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Simple neural network implemented from scratch in C++.
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Neural Network in C++

A neural network implemented with matrices in C++, from scratch !

This program is meant to be used for supervised learning.

What's in there ?

  • src/XOR : Learning XOR operation.
  • src/XOR_PLOT : Learning XOR operation and plot weights variation on a graph (using python).
  • src/Digits-Recognition : Learning to recognize hand-written digits with a training file.

Download, Compile & Run

git clone
cd Neural-Network/src
git submodule update --init

# cd into one of the directories above and:

Network Class

The Network class contains the gradient descent algorithm.

Both src/XOR and src/Digit-Recognition are using it. Quick description :

// constructor
// vector 'neurons' should contain:
// - number of input neurons at first index
// - number of output neurons at last index
// - number of hidden neurons in between
// example: {2,5,3,1} = 2 input neurons, 1 output neuron, 2 hidden layers (5 neurons and 3 neurons respectively)
// learning rate : experimental
Network(std::vector<int> neurons, double learningRate);

// make prediction
Matrix<double> computeOutput(std::vector<double> input);

// learns from the previous computeOutput()
void learn(std::vector<double> expectedOutput);

// save all network's parameters into a file (after a training)
void saveNetworkParams(const char *filepath);

// load network's parameters from a file so you don't have to train it again
void loadNetworkParams(const char *filepath);
// or use the constructor
Network(const char *filepath);


I was curious to see what would've happened if I had plotted the network's parameters on a graph, so I did it, and the result is actually fun :)

The program was learning XOR operation and saving it's weights and error variation over time.

Then I plotted the data using plotly

EDIT: I updated the repo and now there is a python script called at src/ that will do the job instead. Try to compile and run src/XOR_PLOT.

And here is the result :

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We can see that the program is actually working: while the weights are converging to specific values, the error is decreasing.

Amen !

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