This repository contains a Rust implementation of a Convolutional Neural Network (CNN) built from scratch. This repository provides code for training on the MNIST dataset, and the 50States10K dataset.
All machine learning code is written from scratch, however the ndarray crate is used for matrix operations. When tuned correctly, the network should reach 90+% accuracy within one minute on the MNIST dataset.
The repository implements the following features:
- Convolutional, max pooling, and fully connected layers
- ReLU and Softmax activation functions
- Cross-entropy loss function
- SGD, Momentum, RMSProp, and Adam optimizers
- Dropout
- He initialization
To run the demo of the CNN, place the MNIST dataset in a folder named data, and use the following command:
$ cargo run --release
This command will run a demo of the CNN and train it on the MNIST dataset.
For more information about this project, read my blog post on CNNs.
This project is licensed under the GNU Affero General Public License v3.0 - see the LICENSE file for details.