hwenhui1997 / mlp

Simple multilayer perceptron c++ implementation.

MLP

About

MLP stands for multilayer perceptron. This project is a simple & fast C++ implementation of a MLP, oriented towards hacking and rapid prototyping. It is well-tested and includes multiple tests for each component as well as use cases.

This project is maintained by David Nogueira.

Featuring

• C++ implementation.
• Modular-oriented, with classes built on top of each other: Node, Layer and network classes.
• Easy to use and to hack.
• Simple, fast and thread-safe.
• Tests for each component module as well as use-case tests.
• Supports saving & loading models.

OS Support

MLP offers support both for Windows (MVS) & Linux (g++/clang++).

Tests/Example Code

Some example programs are included with the source code.

• IrisDatasetTest.cpp - Using the IRIS data-set trains a MLP using backpropagation and tries to predict the classes.
• MLPTest.cpp - Includes tests to train a MLP for AND, NAND, NOR, OR, NOT and XOR using backpropagation.
• NodeTest.cpp - Includes tests to train a single node (aka, perceptron) for AND, NAND, NOR, OR, NOT and XOR using backpropagation. (A simple perceptron cannot learn the XOR function.)

Example

Let us look at an example. After loading the data and creating the training/dev/test data structures, we will create a MLP with input size 5 (assuming 4 input data features + 1 bias), a hidden layer of 4 neuros and an output layer with 3 outputs (3 possible predicted classes). The activation functions will be a sigmoid for the hidden layer and a linear one for the output layer.

#include "MLP.h"
#include <vector>

// ...
std::vector<TrainingSample> training_set;
// ...

// assuming 4 inputs + 1 bias.
// 1 hidden layer(s) of 4 neurons.
// assuming 3 outputs
MLP my_mlp({ 4 + 1, 4 , 3 }, { "sigmoid", "linear" }, false);

int loops = 5000;
my_mlp.Train(training_set, .01, loops, 0.10, false);

int correct = 0;
for (int j = 0; j < samples; ++j) {
  std::vector<double> guess;
  my_mlp.GetOutput(training_set[j].input_vector(), &guess);
  size_t class_id;
  my_mlp.GetOutputClass(guess, &class_id);

  // Compare class_id with gold class id for each instance
}

Saving and loading models is also very intuitive:

#include "MLP.h"
{
  std::string model = "../../data/iris.mlp";
  //...
  my_mlp.SaveMLPNetwork(model); //saving
}
{
  MLP my_mlp(model); //load a model in constructor
  //...
}