add test documentation and refactor function

candle-nn/src/loss.rs

@@ -62,10 +62,9 @@ pub fn mse(inp: &Tensor, target: &Tensor) -> Result<Tensor> {
 /// The resulting tensor is a scalar containing the average value over the batch.
 pub fn binary_cross_entropy_with_logit(inp: &Tensor, target: &Tensor) -> Result<Tensor> {
  let inp = crate::ops::sigmoid(inp)?;
- let one_tensor = Tensor::new(1.0, &inp.device())?;
 
  let left_side = target * inp.log()?;
- let right_side = (one_tensor.broadcast_sub(&target)?) * (one_tensor.broadcast_sub(&inp)?.log()?);
+ let right_side = (target.affine(-1., 1.))? * inp.affine(-1., 1.)?.log()?;
 
  let loss = left_side? + right_side?;
  let loss = loss?.neg()?.mean_all()?;

candle-nn/tests/loss.rs

@@ -40,6 +40,25 @@ fn nll_and_cross_entropy() -> Result<()> {
  Ok(())
 }
 
+
+/* Equivalent python code:
+import torch
+import torch.nn.functional as F
+
+inp = torch.Tensor([[ 2.3611, -0.8813, -0.5006, -0.2178],
+ [ 0.0419, 0.0763, -1.0457, -1.6692],
+ [-1.0494, 0.8111, 1.5723, 1.2315],
+ [ 1.3081, 0.6641, 1.1802, -0.2547],
+ [ 0.5292, 0.7636, 0.3692, -0.8318]])
+
+target = torch.Tensor([[0., 1., 0., 0.],
+ [0., 1., 0., 0.],
+ [0., 0., 0., 1.],
+ [1., 0., 0., 0.],
+ [0., 0., 1., 0.]])
+
+print(F.binary_cross_entropy_with_logits(inp, target))
+*/
 #[test]
 fn binary_cross_entropy_with_logit() -> Result<()> {
  let cpu = Device::Cpu;
@@ -48,29 +67,19 @@ fn binary_cross_entropy_with_logit() -> Result<()> {
  [ 0.0419, 0.0763, -1.0457, -1.6692],
  [-1.0494, 0.8111, 1.5723, 1.2315],
  [ 1.3081, 0.6641, 1.1802, -0.2547],
- [ 0.5292, 0.7636, 0.3692, -0.8318],
- [ 0.5100, 0.9849, -1.2905, 0.2821],
- [ 1.4662, 0.4550, 0.9875, 0.3143],
- [-1.2121, 0.1262, 0.0598, -1.6363],
- [ 0.3214, -0.8689, 0.0689, -2.5094],
- [ 1.1320, -0.6824, 0.1657, -0.0687]];
+ [ 0.5292, 0.7636, 0.3692, -0.8318]];
 
  let target = [[0.0f64, 1., 0., 0.],
  [0., 1., 0., 0.],
  [0., 0., 0., 1.],
  [1., 0., 0., 0.],
- [0., 0., 1., 0.],
- [1., 0., 0., 0.],
- [0., 0., 1., 0.],
- [0., 0., 1., 0.],
- [0., 1., 0., 0.],
  [0., 0., 1., 0.]];
 
  let inp = Tensor::new(&inp, &cpu)?;
  let target = Tensor::new(&target, &cpu)?;
 
  let loss = candle_nn::loss::binary_cross_entropy_with_logit(&inp, &target)?;
 
- assert_eq!(to_vec0_round(&loss, 4)?, 0.7739);
+ assert_eq!(to_vec0_round(&loss, 4)?, 0.8224);
  Ok(())
 }