorParameter

@property gammaParameter @abstract The gamma tensor parameter used for optimizer update

func (*MLCGroupNormalizationLayer) GroupCount 

func (o *MLCGroupNormalizationLayer) GroupCount() uint

@property groupCount @abstract The number of groups to separate the channels into

func (*MLCGroupNormalizationLayer) VarianceEpsilon 

func (o *MLCGroupNormalizationLayer) VarianceEpsilon() float32

@property varianceEpsilon @abstract A value used for numerical stability

type MLCInferenceGraph 

type MLCInferenceGraph struct {
	MLCGraph
}

An inference graph created from one or more MLCGraph instances plus additional layers added directly to the inference graph.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcinferencegraph

func MLCInferenceGraphFromID 

func MLCInferenceGraphFromID(id objc.ID) *MLCInferenceGraph

func MLCInferenceGraphGraphWithGraphObjects 

func MLCInferenceGraphGraphWithGraphObjects(graphObjects *foundation.NSArray[*MLCGraph]) *MLCInferenceGraph

Creates an inference graph with the layers from the graph objects you specify.

func (*MLCInferenceGraph) AddInputs 

func (o *MLCInferenceGraph) AddInputs(inputs *foundation.NSDictionary[*foundation.NSString, *MLCTensor]) bool

Adds the inputs you specify to the inference graph.

func (*MLCInferenceGraph) AddInputsLossLabelsLossLabelWeights 

func (o *MLCInferenceGraph) AddInputsLossLabelsLossLabelWeights(inputs *foundation.NSDictionary[*foundation.NSString, *MLCTensor], lossLabels *foundation.NSDictionary[*foundation.NSString, *MLCTensor], lossLabelWeights *foundation.NSDictionary[*foundation.NSString, *MLCTensor]) bool

Adds the inputs, loss labels, and loss label weights that you specify to the inference graph.

func (*MLCInferenceGraph) AddOutputs 

func (o *MLCInferenceGraph) AddOutputs(outputs *foundation.NSDictionary[*foundation.NSString, *MLCTensor]) bool

Adds the outputs you specify to the inference graph.

func (*MLCInferenceGraph) CompileWithOptionsDevice 

func (o *MLCInferenceGraph) CompileWithOptionsDevice(options MLCGraphCompilationOptions, device *MLCDevice) bool

Compiles the inference graph for the options and device you specify.

func (*MLCInferenceGraph) CompileWithOptionsDeviceInputTensorsInputTensorsData 

func (o *MLCInferenceGraph) CompileWithOptionsDeviceInputTensorsInputTensorsData(options MLCGraphCompilationOptions, device *MLCDevice, inputTensors *foundation.NSDictionary[*foundation.NSString, *MLCTensor], inputTensorsData *foundation.NSDictionary[*foundation.NSString, *MLCTensorData]) bool

Compiles the inference graph for the options, device, and input tensors you specify.

func (*MLCInferenceGraph) DeviceMemorySize 

func (o *MLCInferenceGraph) DeviceMemorySize() uint

@property The device memory size used by the inference graph @abstract Returns the total size in bytes of device memory used by all intermediate tensors in the inference graph @return A NSUInteger value

func (*MLCInferenceGraph) ExecuteWithInputsDataBatchSizeOptionsCompletionHandler 

func (o *MLCInferenceGraph) ExecuteWithInputsDataBatchSizeOptionsCompletionHandler(inputsData *foundation.NSDictionary[*foundation.NSString, *MLCTensorData], batchSize uint, options MLCExecutionOptions, completionHandler func(*MLCTensor, unsafe.Pointer, float64)) bool

Executes the inference graph with the inputs data, batch size, execution options, and completion handler you specify.

func (*MLCInferenceGraph) ExecuteWithInputsDataLossLabelsDataLossLabelWeightsDataBatchSizeOptionsCompletionHandler 

func (o *MLCInferenceGraph) ExecuteWithInputsDataLossLabelsDataLossLabelWeightsDataBatchSizeOptionsCompletionHandler(inputsData *foundation.NSDictionary[*foundation.NSString, *MLCTensorData], lossLabelsData *foundation.NSDictionary[*foundation.NSString, *MLCTensorData], lossLabelWeightsData *foundation.NSDictionary[*foundation.NSString, *MLCTensorData], batchSize uint, options MLCExecutionOptions, completionHandler func(*MLCTensor, unsafe.Pointer, float64)) bool

Executes the inference graph with the input data, batch size, execution options and completion handler you specify.

func (*MLCInferenceGraph) ExecuteWithInputsDataLossLabelsDataLossLabelWeightsDataOutputsDataBatchSizeOptionsCompletionHandler 

func (o *MLCInferenceGraph) ExecuteWithInputsDataLossLabelsDataLossLabelWeightsDataOutputsDataBatchSizeOptionsCompletionHandler(inputsData *foundation.NSDictionary[*foundation.NSString, *MLCTensorData], lossLabelsData *foundation.NSDictionary[*foundation.NSString, *MLCTensorData], lossLabelWeightsData *foundation.NSDictionary[*foundation.NSString, *MLCTensorData], outputsData *foundation.NSDictionary[*foundation.NSString, *MLCTensorData], batchSize uint, options MLCExecutionOptions, completionHandler func(*MLCTensor, unsafe.Pointer, float64)) bool

Executes the inference graph with the input and output data, batch size, execution options, and completion handler that you specify.

func (*MLCInferenceGraph) ExecuteWithInputsDataOutputsDataBatchSizeOptionsCompletionHandler 

func (o *MLCInferenceGraph) ExecuteWithInputsDataOutputsDataBatchSizeOptionsCompletionHandler(inputsData *foundation.NSDictionary[*foundation.NSString, *MLCTensorData], outputsData *foundation.NSDictionary[*foundation.NSString, *MLCTensorData], batchSize uint, options MLCExecutionOptions, completionHandler func(*MLCTensor, unsafe.Pointer, float64)) bool

Executes the inference graph with the inputs and outputs data, batch size, execution options, and completion handler that you specify.

func (*MLCInferenceGraph) LinkWithGraphs 

func (o *MLCInferenceGraph) LinkWithGraphs(graphs *foundation.NSArray[*MLCInferenceGraph]) bool

Links the inference graphs you specify.

type MLCInstanceNormalizationLayer 

type MLCInstanceNormalizationLayer struct {
	MLCLayer
}

A layer that normalizes all features of one channel.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcinstancenormalizationlayer

func MLCInstanceNormalizationLayerFromID 

func MLCInstanceNormalizationLayerFromID(id objc.ID) *MLCInstanceNormalizationLayer

func MLCInstanceNormalizationLayerLayerWithFeatureChannelCountBetaGammaVarianceEpsilon 

func MLCInstanceNormalizationLayerLayerWithFeatureChannelCountBetaGammaVarianceEpsilon(featureChannelCount uint, beta *MLCTensor, gamma *MLCTensor, varianceEpsilon float32) *MLCInstanceNormalizationLayer

Creates an instance normalization layer with the number of feature channels, beta and gamma tensors, and variance epsilon you specify.

func MLCInstanceNormalizationLayerLayerWithFeatureChannelCountBetaGammaVarianceEpsilonMomentum 

func MLCInstanceNormalizationLayerLayerWithFeatureChannelCountBetaGammaVarianceEpsilonMomentum(featureChannelCount uint, beta *MLCTensor, gamma *MLCTensor, varianceEpsilon float32, momentum float32) *MLCInstanceNormalizationLayer

Creates an instance normalization layer with the number of feature channels, beta and gamma tensors, variance epsilon, and momentum you specify.

func MLCInstanceNormalizationLayerLayerWithFeatureChannelCountMeanVarianceBetaGammaVarianceEpsilonMomentum 

func MLCInstanceNormalizationLayerLayerWithFeatureChannelCountMeanVarianceBetaGammaVarianceEpsilonMomentum(featureChannelCount uint, mean *MLCTensor, variance *MLCTensor, beta *MLCTensor, gamma *MLCTensor, varianceEpsilon float32, momentum float32) *MLCInstanceNormalizationLayer

Creates an instance normalization layer with the number of feature channels, mean, variance, beta and gamma tensors, variance epsilon, and momentum you specify.

func (*MLCInstanceNormalizationLayer) Beta 

func (o *MLCInstanceNormalizationLayer) Beta() *MLCTensor

@property beta @abstract The beta tensor

func (*MLCInstanceNormalizationLayer) BetaParameter 

func (o *MLCInstanceNormalizationLayer) BetaParameter() *MLCTensorParameter

@property betaParameter @abstract The beta tensor parameter used for optimizer update

func (*MLCInstanceNormalizationLayer) FeatureChannelCount 

func (o *MLCInstanceNormalizationLayer) FeatureChannelCount() uint

@property featureChannelCount @abstract The number of feature channels

func (*MLCInstanceNormalizationLayer) Gamma 

func (o *MLCInstanceNormalizationLayer) Gamma() *MLCTensor

@property gamma @abstract The gamma tensor

func (*MLCInstanceNormalizationLayer) GammaParameter 

func (o *MLCInstanceNormalizationLayer) GammaParameter() *MLCTensorParameter

@property gammaParameter @abstract The gamma tensor parameter used for optimizer update

func (*MLCInstanceNormalizationLayer) Mean 

func (o *MLCInstanceNormalizationLayer) Mean() *MLCTensor

@property mean @abstract The running mean tensor

func (*MLCInstanceNormalizationLayer) Momentum 

func (o *MLCInstanceNormalizationLayer) Momentum() float32

@property momentum @abstract The value used for the running mean and variance computation @discussion The default is 0.99f.

func (*MLCInstanceNormalizationLayer) Variance 

func (o *MLCInstanceNormalizationLayer) Variance() *MLCTensor

@property variance @abstract The running variance tensor

func (*MLCInstanceNormalizationLayer) VarianceEpsilon 

func (o *MLCInstanceNormalizationLayer) VarianceEpsilon() float32

@property varianceEpsilon @abstract A value used for numerical stability

type MLCLSTMDescriptor 

type MLCLSTMDescriptor struct {
	foundation.NSObject
}

The configuration object you use to create the LSTM layer.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlclstmdescriptor

func MLCLSTMDescriptorDescriptorWithInputSizeHiddenSizeLayerCount 

func MLCLSTMDescriptorDescriptorWithInputSizeHiddenSizeLayerCount(inputSize uint, hiddenSize uint, layerCount uint) *MLCLSTMDescriptor

Creates a batch first LSTM descriptor with the input size and number of layers you specify.

func MLCLSTMDescriptorDescriptorWithInputSizeHiddenSizeLayerCountUsesBiasesBatchFirstIsBidirectionalDropout 

func MLCLSTMDescriptorDescriptorWithInputSizeHiddenSizeLayerCountUsesBiasesBatchFirstIsBidirectionalDropout(inputSize uint, hiddenSize uint, layerCount uint, usesBiases bool, batchFirst bool, isBidirectional bool, dropout float32) *MLCLSTMDescriptor

Creates a batch first LSTM descriptor that allows you to indicate whether the input and output shape is batch first.

func MLCLSTMDescriptorDescriptorWithInputSizeHiddenSizeLayerCountUsesBiasesBatchFirstIsBidirectionalReturnsSequencesDropout 

func MLCLSTMDescriptorDescriptorWithInputSizeHiddenSizeLayerCountUsesBiasesBatchFirstIsBidirectionalReturnsSequencesDropout(inputSize uint, hiddenSize uint, layerCount uint, usesBiases bool, batchFirst bool, isBidirectional bool, returnsSequences bool, dropout float32) *MLCLSTMDescriptor

Creates a batch first LSTM descriptor that allows you to indicate whether the layer returns output for all sequences, or output for only the last sequence.

func MLCLSTMDescriptorDescriptorWithInputSizeHiddenSizeLayerCountUsesBiasesBatchFirstIsBidirectionalReturnsSequencesDropoutResultMode 

func MLCLSTMDescriptorDescriptorWithInputSizeHiddenSizeLayerCountUsesBiasesBatchFirstIsBidirectionalReturnsSequencesDropoutResultMode(inputSize uint, hiddenSize uint, layerCount uint, usesBiases bool, batchFirst bool, isBidirectional bool, returnsSequences bool, dropout float32, resultMode MLCLSTMResultMode) *MLCLSTMDescriptor

Creates a descriptor with the number of features and layers, dropout, and options for use of biases, batch order, return sequences, bidirectionality, and expected tensors you specify.

func MLCLSTMDescriptorDescriptorWithInputSizeHiddenSizeLayerCountUsesBiasesIsBidirectionalDropout 

func MLCLSTMDescriptorDescriptorWithInputSizeHiddenSizeLayerCountUsesBiasesIsBidirectionalDropout(inputSize uint, hiddenSize uint, layerCount uint, usesBiases bool, isBidirectional bool, dropout float32) *MLCLSTMDescriptor

Creates a batch first LSTM descriptor with bias and bidirectional options you specify.

func MLCLSTMDescriptorFromID 

func MLCLSTMDescriptorFromID(id objc.ID) *MLCLSTMDescriptor

func (*MLCLSTMDescriptor) BatchFirst 

func (o *MLCLSTMDescriptor) BatchFirst() bool

@property batchFirst @abstract LSTM only supports batchFirst=YES. This means the input and output will have shape [batch size, time steps, feature]. Default is YES.

func (*MLCLSTMDescriptor) Dropout 

func (o *MLCLSTMDescriptor) Dropout() float32

@property dropout @abstract If non-zero, intrdouces a dropout layer on the outputs of each LSTM layer except the last layer, with dropout probablity equal to dropout. Default is 0.0.

func (*MLCLSTMDescriptor) HiddenSize 

func (o *MLCLSTMDescriptor) HiddenSize() uint

@property hiddenSize @abstract The number of feature channels in the hidden state

func (*MLCLSTMDescriptor) InputSize 

func (o *MLCLSTMDescriptor) InputSize() uint

@property inputSize @abstract The number of expected feature channels in the input

func (*MLCLSTMDescriptor) IsBidirectional 

func (o *MLCLSTMDescriptor) IsBidirectional() bool

@property isBidirectional @abstract If YES, becomes a bidirectional LSTM. Default is NO.

func (*MLCLSTMDescriptor) LayerCount 

func (o *MLCLSTMDescriptor) LayerCount() uint

@property layerCount @abstract The number of recurrent layers. Default is 1.

func (*MLCLSTMDescriptor) ResultMode 

func (o *MLCLSTMDescriptor) ResultMode() MLCLSTMResultMode

@property resultMode @abstract MLCLSTMResultModeOutput returns output data. MLCLSTMResultModeOutputAndStates returns output data, last hidden state h_n, and last cell state c_n. Default MLCLSTMResultModeOutput.

func (*MLCLSTMDescriptor) ReturnsSequences 

func (o *MLCLSTMDescriptor) ReturnsSequences() bool

@property returnsSequences @abstract if YES return output for all sequences else return output only for the last sequences. Default: YES

func (*MLCLSTMDescriptor) UsesBiases 

func (o *MLCLSTMDescriptor) UsesBiases() bool

@property usesBiases @abstract If NO, the layer does not use bias terms. Default is YES.

type MLCLSTMLayer 

type MLCLSTMLayer struct {
	MLCLayer
}

A layer that represents long short-term memory (LSTM) networks.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlclstmlayer

func MLCLSTMLayerFromID 

func MLCLSTMLayerFromID(id objc.ID) *MLCLSTMLayer

func MLCLSTMLayerLayerWithDescriptorInputWeightsHiddenWeightsBiases 

func MLCLSTMLayerLayerWithDescriptorInputWeightsHiddenWeightsBiases(descriptor *MLCLSTMDescriptor, inputWeights *foundation.NSArray[*MLCTensor], hiddenWeights *foundation.NSArray[*MLCTensor], biases *foundation.NSArray[*MLCTensor]) *MLCLSTMLayer

Creates an LSTM layer with the descriptor, input and hidden weights, and biases you specify.

func MLCLSTMLayerLayerWithDescriptorInputWeightsHiddenWeightsPeepholeWeightsBiases 

func MLCLSTMLayerLayerWithDescriptorInputWeightsHiddenWeightsPeepholeWeightsBiases(descriptor *MLCLSTMDescriptor, inputWeights *foundation.NSArray[*MLCTensor], hiddenWeights *foundation.NSArray[*MLCTensor], peepholeWeights *foundation.NSArray[*MLCTensor], biases *foundation.NSArray[*MLCTensor]) *MLCLSTMLayer

Creates an LSTM layer with the descriptor, weights, and biases you specify.

func MLCLSTMLayerLayerWithDescriptorInputWeightsHiddenWeightsPeepholeWeightsBiasesGateActivationsOutputResultActivation 

func MLCLSTMLayerLayerWithDescriptorInputWeightsHiddenWeightsPeepholeWeightsBiasesGateActivationsOutputResultActivation(descriptor *MLCLSTMDescriptor, inputWeights *foundation.NSArray[*MLCTensor], hiddenWeights *foundation.NSArray[*MLCTensor], peepholeWeights *foundation.NSArray[*MLCTensor], biases *foundation.NSArray[*MLCTensor], gateActivations *foundation.NSArray[*MLCActivationDescriptor], outputResultActivation *MLCActivationDescriptor) *MLCLSTMLayer

Creates an LSTM layer using the descriptor, weights, biases, gate activations, and output result activation that you specify.

func (*MLCLSTMLayer) Biases 

func (o *MLCLSTMLayer) Biases() *foundation.NSArray[*MLCTensor]

@property biases @abstract The array of tensors describing the bias terms for the input, hidden, cell and output gates

func (*MLCLSTMLayer) BiasesParameters 

func (o *MLCLSTMLayer) BiasesParameters() *foundation.NSArray[*MLCTensorParameter]

@property biasesParameters @abstract The bias tensor parameter used for optimizer update

func (*MLCLSTMLayer) Descriptor 

func (o *MLCLSTMLayer) Descriptor() *MLCLSTMDescriptor

@property descriptor @abstract The LSTM descriptor

func (*MLCLSTMLayer) GateActivations 

func (o *MLCLSTMLayer) GateActivations() *foundation.NSArray[*MLCActivationDescriptor]

@property gateActivations @abstract The array of gate activations for input, hidden, cell and output gates @discussion The default gate activations are: sigmoid, sigmoid, tanh, sigmoid

func (*MLCLSTMLayer) HiddenWeights 

func (o *MLCLSTMLayer) HiddenWeights() *foundation.NSArray[*MLCTensor]

@property hiddenWeights @abstract The array of tensors describing the hidden weights for the input, hidden, cell and output gates

func (*MLCLSTMLayer) HiddenWeightsParameters 

func (o *MLCLSTMLayer) HiddenWeightsParameters() *foundation.NSArray[*MLCTensorParameter]

@property hiddenWeightsParameters @abstract The hidden weights tensor parameters used for optimizer update

func (*MLCLSTMLayer) InputWeights 

func (o *MLCLSTMLayer) InputWeights() *foundation.NSArray[*MLCTensor]

@property inputWeights @abstract The array of tensors describing the input weights for the input, hidden, cell and output gates

func (*MLCLSTMLayer) InputWeightsParameters 

func (o *MLCLSTMLayer) InputWeightsParameters() *foundation.NSArray[*MLCTensorParameter]

@property inputWeightsParameters @abstract The input weights tensor parameters used for optimizer update

func (*MLCLSTMLayer) OutputResultActivation 

func (o *MLCLSTMLayer) OutputResultActivation() *MLCActivationDescriptor

@property outputResultActivation @abstract The output activation descriptor

func (*MLCLSTMLayer) PeepholeWeights 

func (o *MLCLSTMLayer) PeepholeWeights() *foundation.NSArray[*MLCTensor]

@property peepholeWeights @abstract The array of tensors describing the peephole weights for the input, hidden, cell and output gates

func (*MLCLSTMLayer) PeepholeWeightsParameters 

func (o *MLCLSTMLayer) PeepholeWeightsParameters() *foundation.NSArray[*MLCTensorParameter]

@property peepholeWeightsParameters @abstract The peephole weights tensor parameters used for optimizer update

type MLCLSTMResultMode 

type MLCLSTMResultMode int64

Constants that describe the result of an LSTM layer. 

const (
	// A result mode that indicates the layer produces a single result tensor that represents the final output of the LSTM.
	MLCLSTMResultModeOutput MLCLSTMResultMode = 0
	// A result mode that indicates the layer produces three result tensors that represent the final output of the LSTM, the last hidden state, and the cell state.
	MLCLSTMResultModeOutputAndStates MLCLSTMResultMode = 1
)

func (MLCLSTMResultMode) String 

func (e MLCLSTMResultMode) String() string

type MLCLayer 

type MLCLayer struct {
	foundation.NSObject
}

The base class for all framework layers.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlclayer

func MLCLayerFromID 

func MLCLayerFromID(id objc.ID) *MLCLayer

func (*MLCLayer) DeviceType 

func (o *MLCLayer) DeviceType() MLCDeviceType

@property deviceType @abstract The device type where this layer will be executed @discussion Typically the MLCDevice passed to compileWithOptions will be the device used to execute layers in the graph. If MLCDeviceTypeANE is selected, it is possible that some of the layers of the graph may not be executed on the ANE but instead on the CPU or GPU. This property can be used to determine which device type the layer will be executed on.

func (*MLCLayer) IsDebuggingEnabled 

func (o *MLCLayer) IsDebuggingEnabled() bool

@property isDebuggingEnabled @abstract A flag to identify if we want to debug this layer when executing a graph that includes this layer @discussion If this is set, we will make sure that the result tensor and gradient tensors are available for reading on CPU The default is NO. If isDebuggingEnabled is set to YES, make sure to set options to enable debugging when compiling the graph. Otherwise this property may be ignored.

func (*MLCLayer) Label 

func (o *MLCLayer) Label() *foundation.NSString

@property label @abstract A string to help identify this object.

func (*MLCLayer) LayerID 

func (o *MLCLayer) LayerID() uint

@property layerID @abstract The layer ID @discussion A unique number to identify each layer. Assigned when the layer is created.

func (*MLCLayer) SetIsDebuggingEnabled 

func (o *MLCLayer) SetIsDebuggingEnabled(isDebuggingEnabled bool)

func (*MLCLayer) SetLabel 

func (o *MLCLayer) SetLabel(label *foundation.NSString)

type MLCLayerNormalizationLayer 

type MLCLayerNormalizationLayer struct {
	MLCLayer
}

A layer that applies layer normalization over inputs.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlclayernormalizationlayer

func MLCLayerNormalizationLayerFromID 

func MLCLayerNormalizationLayerFromID(id objc.ID) *MLCLayerNormalizationLayer

func MLCLayerNormalizationLayerLayerWithNormalizedShapeBetaGammaVarianceEpsilon 

func MLCLayerNormalizationLayerLayerWithNormalizedShapeBetaGammaVarianceEpsilon(normalizedShape *foundation.NSArray[*foundation.NSNumber], beta *MLCTensor, gamma *MLCTensor, varianceEpsilon float32) *MLCLayerNormalizationLayer

Creates a normalization layer with a shape, beta and gamma tensors, and variance epsilon you specify.

func (*MLCLayerNormalizationLayer) Beta 

func (o *MLCLayerNormalizationLayer) Beta() *MLCTensor

@property beta @abstract The beta tensor

func (*MLCLayerNormalizationLayer) BetaParameter 

func (o *MLCLayerNormalizationLayer) BetaParameter() *MLCTensorParameter

@property betaParameter @abstract The beta tensor parameter used for optimizer update

func (*MLCLayerNormalizationLayer) Gamma 

func (o *MLCLayerNormalizationLayer) Gamma() *MLCTensor

@property gamma @abstract The gamma tensor

func (*MLCLayerNormalizationLayer) GammaParameter 

func (o *MLCLayerNormalizationLayer) GammaParameter() *MLCTensorParameter

@property gammaParameter @abstract The gamma tensor parameter used for optimizer update

func (*MLCLayerNormalizationLayer) NormalizedShape 

func (o *MLCLayerNormalizationLayer) NormalizedShape() *foundation.NSArray[*foundation.NSNumber]

@property normalizedShape @abstract The shape of the axes over which normalization occurs, (W), (H,W) or (C,H,W)

func (*MLCLayerNormalizationLayer) VarianceEpsilon 

func (o *MLCLayerNormalizationLayer) VarianceEpsilon() float32

@property varianceEpsilon @abstract A value used for numerical stability

type MLCLossDescriptor 

type MLCLossDescriptor struct {
	foundation.NSObject
}

A configuration object you use to create a loss layer.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlclossdescriptor

func MLCLossDescriptorDescriptorWithTypeReductionType 

func MLCLossDescriptorDescriptorWithTypeReductionType(lossType MLCLossType, reductionType MLCReductionType) *MLCLossDescriptor

Creates a loss descriptor with the loss function and reduction type you specify.

func MLCLossDescriptorDescriptorWithTypeReductionTypeWeight 

func MLCLossDescriptorDescriptorWithTypeReductionTypeWeight(lossType MLCLossType, reductionType MLCReductionType, weight float32) *MLCLossDescriptor

Creates a loss descriptor with the loss function, reduction type, and weight you specify.

func MLCLossDescriptorDescriptorWithTypeReductionTypeWeightLabelSmoothingClassCount 

func MLCLossDescriptorDescriptorWithTypeReductionTypeWeightLabelSmoothingClassCount(lossType MLCLossType, reductionType MLCReductionType, weight float32, labelSmoothing float32, classCount uint) *MLCLossDescriptor

Creates a loss descriptor with the loss function, reduction type, weight, label smoothing, and number of classes you specify.

func MLCLossDescriptorDescriptorWithTypeReductionTypeWeightLabelSmoothingClassCountEpsilonDelta 

func MLCLossDescriptorDescriptorWithTypeReductionTypeWeightLabelSmoothingClassCountEpsilonDelta(lossType MLCLossType, reductionType MLCReductionType, weight float32, labelSmoothing float32, classCount uint, epsilon float32, delta float32) *MLCLossDescriptor

Creates a loss descriptor with the loss function, reduction type, weight, label smoothing, and number of classes, epsilon, and delta that you specify.

func MLCLossDescriptorFromID 

func MLCLossDescriptorFromID(id objc.ID) *MLCLossDescriptor

func (*MLCLossDescriptor) ClassCount 

func (o *MLCLossDescriptor) ClassCount() uint

@property numberOfClasses @abstract The number of classes parameter. The default value is 1. @discussion This parameter is valid only for the loss function MLCLossTypeSoftmaxCrossEntropy.

func (*MLCLossDescriptor) Delta 

func (o *MLCLossDescriptor) Delta() float32

@property delta @abstract The delta parameter. The default value is 1.0f. @discussion This parameter is valid only for the loss function MLCLossTypeHuber.

func (*MLCLossDescriptor) Epsilon 

func (o *MLCLossDescriptor) Epsilon() float32

@property epsilon @abstract The epsilon parameter. The default value is 1e-7. @discussion This parameter is valid only for the loss function MLCLossTypeLog.

func (*MLCLossDescriptor) LabelSmoothing 

func (o *MLCLossDescriptor) LabelSmoothing() float32

@property labelSmoothing @abstract The label smoothing parameter. The default value is 0.0. @discussion This parameter is valid only for the loss functions of the following type(s): MLCLossTypeSoftmaxCrossEntropy and MLCLossTypeSigmoidCrossEntropy.

func (*MLCLossDescriptor) LossType 

func (o *MLCLossDescriptor) LossType() MLCLossType

@property lossType @abstract Specifies the loss function.

func (*MLCLossDescriptor) ReductionType 

func (o *MLCLossDescriptor) ReductionType() MLCReductionType

@property reductionType @abstract The reduction operation performed by the loss function.

func (*MLCLossDescriptor) Weight 

func (o *MLCLossDescriptor) Weight() float32

@property weight @abstract The scale factor to apply to each element of a result. The default value is 1.0.

type MLCLossLayer 

type MLCLossLayer struct {
	MLCLayer
}

A layer that estimates the inaccuracies of the model to reduce the loss on the next evaluation.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlclosslayer

func MLCLossLayerCategoricalCrossEntropyLossWithReductionTypeLabelSmoothingClassCountWeight 

func MLCLossLayerCategoricalCrossEntropyLossWithReductionTypeLabelSmoothingClassCountWeight(reductionType MLCReductionType, labelSmoothing float32, classCount uint, weight float32) *MLCLossLayer

Creates a categorical cross entropy loss layer with the reduction type, label smoothing, number of classes, and weight you specify.

func MLCLossLayerCategoricalCrossEntropyLossWithReductionTypeLabelSmoothingClassCountWeights 

func MLCLossLayerCategoricalCrossEntropyLossWithReductionTypeLabelSmoothingClassCountWeights(reductionType MLCReductionType, labelSmoothing float32, classCount uint, weights *MLCTensor) *MLCLossLayer

Creates a categorical cross entropy loss layer with the reduction type, label smoothing, number of classes, and weights you specify.

func MLCLossLayerCosineDistanceLossWithReductionTypeWeight 

func MLCLossLayerCosineDistanceLossWithReductionTypeWeight(reductionType MLCReductionType, weight float32) *MLCLossLayer

Creates a cosine distance loss layer with the reduction type and weight you specify.

func MLCLossLayerCosineDistanceLossWithReductionTypeWeights 

func MLCLossLayerCosineDistanceLossWithReductionTypeWeights(reductionType MLCReductionType, weights *MLCTensor) *MLCLossLayer

Creates a cosine distance loss layer with the reduction type and weights you specify.

func MLCLossLayerFromID 

func MLCLossLayerFromID(id objc.ID) *MLCLossLayer

func MLCLossLayerHingeLossWithReductionTypeWeight 

func MLCLossLayerHingeLossWithReductionTypeWeight(reductionType MLCReductionType, weight float32) *MLCLossLayer

Creates a hinge loss layer with the reduction type and weight you specify.

func MLCLossLayerHingeLossWithReductionTypeWeights 

func MLCLossLayerHingeLossWithReductionTypeWeights(reductionType MLCReductionType, weights *MLCTensor) *MLCLossLayer

Creates a hinge loss layer with the reduction type and weights you specify.

func MLCLossLayerHuberLossWithReductionTypeDeltaWeight 

func MLCLossLayerHuberLossWithReductionTypeDeltaWeight(reductionType MLCReductionType, delta float32, weight float32) *MLCLossLayer

Creates a huber loss layer with the reduction type, delta, and weight you specify.

func MLCLossLayerHuberLossWithReductionTypeDeltaWeights 

func MLCLossLayerHuberLossWithReductionTypeDeltaWeights(reductionType MLCReductionType, delta float32, weights *MLCTensor) *MLCLossLayer

Creates a huber loss layer with the reduction type, delta, and weights you specify.

func MLCLossLayerLayerWithDescriptor 

func MLCLossLayerLayerWithDescriptor(lossDescriptor *MLCLossDescriptor) *MLCLossLayer

Creates a loss layer with the descriptor you specify.

func MLCLossLayerLayerWithDescriptorWeights 

func MLCLossLayerLayerWithDescriptorWeights(lossDescriptor *MLCLossDescriptor, weights *MLCTensor) *MLCLossLayer

Creates a loss layer with the descriptor and weights you specify.

func MLCLossLayerLogLossWithReductionTypeEpsilonWeight 

func MLCLossLayerLogLossWithReductionTypeEpsilonWeight(reductionType MLCReductionType, epsilon float32, weight float32) *MLCLossLayer

Creates a log loss layer with the reduction type, epsilon, and weight you specify.

func MLCLossLayerLogLossWithReductionTypeEpsilonWeights 

func MLCLossLayerLogLossWithReductionTypeEpsilonWeights(reductionType MLCReductionType, epsilon float32, weights *MLCTensor) *MLCLossLayer

Creates a log loss layer with the reduction type, epsilon, and weights you specify.

func MLCLossLayerMeanAbsoluteErrorLossWithReductionTypeWeight 

func MLCLossLayerMeanAbsoluteErrorLossWithReductionTypeWeight(reductionType MLCReductionType, weight float32) *MLCLossLayer

Creates a mean absolute loss layer with the reduction type and weight.

func MLCLossLayerMeanAbsoluteErrorLossWithReductionTypeWeights 

func MLCLossLayerMeanAbsoluteErrorLossWithReductionTypeWeights(reductionType MLCReductionType, weights *MLCTensor) *MLCLossLayer

Creates a mean absolute loss layer with the reduction type and weights you specify.

func MLCLossLayerMeanSquaredErrorLossWithReductionTypeWeight 

func MLCLossLayerMeanSquaredErrorLossWithReductionTypeWeight(reductionType MLCReductionType, weight float32) *MLCLossLayer

Creates a mean squared loss layer with the reduction type and weight you specify.

func MLCLossLayerMeanSquaredErrorLossWithReductionTypeWeights 

func MLCLossLayerMeanSquaredErrorLossWithReductionTypeWeights(reductionType MLCReductionType, weights *MLCTensor) *MLCLossLayer

Creates a mean squared loss layer with the reduction type and weights you specify.

func MLCLossLayerSigmoidCrossEntropyLossWithReductionTypeLabelSmoothingWeight 

func MLCLossLayerSigmoidCrossEntropyLossWithReductionTypeLabelSmoothingWeight(reductionType MLCReductionType, labelSmoothing float32, weight float32) *MLCLossLayer

Creates a sigmoid cross entropy loss layer with the reduction type, label smoothing, and weight you specify.

func MLCLossLayerSigmoidCrossEntropyLossWithReductionTypeLabelSmoothingWeights 

func MLCLossLayerSigmoidCrossEntropyLossWithReductionTypeLabelSmoothingWeights(reductionType MLCReductionType, labelSmoothing float32, weights *MLCTensor) *MLCLossLayer

Creates a sigmoid cross entropy loss layer with the reduction type, label smoothing, and weights you specify.

func MLCLossLayerSoftmaxCrossEntropyLossWithReductionTypeLabelSmoothingClassCountWeight 

func MLCLossLayerSoftmaxCrossEntropyLossWithReductionTypeLabelSmoothingClassCountWeight(reductionType MLCReductionType, labelSmoothing float32, classCount uint, weight float32) *MLCLossLayer

Creates a softmax cross entropy loss layer with the reduction type, label smoothing, number of classes, and weight you specify.

func MLCLossLayerSoftmaxCrossEntropyLossWithReductionTypeLabelSmoothingClassCountWeights 

func MLCLossLayerSoftmaxCrossEntropyLossWithReductionTypeLabelSmoothingClassCountWeights(reductionType MLCReductionType, labelSmoothing float32, classCount uint, weights *MLCTensor) *MLCLossLayer

Creates a softmax cross entropy loss layer with the reduction type, label smoothing, number of classes, and weights you specify.

func (*MLCLossLayer) Descriptor 

func (o *MLCLossLayer) Descriptor() *MLCLossDescriptor

@property descriptor @abstract The loss descriptor

func (*MLCLossLayer) Weights 

func (o *MLCLossLayer) Weights() *MLCTensor

@property weights @abstract The loss label weights tensor

type MLCLossType 

type MLCLossType int64

A loss function. 

const (
	// The mean absolute error loss.
	MLCLossTypeMeanAbsoluteError MLCLossType = 0
	// The mean squared error loss.
	MLCLossTypeMeanSquaredError MLCLossType = 1
	// The softmax cross entropy loss.
	MLCLossTypeSoftmaxCrossEntropy MLCLossType = 2
	// The sigmoid cross entropy loss.
	MLCLossTypeSigmoidCrossEntropy MLCLossType = 3
	// The categorical cross entropy loss.
	MLCLossTypeCategoricalCrossEntropy MLCLossType = 4
	// The hinge loss.
	MLCLossTypeHinge MLCLossType = 5
	// The Huber loss.
	MLCLossTypeHuber MLCLossType = 6
	// The cosine distance loss.
	MLCLossTypeCosineDistance MLCLossType = 7
	// The log loss.
	MLCLossTypeLog   MLCLossType = 8
	MLCLossTypeCount MLCLossType = 9
)

func (MLCLossType) String 

func (e MLCLossType) String() string

type MLCMatMulDescriptor 

type MLCMatMulDescriptor struct {
	foundation.NSObject
}

A configuration object you use to create a matrix multiplication layer.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcmatmuldescriptor

func MLCMatMulDescriptorDescriptor 

func MLCMatMulDescriptorDescriptor() *MLCMatMulDescriptor

Creates a batched matrix multiplication descriptor.

func MLCMatMulDescriptorDescriptorWithAlphaTransposesXTransposesY 

func MLCMatMulDescriptorDescriptorWithAlphaTransposesXTransposesY(alpha float32, transposesX bool, transposesY bool) *MLCMatMulDescriptor

Creates a batched matrix multiplication descriptor with the alpha value and transpose options you specify.

func MLCMatMulDescriptorFromID 

func MLCMatMulDescriptorFromID(id objc.ID) *MLCMatMulDescriptor

func (*MLCMatMulDescriptor) Alpha 

func (o *MLCMatMulDescriptor) Alpha() float32

@brief a scalar to scale the result in C=alpha x X x Y. Default = 1.0

func (*MLCMatMulDescriptor) TransposesX 

func (o *MLCMatMulDescriptor) TransposesX() bool

@brief if true, transposes the last two dimensions of X. Default = False

func (*MLCMatMulDescriptor) TransposesY 

func (o *MLCMatMulDescriptor) TransposesY() bool

@brief if true, transposes the last two dimensions of Y. Default = False

type MLCMatMulLayer 

type MLCMatMulLayer struct {
	MLCLayer
}

A layer that multiplies matrices.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcmatmullayer

func MLCMatMulLayerFromID 

func MLCMatMulLayerFromID(id objc.ID) *MLCMatMulLayer

func MLCMatMulLayerLayerWithDescriptor 

func MLCMatMulLayerLayerWithDescriptor(descriptor *MLCMatMulDescriptor) *MLCMatMulLayer

Creates a matrix multiplication layer with the specified descriptor you specify.

func (*MLCMatMulLayer) Descriptor 

func (o *MLCMatMulLayer) Descriptor() *MLCMatMulDescriptor

@property descriptor @abstract The matrix multiplication descriptor

type MLCMultiheadAttentionDescriptor 

type MLCMultiheadAttentionDescriptor struct {
	foundation.NSObject
}

A configuration object you use to create a multi-head attention layer.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcmultiheadattentiondescriptor

func MLCMultiheadAttentionDescriptorDescriptorWithModelDimensionHeadCount 

func MLCMultiheadAttentionDescriptorDescriptorWithModelDimensionHeadCount(modelDimension uint, headCount uint) *MLCMultiheadAttentionDescriptor

Creates a multi-head attention descriptor with the model dimension and number of parallel attention heads you specify.

func MLCMultiheadAttentionDescriptorDescriptorWithModelDimensionKeyDimensionValueDimensionHeadCountDropoutHasBiasesHasAttentionBiasesAddsZeroAttention 

func MLCMultiheadAttentionDescriptorDescriptorWithModelDimensionKeyDimensionValueDimensionHeadCountDropoutHasBiasesHasAttentionBiasesAddsZeroAttention(modelDimension uint, keyDimension uint, valueDimension uint, headCount uint, dropout float32, hasBiases bool, hasAttentionBiases bool, addsZeroAttention bool) *MLCMultiheadAttentionDescriptor

Creates a multi-head attention descriptor with the dimensions, number of attention heads, dropout rate, and bias and padding options you specify.

func MLCMultiheadAttentionDescriptorFromID 

func MLCMultiheadAttentionDescriptorFromID(id objc.ID) *MLCMultiheadAttentionDescriptor

func (*MLCMultiheadAttentionDescriptor) AddsZeroAttention 

func (o *MLCMultiheadAttentionDescriptor) AddsZeroAttention() bool

@brief if true, a row of zeroes is added to projected key and value. Default = false

func (*MLCMultiheadAttentionDescriptor) Dropout 

func (o *MLCMultiheadAttentionDescriptor) Dropout() float32

@brief a droupout layer applied to the output projection weights. Default = 0.0

func (*MLCMultiheadAttentionDescriptor) HasAttentionBiases 

func (o *MLCMultiheadAttentionDescriptor) HasAttentionBiases() bool

@brief if true, an array of biases is added to key and value respectively. Default = false

func (*MLCMultiheadAttentionDescriptor) HasBiases 

func (o *MLCMultiheadAttentionDescriptor) HasBiases() bool

@brief if true, bias is used for query/key/value/output projections. Default = true

func (*MLCMultiheadAttentionDescriptor) HeadCount 

func (o *MLCMultiheadAttentionDescriptor) HeadCount() uint

@brief number of parallel attention heads

func (*MLCMultiheadAttentionDescriptor) KeyDimension 

func (o *MLCMultiheadAttentionDescriptor) KeyDimension() uint

@brief total dimension of key space, Default = modelDimension

func (*MLCMultiheadAttentionDescriptor) ModelDimension 

func (o *MLCMultiheadAttentionDescriptor) ModelDimension() uint

@brief model or embedding dimension

func (*MLCMultiheadAttentionDescriptor) ValueDimension 

func (o *MLCMultiheadAttentionDescriptor) ValueDimension() uint

@brief total dimension of value space, Default = modelDimension

type MLCMultiheadAttentionLayer 

type MLCMultiheadAttentionLayer struct {
	MLCLayer
}

A multihead, scaled dot-product attention layer that attends to one or more entries in the input key-value pairs.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcmultiheadattentionlayer

func MLCMultiheadAttentionLayerFromID 

func MLCMultiheadAttentionLayerFromID(id objc.ID) *MLCMultiheadAttentionLayer

func MLCMultiheadAttentionLayerLayerWithDescriptorWeightsBiasesAttentionBiases 

func MLCMultiheadAttentionLayerLayerWithDescriptorWeightsBiasesAttentionBiases(descriptor *MLCMultiheadAttentionDescriptor, weights *foundation.NSArray[*MLCTensor], biases *foundation.NSArray[*MLCTensor], attentionBiases *foundation.NSArray[*MLCTensor]) *MLCMultiheadAttentionLayer

Creates a multi-head attention layer with the descriptor, weights, and biases you specify.

func (*MLCMultiheadAttentionLayer) AttentionBiases 

func (o *MLCMultiheadAttentionLayer) AttentionBiases() *foundation.NSArray[*MLCTensor]

@property attentionBiases @abstract The biases added to key and value

func (*MLCMultiheadAttentionLayer) Biases 

func (o *MLCMultiheadAttentionLayer) Biases() *foundation.NSArray[*MLCTensor]

@property biases @abstract The biases of query, key, value and output projections

func (*MLCMultiheadAttentionLayer) BiasesParameters 

func (o *MLCMultiheadAttentionLayer) BiasesParameters() *foundation.NSArray[*MLCTensorParameter]

@property biasesParameters @abstract The biases tensor parameters used for optimizer update

func (*MLCMultiheadAttentionLayer) Descriptor 

func (o *MLCMultiheadAttentionLayer) Descriptor() *MLCMultiheadAttentionDescriptor

@property descriptor @abstract The multi-head attention descriptor

func (*MLCMultiheadAttentionLayer) Weights 

func (o *MLCMultiheadAttentionLayer) Weights() *foundation.NSArray[*MLCTensor]

@property weights @abstract The weights of query, key, value and output projections

func (*MLCMultiheadAttentionLayer) WeightsParameters 

func (o *MLCMultiheadAttentionLayer) WeightsParameters() *foundation.NSArray[*MLCTensorParameter]

@property weightsParameters @abstract The weights tensor parameters used for optimizer update

type MLCOptimizer 

type MLCOptimizer struct {
	foundation.NSObject
}

The base class for all framework optimizers.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcoptimizer

func MLCOptimizerFromID 

func MLCOptimizerFromID(id objc.ID) *MLCOptimizer

func (*MLCOptimizer) AppliesGradientClipping 

func (o *MLCOptimizer) AppliesGradientClipping() bool

@property appliesGradientClipping @abstract Whether gradient clipping should be applied or not.

func (*MLCOptimizer) CustomGlobalNorm 

func (o *MLCOptimizer) CustomGlobalNorm() float32

@property customGlobalNorm @abstract Used only with MLCGradientClippingTypeByGlobalNorm. If non zero, this norm will be used in place of global norm.

func (*MLCOptimizer) GradientClipMax 

func (o *MLCOptimizer) GradientClipMax() float32

@property gradientClipMax @abstract The maximum gradient value if gradient clipping is enabled before gradient is rescaled.

func (*MLCOptimizer) GradientClipMin 

func (o *MLCOptimizer) GradientClipMin() float32

@property gradientClipMin @abstract The minimum gradient value if gradient clipping is enabled before gradient is rescaled.

func (*MLCOptimizer) GradientClippingType 

func (o *MLCOptimizer) GradientClippingType() MLCGradientClippingType

@property gradientClippingType @abstract The type of clipping applied to gradient

func (*MLCOptimizer) GradientRescale 

func (o *MLCOptimizer) GradientRescale() float32

@property gradientRescale @abstract The rescale value applied to gradients during optimizer update

func (*MLCOptimizer) LearningRate 

func (o *MLCOptimizer) LearningRate() float32

@property learningRate @abstract The learning rate. This property is 'readwrite' so that callers can implement a 'decay' during training

func (*MLCOptimizer) MaximumClippingNorm 

func (o *MLCOptimizer) MaximumClippingNorm() float32

@property maximumClippingNorm @abstract The maximum clipping value

func (*MLCOptimizer) RegularizationScale 

func (o *MLCOptimizer) RegularizationScale() float32

@property regularizationScale @abstract The regularization scale.

func (*MLCOptimizer) RegularizationType 

func (o *MLCOptimizer) RegularizationType() MLCRegularizationType

@property regularizationType @abstract The regularization type.

func (*MLCOptimizer) SetAppliesGradientClipping 

func (o *MLCOptimizer) SetAppliesGradientClipping(appliesGradientClipping bool)

func (*MLCOptimizer) SetLearningRate 

func (o *MLCOptimizer) SetLearningRate(learningRate float32)

type MLCOptimizerDescriptor 

type MLCOptimizerDescriptor struct {
	foundation.NSObject
}

A configuration object you use to create an optimizer.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcoptimizerdescriptor

func MLCOptimizerDescriptorDescriptorWithLearningRateGradientRescaleAppliesGradientClippingGradientClipMaxGradientClipMinRegularizationTypeRegularizationScale 

func MLCOptimizerDescriptorDescriptorWithLearningRateGradientRescaleAppliesGradientClippingGradientClipMaxGradientClipMinRegularizationTypeRegularizationScale(learningRate float32, gradientRescale float32, appliesGradientClipping bool, gradientClipMax float32, gradientClipMin float32, regularizationType MLCRegularizationType, regularizationScale float32) *MLCOptimizerDescriptor

Creates a descriptor with the learning rate, gradient rescale, clipping option and values, and regularization type and scale that you specify.

func MLCOptimizerDescriptorDescriptorWithLearningRateGradientRescaleAppliesGradientClippingGradientClippingTypeGradientClipMaxGradientClipMinMaximumClippingNormCustomGlobalNormRegularizationTypeRegularizationScale 

func MLCOptimizerDescriptorDescriptorWithLearningRateGradientRescaleAppliesGradientClippingGradientClippingTypeGradientClipMaxGradientClipMinMaximumClippingNormCustomGlobalNormRegularizationTypeRegularizationScale(learningRate float32, gradientRescale float32, appliesGradientClipping bool, gradientClippingType MLCGradientClippingType, gradientClipMax float32, gradientClipMin float32, maximumClippingNorm float32, customGlobalNorm float32, regularizationType MLCRegularizationType, regularizationScale float32) *MLCOptimizerDescriptor

Creates a descriptor with the learning rate, gradient rescale, clipping option and values, and regularization type and scale that you specify.

func MLCOptimizerDescriptorDescriptorWithLearningRateGradientRescaleRegularizationTypeRegularizationScale 

func MLCOptimizerDescriptorDescriptorWithLearningRateGradientRescaleRegularizationTypeRegularizationScale(learningRate float32, gradientRescale float32, regularizationType MLCRegularizationType, regularizationScale float32) *MLCOptimizerDescriptor

Creates an optimizer descriptor with the learning rate, gradient rescale, regularization type, and regulation scale that you specify.

func MLCOptimizerDescriptorFromID 

func MLCOptimizerDescriptorFromID(id objc.ID) *MLCOptimizerDescriptor

func (*MLCOptimizerDescriptor) AppliesGradientClipping 

func (o *MLCOptimizerDescriptor) AppliesGradientClipping() bool

@property appliesGradientClipping @abstract Whether gradient clipping should be applied or not. @discussion The default is false

func (*MLCOptimizerDescriptor) CustomGlobalNorm 

func (o *MLCOptimizerDescriptor) CustomGlobalNorm() float32

@property customGlobalNorm @abstract Used only with MLCGradientClippingTypeByGlobalNorm. If non zero, this norm will be used in place of global norm.

func (*MLCOptimizerDescriptor) GradientClipMax 

func (o *MLCOptimizerDescriptor) GradientClipMax() float32

@property gradientClipMax @abstract The maximum gradient value if gradient clipping is enabled before gradient is rescaled.

func (*MLCOptimizerDescriptor) GradientClipMin 

func (o *MLCOptimizerDescriptor) GradientClipMin() float32

@property gradientClipMin @abstract The minimum gradient value if gradient clipping is enabled before gradient is rescaled.

func (*MLCOptimizerDescriptor) GradientClippingType 

func (o *MLCOptimizerDescriptor) GradientClippingType() MLCGradientClippingType

@property gradientClippingType @abstract The type of clipping applied to gradient

func (*MLCOptimizerDescriptor) GradientRescale 

func (o *MLCOptimizerDescriptor) GradientRescale() float32

@property gradientRescale @abstract The rescale value applied to gradients during optimizer update

func (*MLCOptimizerDescriptor) LearningRate 

func (o *MLCOptimizerDescriptor) LearningRate() float32

@property learningRate @abstract The learning rate

func (*MLCOptimizerDescriptor) MaximumClippingNorm 

func (o *MLCOptimizerDescriptor) MaximumClippingNorm() float32

@property maximumClippingNorm @abstract The maximum clipping value

func (*MLCOptimizerDescriptor) RegularizationScale 

func (o *MLCOptimizerDescriptor) RegularizationScale() float32

@property regularizationScale @abstract The regularization scale.

func (*MLCOptimizerDescriptor) RegularizationType 

func (o *MLCOptimizerDescriptor) RegularizationType() MLCRegularizationType

@property regularizationType @abstract The regularization type.

type MLCPaddingLayer 

type MLCPaddingLayer struct {
	MLCLayer
}

A layer that pads a tensor with the padding sizes you specify.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcpaddinglayer

func MLCPaddingLayerFromID 

func MLCPaddingLayerFromID(id objc.ID) *MLCPaddingLayer

func MLCPaddingLayerLayerWithConstantPaddingConstantValue 

func MLCPaddingLayerLayerWithConstantPaddingConstantValue(padding *foundation.NSArray[*foundation.NSNumber], constantValue float32) *MLCPaddingLayer

Creates a padding layer with the constant padding sizes and constant valu you specify.

func MLCPaddingLayerLayerWithReflectionPadding 

func MLCPaddingLayerLayerWithReflectionPadding(padding *foundation.NSArray[*foundation.NSNumber]) *MLCPaddingLayer

Creates a padding layer with the reflection padding sizes you specify.

func MLCPaddingLayerLayerWithSymmetricPadding 

func MLCPaddingLayerLayerWithSymmetricPadding(padding *foundation.NSArray[*foundation.NSNumber]) *MLCPaddingLayer

Creates a padding layer with the symmetric padding sizes you specify.

func MLCPaddingLayerLayerWithZeroPadding 

func MLCPaddingLayerLayerWithZeroPadding(padding *foundation.NSArray[*foundation.NSNumber]) *MLCPaddingLayer

Creates a padding layer with the zero padding sizes you specify.

func (*MLCPaddingLayer) ConstantValue 

func (o *MLCPaddingLayer) ConstantValue() float32

@property constantValue @abstract The constant value to use if padding type is constant.

func (*MLCPaddingLayer) PaddingBottom 

func (o *MLCPaddingLayer) PaddingBottom() uint

@property paddingBottom @abstract The bottom padding size

func (*MLCPaddingLayer) PaddingLeft 

func (o *MLCPaddingLayer) PaddingLeft() uint

@property paddingLeft @abstract The left padding size

func (*MLCPaddingLayer) PaddingRight 

func (o *MLCPaddingLayer) PaddingRight() uint

@property paddingRight @abstract The right padding size

func (*MLCPaddingLayer) PaddingTop 

func (o *MLCPaddingLayer) PaddingTop() uint

@property paddingTop @abstract The top padding size

func (*MLCPaddingLayer) PaddingType 

func (o *MLCPaddingLayer) PaddingType() MLCPaddingType

@property paddingType @abstract The padding type i.e. constant, zero, reflect or symmetric

type MLCPaddingPolicy 

type MLCPaddingPolicy int64

A padding policy that you specify for a convolution or pooling layer. 

const (
	// The "same" padding policy.
	MLCPaddingPolicySame MLCPaddingPolicy = 0
	// The "valid" padding policy.
	MLCPaddingPolicyValid MLCPaddingPolicy = 1
	// The choice to use explicitly specified padding sizes.
	MLCPaddingPolicyUsePaddingSize MLCPaddingPolicy = 2
)

func (MLCPaddingPolicy) String 

func (e MLCPaddingPolicy) String() string

type MLCPaddingType 

type MLCPaddingType int64

A padding type that you specify for a padding layer. 

const (
	// The zero padding type.
	MLCPaddingTypeZero MLCPaddingType = 0
	// The reflect padding type.
	MLCPaddingTypeReflect MLCPaddingType = 1
	// The symmetric padding type.
	MLCPaddingTypeSymmetric MLCPaddingType = 2
	// The constant padding type.
	MLCPaddingTypeConstant MLCPaddingType = 3
)

func (MLCPaddingType) String 

func (e MLCPaddingType) String() string

type MLCPlatform 

type MLCPlatform struct {
	foundation.NSObject
}

A utility class for setting global properties in the framework.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcplatform

func MLCPlatformFromID 

func MLCPlatformFromID(id objc.ID) *MLCPlatform

type MLCPoolingDescriptor 

type MLCPoolingDescriptor struct {
	foundation.NSObject
}

A configuration object you use to create a pooling layer.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcpoolingdescriptor

func MLCPoolingDescriptorAveragePoolingDescriptorWithKernelSizesStridesDilationRatesPaddingPolicyPaddingSizesCountIncludesPadding 

func MLCPoolingDescriptorAveragePoolingDescriptorWithKernelSizesStridesDilationRatesPaddingPolicyPaddingSizesCountIncludesPadding(kernelSizes *foundation.NSArray[*foundation.NSNumber], strides *foundation.NSArray[*foundation.NSNumber], dilationRates *foundation.NSArray[*foundation.NSNumber], paddingPolicy MLCPaddingPolicy, paddingSizes *foundation.NSArray[*foundation.NSNumber], countIncludesPadding bool) *MLCPoolingDescriptor

Creates an average pooling descriptor with the kernel sizes, strides, dilution rates, padding policy and sizes, and zero padding option you specify.

func MLCPoolingDescriptorAveragePoolingDescriptorWithKernelSizesStridesPaddingPolicyPaddingSizesCountIncludesPadding 

func MLCPoolingDescriptorAveragePoolingDescriptorWithKernelSizesStridesPaddingPolicyPaddingSizesCountIncludesPadding(kernelSizes *foundation.NSArray[*foundation.NSNumber], strides *foundation.NSArray[*foundation.NSNumber], paddingPolicy MLCPaddingPolicy, paddingSizes *foundation.NSArray[*foundation.NSNumber], countIncludesPadding bool) *MLCPoolingDescriptor

Creates an average pooling descriptor with the kernel sizes, strides, padding policy, padding sizes, and zero padding option that you specify.

func MLCPoolingDescriptorFromID 

func MLCPoolingDescriptorFromID(id objc.ID) *MLCPoolingDescriptor

func MLCPoolingDescriptorL2NormPoolingDescriptorWithKernelSizesStridesDilationRatesPaddingPolicyPaddingSizes 

func MLCPoolingDescriptorL2NormPoolingDescriptorWithKernelSizesStridesDilationRatesPaddingPolicyPaddingSizes(kernelSizes *foundation.NSArray[*foundation.NSNumber], strides *foundation.NSArray[*foundation.NSNumber], dilationRates *foundation.NSArray[*foundation.NSNumber], paddingPolicy MLCPaddingPolicy, paddingSizes *foundation.NSArray[*foundation.NSNumber]) *MLCPoolingDescriptor

Creates a descriptor for an L2 norm pooling function with the kernel sizes, strides, dilation rates, padding policy, and padding sizes you specify.

func MLCPoolingDescriptorL2NormPoolingDescriptorWithKernelSizesStridesPaddingPolicyPaddingSizes 

func MLCPoolingDescriptorL2NormPoolingDescriptorWithKernelSizesStridesPaddingPolicyPaddingSizes(kernelSizes *foundation.NSArray[*foundation.NSNumber], strides *foundation.NSArray[*foundation.NSNumber], paddingPolicy MLCPaddingPolicy, paddingSizes *foundation.NSArray[*foundation.NSNumber]) *MLCPoolingDescriptor

Creates a descriptor for an L2 norm pooling function with the kernel sizes, strides, padding policy, and padding sizes that you specify.

func MLCPoolingDescriptorMaxPoolingDescriptorWithKernelSizesStridesDilationRatesPaddingPolicyPaddingSizes 

func MLCPoolingDescriptorMaxPoolingDescriptorWithKernelSizesStridesDilationRatesPaddingPolicyPaddingSizes(kernelSizes *foundation.NSArray[*foundation.NSNumber], strides *foundation.NSArray[*foundation.NSNumber], dilationRates *foundation.NSArray[*foundation.NSNumber], paddingPolicy MLCPaddingPolicy, paddingSizes *foundation.NSArray[*foundation.NSNumber]) *MLCPoolingDescriptor

Creates a descriptor for a max pooling function with the kernel sizes, strides, dilation rates, padding policy, and padding sizes that you specify.

func MLCPoolingDescriptorMaxPoolingDescriptorWithKernelSizesStridesPaddingPolicyPaddingSizes 

func MLCPoolingDescriptorMaxPoolingDescriptorWithKernelSizesStridesPaddingPolicyPaddingSizes(kernelSizes *foundation.NSArray[*foundation.NSNumber], strides *foundation.NSArray[*foundation.NSNumber], paddingPolicy MLCPaddingPolicy, paddingSizes *foundation.NSArray[*foundation.NSNumber]) *MLCPoolingDescriptor

Creates a descriptor for a max pooling function with the kernel sizes, strides, padding policy, and padding sizes that you specify.

func MLCPoolingDescriptorPoolingDescriptorWithTypeKernelSizeStride 

func MLCPoolingDescriptorPoolingDescriptorWithTypeKernelSizeStride(poolingType MLCPoolingType, kernelSize uint, stride uint) *MLCPoolingDescriptor

Creates a pooling descriptor with the pooling function, kernel size, and stride you specify.

func (*MLCPoolingDescriptor) CountIncludesPadding 

func (o *MLCPoolingDescriptor) CountIncludesPadding() bool

@property countIncludesPadding @abstract Include the zero-padding in the averaging calculation if true. Used only with average pooling.

func (*MLCPoolingDescriptor) DilationRateInX 

func (o *MLCPoolingDescriptor) DilationRateInX() uint

@property dilationRateInX @abstract The dilation rate i.e. stride of elements in the kernel in x.

func (*MLCPoolingDescriptor) DilationRateInY 

func (o *MLCPoolingDescriptor) DilationRateInY() uint

@property dilationRateInY @abstract The dilation rate i.e. stride of elements in the kernel in y.

func (*MLCPoolingDescriptor) KernelHeight 

func (o *MLCPoolingDescriptor) KernelHeight() uint

@property kernelHeight @abstract The pooling kernel size in y.

func (*MLCPoolingDescriptor) KernelWidth 

func (o *MLCPoolingDescriptor) KernelWidth() uint

@property kernelWidth @abstract The pooling kernel size in x.

func (*MLCPoolingDescriptor) PaddingPolicy 

func (o *MLCPoolingDescriptor) PaddingPolicy() MLCPaddingPolicy

@property paddingPolicy @abstract The padding policy to use.

func (*MLCPoolingDescriptor) PaddingSizeInX 

func (o *MLCPoolingDescriptor) PaddingSizeInX() uint

@property paddingSizeInX @abstract The padding size in x (left and right) to use if paddingPolicy is MLCPaddingPolicyUsePaddingSize

func (*MLCPoolingDescriptor) PaddingSizeInY 

func (o *MLCPoolingDescriptor) PaddingSizeInY() uint

@property paddingSizeInY @abstract The padding size in y (top and bottom) to use if paddingPolicy is MLCPaddingPolicyUsePaddingSize

func (*MLCPoolingDescriptor) PoolingType 

func (o *MLCPoolingDescriptor) PoolingType() MLCPoolingType

@property poolingType @abstract The pooling operation

func (*MLCPoolingDescriptor) StrideInX 

func (o *MLCPoolingDescriptor) StrideInX() uint

@property strideInX @abstract The stride of the kernel in x.

func (*MLCPoolingDescriptor) StrideInY 

func (o *MLCPoolingDescriptor) StrideInY() uint

@property strideInY @abstract The stride of the kernel in y.

type MLCPoolingLayer 

type MLCPoolingLayer struct {
	MLCLayer
}

A layer that summarizes the average presence of a feature.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcpoolinglayer

func MLCPoolingLayerFromID 

func MLCPoolingLayerFromID(id objc.ID) *MLCPoolingLayer

func MLCPoolingLayerLayerWithDescriptor 

func MLCPoolingLayerLayerWithDescriptor(descriptor *MLCPoolingDescriptor) *MLCPoolingLayer

Creates a pooling layer with the descriptor you specify.

func (*MLCPoolingLayer) Descriptor 

func (o *MLCPoolingLayer) Descriptor() *MLCPoolingDescriptor

@property descriptor @abstract The pooling descriptor

type MLCPoolingType 

type MLCPoolingType int64

A pooling function type for a pooling layer. 

const (
	// The max pooling type.
	MLCPoolingTypeMax MLCPoolingType = 1
	// The average pooling type.
	MLCPoolingTypeAverage MLCPoolingType = 2
	// The L2-norm pooling type.
	MLCPoolingTypeL2Norm MLCPoolingType = 3
	MLCPoolingTypeCount  MLCPoolingType = 4
)

func (MLCPoolingType) String 

func (e MLCPoolingType) String() string

type MLCRMSPropOptimizer 

type MLCRMSPropOptimizer struct {
	MLCOptimizer
}

An optimizer that represents the root mean square propagation algorithm.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcrmspropoptimizer Deprecated: Use Metal Performance Shaders Graph or BNNS instead.

func MLCRMSPropOptimizerFromID 

func MLCRMSPropOptimizerFromID(id objc.ID) *MLCRMSPropOptimizer

func MLCRMSPropOptimizerOptimizerWithDescriptor 

func MLCRMSPropOptimizerOptimizerWithDescriptor(optimizerDescriptor *MLCOptimizerDescriptor) *MLCRMSPropOptimizer

Creates an RMSProp optimizer with the descriptor you specify.

func MLCRMSPropOptimizerOptimizerWithDescriptorMomentumScaleAlphaEpsilonIsCentered 

func MLCRMSPropOptimizerOptimizerWithDescriptorMomentumScaleAlphaEpsilonIsCentered(optimizerDescriptor *MLCOptimizerDescriptor, momentumScale float32, alpha float32, epsilon float32, isCentered bool) *MLCRMSPropOptimizer

Creates an RMSProp optimizer with the descriptor, momentum scale, smoothing, epsilon, and option to compute the centered RMSProp that you specify.

func (*MLCRMSPropOptimizer) Alpha 

func (o *MLCRMSPropOptimizer) Alpha() float32

@property alpha @abstract The smoothing constant. @discussion The default is 0.99.

func (*MLCRMSPropOptimizer) Epsilon 

func (o *MLCRMSPropOptimizer) Epsilon() float32

@property epsilon @abstract A term added to improve numerical stability. @discussion The default is 1e-8.

func (*MLCRMSPropOptimizer) IsCentered 

func (o *MLCRMSPropOptimizer) IsCentered() bool

@property isCentered @abstract If True, compute the centered RMSProp, the gradient is normalized by an estimation of its variance. @discussion The default is false.

func (*MLCRMSPropOptimizer) MomentumScale 

func (o *MLCRMSPropOptimizer) MomentumScale() float32

@property momentumScale @abstract The momentum factor. A hyper-parameter. @discussion The default is 0.0.

type MLCRandomInitializerType 

type MLCRandomInitializerType int64

An initializer type you use to create a tensor with random data. 

const (
	MLCRandomInitializerTypeInvalid MLCRandomInitializerType = 0
	// The uniform random initializer type.
	MLCRandomInitializerTypeUniform MLCRandomInitializerType = 1
	// The glorot uniform random initializer type.
	MLCRandomInitializerTypeGlorotUniform MLCRandomInitializerType = 2
	// The Xavier random initializer type.
	MLCRandomInitializerTypeXavier MLCRandomInitializerType = 3
	MLCRandomInitializerTypeCount  MLCRandomInitializerType = 4
)

func (MLCRandomInitializerType) String 

func (e MLCRandomInitializerType) String() string

type MLCReductionLayer 

type MLCReductionLayer struct {
	MLCLayer
}

A layer that reduces tensor values across a specific dimension to a scalar value.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcreductionlayer

func MLCReductionLayerFromID 

func MLCReductionLayerFromID(id objc.ID) *MLCReductionLayer

func MLCReductionLayerLayerWithReductionTypeDimension 

func MLCReductionLayerLayerWithReductionTypeDimension(reductionType MLCReductionType, dimension uint) *MLCReductionLayer

Creates a reduction layer using the reduction type and dimension you specify.

func MLCReductionLayerLayerWithReductionTypeDimensions 

func MLCReductionLayerLayerWithReductionTypeDimensions(reductionType MLCReductionType, dimensions *foundation.NSArray[*foundation.NSNumber]) *MLCReductionLayer

Creates a reduction layer using the reduction type and dimensions you specify.

func (*MLCReductionLayer) Dimension 

func (o *MLCReductionLayer) Dimension() uint

@property dimension @abstract The dimension over which to perform the reduction operation

func (*MLCReductionLayer) Dimensions 

func (o *MLCReductionLayer) Dimensions() *foundation.NSArray[*foundation.NSNumber]

@property dimensions @abstract The dimensions over which to perform the reduction operation

func (*MLCReductionLayer) ReductionType 

func (o *MLCReductionLayer) ReductionType() MLCReductionType

@property reductionType @abstract The reduction type

type MLCReductionType 

type MLCReductionType int64

Constants that describe a reduction operation type. 

const (
	// A reduction operation that applies no reduction.
	MLCReductionTypeNone MLCReductionType = 0
	// A reduction operation that applies to the sum of the dimensions.
	MLCReductionTypeSum MLCReductionType = 1
	// A reduction operation that applies to the mean of the dimensions.
	MLCReductionTypeMean MLCReductionType = 2
	// A reduction operation that applies to the maximum dimension.
	MLCReductionTypeMax MLCReductionType = 3
	// A reduction operation that applies to the minimum dimension.
	MLCReductionTypeMin MLCReductionType = 4
	// A reduction operation that applies to the maximum dimension you specify.
	MLCReductionTypeArgMax MLCReductionType = 5
	// A reduction operation that applies to the minimum dimension you specify.
	MLCReductionTypeArgMin MLCReductionType = 6
	// A reduction operation that applies a lasso regularization penalty.
	MLCReductionTypeL1Norm MLCReductionType = 7
	// A reduction operation that applies to any dimension.
	MLCReductionTypeAny MLCReductionType = 8
	// A reduction operation that applies to all dimensions.
	MLCReductionTypeAll MLCReductionType = 9
	// The total number of reduction operations.
	MLCReductionTypeCount MLCReductionType = 10
)

func (MLCReductionType) String 

func (e MLCReductionType) String() string

type MLCRegularizationType 

type MLCRegularizationType int64

A regularization function to use with an optimizer. 

const (
	// No regularization.
	MLCRegularizationTypeNone MLCRegularizationType = 0
	// The L1 regularization.
	MLCRegularizationTypeL1 MLCRegularizationType = 1
	// The L2 regularization.
	MLCRegularizationTypeL2 MLCRegularizationType = 2
)

func (MLCRegularizationType) String 

func (e MLCRegularizationType) String() string

type MLCReshapeLayer 

type MLCReshapeLayer struct {
	MLCLayer
}

A layer that reshapes a tensor with the shape you specify.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcreshapelayer

func MLCReshapeLayerFromID 

func MLCReshapeLayerFromID(id objc.ID) *MLCReshapeLayer

func MLCReshapeLayerLayerWithShape 

func MLCReshapeLayerLayerWithShape(shape *foundation.NSArray[*foundation.NSNumber]) *MLCReshapeLayer

Creates a reshape layer with the shape you specify.

func (*MLCReshapeLayer) Shape 

func (o *MLCReshapeLayer) Shape() *foundation.NSArray[*foundation.NSNumber]

@property shape @abstract The target shape.

type MLCSGDOptimizer 

type MLCSGDOptimizer struct {
	MLCOptimizer
}

An optimizer that represents the stochastic gradient decent algorithm.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcsgdoptimizer

func MLCSGDOptimizerFromID 

func MLCSGDOptimizerFromID(id objc.ID) *MLCSGDOptimizer

func MLCSGDOptimizerOptimizerWithDescriptor 

func MLCSGDOptimizerOptimizerWithDescriptor(optimizerDescriptor *MLCOptimizerDescriptor) *MLCSGDOptimizer

Creates an SGD optimizer with the descriptor you specify.

func MLCSGDOptimizerOptimizerWithDescriptorMomentumScaleUsesNesterovMomentum 

func MLCSGDOptimizerOptimizerWithDescriptorMomentumScaleUsesNesterovMomentum(optimizerDescriptor *MLCOptimizerDescriptor, momentumScale float32, usesNesterovMomentum bool) *MLCSGDOptimizer

Create an SGD optimizer with the descriptor, momentum scale, and option to enable Nesterov momentum that you specify.

func (*MLCSGDOptimizer) MomentumScale 

func (o *MLCSGDOptimizer) MomentumScale() float32

@property momentumScale @abstract The momentum factor. A hyper-parameter. @discussion The default is 0.0.

func (*MLCSGDOptimizer) UsesNesterovMomentum 

func (o *MLCSGDOptimizer) UsesNesterovMomentum() bool

@property usesNesterovMomentum @abstract A boolean that specifies whether to apply nesterov momentum or not. @discussion The default is false.

type MLCSampleMode 

type MLCSampleMode int64

A sampling mode for an upsample layer. 

const (
	// The nearest sample mode.
	MLCSampleModeNearest MLCSampleMode = 0
	// The linear sample mode.
	MLCSampleModeLinear MLCSampleMode = 1
)

func (MLCSampleMode) String 

func (e MLCSampleMode) String() string

type MLCScatterLayer 

type MLCScatterLayer struct {
	MLCLayer
}

A layer that updates the output at an index you specify.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcscatterlayer

func MLCScatterLayerFromID 

func MLCScatterLayerFromID(id objc.ID) *MLCScatterLayer

func MLCScatterLayerLayerWithDimensionReductionType 

func MLCScatterLayerLayerWithDimensionReductionType(dimension uint, reductionType MLCReductionType) *MLCScatterLayer

Creates a scatter layer with the dimension and reduction type you specify.

func (*MLCScatterLayer) Dimension 

func (o *MLCScatterLayer) Dimension() uint

@property dimension @abstract The dimension along which to index

func (*MLCScatterLayer) ReductionType 

func (o *MLCScatterLayer) ReductionType() MLCReductionType

@property reductionType @abstract The reduction type applied for all values in source tensor that are scattered to a specific location in the result tensor. Must be: MLCReductionTypeNone or MLCReductionTypeSum.

type MLCSelectionLayer 

type MLCSelectionLayer struct {
	MLCLayer
}

A layer for selecting elements from two tensors.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcselectionlayer

func MLCSelectionLayerFromID 

func MLCSelectionLayerFromID(id objc.ID) *MLCSelectionLayer

func MLCSelectionLayerLayer 

func MLCSelectionLayerLayer() *MLCSelectionLayer

Creates a selection layer.

type MLCSliceLayer 

type MLCSliceLayer struct {
	MLCLayer
}

A layer that extracts a slice from a tensor.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcslicelayer

func MLCSliceLayerFromID 

func MLCSliceLayerFromID(id objc.ID) *MLCSliceLayer

func MLCSliceLayerSliceLayerWithStartEndStride 

func MLCSliceLayerSliceLayerWithStartEndStride(start *foundation.NSArray[*foundation.NSNumber], end *foundation.NSArray[*foundation.NSNumber], stride *foundation.NSArray[*foundation.NSNumber]) *MLCSliceLayer

Creates a slice layer with the specified start, end, and stride.

func (*MLCSliceLayer) End 

func (o *MLCSliceLayer) End() *foundation.NSArray[*foundation.NSNumber]

@property end @abstract A vector of length equal to that of source. The element at index i specifies the end of slice in dimension i.

func (*MLCSliceLayer) Start 

func (o *MLCSliceLayer) Start() *foundation.NSArray[*foundation.NSNumber]

@property start @abstract A vector of length equal to that of source. The element at index i specifies the beginning of slice in dimension i.

func (*MLCSliceLayer) Stride 

func (o *MLCSliceLayer) Stride() *foundation.NSArray[*foundation.NSNumber]

@property stride @abstract A vector of length equal to that of source. The element at index i specifies the stride of slice in dimension i.

type MLCSoftmaxLayer 

type MLCSoftmaxLayer struct {
	MLCLayer
}

A layer that outputs a probability distribution as attention weights.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcsoftmaxlayer

func MLCSoftmaxLayerFromID 

func MLCSoftmaxLayerFromID(id objc.ID) *MLCSoftmaxLayer

func MLCSoftmaxLayerLayerWithOperation 

func MLCSoftmaxLayerLayerWithOperation(operation MLCSoftmaxOperation) *MLCSoftmaxLayer

Creates a softmax layer with the operation you specify.

func MLCSoftmaxLayerLayerWithOperationDimension 

func MLCSoftmaxLayerLayerWithOperationDimension(operation MLCSoftmaxOperation, dimension uint) *MLCSoftmaxLayer

Creates a softmax layer with the operation and dimension you specify.

func (*MLCSoftmaxLayer) Dimension 

func (o *MLCSoftmaxLayer) Dimension() uint

@property dimension @abstract The dimension over which softmax operation should be performed

func (*MLCSoftmaxLayer) Operation 

func (o *MLCSoftmaxLayer) Operation() MLCSoftmaxOperation

@property operation @abstract The softmax operation. Supported values are softmax and log softmax.

type MLCSoftmaxOperation 

type MLCSoftmaxOperation int64

A softmax operation. 

const (
	// The standard softmax operation.
	MLCSoftmaxOperationSoftmax MLCSoftmaxOperation = 0
	// The log softmax operation.
	MLCSoftmaxOperationLogSoftmax MLCSoftmaxOperation = 1
)

func (MLCSoftmaxOperation) String 

func (e MLCSoftmaxOperation) String() string

type MLCSplitLayer 

type MLCSplitLayer struct {
	MLCLayer
}

A layer that splits a tensor value into a list of subtensors.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcsplitlayer

func MLCSplitLayerFromID 

func MLCSplitLayerFromID(id objc.ID) *MLCSplitLayer

func MLCSplitLayerLayerWithSplitCountDimension 

func MLCSplitLayerLayerWithSplitCountDimension(splitCount uint, dimension uint) *MLCSplitLayer

Creates a split layer with the number of splits and dimension you specify.

func MLCSplitLayerLayerWithSplitSectionLengthsDimension 

func MLCSplitLayerLayerWithSplitSectionLengthsDimension(splitSectionLengths *foundation.NSArray[*foundation.NSNumber], dimension uint) *MLCSplitLayer

Creates a split layer with the lengths of each split section and dimension you specify.

func (*MLCSplitLayer) Dimension 

func (o *MLCSplitLayer) Dimension() uint

@property dimension @abstract The dimension (or axis) along which to split tensor

func (*MLCSplitLayer) SplitCount 

func (o *MLCSplitLayer) SplitCount() uint

@property splitCount @abstract The number of splits. @discussion The tensor will be split into equally sized chunks. The last chunk may be smaller in size.

func (*MLCSplitLayer) SplitSectionLengths 

func (o *MLCSplitLayer) SplitSectionLengths() *foundation.NSArray[*foundation.NSNumber]

@property splitSectionLengths @abstract Lengths of each split section. @discussion The tensor will be split into chunks along dimensions with sizes given in \p splitSectionLengths .

type MLCTensor 

type MLCTensor struct {
	foundation.NSObject
}

The data object you use throughout the framework.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlctensor

func MLCTensorFromID 

func MLCTensorFromID(id objc.ID) *MLCTensor

func MLCTensorTensorWithDescriptor 

func MLCTensorTensorWithDescriptor(tensorDescriptor *MLCTensorDescriptor) *MLCTensor

Creates a tensor without data, using the descriptor you specify.

func MLCTensorTensorWithDescriptorData 

func MLCTensorTensorWithDescriptorData(tensorDescriptor *MLCTensorDescriptor, data *MLCTensorData) *MLCTensor

Creates a tensor with the descriptor and data you specify.

func MLCTensorTensorWithDescriptorFillWithData 

func MLCTensorTensorWithDescriptorFillWithData(tensorDescriptor *MLCTensorDescriptor, fillData *foundation.NSNumber) *MLCTensor

Creates a tensor with the descriptor and scalar value you specify.

func MLCTensorTensorWithDescriptorRandomInitializerType 

func MLCTensorTensorWithDescriptorRandomInitializerType(tensorDescriptor *MLCTensorDescriptor, randomInitializerType MLCRandomInitializerType) *MLCTensor

Creates a tensor with the descriptor and random initializer type you specify.

func MLCTensorTensorWithSequenceLengthFeatureChannelCountBatchSize 

func MLCTensorTensorWithSequenceLengthFeatureChannelCountBatchSize(sequenceLength uint, featureChannelCount uint, batchSize uint) *MLCTensor

Creates a tensor without data, with the sequence length, number of feature channels, and batch size you specify.

func MLCTensorTensorWithSequenceLengthFeatureChannelCountBatchSizeData 

func MLCTensorTensorWithSequenceLengthFeatureChannelCountBatchSizeData(sequenceLength uint, featureChannelCount uint, batchSize uint, data *MLCTensorData) *MLCTensor

Creates a tensor with the sequence length, number of feature channels, batch size, and data you specify.

func MLCTensorTensorWithSequenceLengthFeatureChannelCountBatchSizeRandomInitializerType 

func MLCTensorTensorWithSequenceLengthFeatureChannelCountBatchSizeRandomInitializerType(sequenceLength uint, featureChannelCount uint, batchSize uint, randomInitializerType MLCRandomInitializerType) *MLCTensor

Creates a tensor with the sequence length, number of feature channels, batch size, and random initializer type you specify.

func MLCTensorTensorWithSequenceLengthsSortedSequencesFeatureChannelCountBatchSizeData 

func MLCTensorTensorWithSequenceLengthsSortedSequencesFeatureChannelCountBatchSizeData(sequenceLengths *foundation.NSArray[*foundation.NSNumber], sortedSequences bool, featureChannelCount uint, batchSize uint, data *MLCTensorData) *MLCTensor

Creates a tensor with the sequence lengths, sorting indicator, number of feature channels, batch size, and data you specify.

func MLCTensorTensorWithSequenceLengthsSortedSequencesFeatureChannelCountBatchSizeRandomInitializerType 

func MLCTensorTensorWithSequenceLengthsSortedSequencesFeatureChannelCountBatchSizeRandomInitializerType(sequenceLengths *foundation.NSArray[*foundation.NSNumber], sortedSequences bool, featureChannelCount uint, batchSize uint, randomInitializerType MLCRandomInitializerType) *MLCTensor

Creates a tensor with the sequence lengths, sorting indicator, number of feature channels, batch size, and random initializer type you specify.

func MLCTensorTensorWithShape 

func MLCTensorTensorWithShape(shape *foundation.NSArray[*foundation.NSNumber]) *MLCTensor

Creates a tensor without data, with the shape you specify.

func MLCTensorTensorWithShapeDataDataType 

func MLCTensorTensorWithShapeDataDataType(shape *foundation.NSArray[*foundation.NSNumber], data *MLCTensorData, dataType MLCDataType) *MLCTensor

Creates a tensor with the shape, data, and data type you specify.

func MLCTensorTensorWithShapeDataType 

func MLCTensorTensorWithShapeDataType(shape *foundation.NSArray[*foundation.NSNumber], dataType MLCDataType) *MLCTensor

Creates a tensor without data, with the shape and data type you specify.

func MLCTensorTensorWithShapeFillWithDataDataType 

func MLCTensorTensorWithShapeFillWithDataDataType(shape *foundation.NSArray[*foundation.NSNumber], fillData *foundation.NSNumber, dataType MLCDataType) *MLCTensor

Creates a tensor with the shape, scalar value, and data type you specify.

func MLCTensorTensorWithShapeRandomInitializerType 

func MLCTensorTensorWithShapeRandomInitializerType(shape *foundation.NSArray[*foundation.NSNumber], randomInitializerType MLCRandomInitializerType) *MLCTensor

Creates a tensor with the shape and random initializer type you specify.

func MLCTensorTensorWithShapeRandomInitializerTypeDataType 

func MLCTensorTensorWithShapeRandomInitializerTypeDataType(shape *foundation.NSArray[*foundation.NSNumber], randomInitializerType MLCRandomInitializerType, dataType MLCDataType) *MLCTensor

Creates a tensor with the shape, random initializer, and data type you specify.

func MLCTensorTensorWithWidthHeightFeatureChannelCountBatchSize 

func MLCTensorTensorWithWidthHeightFeatureChannelCountBatchSize(width uint, height uint, featureChannelCount uint, batchSize uint) *MLCTensor

Creates a tensor without data, with the sizes and number of feature channels you specify.

func MLCTensorTensorWithWidthHeightFeatureChannelCountBatchSizeData 

func MLCTensorTensorWithWidthHeightFeatureChannelCountBatchSizeData(width uint, height uint, featureChannelCount uint, batchSize uint, data *MLCTensorData) *MLCTensor

Creates a tensor with the sizes, number of feature channels, and data you specify.

func MLCTensorTensorWithWidthHeightFeatureChannelCountBatchSizeDataDataType 

func MLCTensorTensorWithWidthHeightFeatureChannelCountBatchSizeDataDataType(width uint, height uint, featureChannelCount uint, batchSize uint, data *MLCTensorData, dataType MLCDataType) *MLCTensor

Creates a tensor with the sizes, number of feature channels, data, and data type you specify.

func MLCTensorTensorWithWidthHeightFeatureChannelCountBatchSizeFillWithDataDataType 

func MLCTensorTensorWithWidthHeightFeatureChannelCountBatchSizeFillWithDataDataType(width uint, height uint, featureChannelCount uint, batchSize uint, fillData float32, dataType MLCDataType) *MLCTensor

Creates a tensor with the sizes and number of feature channels, and filled with the data and type you specify.

func MLCTensorTensorWithWidthHeightFeatureChannelCountBatchSizeRandomInitializerType 

func MLCTensorTensorWithWidthHeightFeatureChannelCountBatchSizeRandomInitializerType(width uint, height uint, featureChannelCount uint, batchSize uint, randomInitializerType MLCRandomInitializerType) *MLCTensor

Creates a tensor with the sizes, number of feature channels, and random data using the random initializer type you specify.

func (*MLCTensor) BindAndWriteDataToDevice 

func (o *MLCTensor) BindAndWriteDataToDevice(data *MLCTensorData, device *MLCDevice) bool

Associates the given data to the tensor, and if the device is a GPU, also copies the data to the device memory.

func (*MLCTensor) BindOptimizerDataDeviceData 

func (o *MLCTensor) BindOptimizerDataDeviceData(data *foundation.NSArray[*MLCTensorData], deviceData *foundation.NSArray[*MLCTensorOptimizerDeviceData]) bool

Associates the optimizer and device data buffers you specify to the tensor.

func (*MLCTensor) CopyDataFromDeviceMemoryToBytesLengthSynchronizeWithDevice 

func (o *MLCTensor) CopyDataFromDeviceMemoryToBytesLengthSynchronizeWithDevice(bytes_ unsafe.Pointer, length uint, synchronizeWithDevice bool) bool

Copies tensor data from device memory to user-specified memory.

func (*MLCTensor) Data 

func (o *MLCTensor) Data() *foundation.NSData

@property data @abstract The tensor data

func (*MLCTensor) Descriptor 

func (o *MLCTensor) Descriptor() *MLCTensorDescriptor

@property descriptor @abstract The tensor descriptor

func (*MLCTensor) Device 

func (o *MLCTensor) Device() *MLCDevice

@property device @abstract The device associated with this tensor.

func (*MLCTensor) HasValidNumerics 

func (o *MLCTensor) HasValidNumerics() bool

@abstract Returns a Boolean value indicating whether the underlying data has valid floating-point numerics, i.e. it does not contain NaN or INF floating-point values.

func (*MLCTensor) Label 

func (o *MLCTensor) Label() *foundation.NSString

@property label @abstract A string to help identify this object.

func (*MLCTensor) OptimizerData 

func (o *MLCTensor) OptimizerData() *foundation.NSArray[*MLCTensorData]

@property optimizer buffers to use if tensor is used as a parameter @abstract These are the host side optimizer (momentum and velocity) buffers which developers can query and initialize @discussion When customizing optimizer data, the contents of these buffers must be initialized before executing optimizer update for a graph.

func (*MLCTensor) OptimizerDeviceData 

func (o *MLCTensor) OptimizerDeviceData() *foundation.NSArray[*MLCTensorOptimizerDeviceData]

@property optimizer device buffers to use if tensor is used as a parameter @abstract These are the device side optimizer (momentum and velocity) buffers which developers can query

func (*MLCTensor) SetLabel 

func (o *MLCTensor) SetLabel(label *foundation.NSString)

func (*MLCTensor) SynchronizeData 

func (o *MLCTensor) SynchronizeData() bool

Synchronizes the data in host memory.

func (*MLCTensor) SynchronizeOptimizerData 

func (o *MLCTensor) SynchronizeOptimizerData() bool

Synchronizes the optimizer data in host memory.

func (*MLCTensor) TensorByDequantizingToTypeScaleBias 

func (o *MLCTensor) TensorByDequantizingToTypeScaleBias(type_ MLCDataType, scale *MLCTensor, bias *MLCTensor) *MLCTensor

Converts a tensor you quantize to a 32-bit floating-point tensor.

func (*MLCTensor) TensorByDequantizingToTypeScaleBiasAxis 

func (o *MLCTensor) TensorByDequantizingToTypeScaleBiasAxis(type_ MLCDataType, scale *MLCTensor, bias *MLCTensor, axis int) *MLCTensor

Converts a tensor you quantize to a 32-bit floating-point tensor.

func (*MLCTensor) TensorByQuantizingToTypeScaleBias 

func (o *MLCTensor) TensorByQuantizingToTypeScaleBias(type_ MLCDataType, scale float32, bias int) *MLCTensor

Converts a 32-bit floating-point tensor with the scale and bias you specify.

func (*MLCTensor) TensorByQuantizingToTypeScaleBiasAxis 

func (o *MLCTensor) TensorByQuantizingToTypeScaleBiasAxis(type_ MLCDataType, scale *MLCTensor, bias *MLCTensor, axis int) *MLCTensor

Converts a 32-bit floating-point tensor with the scale and bias you specify.

func (*MLCTensor) TensorID 

func (o *MLCTensor) TensorID() uint

@property tensorID @abstract The tensor ID @discussion A unique number to identify each tensor. Assigned when the tensor is created.

type MLCTensorData 

type MLCTensorData struct {
	foundation.NSObject
}

An encapsulation of the memory that tensor data uses.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlctensordata

func MLCTensorDataDataWithBytesNoCopyLength 

func MLCTensorDataDataWithBytesNoCopyLength(bytes_ unsafe.Pointer, length uint) *MLCTensorData

Creates a tensor data instance with the buffer of data and length of bytes you specify.

func MLCTensorDataDataWithBytesNoCopyLengthDeallocator 

func MLCTensorDataDataWithBytesNoCopyLengthDeallocator(bytes_ unsafe.Pointer, length uint, deallocator func(unsafe.Pointer, uint)) *MLCTensorData

Creates a tensor data instance with a data buffer, byte length, and custom deallocator closure you specify.

func MLCTensorDataDataWithImmutableBytesNoCopyLength 

func MLCTensorDataDataWithImmutableBytesNoCopyLength(bytes_ unsafe.Pointer, length uint) *MLCTensorData

Creates a tensor data instance with the buffer of immutable data and length of bytes you specify.

func MLCTensorDataFromID 

func MLCTensorDataFromID(id objc.ID) *MLCTensorData

func (*MLCTensorData) Bytes 

func (o *MLCTensorData) Bytes() unsafe.Pointer

@property bytes @abstract Pointer to memory that contains or will be used for tensor data

func (*MLCTensorData) Length 

func (o *MLCTensorData) Length() uint

@property length @abstract The size in bytes of the tensor data

type MLCTensorDescriptor 

type MLCTensorDescriptor struct {
	foundation.NSObject
}

A configuration object you use to create a tensor.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlctensordescriptor

func MLCTensorDescriptorConvolutionBiasesDescriptorWithFeatureChannelCountDataType 

func MLCTensorDescriptorConvolutionBiasesDescriptorWithFeatureChannelCountDataType(featureChannelCount uint, dataType MLCDataType) *MLCTensorDescriptor

Creates a tensor descriptor with the number of feature channels and data type you specify.

func MLCTensorDescriptorConvolutionWeightsDescriptorWithInputFeatureChannelCountOutputFeatureChannelCountDataType 

func MLCTensorDescriptorConvolutionWeightsDescriptorWithInputFeatureChannelCountOutputFeatureChannelCountDataType(inputFeatureChannelCount uint, outputFeatureChannelCount uint, dataType MLCDataType) *MLCTensorDescriptor

Creates a tensor descriptor with the number of feature channels and data type you specify.

func MLCTensorDescriptorConvolutionWeightsDescriptorWithWidthHeightInputFeatureChannelCountOutputFeatureChannelCountDataType 

func MLCTensorDescriptorConvolutionWeightsDescriptorWithWidthHeightInputFeatureChannelCountOutputFeatureChannelCountDataType(width uint, height uint, inputFeatureChannelCount uint, outputFeatureChannelCount uint, dataType MLCDataType) *MLCTensorDescriptor

Creates a tensor descriptor with the sizing, number of feature channels, and data type you specify.

func MLCTensorDescriptorDescriptorWithShapeDataType 

func MLCTensorDescriptorDescriptorWithShapeDataType(shape *foundation.NSArray[*foundation.NSNumber], dataType MLCDataType) *MLCTensorDescriptor

Creates a tensor descriptor with the shape and data type you specify.

func MLCTensorDescriptorDescriptorWithShapeSequenceLengthsSortedSequencesDataType 

func MLCTensorDescriptorDescriptorWithShapeSequenceLengthsSortedSequencesDataType(shape *foundation.NSArray[*foundation.NSNumber], sequenceLengths *foundation.NSArray[*foundation.NSNumber], sortedSequences bool, dataType MLCDataType) *MLCTensorDescriptor

Creates a tensor descriptor with the shape, variable sequence lengths, sorting indicator, and data type you specify.

func MLCTensorDescriptorDescriptorWithWidthHeightFeatureChannelCountBatchSize 

func MLCTensorDescriptorDescriptorWithWidthHeightFeatureChannelCountBatchSize(width uint, height uint, featureChannels uint, batchSize uint) *MLCTensorDescriptor

Creates a tensor descriptor with the width and height, number of feature channels, and batch size you specify.

func MLCTensorDescriptorDescriptorWithWidthHeightFeatureChannelCountBatchSizeDataType 

func MLCTensorDescriptorDescriptorWithWidthHeightFeatureChannelCountBatchSizeDataType(width uint, height uint, featureChannelCount uint, batchSize uint, dataType MLCDataType) *MLCTensorDescriptor

Creates a tensor descriptor with the width and height, number of feature channels, batch size, and data type you specify.

func MLCTensorDescriptorFromID 

func MLCTensorDescriptorFromID(id objc.ID) *MLCTensorDescriptor

func (*MLCTensorDescriptor) BatchSizePerSequenceStep 

func (o *MLCTensorDescriptor) BatchSizePerSequenceStep() *foundation.NSArray[*foundation.NSNumber]

@property batchSizePerSequenceStep @abstract The batch size for each sequence @discussion We populate this only when sequenceLengths is valid. The length of this array should be the maximum sequence length in sequenceLengths (i.e sequenceLengths[0]).

func (*MLCTensorDescriptor) DataType 

func (o *MLCTensorDescriptor) DataType() MLCDataType

@property dataType @abstract The tensor data type. The default is MLCDataTypeFloat32.

func (*MLCTensorDescriptor) DimensionCount 

func (o *MLCTensorDescriptor) DimensionCount() uint

@property dimensionCount @abstract The number of dimensions in the tensor

func (*MLCTensorDescriptor) SequenceLengths 

func (o *MLCTensorDescriptor) SequenceLengths() *foundation.NSArray[*foundation.NSNumber]

@property sequenceLengths @abstract TODO

func (*MLCTensorDescriptor) Shape 

func (o *MLCTensorDescriptor) Shape() *foundation.NSArray[*foundation.NSNumber]

@property shape @abstract The size in each dimension

func (*MLCTensorDescriptor) SortedSequences 

func (o *MLCTensorDescriptor) SortedSequences() bool

@property sortedSequences @abstract Specifies whether the sequences are sorted or not.

func (*MLCTensorDescriptor) Stride 

func (o *MLCTensorDescriptor) Stride() *foundation.NSArray[*foundation.NSNumber]

@property stride @abstract The stride in bytes in each dimension

func (*MLCTensorDescriptor) TensorAllocationSizeInBytes 

func (o *MLCTensorDescriptor) TensorAllocationSizeInBytes() uint

@property tensorAllocationSizeInBytes @abstract The allocation size in bytes for a tensor.

type MLCTensorOptimizerDeviceData 

type MLCTensorOptimizerDeviceData struct {
	foundation.NSObject
}

An encapsulation of the device memory associated with a tensor that an optimizer uses.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlctensoroptimizerdevicedata

func MLCTensorOptimizerDeviceDataFromID 

func MLCTensorOptimizerDeviceDataFromID(id objc.ID) *MLCTensorOptimizerDeviceData

type MLCTensorParameter 

type MLCTensorParameter struct {
	foundation.NSObject
}

A tensor parameter object.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlctensorparameter

func MLCTensorParameterFromID 

func MLCTensorParameterFromID(id objc.ID) *MLCTensorParameter

func MLCTensorParameterParameterWithTensor 

func MLCTensorParameterParameterWithTensor(tensor *MLCTensor) *MLCTensorParameter

Creates a tensor parameter with the tensor you specify.

func MLCTensorParameterParameterWithTensorOptimizerData 

func MLCTensorParameterParameterWithTensorOptimizerData(tensor *MLCTensor, optimizerData *foundation.NSArray[*MLCTensorData]) *MLCTensorParameter

Creates a tensor parameter with the tensor and optimizer data you specify.

func (*MLCTensorParameter) IsUpdatable 

func (o *MLCTensorParameter) IsUpdatable() bool

@property isUpdatable @abstract Specifies whether this tensor parameter is updatable

func (*MLCTensorParameter) SetIsUpdatable 

func (o *MLCTensorParameter) SetIsUpdatable(isUpdatable bool)

func (*MLCTensorParameter) Tensor 

func (o *MLCTensorParameter) Tensor() *MLCTensor

@property tensor @abstract The underlying tensor

type MLCTrainingGraph 

type MLCTrainingGraph struct {
	MLCGraph
}

A training graph that you create from one or more graph objects plus additional layers you add directly to the training graph.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlctraininggraph

func MLCTrainingGraphFromID 

func MLCTrainingGraphFromID(id objc.ID) *MLCTrainingGraph

func MLCTrainingGraphGraphWithGraphObjectsLossLayerOptimizer 

func MLCTrainingGraphGraphWithGraphObjectsLossLayerOptimizer(graphObjects *foundation.NSArray[*MLCGraph], lossLayer *MLCLayer, optimizer *MLCOptimizer) *MLCTrainingGraph

Creates a training graph with the layers from the graph objects, loss layer, and optimizer you specify.

func (*MLCTrainingGraph) AddInputsLossLabels 

func (o *MLCTrainingGraph) AddInputsLossLabels(inputs *foundation.NSDictionary[*foundation.NSString, *MLCTensor], lossLabels *foundation.NSDictionary[*foundation.NSString, *MLCTensor]) bool

Adds the inputs and loss label inputs that you specify to the training graph.

func (*MLCTrainingGraph) AddInputsLossLabelsLossLabelWeights 

func (o *MLCTrainingGraph) AddInputsLossLabelsLossLabelWeights(inputs *foundation.NSDictionary[*foundation.NSString, *MLCTensor], lossLabels *foundation.NSDictionary[*foundation.NSString, *MLCTensor], lossLabelWeights *foundation.NSDictionary[*foundation.NSString, *MLCTensor]) bool

Adds the inputs, loss labels, and loss label weights that you specify to the training graph.

func (*MLCTrainingGraph) AddOutputs 

func (o *MLCTrainingGraph) AddOutputs(outputs *foundation.NSDictionary[*foundation.NSString, *MLCTensor]) bool

Adds the outputs to the training graph you specify.

func (*MLCTrainingGraph) AllocateUserGradientForTensor 

func (o *MLCTrainingGraph) AllocateUserGradientForTensor(tensor *MLCTensor) *MLCTensor

Allocates an entry for a gradient for the result tensor you specify.

func (*MLCTrainingGraph) BindOptimizerDataDeviceDataWithTensor 

func (o *MLCTrainingGraph) BindOptimizerDataDeviceDataWithTensor(data *foundation.NSArray[*MLCTensorData], deviceData *foundation.NSArray[*MLCTensorOptimizerDeviceData], tensor *MLCTensor) bool

Associates the optimizer and device data you specify along with the tensor.

func (*MLCTrainingGraph) CompileOptimizer 

func (o *MLCTrainingGraph) CompileOptimizer(optimizer *MLCOptimizer) bool

Compiles the optimizer to use with a training graph you specify.

func (*MLCTrainingGraph) CompileWithOptionsDevice 

func (o *MLCTrainingGraph) CompileWithOptionsDevice(options MLCGraphCompilationOptions, device *MLCDevice) bool

Compiles the training graph for the options and device you specify.

func (*MLCTrainingGraph) CompileWithOptionsDeviceInputTensorsInputTensorsData 

func (o *MLCTrainingGraph) CompileWithOptionsDeviceInputTensorsInputTensorsData(options MLCGraphCompilationOptions, device *MLCDevice, inputTensors *foundation.NSDictionary[*foundation.NSString, *MLCTensor], inputTensorsData *foundation.NSDictionary[*foundation.NSString, *MLCTensorData]) bool

Compiles the training graph for the options, device, and input tensors you specify.

func (*MLCTrainingGraph) DeviceMemorySize 

func (o *MLCTrainingGraph) DeviceMemorySize() uint

@property The device memory size used by the training graph @abstract Returns the total size in bytes of device memory used for all intermediate tensors for forward, gradient passes and optimizer update for all layers in the training graph. We recommend executing an iteration before checking the device memory size as the buffers needed get allocated when the corresponding pass such as gradient, optimizer update is executed. @return A NSUInteger value

func (*MLCTrainingGraph) ExecuteForwardWithBatchSizeOptionsCompletionHandler 

func (o *MLCTrainingGraph) ExecuteForwardWithBatchSizeOptionsCompletionHandler(batchSize uint, options MLCExecutionOptions, completionHandler func(*MLCTensor, unsafe.Pointer, float64)) bool

Executes the forward pass of the training graph with the batch size, execution options, and completion handler you specify.

func (*MLCTrainingGraph) ExecuteForwardWithBatchSizeOptionsOutputsDataCompletionHandler 

func (o *MLCTrainingGraph) ExecuteForwardWithBatchSizeOptionsOutputsDataCompletionHandler(batchSize uint, options MLCExecutionOptions, outputsData *foundation.NSDictionary[*foundation.NSString, *MLCTensorData], completionHandler func(*MLCTensor, unsafe.Pointer, float64)) bool

Executes the forward pass of the training graph with the batch size, execution options, output data, and completion handler you specify.

func (*MLCTrainingGraph) ExecuteGradientWithBatchSizeOptionsCompletionHandler 

func (o *MLCTrainingGraph) ExecuteGradientWithBatchSizeOptionsCompletionHandler(batchSize uint, options MLCExecutionOptions, completionHandler func(*MLCTensor, unsafe.Pointer, float64)) bool

Executes the gradient pass of the training graph with the batch size, execution options, and completion handler you specify.

func (*MLCTrainingGraph) ExecuteGradientWithBatchSizeOptionsOutputsDataCompletionHandler 

func (o *MLCTrainingGraph) ExecuteGradientWithBatchSizeOptionsOutputsDataCompletionHandler(batchSize uint, options MLCExecutionOptions, outputsData *foundation.NSDictionary[*foundation.NSString, *MLCTensorData], completionHandler func(*MLCTensor, unsafe.Pointer, float64)) bool

Executes the gradient pass of the training graph with the batch size, execution options, output data, and completion handler you specify.

func (*MLCTrainingGraph) ExecuteOptimizerUpdateWithOptionsCompletionHandler 

func (o *MLCTrainingGraph) ExecuteOptimizerUpdateWithOptionsCompletionHandler(options MLCExecutionOptions, completionHandler func(*MLCTensor, unsafe.Pointer, float64)) bool

Executes the optimizer update pass of the training graph with the execution options and completion handler you specify.

func (*MLCTrainingGraph) ExecuteWithInputsDataLossLabelsDataLossLabelWeightsDataBatchSizeOptionsCompletionHandler 

func (o *MLCTrainingGraph) ExecuteWithInputsDataLossLabelsDataLossLabelWeightsDataBatchSizeOptionsCompletionHandler(inputsData *foundation.NSDictionary[*foundation.NSString, *MLCTensorData], lossLabelsData *foundation.NSDictionary[*foundation.NSString, *MLCTensorData], lossLabelWeightsData *foundation.NSDictionary[*foundation.NSString, *MLCTensorData], batchSize uint, options MLCExecutionOptions, completionHandler func(*MLCTensor, unsafe.Pointer, float64)) bool

Executes the training graph with the input data, batch size, execution options, and completion handler you specify.

func (*MLCTrainingGraph) ExecuteWithInputsDataLossLabelsDataLossLabelWeightsDataOutputsDataBatchSizeOptionsCompletionHandler 

func (o *MLCTrainingGraph) ExecuteWithInputsDataLossLabelsDataLossLabelWeightsDataOutputsDataBatchSizeOptionsCompletionHandler(inputsData *foundation.NSDictionary[*foundation.NSString, *MLCTensorData], lossLabelsData *foundation.NSDictionary[*foundation.NSString, *MLCTensorData], lossLabelWeightsData *foundation.NSDictionary[*foundation.NSString, *MLCTensorData], outputsData *foundation.NSDictionary[*foundation.NSString, *MLCTensorData], batchSize uint, options MLCExecutionOptions, completionHandler func(*MLCTensor, unsafe.Pointer, float64)) bool

Executes the training graph with the input data, output data, batch size, execution options, and completion handler that you specify.

func (*MLCTrainingGraph) GradientDataForParameterLayer 

func (o *MLCTrainingGraph) GradientDataForParameterLayer(parameter *MLCTensor, layer *MLCLayer) *foundation.NSData

Gets the gradient data for the trainable parameter and associated layer you specify.

func (*MLCTrainingGraph) GradientTensorForInput 

func (o *MLCTrainingGraph) GradientTensorForInput(input *MLCTensor) *MLCTensor

Gets the gradient tensor for the input tensor you specify.

func (*MLCTrainingGraph) LinkWithGraphs 

func (o *MLCTrainingGraph) LinkWithGraphs(graphs *foundation.NSArray[*MLCTrainingGraph]) bool

Links the training graphs you specify.

func (*MLCTrainingGraph) Optimizer 

func (o *MLCTrainingGraph) Optimizer() *MLCOptimizer

@property optimizer @abstract The optimizer to be used with the training graph

func (*MLCTrainingGraph) ResultGradientTensorsForLayer 

func (o *MLCTrainingGraph) ResultGradientTensorsForLayer(layer *MLCLayer) *foundation.NSArray[*MLCTensor]

Gets the result gradient tensors for the layer in the training graph you specify.

func (*MLCTrainingGraph) SetTrainingTensorParameters 

func (o *MLCTrainingGraph) SetTrainingTensorParameters(parameters *foundation.NSArray[*MLCTensorParameter]) bool

Sets the input tensor parameters, which the optimizer then updates.

func (*MLCTrainingGraph) SourceGradientTensorsForLayer 

func (o *MLCTrainingGraph) SourceGradientTensorsForLayer(layer *MLCLayer) *foundation.NSArray[*MLCTensor]

Gets the source gradient tensors for the layer in the training graph you specify.

func (*MLCTrainingGraph) StopGradientForTensors 

func (o *MLCTrainingGraph) StopGradientForTensors(tensors *foundation.NSArray[*MLCTensor]) bool

Adds the tensors that you specify, to indicate which contributions the graph excludes when computing gradients during gradient pass.

func (*MLCTrainingGraph) SynchronizeUpdates 

func (o *MLCTrainingGraph) SynchronizeUpdates()

Synchronizes updates from device memory.

type MLCTransposeLayer 

type MLCTransposeLayer struct {
	MLCLayer
}

A layer that permutes the dimensions you specify.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlctransposelayer

func MLCTransposeLayerFromID 

func MLCTransposeLayerFromID(id objc.ID) *MLCTransposeLayer

func MLCTransposeLayerLayerWithDimensions 

func MLCTransposeLayerLayerWithDimensions(dimensions *foundation.NSArray[*foundation.NSNumber]) *MLCTransposeLayer

Creates a transpose layer with the dimensions you specify.

func (*MLCTransposeLayer) Dimensions 

func (o *MLCTransposeLayer) Dimensions() *foundation.NSArray[*foundation.NSNumber]

@property dimensions @abstract Permutes the dimensions according to 'dimensions'. @discussion The returned tensor's dimension i will correspond to dimensions[i].

type MLCUpsampleLayer 

type MLCUpsampleLayer struct {
	MLCLayer
}

A layer that applies upsampling with the shape you specify.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcupsamplelayer

func MLCUpsampleLayerFromID 

func MLCUpsampleLayerFromID(id objc.ID) *MLCUpsampleLayer

func MLCUpsampleLayerLayerWithShape 

func MLCUpsampleLayerLayerWithShape(shape *foundation.NSArray[*foundation.NSNumber]) *MLCUpsampleLayer

Creates an upsample layer with the shape you specify.

func MLCUpsampleLayerLayerWithShapeSampleModeAlignsCorners 

func MLCUpsampleLayerLayerWithShapeSampleModeAlignsCorners(shape *foundation.NSArray[*foundation.NSNumber], sampleMode MLCSampleMode, alignsCorners bool) *MLCUpsampleLayer

Creates an upsample layer with the shape, upsampling algorithm, and corner alignement option you specify.

func (*MLCUpsampleLayer) AlignsCorners 

func (o *MLCUpsampleLayer) AlignsCorners() bool

@property alignsCorners @abstract A boolean that specifies whether the corner pixels of the source and result tensors are aligned. @discussion If True, the corner pixels of the source and result tensors are aligned, and thus preserving the values at those pixels. This only has effect when mode is 'bilinear'. Default is NO.

func (*MLCUpsampleLayer) SampleMode 

func (o *MLCUpsampleLayer) SampleMode() MLCSampleMode

@property sampleMode @abstract The sampling mode to use when performing the upsample.

func (*MLCUpsampleLayer) Shape 

func (o *MLCUpsampleLayer) Shape() *foundation.NSArray[*foundation.NSNumber]

@property shape @abstract A NSArray<NSNumber *> representing just the width if number of entries in shape array is 1 or the height followed by width of result tensor if the number of entries in shape array is 2.

type MLCYOLOLossDescriptor 

type MLCYOLOLossDescriptor struct {
	foundation.NSObject
}

The configuration object you use to create the YOLO loss layer.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcyololossdescriptor

func MLCYOLOLossDescriptorDescriptorWithAnchorBoxesAnchorBoxCount 

func MLCYOLOLossDescriptorDescriptorWithAnchorBoxesAnchorBoxCount(anchorBoxes *foundation.NSData, anchorBoxCount uint) *MLCYOLOLossDescriptor

Creates a YOLO loss filter descriptor with the anchor box data and number of anchor boxes you specify.

func MLCYOLOLossDescriptorFromID 

func MLCYOLOLossDescriptorFromID(id objc.ID) *MLCYOLOLossDescriptor

func (*MLCYOLOLossDescriptor) AnchorBoxCount 

func (o *MLCYOLOLossDescriptor) AnchorBoxCount() uint

@property anchorBoxCount @abstract number of anchor boxes used to detect object per grid cell

func (*MLCYOLOLossDescriptor) AnchorBoxes 

func (o *MLCYOLOLossDescriptor) AnchorBoxes() *foundation.NSData

@property anchorBoxes @abstract \p NSData containing the width and height for \p anchorBoxCount anchor boxes This \p NSData should have 2 floating-point values per anchor box which represent the width and height of the anchor box.

func (*MLCYOLOLossDescriptor) MaximumIOUForObjectAbsence 

func (o *MLCYOLOLossDescriptor) MaximumIOUForObjectAbsence() float32

@property negative IOU @abstract If the prediction IOU with groundTruth is lower than this value we consider it a confident object absence. The default is 0.3

func (*MLCYOLOLossDescriptor) MinimumIOUForObjectPresence 

func (o *MLCYOLOLossDescriptor) MinimumIOUForObjectPresence() float32

@property positive IOU @abstract If the prediction IOU with groundTruth is higher than this value we consider it a confident object presence, The default is 0.7

func (*MLCYOLOLossDescriptor) ScaleClassLoss 

func (o *MLCYOLOLossDescriptor) ScaleClassLoss() float32

@property scaleClass @abstract The scale factor for no object classes loss and loss gradient. The default is 2.0

func (*MLCYOLOLossDescriptor) ScaleNoObjectConfidenceLoss 

func (o *MLCYOLOLossDescriptor) ScaleNoObjectConfidenceLoss() float32

@property scaleNoObject @abstract The scale factor for no object confidence loss and loss gradient. The default is 5.0

func (*MLCYOLOLossDescriptor) ScaleObjectConfidenceLoss 

func (o *MLCYOLOLossDescriptor) ScaleObjectConfidenceLoss() float32

@property scaleObject @abstract The scale factor for object confidence loss and loss gradient. The default is 100.0

func (*MLCYOLOLossDescriptor) ScaleSpatialPositionLoss 

func (o *MLCYOLOLossDescriptor) ScaleSpatialPositionLoss() float32

@property scaleSpatialPositionLoss @abstract The scale factor for spatial position loss and loss gradient. The default is 10.0

func (*MLCYOLOLossDescriptor) ScaleSpatialSizeLoss 

func (o *MLCYOLOLossDescriptor) ScaleSpatialSizeLoss() float32

@property scaleSpatialSizeLoss @abstract The scale factor for spatial size loss and loss gradient. The default is 10.0

func (*MLCYOLOLossDescriptor) SetMaximumIOUForObjectAbsence 

func (o *MLCYOLOLossDescriptor) SetMaximumIOUForObjectAbsence(maximumIOUForObjectAbsence float32)

func (*MLCYOLOLossDescriptor) SetMinimumIOUForObjectPresence 

func (o *MLCYOLOLossDescriptor) SetMinimumIOUForObjectPresence(minimumIOUForObjectPresence float32)

func (*MLCYOLOLossDescriptor) SetScaleClassLoss 

func (o *MLCYOLOLossDescriptor) SetScaleClassLoss(scaleClassLoss float32)

func (*MLCYOLOLossDescriptor) SetScaleNoObjectConfidenceLoss 

func (o *MLCYOLOLossDescriptor) SetScaleNoObjectConfidenceLoss(scaleNoObjectConfidenceLoss float32)

func (*MLCYOLOLossDescriptor) SetScaleObjectConfidenceLoss 

func (o *MLCYOLOLossDescriptor) SetScaleObjectConfidenceLoss(scaleObjectConfidenceLoss float32)

func (*MLCYOLOLossDescriptor) SetScaleSpatialPositionLoss 

func (o *MLCYOLOLossDescriptor) SetScaleSpatialPositionLoss(scaleSpatialPositionLoss float32)

func (*MLCYOLOLossDescriptor) SetScaleSpatialSizeLoss 

func (o *MLCYOLOLossDescriptor) SetScaleSpatialSizeLoss(scaleSpatialSizeLoss float32)

func (*MLCYOLOLossDescriptor) SetShouldRescore 

func (o *MLCYOLOLossDescriptor) SetShouldRescore(shouldRescore bool)

func (*MLCYOLOLossDescriptor) ShouldRescore 

func (o *MLCYOLOLossDescriptor) ShouldRescore() bool

@property shouldRescore @abstract Rescore pertains to multiplying the confidence groundTruth with IOU (intersection over union) of predicted bounding box and the groundTruth boundingBox. The default is YES

type MLCYOLOLossLayer 

type MLCYOLOLossLayer struct {
	MLCLossLayer
}

A layer that estimates loss for the YOLO algorithm.

Apple documentation: https://developer.apple.com/documentation/mlcompute/mlcyololosslayer

func MLCYOLOLossLayerFromID 

func MLCYOLOLossLayerFromID(id objc.ID) *MLCYOLOLossLayer

func MLCYOLOLossLayerLayerWithDescriptor 

func MLCYOLOLossLayerLayerWithDescriptor(lossDescriptor *MLCYOLOLossDescriptor) *MLCYOLOLossLayer

Creates a YOLO loss layer with the descriptor you specify.

func (*MLCYOLOLossLayer) YoloLossDescriptor 

func (o *MLCYOLOLossLayer) YoloLossDescriptor() *MLCYOLOLossDescriptor

@property yoloLossDescriptor @abstract The YOLO loss descriptor

type Mach_vm_range_flags_t 

type Mach_vm_range_flags_t int64
const (
	MACH_VM_RANGE_NONE Mach_vm_range_flags_t = 0
)

func (Mach_vm_range_flags_t) String 

func (e Mach_vm_range_flags_t) String() string

type Mach_vm_range_flavor_t 

type Mach_vm_range_flavor_t int64
const (
	MACH_VM_RANGE_FLAVOR_INVALID Mach_vm_range_flavor_t = 0
	MACH_VM_RANGE_FLAVOR_V1      Mach_vm_range_flavor_t = 1
)

func (Mach_vm_range_flavor_t) String 

func (e Mach_vm_range_flavor_t) String() string

type Mach_vm_range_tag_t 

type Mach_vm_range_tag_t int64
const (
	MACH_VM_RANGE_DEFAULT Mach_vm_range_tag_t = 0
	MACH_VM_RANGE_DATA    Mach_vm_range_tag_t = 1
	MACH_VM_RANGE_FIXED   Mach_vm_range_tag_t = 2
)

func (Mach_vm_range_tag_t) String 

func (e Mach_vm_range_tag_t) String() string

type Mpo_flags_t 

type Mpo_flags_t int64
const (
	MPO_PORT                            Mpo_flags_t = 0
	MPO_SERVICE_PORT                    Mpo_flags_t = 1024
	MPO_CONNECTION_PORT                 Mpo_flags_t = 2048
	MPO_REPLY_PORT                      Mpo_flags_t = 4096
	MPO_WEAK_REPLY_PORT                 Mpo_flags_t = 16384
	MPO_NOTIFICATION_PORT               Mpo_flags_t = 17408
	MPO_EXCEPTION_PORT                  Mpo_flags_t = 32768
	MPO_CONNECTION_PORT_WITH_PORT_ARRAY Mpo_flags_t = 65536
)

func (Mpo_flags_t) String 

func (e Mpo_flags_t) String() string

type Os_clockid_t 

type Os_clockid_t int64
const (
	OS_CLOCK_MACH_ABSOLUTE_TIME Os_clockid_t = 32
)

func (Os_clockid_t) String 

func (e Os_clockid_t) String() string

type Ptrauth_key 

type Ptrauth_key int64
const (
	Ptrauth_key_none                     Ptrauth_key = -1
	Ptrauth_key_asia                     Ptrauth_key = 0
	Ptrauth_key_asib                     Ptrauth_key = 1
	Ptrauth_key_asda                     Ptrauth_key = 2
	Ptrauth_key_asdb                     Ptrauth_key = 3
	Ptrauth_key_process_independent_code Ptrauth_key = 0
	Ptrauth_key_process_dependent_code   Ptrauth_key = 1
	Ptrauth_key_process_independent_data Ptrauth_key = 2
	Ptrauth_key_process_dependent_data   Ptrauth_key = 3
	Ptrauth_key_return_address           Ptrauth_key = 1
	Ptrauth_key_frame_pointer            Ptrauth_key = 3
	Ptrauth_key_function_pointer         Ptrauth_key = 0
	Ptrauth_key_block_function           Ptrauth_key = 0
	Ptrauth_key_cxx_vtable_pointer       Ptrauth_key = 2
	Ptrauth_key_method_list_pointer      Ptrauth_key = 2
	Ptrauth_key_objc_isa_pointer         Ptrauth_key = 2
	Ptrauth_key_objc_super_pointer       Ptrauth_key = 2
	Ptrauth_key_objc_sel_pointer         Ptrauth_key = 3
	Ptrauth_key_objc_class_ro_pointer    Ptrauth_key = 2
	Ptrauth_key_block_descriptor_pointer Ptrauth_key = 2
	Ptrauth_key_init_fini_pointer        Ptrauth_key = 0
)

func (Ptrauth_key) String 

func (e Ptrauth_key) String() string

type Qos_class_t 

type Qos_class_t uint32
const (
	QOS_CLASS_USER_INTERACTIVE Qos_class_t = 33
	QOS_CLASS_USER_INITIATED   Qos_class_t = 25
	QOS_CLASS_DEFAULT          Qos_class_t = 21
	QOS_CLASS_UTILITY          Qos_class_t = 17
	QOS_CLASS_BACKGROUND       Qos_class_t = 9
	QOS_CLASS_UNSPECIFIED      Qos_class_t = 0
)

func (Qos_class_t) String 

func (e Qos_class_t) String() string

type Virtual_memory_guard_exception_code_t 

type Virtual_memory_guard_exception_code_t int64
const (
	KGUARD_EXC_DEALLOC_GAP                   Virtual_memory_guard_exception_code_t = 1
	KGUARD_EXC_RECLAIM_COPYIO_FAILURE        Virtual_memory_guard_exception_code_t = 2
	KGUARD_EXC_RECLAIM_INDEX_FAILURE         Virtual_memory_guard_exception_code_t = 4
	KGUARD_EXC_RECLAIM_DEALLOCATE_FAILURE    Virtual_memory_guard_exception_code_t = 8
	KGUARD_EXC_RECLAIM_ACCOUNTING_FAILURE    Virtual_memory_guard_exception_code_t = 9
	KGUARD_EXC_SEC_IOPL_ON_EXEC_PAGE         Virtual_memory_guard_exception_code_t = 10
	KGUARD_EXC_SEC_EXEC_ON_IOPL_PAGE         Virtual_memory_guard_exception_code_t = 11
	KGUARD_EXC_SEC_UPL_WRITE_ON_EXEC_REGION  Virtual_memory_guard_exception_code_t = 12
	KGUARD_EXC_LARGE_ALLOCATION_TELEMETRY    Virtual_memory_guard_exception_code_t = 13
	KGUARD_EXC_SEC_ACCESS_FAULT              Virtual_memory_guard_exception_code_t = 98
	KGUARD_EXC_SEC_ASYNC_ACCESS_FAULT        Virtual_memory_guard_exception_code_t = 99
	KGUARD_EXC_SEC_COPY_DENIED               Virtual_memory_guard_exception_code_t = 100
	KGUARD_EXC_SEC_SHARING_DENIED            Virtual_memory_guard_exception_code_t = 101
	KGUARD_EXC_MTE_SYNC_FAULT                Virtual_memory_guard_exception_code_t = 200
	KGUARD_EXC_MTE_ASYNC_USER_FAULT          Virtual_memory_guard_exception_code_t = 201
	KGUARD_EXC_MTE_ASYNC_KERN_FAULT          Virtual_memory_guard_exception_code_t = 202
	KGUARD_EXC_GUARD_OBJECT_ASYNC_USER_FAULT Virtual_memory_guard_exception_code_t = 203
	KGUARD_EXC_GUARD_OBJECT_ASYNC_KERN_FAULT Virtual_memory_guard_exception_code_t = 204
)

func (Virtual_memory_guard_exception_code_t) String 

func (e Virtual_memory_guard_exception_code_t) String() string

type Xpc_listener_create_flags_t 

type Xpc_listener_create_flags_t int64
const (
	XPC_LISTENER_CREATE_NONE             Xpc_listener_create_flags_t = 0
	XPC_LISTENER_CREATE_INACTIVE         Xpc_listener_create_flags_t = 1
	XPC_LISTENER_CREATE_FORCE_MACH       Xpc_listener_create_flags_t = 2
	XPC_LISTENER_CREATE_FORCE_XPCSERVICE Xpc_listener_create_flags_t = 4
)

func (Xpc_listener_create_flags_t) String 

func (e Xpc_listener_create_flags_t) String() string

type Xpc_session_create_flags_t 

type Xpc_session_create_flags_t int64
const (
	XPC_SESSION_CREATE_NONE            Xpc_session_create_flags_t = 0
	XPC_SESSION_CREATE_INACTIVE        Xpc_session_create_flags_t = 1
	XPC_SESSION_CREATE_MACH_PRIVILEGED Xpc_session_create_flags_t = 2
)

func (Xpc_session_create_flags_t) String 

func (e Xpc_session_create_flags_t) String() string

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