Add the cross-entropy loss. (#287)

candle-nn/src/loss.rs

@@ -26,3 +26,20 @@ pub fn nll(inp: &Tensor, target: &Tensor) -> Result<Tensor> {
         .sum_all()?
         .affine(-1f64 / b_sz as f64, 0.)
 }
+
+/// The cross-entropy loss.
+///
+/// Arguments
+///
+/// * [inp]: The input tensor of dimensions `N, C` where `N` is the batch size and `C` the number
+///          of categories. This is expected to raw logits.
+/// * [target]: The ground truth labels as a tensor of u32 of dimension `N`.
+///
+/// The resulting tensor is a scalar containing the average value over the batch.
+pub fn cross_entropy(inp: &Tensor, target: &Tensor) -> Result<Tensor> {
+    if inp.rank() != 2 {
+        candle::bail!("cross_entropy expects an input tensor of rank 2")
+    }
+    let inp = crate::ops::log_softmax(inp, 1)?;
+    nll(&inp, target)
+}

candle-nn/tests/loss.rs

@@ -10,9 +10,10 @@ input = torch.tensor([
 
 target = torch.tensor([1, 0, 4])
 print(F.nll_loss(F.log_softmax(input, dim=1), target))
+print(F.cross_entropy(input, target))
 */
 #[test]
-fn nll() -> Result<()> {
+fn nll_and_cross_entropy() -> Result<()> {
     let cpu = Device::Cpu;
     let input = Tensor::new(
         &[
@@ -27,5 +28,7 @@ fn nll() -> Result<()> {
     let log_softmax = candle_nn::ops::log_softmax(&input, 1)?;
     let loss = candle_nn::loss::nll(&log_softmax, &target)?;
     assert_eq!(loss.to_vec0::<f32>()?, 1.1312335);
+    let loss = candle_nn::loss::cross_entropy(&input, &target)?;
+    assert_eq!(loss.to_vec0::<f32>()?, 1.1312335);
     Ok(())
 }