nnCore: nnCore

In hsamuelson/nnCore: A Light Weight Deep Learning Core for R

Description

A Light Weight Deep Learning Core in R. This library was built to have a simple yet comprehensive neural network library that is well commented. This library was built to be light weight in comparison to most bloated neuralnet libraries.

Arguments

nnCore$new(Species

~ ., data = iris, hidden = 6)

Format

Creating a new Network nn <- nnCore$new(Species ~ ., data = iris, hidden = 6) For the formula Species~. Means that column named “Species” Correlates to . which means the reaming columns in the data set. If you do not want to include all columns in the data set you can name the specific columns separated with “+”s. Species ~ Sepal.Length+Sepal.Width. ## Training A Network nn$train(9999, trace = 1e3, learn_rate = .0001) 9999 is the number of training iterations. Default the number of iterations is set to 1e4 or the default tolerance threshold which is 0.01 if the threshold is achived before the set number of iterations is passed.

## Compute New Observations nn$computeNN(iris[,-5]) iris[,-5] is simply the iris training set with the class column removed, which will overlap with the dataset, but any new observations can be tested with $computeNN. The function will return the predicted classifications. ## Linking and Predicting The $ syntax of the R6 library allows for commands to be strung together in one line. It is important to note that order does matter. nn$train(9999, trace = 1e3, learn_rate = .0001)$predict(data.matrix(cbind(1, iris[,-5]))) Hidden Layer Modifications You can either enter a specific number of hidden nodes or you can put one of three options in as a character, to create a generic number of nodes. This is valuable if you are generating neural networks within a program and do not have time to run a hyperparameters test. These three options have been defined as generic ways of determining an accurate number of hidden nodes. Choose 1,2,3 to decide method for choosing # of hidden nodes.

“1” # of hidden nodes = # of inputnodes “2” # of hidden nodes = # of (# of input nodes + output nodes)/ (2/3) “3” # of hidden nodes = sqrt(# of input nodes * # of attributes) ## Examples This will use option one, which will simply be the number of input nodes nn <- nnCore$new(Species ~ ., data = iris, hidden = "1")

Examples

nn <- nnCore$new(Species ~ ., data = iris, hidden = 6)
nn$train(9999, trace = 1e3, learn_rate = .0001)
nn$computeNN(iris[,-5])

hsamuelson/nnCore documentation built on May 8, 2019, 6:48 p.m.