In cvazquezlos/NNEvol: A Simple Method to Obtain the Best Neural Network that Fixes a given Problem

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NNEvol

The goal of NNEvol is to solve one of the open problems in Machine and Deep Learning that is about "which does the best neural network architecture that fixes a given problem is?". NNEvol uses Grammar-Guided Genetic Programming for solving it.

Installation

You can install the released version of NNEvol from CRAN with:

install.packages("NNEvol")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("cvazquezlos/NNEvol")

Introduction

This library just has an unique function: get_best_nn. It receives some parameters:

• data. Is the element that individuals (neural networks) use in the evolution process. The received dataset will be a Dataframe whose inputs are displayed before the outputs, in any order.
• input. Number of inputs.
• output. Number of outputs.
• train. Proportion of the instances within the dataset that are used in the training process (default 0.70).
• validation. Proportion of the instances within the dataset that are used in the validation process (default 0.20).
• test. Proportion of the instances within the dataset that are used in the test process (default 0.10). This data is only used when train_final_nn is selected. In addition, the three above values (including this) would add 1.
• train_mode. Mode of the training process. 0 means multi-class, while 1 means regression. Binary classification could be expressed as a multi-class with n = 2 classes (default 0).
• train_final_nn. If the user wants NNEvol to train the final individual totally. If checked, this options allows you to know which are the metrics of the best neural network selected (default FALSE).
• seed. Seed that is used in the random processes of the package (default 123).
• population_size. Size of the population that participates in the evolution process (default 30). The bigger the number is, the longer the library takes to find a solution.
• genetation_number. Number of generations within the evolution process (default 30). The bigger the number is, the longer the library takes to find a solution.

cvazquezlos/NNEvol documentation built on Nov. 4, 2019, 9:20 a.m.