1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39

//! Optimizers such as [Sgd], [Adam], and [RMSprop] that can optimize neural networks.
//!
//! # Initializing
//!
//! All the optimizer's provide [Default] implementations, and also provide a way to specify
//! all the relevant parameters through the corresponding config object:
//! - [Sgd::new()] with [SgdConfig]
//! - [Adam::new()] with [AdamConfig]
//! - [RMSprop::new()] with [RMSpropConfig]
//!
//! # Updating network parameters
//!
//! This is done via [Optimizer::update()], where you pass in a mutable [crate::nn::Module], and
//! the [crate::gradients::Gradients]:
//!
//! ```rust
//! # use dfdx::{prelude::*, gradients::*};
//! # type MyModel = Linear<5, 2>;
//! let mut model: MyModel = Default::default();
//! let mut opt: Sgd<MyModel> = Default::default();
//! # let y = model.forward(Tensor1D::zeros().traced());
//! # let loss = mse_loss(y, Tensor1D::zeros());
//! // -- snip loss computation --
//!
//! let gradients: Gradients = backward(loss);
//! opt.update(&mut model, gradients);
//! ```

mod adam;
mod optimizer;
mod rmsprop;
mod sgd;
mod weight_decay;

pub use adam::*;
pub use optimizer::*;
pub use rmsprop::*;
pub use sgd::*;
pub use weight_decay::*;