Kaffe - A Pytorch inspired library written in rust
Kaffe is as per the title, a way to create Neural Networks in Rust.
The goal is to create a simple way to write your own models, and test them.
Syntax should be familiar to pytorch, but some features might take
names from numpy or even tensorflow.
In the future, the matrix library might be completely moved to its
own project, but for now they're all in the same crate;
Why? Because sometimes you wanna make cool and fast stuff in rust :)
Examples
Matrix basic example
use kaffe::tensor::Tensor;
fn main() {
let t = Tensor::init(10f32, vec![2, 2, 2]);
let res = t.log(10.0);
println!("{:?}", res.data);
let tensor = Tensor::randomize_range(1.0, 4.0, vec![2, 4]);
assert_eq!(tensor.all(|&e| e >= 1.0), true);
let tensor = Tensor::init(20.0, vec![2, 2]);
let value: f32 = 2.0;
let result_mat = tensor.div_val(value);
assert_eq!(result_mat.data, vec![10.0; 4]);
let tensor = Tensor::init(4f32, vec![1, 1, 1, 4]);
assert_eq!(tensor.data, vec![4f32; 4]);
assert_eq!(tensor.shape, vec![1, 1, 1, 4]);
let mut tensor = Tensor::init(2.0, vec![2, 4]);
println!("{}", tensor.data[0]);
tensor.set_where(|e| {
if *e == 2.0 {
*e = 2.3;
}
});
println!("{}", tensor.data[0]);
assert_eq!(tensor.data[0], 2.3);
println!("{}", tensor.get(vec![0, 0]).unwrap());
}
Neural net basic example - To Be Implemented
use kaffe::Matrix;
use kaffe::{Net, Layer, optimizer::*, loss::*};
struct MyNet {
layers: Vec<Layer>
}
impl Net for MyNet {
fn init() -> Self {
let mut layers: Vec<Layers> = Vec::new();
self.layers.push(nn.Conv2d(1,32,3,1));
self.layers.push(nn.Conv2d(32,64,3,1));
self.layers.push(nn.Dropout(0.25));
self.layers.push(nn.Dropout(0.5));
self.layers.push(nn.FCL(9216, 128));
self.layers.push(nn.FCL(128,10));
Self { layers }
}
fn forward(x: &Matrix) {
x = layers[0](x)
x = ReLU(x);
x = layers[1](x)
x = ReLU(x);
x = layers[2](x)
x = ReLU(x);
let output = log_softmax(x);
return output;
}
}
fn train(model: &Model,
train_dataloader: &DataLoader,
optimizer: &Optimizer,
epoch: usize) {
model.train();
for (batch_idx, (data, target)) in train_dataloader.iter().enumerate() {
optimizer.zero_grad();
let output = model(data);
let loss = BCELoss(output, target);
loss.backward();
optimizer.step();
}
}
fn test(model: &Model,
test_dataloader: &DataLoader,
optimizer: &Optimizer,
epoch: usize) {
model.eval();
let mut test_loss = 0.0;
let mut correct = 0.0;
optimizer.no_grad();
for (batch_idx, (data, target)) in train_dataloader.iter().enumerate() {
let output = model(data);
test_loss += BCELoss(output, target);
let pred = output.argmax(Dimension::Row);
correct += pred.eq(target.view_as(pred)).sum();
}
test_loss /= test_dataloader.count();
}
fn main() {
let d1 = download_dataset(url, "../data", true, true, transform);
let d2 = download_dataset(url, "../data", false, false, transform);
let train_dl = DataLoader::new(&d1);
let test_dl = DataLoader::new(&d2);
let model = Net::init();
let optimizer = SGD::init(0.001, 0.8);
for epoch in 1..EPOCHS+1 {
train(&model, &train_dl, &optimizer, epoch);
test(&model, &test_dl, &optimizer, epoch);
}
if args.SAVE_MODEL {
model.save_model("mnist_test.kaffe_pt");
}
}
GPU Support
As per right now, support for training on GPU is not happening anytime soon.
Although.. transpilation IS a thing you know.
For more examples, please see examples
Documentation
Full API documentation can be found here.
Features
