jacobian:
Jacobian Matrix of Gradient Function for Training Datasets

Description

Calculate the Jacobian matrix of gradient function for the training dataset. It takes input from neural network models and the gradient at each weight parameters. The matrix has dimension of R [nObs * nPara], nObs denotes the number of training observations and nPara denotes the number of weights parameters.

Usage

jacobian(object, ...)
"jacobian"(object, xTrain, funName = 'sigmoid',...)
"jacobian"(object, xTrain, funName = 'sigmoid',...)
"jacobian"(object, xTrain, funName = 'sigmoid',...)

Arguments

object

object of class: nnet as returned by 'nnet' package, nn as returned by 'neuralnet' package, rsnns as returned by 'RSNNS' package.

xTrain

matrix or data frame of input values for the training dataset.

funName

activation function name of neuron, e.g. 'sigmoid', 'tanh', etc. In default, it is set to 'sigmoid'.

...

additional arguments passed to the method.

Value

matrix which denotes the Jacobian matrix for training datasets.

Details

Jacobian matrix with gradient function, in which J[ij] element denotes the gradient function at the jth weight parameters for the ith training observation. The dimension is equal to nObs * nPara.

Examples

Run this code

library(nnet)
xTrain <- rbind(cbind(runif(150,min = 0, max = 0.5),runif(150,min = 0, max = 0.5)) ,
		cbind(runif(150,min = 0.5, max = 1),runif(150,min = 0.5, max = 1))
		)
nObs <- dim(xTrain)[1]
yTrain <- 0.5 + 0.4 * sin(2* pi * xTrain %*% c(0.4,0.6)) +rnorm(nObs,mean = 0, sd = 0.05)
# Training nnet models
net <- nnet(yTrain ~ xTrain,size = 3, rang = 0.1,decay = 5e-4, maxit = 500)

# Calculating Jacobian Matrix of the training samples
library(nnetpredint)
jacobMat = jacobian(net,xTrain)
dim(jacobMat)