deep

A small R library for creating deep neural networks.

Installing

I am pleased to say that this package is on CRAN, so one can install it by running install.packages("deep").

Using

Here is an example:

First load the package: library(deep), then prepare a dataset, here we used the classic iris dataset.

dataset <- iris
dataset$Petal.Length <- NULL
dataset$Petal.Width <- NULL
dataset <- dataset[dataset$Species != "versicolor",]
dataset$Code <- as.integer(dataset$Species == "virginica")
dataset <- dataset[sample(nrow(dataset)),]

Now that we have the data on the format we whant, let's create the net.

net <- neuralNet(2, perceptronLayer(1))

The neuralNet class contructor takes arguments that control the shape of the net: the length of the input vector, this case, 2, and the layers, which is just one layer with one perceptron neuron. Now we can train the net.

net$train(dataset[,c(1,2)], dataset$Code, epochs = 5000)

Check if the accuracy is satisfying:

dataset$Calc <- sapply(1:nrow(dataset), function(x) net$output(dataset[x,c(1,2)]))
length(which(dataset$Code==dataset$Calc))/nrow(dataset)

You can train it for more epochs if needed, to get the method use the function output:

net$output(c(1,2))

For more help consult the man folder or run ?neuralNet on your console.

Note to contributors

This project is in a very initial state and many features are missing:

• new layers, like dropout, pooling, convolution etc
• a better implementation of the backpropagation algorithm
• new training algorithms
• a plot method tho view all neurons and weights in a nice way
• a plot during training, just because it's nice
• anything you propose that makes sense

I intend to convert some methods using RCPP for better performance.