computeGrad1: Computes a gradient

In TeachNet: Fits Neural Networks to Learn About Backpropagation

Description

This function computes the gradient for a one hidden layer network.

Usage

computeGrad1(x, y, I, H, weights, f, f_d, m_f)

Arguments

x	properties of observation
y	characteristic of observation (zero or one)
I	numbers of input neurons
H	numbers of hidden neurons
weights	the weights with that the gradient should be computed
f	the activation function of the neural network
f_d	the derivative of the activation function
m_f	the function for the interim value m. It is two times the output of the network minus the observed characteristic.

Value

A Weights class with the gradient parts

Author(s)

Georg Steinbuss

See Also

Weights-class computeGrad2

TeachNet documentation built on May 2, 2019, 7 a.m.