diff --git a/candle-nn/src/loss.rs b/candle-nn/src/loss.rs
index 669a8c684..2e9dbd2fc 100644
--- a/candle-nn/src/loss.rs
+++ b/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()?;
diff --git a/candle-nn/tests/loss.rs b/candle-nn/tests/loss.rs
index 718fb0719..9027de7f6 100644
--- a/candle-nn/tests/loss.rs
+++ b/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;
@@ -48,22 +67,12 @@ 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)?;
@@ -71,6 +80,6 @@ fn binary_cross_entropy_with_logit() -> Result<()> {
 
     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(())
 }