#include <NeuralNetwork.hpp> #include <Logger.hpp> // Create a Neural Network like so std::shared_ptr<NeuralNetwork> neuralNetworkPointer( new NeuralNetwork( // Layer sizes std::vector<unsigned long>({ 2, 3, 1 }), // ActivationType. Can be one of: Sigmoid, Linear, Swish, Tanh NeuralNetwork::Sigmoid ) ); auto &network = *neuralNetworkPointer; // Training // Declare some training inputs/outputs // Simple XOR example std::vector<std::vector<long double>> trainingInputs = {{{{0, 0}}, {{0, 1}}, {{1, 0}}, {{1, 1}}}}; std::vector<std::vector<long double>> trainingOutputs = {{{{0}}, {{1}}, {{1}}, {{0}}}}; // Train // Set learning rate network.learningRate = 20; // Train a number of iterations auto trainingInputsSize = trainingInputs.size(); unsigned long trainingIteration = 0; for (; trainingIteration < 4096; trainingIteration++) { for (unsigned long trainingIndex = 0; trainingIndex < trainingInputsSize; trainingIndex++) { auto &input = trainingInputs[trainingIndex]; auto &output = trainingOutputs[trainingIndex]; // Activate the network with input network.feedforward(input); // Backpropagate the network with expected output network.backpropagate(output); } } // Use the network std::vector<long double> input({ {0, 1} }); network.feedforward(input); auto outputs = network.getOutputs(); logger(Logger::Info, "Output: " + std::to_string(outputs[0]));