Ann 2.0

*a simple implementation of a neural network in pure c++, only one file *

Build

you can build it with visual studio 19

code example

NeuralNetwork ann;
ann.SetInputSize(2);
ann.AddLayer(32, ReLu, ReLuDerivative);
ann.AddLayer(32, ReLu, ReLuDerivative); 
ann.AddLayer(32, ReLu, ReLuDerivative);
ann.AddLayer(1, ReLu, ReLuDerivative);
ann.LinkLayers();

//two elemets vector
std::vector<double> input;
input.push_back(1.f);
input.push_back(1.f);

std::vector<double> output = ann.ComputeOutput(input);

for (unsigned int i = 0; i < output.size(); i++)
{
	std::cout << "[" << i << "]: " << output[i] << std::endl;
}

instantiate the object of the class NeuralNetwork

NeuralNetwork ann

set the input size of the neural network

ann.SetInputSize(size);

add a new layer to the neural network

ann.AddLayer(NumberOfNeurons, ActivationFunction, ActivationFunctionDerivative);

link all layers of neuron together (very important)

ann.LinkLayers();

to train the neural network use the function ann.Learn(Input, ExpectedOutput, LearningRate) or ann.LearnMultiThread(Input, ExpectedOutput, LearningRate, NeuronsCalculatedPerTask)Example:

for(int i = 0; i < epoch; i++)
{
    float error = ann.Learn(your_input, expected_output, learning_rate);
    std::cout << "Error: " << error << std::endl;
}

compute the output of the neural network with ann.ComputeOutput(input) or ann.ComputeOutputMultiThread(Input, NeuronsCalculatedPerTask) Example:

std::vector<double> output = ann.ComputeOutput(input);

for (unsigned int i = 0; i < output.size(); i++)
{
	std::cout << "output[" << i << "]: " << output[i] << std::endl;
}