Package deepnetts.net.loss

Class BinaryCrossEntropyLoss

java.lang.Object

deepnetts.net.loss.BinaryCrossEntropyLoss

All Implemented Interfaces:

LossFunction, Serializable

public class BinaryCrossEntropyLoss
extends Object
implements LossFunction, Serializable

Cross Entropy Loss is a loss function used for binary classification tasks (two classes, single output which represents probability ). It should be used in combination with sigmoid output activation function. The formula: E = (1/n) * -SUM( t * ln(y) + (1-t) * ln(1-y) ) where t is target, and y actual output Bishop, C. pg. 231, eq. 6.120

See Also:

• Serialized Form

Constructor Summary

Constructors

Constructor	Description
BinaryCrossEntropyLoss(NeuralNetwork neuralNet)

Method Summary

Modifier and Type	Method	Description
float[]	addPatternError(float[] pred, float[] target)	Calculates error for given actual and target patterns and adds that error to total error.
TensorBase	addPatternError(TensorBase predictedOutput, TensorBase targetOutput)
void	addRegularizationSum(float regSum)	Adds specified regularization sum to total loss.
float	getPatternLoss()
float	getTotal()	Returns the total error calculated by this loss function.
void	reset()	Resets the total error and pattern counter.

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface deepnetts.net.loss.LossFunction

valueFor

Constructor Details

BinaryCrossEntropyLoss

public BinaryCrossEntropyLoss(NeuralNetwork neuralNet)

Method Details

addPatternError

public float[] addPatternError(float[] pred,
 float[] target)

Calculates error for given actual and target patterns and adds that error to total error. Returns output error vector for specified actual and target outputs.

Specified by:

addPatternError in interface LossFunction

Parameters:

pred - predicted output of a neural network

target - target output of a neural network

Returns:

error vector for specified predicted and target outputs

addPatternError

public TensorBase addPatternError(TensorBase predictedOutput,
 TensorBase targetOutput)

Specified by:

addPatternError in interface LossFunction

getPatternLoss

public float getPatternLoss()

Specified by:

getPatternLoss in interface LossFunction

addRegularizationSum

public void addRegularizationSum(float regSum)

Description copied from interface: LossFunction

Adds specified regularization sum to total loss.

Specified by:

addRegularizationSum in interface LossFunction

Parameters:

regSum - regularization sum

getTotal

public float getTotal()

Description copied from interface: LossFunction

Returns the total error calculated by this loss function.

Specified by:

getTotal in interface LossFunction

Returns:

total error calculated by this loss function

reset

public void reset()

Description copied from interface: LossFunction

Resets the total error and pattern counter.

Specified by:

reset in interface LossFunction