BroadcastOperator

public class BroadcastOperator: ComputableOperator

Doing broadcasting on input tensors and store result in output tensors. Serrano follows the broadcasting rule of Scipy.

Attributes

operatorLabel
Operator label. Conforms to ComputableOperator
Declaration
Swift

public var operatorLabel: String = ""
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metalKernelFuncLabel
This operator does not operator on GPU. Conforms to ComputableOperator
Declaration
Swift

public var metalKernelFuncLabel:String
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computationDelegate
Conforms to ComputableOperator
Declaration
Swift

public var computationDelegate: OperatorCalculationDelegate?
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inputTensors
Conforms to ComputableOperator
Declaration
Swift

public var inputTensors: [Tensor]?
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outputTensors
Conforms to ComputableOperator
Declaration
Swift

public var outputTensors: [Tensor]?
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targetShape
Target shape.

Note
This atrribute could be nil when initialize an object. If it is nil doing calculation, a fatalError() will be raise.
Declaration
Swift

public var targetShape: TensorShape?
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disableUpGradShapeCheck
If true, operator will not check the upGrads‘s shape. This is used inside framework to speed up in situation we know it will not be wrong. Cases like auto generated differentiation graph.
Declaration
Swift

public var disableUpGradShapeCheck: Bool = false
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disableInputOutputCheck
If true, operator will not call inputOutputTensorsCheck() before doing calculation. This is used inside framework to speed up in situation we know it will not be wrong.
Declaration
Swift

public var disableInputOutputCheck: Bool = false
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trainable
Indicate if this operator would do paramter update.

Note
BroadcastOperator is not trainable.
Declaration
Swift

public var trainable: Bool = false
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mapType
The mapping type of this operator. OneToOne for this operator.
Declaration
Swift

public var mapType: OperatorMappingType
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inPlaceble
Broadcast operator cannot do in-place calculation
Declaration
Swift

public var inPlaceble: Bool = false
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Initializers


                    
                    
                    init(operatorLabel:metalKernelFuncLabel:computationDelegate:inputTensors:outputTensors:targetShape:)

Initializers

Note

In most cases, this initializer should not be called. Call one of the convenience initializers instead.

Declaration

Swift

public init(operatorLabel label: String,
            metalKernelFuncLabel kernelLabel: String,
            computationDelegate: OperatorCalculationDelegate?,
            inputTensors: [Tensor]?,
            outputTensors: [Tensor]?,
            targetShape: TensorShape?)

Parameters

`label`
`kernelLabel`
`computationDelegate`
`inputTensors`
`outputTensors`
`targetShape`

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                    init(computationDelegate:targetShape:)

Declaration

Swift

public convenience init(computationDelegate: OperatorCalculationDelegate, targetShape: TensorShape?)

Parameters

`computationDelegate`
`targetShape`

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                    init(targetShape:inputTensors:outputTensors:)

Initializer

Declaration

Swift

public convenience init(targetShape: TensorShape? = nil,  inputTensors: [Tensor]? = nil,
						outputTensors: [Tensor]? = nil)

Parameters


                                targetShape

target broadcast shape

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Conforms to `ComputableOperator`


                    
                    
                    outputShape(shapeArray:)

The input shapes and target shape should follow the scipy rule

Declaration

Swift

public func outputShape(shapeArray shapes: [TensorShape]) -> [TensorShape]?

Parameters


                                shapes

input shapes

Return Value

return shapes. nil if not valid

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inputOutputTensorsCheck()
Check if shapes of all input tensors and output tensors are compatible with targetShape
Declaration
Swift

public func inputOutputTensorsCheck() -> (check: Bool, msg: String)
Return Value

check and message

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                    compute(_:)

Declaration

Swift

public func compute(_ computationMode: OperatorComputationMode = SerranoEngine.configuredEngine.defaultComputationMode)

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                    computeAsync(_:)

Declaration

Swift

public func computeAsync(_ computationMode: OperatorComputationMode = SerranoEngine.configuredEngine.defaultComputationMode)

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                    gradCompute(_:)

Calulate grads sync. Broadcast operator itself does not generate any grads. Should be ignored in gaph AD.

Declaration

Swift

public func gradCompute(_ computationMode: OperatorComputationMode) -> [String: DataSymbolSupportedDataType]

Parameters


                                computationMode

computationMode

Return Value

return upGrads if not nil. Else return an empty array.

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                    gradComputAsync(_:)

Cal grads async

Declaration

Swift

public func gradComputAsync(_ computationMode: OperatorComputationMode)

Parameters


                                computationMode

computationMode

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                    updateParams(grads:LR:)

No updatable parameters. This function just returns.

Declaration

Swift

public func updateParams(grads: [Tensor], LR: Float)

Parameters

`grads`	grads
`LR`	LR

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bindParamSymbols(_:)
This operator has no parameters. Do nothing
Declaration
Swift

public func bindParamSymbols(_ symbols: [GraphSymbol])
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paramSymbols()
This operator has no parameters.
Declaration
Swift

public func paramSymbols() -> [GraphSymbol]
Return Value

An empty array

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cpu()
Undocumented
Declaration
Swift

public func cpu()
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