BrabeNetz is a supervised neural network written in C++, aiming to be as fast as possible by using bare values instead of objects

BrabeNetz has no bounds- or error-checking for performance reasons, be careful what you feed it.

I've currently trained it to solve XOR, once I fix this, I'll train it to recognize handwritten characters.

Be sure to read the network description

It's pretty fast (TODO)

• Fast Feed-Forward algorithm
• Fast Backwards-Propagation algorithm
• Easy to use (Inputs, outputs)
• Pointer arrays instead of std::vector
• Fast binary network state saving via state.nn file (Weights, Biases, Sizes)
• Multithreaded if worth the spawn-overhead (std::thread or NVIDIA CUDA)
• Scalability (Neuron size, Layer count) - only limited by hardware

• Randomly generated values to begin with
• Easily save/load with network::save(string)/network::load(string)
• Sigmoid squashing function (TODO: ReLU?)
• Biases for each neuron
• network_topology helper objects for loading/saving state and inspecting network (TODO: Remove totally?)

1. Constructors

   • network(initializer_list<int>): Create a new neural network with the given topology vector and fill it with random numbers ({ 2, 3, 4, 1} = 2 Input, 3 Hidden, 4 Hidden, 1 Output Neurons - total of 4 layers)
   • network(network_topology&): Create a new neural network with the given network topology and load_ it's values
   • network(string): Create a new neural network with the given path to the sate.nn file and load it.

2. Functions

   • double feed(double* input_values, int length, int& out_length): Feed the network input_values and return an array of output values (where out_length will be set to the length of returned output values, that's the size of the output layer in topology)
   • double train(double* input_values, int length, double* expected_output): Feed the network input_values and compare predicted output with expected_output, Backwards-Propagate (adjust weights/biases) if needed. Returns the total error of the output layer.
   • void save(string path): Save the current network state (topology, weights, biases) to disk (with the given path or default: state.nn)
   • void set_learnrate(double value): Set the learn rate of the network (used by train(..) function). Should always be 1 / (total train times + 1)

An example of this can be found here, and here