mynn 0.1.2

# mynn 

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[![MIT licensed](https://img.shields.io/badge/license-MIT-blue.svg)](./LICENCE)

A hobbyist no-std neural network library. 

## Explanation 

This is a small library (currently ~200 lines minus doc comments and helper macros) I initially created during my lunch break when I had attempted to represent the shape of a neural network in Rust's type system, the result was I was able to make all the vectors into fixed sized arrays and allow the neural network to be no-std and in theory usable on microcontroller and embedded platforms. 

See this [example](https://github.com/jasonalexander-ja/mynn-attiny-example) of a pre-trained model approximating an XOR running on an ATtiny85. 

## Installation 

Command line: 
```text 
cargo add mynn 
```

Cargo.toml: 
```text 
mynn = "0.1.2" 
``` 

To use `f32` in all operations, supply the `f32` flag:

```text
mynn = { version = "0.1.2", features = ["f32"] }
```

To remove recursion, use `recurse-opt`:

```text
mynn = { version = "0.1.2", features = ["recurse-opt"] }
```

This will cause all the recursive method calls on each layer to be inlined, on larger models this may increase the size of the generated code, tradeoffs need to be considered.

## Example  

Short example approximates the output of a XOR gate. 

```rust
use mynn::make_network;
use mynn::activations::SIGMOID;

fn main() {
    let inputs = [[0.0, 0.0],  [0.0, 1.0], [1.0, 0.0],  [1.0, 1.0]];
    let targets = [[0.0], [1.0], [1.0], [0.0]];


    let mut network = make_network!(2, 3, 1);
    network.train(0.5, inputs, targets, 10_000, &SIGMOID);


    println!("0 and 0: {:?}", network.predict([0.0, 0.0], &SIGMOID));
    println!("1 and 0: {:?}", network.predict([1.0, 0.0], &SIGMOID));
    println!("0 and 1: {:?}", network.predict([0.0, 1.0], &SIGMOID));
    println!("1 and 1: {:?}", network.predict([1.0, 1.0], &SIGMOID));
}
```