Class BiasAdd

Namespace

NeuralNetworks.Operations.Parameterized

Assembly

NeuralNetworks.dll

Computes bias addition.

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

Inheritance

object

Operation

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

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

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

BiasAdd

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

BiasAdd(float[])

Computes bias addition.

public BiasAdd(float[] bias)

Parameters

bias float[]

Bias matrix.

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[]