stochastic-gradient-descent

Very simple c++ DNN implementation that uses the stochastic gradient descent optimization algorithm

How to use it?

1. Create an object of type "NeuralNetwork":

    //specify the neurons cuont at each layer
    std::vector<uint32_t> layers_lengths = { 250, 40, 30, 5 };
   
    //specify the activation functions
    NeuralNetwork::func_ptr activations = { 3, tanh };
    
    //specify the activation functions derivatives
    NeuralNetwork::func_ptr activations_derivatives = { 3, [](double x) {return 1.0 - x * x; } };
   
    NeuralNetwork NN(layers_lengths, activations, activations_derivatives);

2. Call the "forward_pass" function to calculate the output layer values:

   //assuming that the vector "inputs" is defined somewhere
   NN.forward_pass(inputs);

3. You can read the output layer values from the member variable "neurons":

   for (size_t i = 0; i < NN.neurons.back().size(); i++)
   {
     double output = NN.neurons.back()[i];
     //...
   }

4. To optimize the Neural Network, call the "backward_pass" function

   //the vector "desired_outputs" holds the correct values that the neural network was supposed to give 
   NN.backward_pass(desired_outputs, 0.3/*learning rate*/);

License

MIT License