candle-optimisers 0.10.2

Optimisers for use with candle, the minimalist ML framework
Documentation 
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use candle_core::{Result, Tensor};
use candle_nn::{Linear, Module, VarBuilder};

const IMAGE_DIM: usize = 784;
const LABELS: usize = 10;

fn linear_z(in_dim: usize, out_dim: usize, vs: &VarBuilder) -> Result<Linear> {
    let ws = vs.get_with_hints((out_dim, in_dim), "weight", candle_nn::init::ZERO)?;
    let bs = vs.get_with_hints(out_dim, "bias", candle_nn::init::ZERO)?;
    Ok(Linear::new(ws, Some(bs)))
}

pub trait Model: Sized {
    fn new(vs: VarBuilder) -> Result<Self>;
    fn forward(&self, xs: &Tensor) -> Result<Tensor>;
}

pub struct LinearModel {
    linear: Linear,
}

impl Model for LinearModel {
    fn new(vs: VarBuilder) -> Result<Self> {
        let linear = linear_z(IMAGE_DIM, LABELS, &vs)?;
        Ok(Self { linear })
    }

    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
        self.linear.forward(xs)
    }
}

pub struct Mlp {
    ln1: Linear,
    ln2: Linear,
}

impl Model for Mlp {
    fn new(vs: VarBuilder) -> Result<Self> {
        let ln1 = candle_nn::linear(IMAGE_DIM, 100, vs.pp("ln1"))?;
        let ln2 = candle_nn::linear(100, LABELS, vs.pp("ln2"))?;
        Ok(Self { ln1, ln2 })
    }

    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
        let xs = self.ln1.forward(xs)?;
        let xs = xs.relu()?;
        self.ln2.forward(&xs)
    }
}