Add a basic optimizer example. (#454)

candle-nn/examples/basic_optimizer.rs

@@ -0,0 +1,33 @@
+use candle::{DType, Device, Result, Tensor};
+use candle_nn::{linear, AdamW, Linear, ParamsAdamW, VarBuilder, VarMap};
+
+fn gen_data() -> Result<(Tensor, Tensor)> {
+    // Generate some sample linear data.
+    let w_gen = Tensor::new(&[[3f32, 1.]], &Device::Cpu)?;
+    let b_gen = Tensor::new(-2f32, &Device::Cpu)?;
+    let gen = Linear::new(w_gen, Some(b_gen));
+    let sample_xs = Tensor::new(&[[2f32, 1.], [7., 4.], [-4., 12.], [5., 8.]], &Device::Cpu)?;
+    let sample_ys = gen.forward(&sample_xs)?;
+    Ok((sample_xs, sample_ys))
+}
+
+fn main() -> Result<()> {
+    let (sample_xs, sample_ys) = gen_data()?;
+
+    // Use backprop to run a linear regression between samples and get the coefficients back.
+    let varmap = VarMap::new();
+    let vb = VarBuilder::from_varmap(&varmap, DType::F32, &Device::Cpu);
+    let model = linear(2, 1, vb.pp("linear"))?;
+    let params = ParamsAdamW {
+        lr: 0.1,
+        ..Default::default()
+    };
+    let mut opt = AdamW::new(varmap.all_vars(), params)?;
+    for step in 0..10000 {
+        let ys = model.forward(&sample_xs)?;
+        let loss = ys.sub(&sample_ys)?.sqr()?.sum_all()?;
+        opt.backward_step(&loss)?;
+        println!("{step} {}", loss.to_vec0::<f32>()?);
+    }
+    Ok(())
+}