Class MeanSquaredErrorLoss

Namespace

NeuralNetworks.Losses

Assembly

NeuralNetworks.dll

Represents a loss function that computes the mean squared error between predicted and target values.

public class MeanSquaredErrorLoss : Loss<float[,]>

Inheritance

object

Loss<float[,]>

MeanSquaredErrorLoss

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

This class is typically used in regression tasks (for example, Boston Housing dataset, Sine wave prediction) to measure the average squared difference between predicted and actual values. It can be used as a loss function in training neural networks or other predictive models where minimizing the mean squared error is desired.

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.