## Example Usage Below is a minimal example of how to use the FlexNN NeuralNetwork library in your own C++ project: ```cpp #include #include "FlexNN.h" int main() { // Prepare your data (X: features, Y: labels) Eigen::MatrixXd X; // shape: (num_samples, num_features) Eigen::VectorXd Y; // shape: (num_samples) FlexNN::readCSV_XY("data/mnist-digit-recognition.csv", X, Y); // Normalize input data if needed X = X.array() / 255.0; // (Optional) Split data into training and test sets auto splits = FlexNN::splitXY(X, Y, {0.8, 0.2}); Eigen::MatrixXd X_train = splits[0].first; Eigen::VectorXd Y_train = splits[0].second; Eigen::MatrixXd X_test = splits[1].first; Eigen::VectorXd Y_test = splits[1].second; // Transpose for (features, samples) shape expected by the network X_train.transposeInPlace(); X_test.transposeInPlace(); // Create a neural network with desired layers FlexNN::NeuralNetwork nn({ FlexNN::Layer(X_train.rows(), 64, "relu"), FlexNN::Layer(64, 10, "softmax") }); // Train the network nn.train(X_train, Y_train, 0.5, 100); // Evaluate accuracy double train_acc = nn.accuracy(X_train, Y_train); double test_acc = nn.accuracy(X_test, Y_test); std::cout << "Training accuracy: " << train_acc * 100 << "%" << std::endl; std::cout << "Test accuracy: " << test_acc * 100 << "%" << std::endl; return 0; } ``` **Note:** - You can customize the network architecture by changing the number and type of layers. - Make sure your data is in the correct format and normalized as needed. - See the `src/main.cpp` file for a more complete example. ## Requirements The C++ Neural Network library uses `Eigen3` for matrix operations and `OpenMP` for multithreading. The project uses `CMake` for building the code. On Ubuntu, you can install these requirements using: ``` sudo apt install cmake libeigen3-dev ``` ## Building and Running the Project To build the project, follow these steps: 1. Open a terminal and navigate to the project directory. 2. Create a build directory: ``` mkdir build cd build ``` 3. Run CMake to configure the project: ``` cmake .. ``` 4. Build the project: ``` cmake --build . ``` 5. To run the program, go into the project root directory (so that dataset path is correctly resolved) ``` cd .. ``` And then run the `main` executable present inside the `build` folder by: ``` ./build/main ``` ## API Reference For details on the code structure, available classes, and how to use FlexNN in your own projects, please visit the full documentation here: [https://docs.nalinangrish.me/FlexNN](https://docs.nalinangrish.me/FlexNN). ## Known Issues / Limitations - Only CPU computation is supported (no GPU). - No support for convolutional or recurrent layers. - Training large models may be slow. - Currently there is no support for saving model architecture and weights, so you need to train the model on every execution.

FlexNN - Flexible Neural Network Library in C++