Interface LossFunction

All Known Implementing Classes:
BinaryCrossEntropyLoss, CrossEntropyLoss, MeanSquaredErrorLoss
public interface LossFunction
Base Interface for all loss functions. Loss function is a component of a deep learning algorithm which calculates an error, as a difference of actual (or predicted) and desired (target) output of a neural network. The total error for some training is usually calculated as a average of errors for all individual input-output pairs. The higher value of loss function, means higher error and lower accuracy of prediction.
See Also:

  • Method Summary

    Modifier and Type
    Method
    Description
    TensorBase
    addPatternError(TensorBase predictedOutput, TensorBase targetOutput)
    Calculates pattern error for singe pattern for the specified predicted and target outputs, adds the error to total error, and returns the pattern error.
    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.
    default float
    valueFor(NeuralNetwork nnet, javax.visrec.ml.data.DataSet<? extends MLDataItem> dataSet)
    Calculates and returns loss function value for the given neural network and data set.

  • Method Details

    • addPatternError

      TensorBase addPatternError(TensorBase predictedOutput, TensorBase targetOutput)
      Calculates pattern error for singe pattern for the specified predicted and target outputs, adds the error to total error, and returns the pattern error.
      Parameters:
      predictedOutput - predicted/actual network output vector
      targetOutput - target network output vector
      Returns:
      error vector error vector for the given predicted and target vectors

    • addRegularizationSum

      void addRegularizationSum(float regSum)
      Adds specified regularization sum to total loss.
      Parameters:
      regSum - regularization sum

    • getTotal

      float getTotal()
      Returns the total error calculated by this loss function.
      Returns:
      total error calculated by this loss function

    • getPatternLoss

      float getPatternLoss()

    • reset

      void reset()
      Resets the total error and pattern counter.

    • valueFor

      default float valueFor(NeuralNetwork nnet, javax.visrec.ml.data.DataSet<? extends MLDataItem> dataSet)
      Calculates and returns loss function value for the given neural network and data set.
      Parameters:
      nnet -
      dataSet -
      Returns: