.ipynb_checkpoints/README-checkpoint.md

In Greg-Hallenbeck/easy.mlp: Easy-To-Use Multilayer Perceptron

output: github_document

easy.mlp

The goal of easy.mlp is to quickly and easily build a neural network to fit tabular data.

• Supports the usage of the R formula class.
• Normalizes input and output data to the range of 0-1.
• One-hot encodes input factor data.
• Allows for any number of hidden layers and nodes.
• Supports regression, classification, and binary classification networks.
• Multiple pre-defined activation functions (and supports user-defined functions).
• Mini-batch gradient descent using the ADAM optimizer.
• Randomly splits data into training and validation data.
• Simple plotting of metrics and predictions for new data.

Installation

You can install the released version of easy.mlp from github with:

library(devtools)

install_github("Greg-Hallenbeck/easy.mlp")

Example

This is a basic example which shows you how to solve a common problem:

library(easy.mlp)

data(iris)

# Initial network creation is stochastic.
set.seed(8675309)
net <- create.mlp(Species ~ ., data=iris, hidden=c(5,5,5), type="classification")

# This line can be run multiple times to train another 1,000 epochs
net <- train(net, 1000)

# Plot the loss and accuracy of the network
par(mfrow=c(1,2))
options(repr.plot.width=10, repr.plot.height=5.5)

plot(net, ylim=c(0.03, 2))
plot(net, metric="accuracy", ylim=c(0,1))

# Predict species for a new data point.

What is special about using README.Rmd instead of just README.md? You can include R chunks like so:

summary(cars)
#>      speed           dist       
#>  Min.   : 4.0   Min.   :  2.00  
#>  1st Qu.:12.0   1st Qu.: 26.00  
#>  Median :15.0   Median : 36.00  
#>  Mean   :15.4   Mean   : 42.98  
#>  3rd Qu.:19.0   3rd Qu.: 56.00  
#>  Max.   :25.0   Max.   :120.00

You'll still need to render README.Rmd regularly, to keep README.md up-to-date. devtools::build_readme() is handy for this. You could also use GitHub Actions to re-render README.Rmd every time you push. An example workflow can be found here: https://github.com/r-lib/actions/tree/master/examples.

You can also embed plots, for example:

In that case, don't forget to commit and push the resulting figure files, so they display on GitHub and CRAN.

Greg-Hallenbeck/easy.mlp documentation built on March 10, 2023, 6:31 a.m.