BrabeNetz 1.5.2

BrabeNetz

🌐 A fast and clean supervised neural network in C++, capable of effectively using multiple cores 🧠

Usage

1. Build & link library

   • See: build/link instructions

2. Choose your interface

   1. brabe_netz.h: [Recommended] A wrapper for the raw network.h interface, but with error handling and modern C++ interface styling such as std::vectors, std::exceptions, etc.
   2. network.h: The raw network with C-style arrays and no bound/error checking. Only use this if performance is important.

3. Constructors

   1. (initializer_list<int>, properties): Construct a new neural network with the given network size (e.g. { 2, 3, 4, 1 }) and randomize all base weights and biases - ref
   2. (network_topology&, properties): Construct a new neural network with the given network topology and import it's existing weights and biases - ref
   3. (string, properties): Construct a new neural network with and load the neural network state from the file specified in properties.state_file - ref

4. Functions

   1. network_result brabe_netz::feed(std::vector<double>& input_values): Feed the network input values and forward propagate through all neurons to estimate a possible output (Use the network_result structure (ref) to access the result of the forward propagation, such as .values to view the output) - ref
   2. double network_result::adjust(std::vector<double>& expected_output): Backwards propagate through the whole network to adjust wrong neurons for result trimming and return the total network error - ref
   3. void save(string path): Save the network's state to disk by serializing weights
   4. void set_learnrate(double value): Set the network's learning rate. It is good practice and generally recommended to use one divided by the train count, so the learn rate decreases the more often you train - ref
   5. network_topology& build_topology(): Build and set the network topology object of the current network's state (can be used for network visualization or similar) - ref

Usage examples can be found here, and here