Class Conv2D

Namespace

NeuralNetworks.Operations.Parameterized

Assembly

NeuralNetworks.dll

Dimensions of the input are: [batch, channels, height, width]. Dimensions of the param array are: [channels, filters, kernelSize, kernelSize]. CalcOutput returns an array of dimensions: [batch, filters, height, width]. Padding is assumed to be the same on all sides = kernelSize / 2

public class Conv2D : ParamOperation<float[,,,], float[,,,], float[,,,]>, IParamOperation

Inheritance

object

Operation

Operation<float[,,,], float[,,,]>

ParamOperation<float[,,,], float[,,,]>

ParamOperation<float[,,,], float[,,,], float[,,,]>

Conv2D

Implements

IParamOperation

Inherited Members

ParamOperation<float[,,,], float[,,,], float[,,,]>.Param

ParamOperation<float[,,,], float[,,,], float[,,,]>.Backward(float[,,,])

ParamOperation<float[,,,], float[,,,], float[,,,]>.CalcParamGradient(float[,,,])

ParamOperation<float[,,,], float[,,,], float[,,,]>.GetData()

ParamOperation<float[,,,], float[,,,], float[,,,]>.ApplyData(ParamOperationData, int, int)

ParamOperation<float[,,,], float[,,,], float[,,,]>.GetParamCount()

ParamOperation<float[,,,], float[,,,], float[,,,]>.UpdateParams(Optimizer)

ParamOperation<float[,,,], float[,,,]>.GetParamCount()

ParamOperation<float[,,,], float[,,,]>.UpdateParams(Optimizer)

ParamOperation<float[,,,], float[,,,]>.GetData()

ParamOperation<float[,,,], float[,,,]>.ApplyData(ParamOperationData, int, int)

Operation<float[,,,], float[,,,]>.Input

Operation<float[,,,], float[,,,]>.Output

Operation<float[,,,], float[,,,]>.Forward(float[,,,], bool)

Operation<float[,,,], float[,,,]>.Backward(float[,,,])

Operation<float[,,,], float[,,,]>.CalcOutput(bool)

Operation<float[,,,], float[,,,]>.CalcInputGradient(float[,,,])

Operation<float[,,,], float[,,,]>.Forward(object, bool)

Operation<float[,,,], float[,,,]>.Backward(object)

Operation<float[,,,], float[,,,]>.GetOutputType()

Operation<float[,,,], float[,,,]>.GetInputType()

Operation.SetRegistered()

Operation.GetOutputType()

Operation.GetInputType()

Operation.Forward(object, bool)

Operation.Backward(object)

object.Equals(object)

object.Equals(object, object)

object.GetHashCode()

object.GetType()

object.MemberwiseClone()

object.ReferenceEquals(object, object)

object.ToString()

Constructors

Conv2D(float[,,,], int, int, int, int, int, int)

Dimensions of the input are: [batch, channels, height, width]. Dimensions of the param array are: [channels, filters, kernelSize, kernelSize]. CalcOutput returns an array of dimensions: [batch, filters, height, width]. Padding is assumed to be the same on all sides = kernelSize / 2

public Conv2D(float[,,,] weights, int paddingHeight, int paddingWidth, int strideHeight = 1, int strideWidth = 1, int dilatationHeight = 1, int dilatationWidth = 1)

Parameters

weights float[,,,]

paddingHeight int

paddingWidth int

strideHeight int

strideWidth int

dilatationHeight int

dilatationWidth int

Methods

CalcInputGradient(float[,,,])

Calculates input gradient.

protected override float[,,,] CalcInputGradient(float[,,,] outputGradient)

Parameters

outputGradient float[,,,]

Returns

float[,,,]

Remarks

Based on outputGradient, calculates changes in input.

CalcOutput(bool)

Computes output.

protected override float[,,,] CalcOutput(bool inference)

Parameters

inference bool

Returns

float[,,,]

CalcParamGradient(float[,,,])

protected override float[,,,] CalcParamGradient(float[,,,] outputGradient)

Parameters

outputGradient float[,,,]

Returns

float[,,,]