Package deepboof.backward

Interface NumericalGradient<T extends deepboof.Tensor<T>>

All Known Implementing Classes:
NumericalGradient_F64
public interface NumericalGradient<T extends deepboof.Tensor<T>>

Given a Function implementations of this interface will compute the gradient of its inputs and parameters. Numerical differentiation is done using a symmetric sample, e.g. dx = [f(x+T)-f(x-T)]/T

  • Method Summary

    Modifier and Type Method Description
    void configure​(double T)
    Overrides default settings for computing numerical gradient.
    void differentiate​(T input, List<T> parameters, T dout, T gradientInput, List<T> gradientParameters)
    Performs numerical differentiation to compute the gradients of input and parameters.
    void setFunction​(deepboof.Function<T> function)
    Sets the function which will be differentiated and other parameters.

  • Method Details

    • configure

      void configure​(double T)
      Overrides default settings for computing numerical gradient.
      Parameters:
      T - Sampling distance used for numerical differentiation

    • setFunction

      void setFunction​(deepboof.Function<T> function)
      Sets the function which will be differentiated and other parameters. Function.initialize(int...) should have already been called.
      Parameters:
      function - The function which is to be differentiated

    • differentiate

      void differentiate​(T input, List<T> parameters, T dout, T gradientInput, List<T> gradientParameters)
      Performs numerical differentiation to compute the gradients of input and parameters. When numerical differentiation is being performed input and parameters will be modified and then returned to their original state.
      Parameters:
      input - The same input tensor which was passed in during the forward pass.
      parameters - The same parameters which was passed in during the forward pass.
      dout - Derivative of output, computed from next layer.
      gradientInput - Storage for gradient of input
      gradientParameters - Storage for gradients of parameters