Class MeanSquaredErrorLoss4D

Namespace

NeuralNetworks.Losses

Assembly

NeuralNetworks.dll

public class MeanSquaredErrorLoss4D : Loss<float[,,,]>

Inheritance

object

Loss<float[,,,]>

MeanSquaredErrorLoss4D

Inherited Members

Loss<float[,,,]>.Prediction

Loss<float[,,,]>.Target

Loss<float[,,,]>.Forward(float[,,,], float[,,,])

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

Loss<float[,,,]>.CalculateLoss()

Loss<float[,,,]>.CalculateLossGradient()

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

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

object.Equals(object)

object.Equals(object, object)

object.GetHashCode()

object.GetType()

object.MemberwiseClone()

object.ReferenceEquals(object, object)

Remarks

Usually used in convolutional neural networks (CNNs) for autoencoding tasks, where the input and output are 4D tensors (e.g., batch size, channels, height, width).

Constructors

MeanSquaredErrorLoss4D(MseReduction)

public MeanSquaredErrorLoss4D(MseReduction mseReduction = MseReduction.ElementMean)

Parameters

mseReduction MseReduction

Remarks

Usually used in convolutional neural networks (CNNs) for autoencoding tasks, where the input and output are 4D tensors (e.g., batch size, channels, height, width).

Methods

CalculateLoss()

protected override float CalculateLoss()

Returns

float

CalculateLossGradient()

protected override float[,,,] CalculateLossGradient()

Returns

float[,,,]

ToString()

Returns a string that represents the current object.

public override string ToString()

Returns

string

A string that represents the current object.