A comprehensive C++ implementation of neural networks with optimization algorithms and evaluation on the Iris dataset.
Features
- Custom neural network with configurable layers and activation functions.
- Sigmoid, ReLU, and Softmax activation support.
- Full evaluation pipeline: accuracy, precision, recall, F1-score, and confusion matrix.
- K-fold cross-validation with stratified sampling.
- Performance benchmarking with Google Benchmark.
Dependencies
- Eigen3 for linear algebra.
- Google Test for unit/integration tests.
- Google Benchmark for performance measurements.
- CMake as build system.
Cross-validation snapshot
- Mean accuracy: 76.00% +/- 18.79%
- Best fold: 100% accuracy
- Worst fold: 53.33% accuracy