**fksainetwork
**
[
](https://github.com/Felix1G/fksainetwork)
[
](https://crates.io/crates/fksainetwork)
[
](https://docs.rs/fksainetwork)
A **neural network** capable of **learning** and calculating output.
This project is just made for fun :)
---
Please go to [Inner Workings](https://github.com/Felix1G/fksainetwork/blob/main/INNERWORKINGS.md) to read more.
---
Usage
---
``````toml
[dependencies]
fksainetwork = "0.2.0"
``````
---
Example (Feed Forward)
---
``````rust
let mut network = Network::new(2, &[ //2 neuron inputs
(10, Initialization::He, Activation::Sigmoid, false), //10 hidden neurons
(2, Initialization::Xavier, Activation::LeakyReLU, true) //2 neuron outputs, true: batch normalisation
], Loss::BinaryCrossEntropy, true);
//or: let network = load_network("path/to/network-file");
//calculating
let input = vec![1.0, 1.0];
let output = network.calculate(&input); //calculate
println!("{:?}", output);
//learning
//batch size of 2
network.learn(0.01,
&vec![vec![0.0, 1.0], vec![0.0, 3.0]], //input values, batch size of 2
&vec![vec![1.0, 0.0], vec![0.0, 1.0]] //expected values
);
//NOTE: if you call 'learn', u do not need to call 'calculate' beforehand
//save
save_network("path/network-file", &network);
``````
---
Example (Convolutional)
---
``````rust
let network = ConvolutionalNetwork::new(
//convolution layers
&[
(2, &[Initialization::Xavier;20], Activation::ReLU, 2, Pooling::Max), //20 channels, kernel 2x2, pooling max 2.0
(3, &[Initialization::Xavier;40], Activation::ReLU, 2, Pooling::Max) //40 channels, kernel 3x3, pooling max 2.0
],
13, 13, 1, //input size of w: 13, h: 13, channels: 1
//input similar to the Feed Forward Network
&[
(3, Initialization::Xavier, Activation::LeakyReLU, false)
],
Loss::BinaryCrossEntropy, true
);
//or: let network = load_cnn_network("path/to/network-file");
//pretend these samples are actual images of something
let sample0 = Matrix::new(13, 13);
let sample1 = Matrix::new(13, 13);
let sample2 = Matrix::new(13, 13);
//calculate
let output = network.calculate(&vec![&sample0, &sample1, &sample2]);
println!("{:?}", output);
//learn
network.learn(0.04, &vec![&sample0, &sample1, &sample2], &vec![[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]);
//print outputs
cnn_network_bmp("path/to/directory", &network);
//save network
save_cnn_network("path/to/network-file", &network);
``````
---
Update Patches :)
---
0.1.3: Added Tanh Activation Function.
0.1.3: Improved Docs.
0.2.0: The save_network and load_network functions now use str instead of String for the path.
0.2.0: Improved learning algorithm.
0.2.0: Added the Convolutional Neural Network.
0.2.0: Added more Loss functions and Initialization functions.
0.2.0: Changed learn_bpg_mse to learn as the loss function is now a function parameter.
0.2.0: Changes to the feed forward network "new()" function parameter.