# Rust DNN
Create Modular Deep Neural Networks in Rust easy
# In progress
If literally anyone stars this project I will add convolutional layers.
If this project get 20 stars I add everything
# Installation
After running
```
cargo add rdnn
```
Then you must put these in your rust code
```rust
use Rust_Simple_DNN::rdnn::layers::*;
use Rust_Simple_DNN::rdnn::*;
```
# Mini tutorial
This is how you make a neural network that looks like this
<br>
<img src="network.png" alt="image-alt-text-check-github-to-see-image" width="300"/>
Use this code to make it:
```rust
//FC layers are dense layers.
//Sig layers are sigmoid activation
let mut net = Net::new(
vec![
FC::new(3, 4), //input 3, output 4
Sig::new(4), //sigmoid, input 4 output 4
FC::new(4, 4),
Sig::new(4), //sigmoid
FC::new(4, 1),// input 4 output 1
Sig::new(1), //sigmoid
],
1, //batch size
0.1, //learning rate
);
//"net" is the variable representing your entire network
```
<br>
<br>
This is how you propagate data through the network:
```rust
net.forward_data(&vec![1.0, 0.0, -69.0]); //returns the output vector
```
After propagating some data through, you can then also backpropagate some like this:
```rust
net.backward_data(&vec![0.0]); //a vector of what you want the nn to output
```
The network will automatically store and apply the gradients, so to train the network, all you need to do is repeatedly forward and backpropagate your data
```rust
let mut x = 0;
while x < 5000 {
net.forward_data(&vec![1.0, 0.0, 0.0]);
net.backward_data(&vec![1.0]);
net.forward_data(&vec![1.0, 1.0, 0.0]);
net.backward_data(&vec![0.0]);
net.forward_data(&vec![0.0, 1.0, 0.0]);
net.backward_data(&vec![1.0]);
net.forward_data(&vec![0.0, 0.0, 0.0]);
net.backward_data(&vec![0.0]);
x += 1;
}
//at this point its trained
```
This is Pytorch if it wasn't needlessly complicated be like hahahaha
