Skip to main content

Micro Neural Network framework implemented in Rust w/ Python bindings

Project description

# pyrus-nn

[![Build Status](https://milesgranger.visualstudio.com/builds/_apis/build/status/pyrus-nn?branchName=master)](https://milesgranger.visualstudio.com/builds/_build/latest?definitionId=1&branchName=master)
[![Dependabot Status](https://api.dependabot.com/badges/status?host=github&repo=milesgranger/black-jack)](https://dependabot.com)
[![crates.io](http://meritbadge.herokuapp.com/pyrus-nn)](https://crates.io/crates/pyrus-nn)

[Rust API Documentation](https://docs.rs/pyrus-nn)

Lightweight neural network framework written in Rust, with _thin_ python bindings.

- Features:
- Serialize networks into/from YAML & JSON!
- Rust -> serde compatible
- Python -> `network.to_dict()` & `Sequential.from_dict()`
- Python install requires _zero_ dependencies
- No external system libs to install

- Draw backs:
- Only supports generic gradient descent.
- Fully connected (Dense) layers only so far
- Activation functions limited to linear, tanh, sigmoid and softmax
- Cost functions limited to MSE, MAE, Cross Entropy and Accuracy

### Install:

Python:
```
pip install pyrus-nn # Has ZERO dependencies!
```

Rust:
```toml
[dependencies]
pyrus-nn = "0.2.1"
```



### From Python
```python
from pyrus_nn.models import Sequential
from pyrus_nn.layers import Dense

model = Sequential(lr=0.001, n_epochs=10)
model.add(Dense(n_input=12, n_output=24, activation='sigmoid'))
model.add(Dense(n_input=24, n_output=1, activation='sigmoid'))

# Create some X and y, each of which must be 2d
X = [list(range(12)) for _ in range(10)]
y = [[i] for i in range(10)]

model.fit(X, y)
out = model.predict(X)

```

---

### From Rust
```rust
use ndarray::Array2;
use pyrus_nn::{network::Sequential, layers::Dense};


// Network with 4 inputs and 1 output.
fn main() {
let mut network = Sequential::new(0.001, 100, 32, CostFunc::CrossEntropy);
assert!(
network.add(Dense::new(4, 5)).is_ok()
);
assert!(
network.add(Dense::new(5, 6)).is_ok()
);
assert!(
network.add(Dense::new(6, 4)).is_ok()
);
assert!(
network.add(Dense::new(4, 1)).is_ok()
);

let X: Array2<f32> = ...
let y: Array2<f32> = ...

network.fit(X.view(), y.view());

let yhat: Array2<f32> = network.predict(another_x.view());
}

```

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for pyrus-nn, version 0.2.1
Filename, size File type Python version Upload date Hashes
Filename, size pyrus_nn-0.2.1-cp27-cp27m-manylinux1_x86_64.whl (989.5 kB) File type Wheel Python version cp27 Upload date Hashes View
Filename, size pyrus_nn-0.2.1-cp27-cp27mu-manylinux1_x86_64.whl (989.5 kB) File type Wheel Python version cp27 Upload date Hashes View
Filename, size pyrus_nn-0.2.1-cp35-cp35m-manylinux1_x86_64.whl (2.0 MB) File type Wheel Python version cp35 Upload date Hashes View
Filename, size pyrus_nn-0.2.1-cp36-cp36m-manylinux1_x86_64.whl (3.0 MB) File type Wheel Python version cp36 Upload date Hashes View
Filename, size pyrus_nn-0.2.1-cp36-cp36m-win_amd64.whl (488.6 kB) File type Wheel Python version cp36 Upload date Hashes View
Filename, size pyrus_nn-0.2.1-cp37-cp37m-manylinux1_x86_64.whl (3.9 MB) File type Wheel Python version cp37 Upload date Hashes View
Filename, size pyrus_nn-0.2.1-cp37-cp37m-win_amd64.whl (488.5 kB) File type Wheel Python version cp37 Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page