https://github.com/spt-development/spt-neural-net
Native Rust implementation of the Neural Network defined in Tariq Rashid's excellent book "Make Your Own Neural Network".
https://github.com/spt-development/spt-neural-net
Last synced: about 2 months ago
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Native Rust implementation of the Neural Network defined in Tariq Rashid's excellent book "Make Your Own Neural Network".
- Host: GitHub
- URL: https://github.com/spt-development/spt-neural-net
- Owner: spt-development
- License: gpl-2.0
- Created: 2024-12-10T20:46:07.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-12-15T17:29:05.000Z (5 months ago)
- Last Synced: 2025-02-06T18:50:38.848Z (4 months ago)
- Language: Rust
- Homepage:
- Size: 50.8 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# spt-neural-net
Native Rust implementation of the Neural Network defined in Tariq Rashid's excellent book
[\"Make Your Own Neural Network\"](https://github.com/makeyourownneuralnetwork/makeyourownneuralnetwork/tree/master).
## Pre-requisites
The application takes a single (optional) boolean argument that results in each of the images used
for training and testing the [NeuralNetwork](src/neural_network.rs) being displayed - this is
useful when debugging the application. The display of the images uses the
[matplotlib](https://crates.io/crates/matplotlib) crate, which simply wraps the Python library of
the same name. In order therefore, for the image display to work, the following Python library
pre-requisites must be installed.
```shell
$ pip install --user matplotlib
$ pip install --user PyQt5
```
## Building and running the application
The standard `cargo` commands can be used for building, testing and running the application.
```shell
$ cargo clippy
$ cargo test
$ cargo build --release
$ cargo run --release
```
Example output when running the application.
```shell
$ cargo run --release
Finished `release` profile [optimized] target(s) in 0.13s
Running `target/release/spt-neural-net`
2024-12-15T17:13:51.391128 Training the network...
2024-12-15T17:14:13.786799 Network trained with 10000 records...
2024-12-15T17:14:35.126296 Network trained with 20000 records...
2024-12-15T17:14:55.012176 Network trained with 30000 records...
2024-12-15T17:15:15.502282 Network trained with 40000 records...
2024-12-15T17:15:35.603023 Network trained with 50000 records...
2024-12-15T17:15:55.829038 Network trained with 60000 records...
2024-12-15T17:15:55.829095 Testing the network...
2024-12-15T17:15:57.872578 Network performance: 0.97
```
**NOTE** In order to get results in the region of 97% as shown in the example above, it will be
necessary to train the neural network with a larger data set than
[mnist_train_100.csv](mnist_dataset/mnist_train_100.csv); the full dataset is available
[here](https://pjreddie.com/projects/mnist-in-csv/).