Class Conv2D

Namespace

NeuralNetworks.Operations

Assembly

NeuralNetworks.dll

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

public class Conv2D : ParamOperation4D<float[,,,]>

Inheritance

object

Operation

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

Operation4D

ParamOperation4D

ParamOperation4D<float[,,,]>

Conv2D

Inherited Members

ParamOperation4D<float[,,,]>.Param

ParamOperation4D<float[,,,]>.Backward(float[,,,])

ParamOperation4D<float[,,,]>.EnsureSameShapeForParam(float[,,,], float[,,,])

ParamOperation4D<float[,,,]>.CalcParamGradient(float[,,,])

ParamOperation4D<float[,,,]>.CloneBase()

Operation4D.EnsureSameShapeForInput(float[,,,], float[,,,])

Operation4D.EnsureSameShapeForOutput(float[,,,], float[,,,])

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

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

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

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

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

Operation<float[,,,], float[,,,]>.EnsureSameShapeForOutput(float[,,,], 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<float[,,,], float[,,,]>.CloneBase()

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

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[,,,])

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

public Conv2D(float[,,,] weights)

Parameters

weights float[,,,]

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[,,,]

EnsureSameShapeForParam(float[,,,]?, float[,,,])

protected override void EnsureSameShapeForParam(float[,,,]? param, float[,,,] paramGradient)

Parameters

param float[,,,]

paramGradient float[,,,]

GetParamCount()

public override int GetParamCount()

Returns

int

UpdateParams(Layer?, Optimizer)

public override void UpdateParams(Layer? layer, Optimizer optimizer)

Parameters

layer Layer

optimizer Optimizer