rneatneuralnetcontinuetraining: Continues the training of the neural networks / runs more...

In ahunteruk/RNeat: Neuroevolution of Augmenting Topologies - NEAT

Description

Continues the training of the neural networks / runs more generations

Usage

rneatneuralnetcontinuetraining(rneatsim, nTrainingGenerations)

Arguments

rneatsim	Is the class rneatneuralnet created using rneatneuralnet function
nTrainingGenerations	Number of new generations to train

Value

rneatneuralnet class with pool of genomes and training data

Examples

#Generate traing data y = sqrt(x)
trainingData <- as.data.frame(cbind(sqrt(seq(0.1,1,0.1)),seq(0.1,1,0.1)))
colnames(trainingData) <- c("y","x")

#Train the neural network for 5 generations, and plot the fitness
rneatsim <- rneatneuralnet(y~x,trainingData,5)
plot(rneatsim)

#Continue training the network for another 5 generations
rneatsim <- rneatneuralnetcontinuetraining(rneatsim,5)
plot(rneatsim)

#Construct some fresh data to stick through the neural network and hopefully get square rooted
liveData <- as.data.frame(seq(0.1,1,0.01))
colnames(liveData) <- c("x")

liveDataExpectedOutput <- sqrt(liveData)
colnames(liveDataExpectedOutput) <- "yExpected"

#Pass the data through the network
results <- compute(rneatsim,liveData)

#Calculate the difference between yPred the neural network output, and yExpected the actual square root of the input
error <- liveDataExpectedOutput[,"yExpected"] - results[,"yPred"]
results <- cbind(results,liveDataExpectedOutput,error)
print(results)

dev.new()
layout(matrix(c(3,3,3,1,4,2), 2, 3, byrow = TRUE),heights=c(1,2))
plot(x=results[,"x"],y=results[,"yExpected"],type="l", main="Neural Network y=sqrt(x) expected vs predicted",xlab="x",ylab="y")
lines(x=results[,"x"],y=results[,"yPred"],col="red",type="l")
legend(x='bottomright', c('yExpected','yPredicted'), col=c("black","red"), fill=1:2, bty='n')
plot(rneatsim)
plot(rneatsim$simulation)

ahunteruk/RNeat documentation built on May 12, 2019, 2:31 a.m.