Only using the C++ STL (standard template library).
This was not my idea. I merely followed up on this great tutorial, written by Jason Brownlee, where he explains the steps of programming a neural network from scratch in Python without the use of any library.
Porting the python code from Jason Brownlee to c++ is a great exercise to freshen up a bit on object oriented programming (using classes to represent neurons and layers) and vector handling. It also helps a great deal with understanding how neural networks work on the inside. However, there are many ways that a network like this can be build in c++ and I chose to dive into it without much planning. So the code might not be very beautiful, perfectly encapsulated or easy to comprehend (as a matter of fact, I know it isn't😔) but it was fun nevertheless.
This code was built in VisualStudio (2019 v16.9.4) with g++ 8.1.0 (using C++14).
You can either import the code in an empty console project in VisualStudio or compile it directly on the command line with:
g++ -Wall -Wpedantic main.cpp NeuralNetwork.cpp
Simply running it will read the provided csv file from the same directory, train the network and attempt predictions afterwards. Based on the results of the predictions the accuracy is calculated and subsequently printed to stdout.
Using this network on the given seed dataset (and cross-validation) we are able to achieve an approximate accuracy of more than 90%.
The following result was achieved using a learning rate of 0.3 with 500 epochs and 5 neurons in the first hidden layer on a network with one hidden and one output layer.
