gradients

Deep Learning library using [custos] and [custos-math].

external (C) dependencies: OpenCL, CUDA, nvrtc, cublas, a BLAS lib (OpenBLAS, Intel MKL, ...)

Installation

There are two features available that are enabled by default: - cuda ... CUDA, nvrtc and cublas must be installed - opencl ... OpenCL is needed

If you deactivate them (add default-features = false and provide no additional features), only the CPU device can be used.

For all feature-configurations, a BLAS library needs to be installed on the system.

```toml [dependencies] gradients = "0.3.3"

to disable the default features (cuda, opencl) and use an own set of features:

gradients = {version = "0.3.3", default-features = false, features=["opencl"]}

```

MNIST [example]

(if this example does not compile, consider looking here)

Use a struct that implements the NeuralNetwork trait (it is implemented via the network attribute) to define which layers you want to use:

```rust use gradients::purpur::{CSVLoader, CSVReturn, Converter}; use gradients::OneHotMat; use gradients::{ correctclasses, nn::{cce, ccegrad}, range, Adam, CLDevice, Linear, network, ReLU, Softmax, };

[network]

pub struct Network { lin1: Linear<784, 128>, relu1: ReLU, lin2: Linear<128, 10>, relu2: ReLU, lin3: Linear<10, 10>, softmax: Softmax, } ``` Load [data] and create an instance of Network:

You can download the mnist dataset here.

```rust // use cpu (no features enabled): let device = gradients::CPU::new().select(); // use cuda device (cuda feature enabled): let device = gradients::CudaDevice::new(0).unwrap().select(); // use opencl device (opencl feature enabled): let device = CLDevice::new(0)?;

let mut net = Network::with_device(&device);

let loader = CSVLoader::new(true); let loadeddata: CSVReturn = loader.load("PATH/TO/DATASET/mnisttrain.csv")?;

let i = Matrix::from(( &device, (loadeddata.samplecount, loadeddata.features), &loadeddata.x, )); let i = i / 255.;

let y = Matrix::from((&device, (loadeddata.samplecount, 1), &loaded_data.y)); let y = y.onehot(); ```

Training loop:

```rust let mut opt = Adam::new(0.01);

for epoch in range(200) { let preds = net.forward(&i); let correcttraining = correctclasses(&loadeddata.y.asusize(), &preds) as f32;

let loss = cce(&device, &preds, &y);
println!(
    "epoch: {epoch}, loss: {loss}, training_acc: {acc}",
    acc = correct_training / loaded_data.sample_count() as f32
);

let grad = cce_grad(&device, &preds, &y);
net.backward(&grad);
opt.step(&device, net.params());

} ```