zhangyiss/mlx
1
0
Fork 0
mirror of https://github.com/ml-explore/mlx.git synced 2025-10-18 15:28:16 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

fix loss tests (#118)

Browse Source 
* fix loss tests

* use none as default
This commit is contained in:
Awni Hannun
2023-12-10 10:08:19 -08:00
committed by GitHub
parent c1e1c1443f
commit 2d0130f80f
2 changed files with 27 additions and 21 deletions
Download Patch File Download Diff File Expand all files Collapse all files

14
python/tests/test_nn.py
View File

@@ -50,7 +50,7 @@ class TestNN(mlx_tests.MLXTestCase):
# Test with reduction 'none'
losses_none = nn.losses.mse_loss(predictions, targets, reduction="none")
expected_none = mx.array([0.04, 0.01, 0.01, 0.04])
self.assertTrue(mx.array_equal(losses_none, expected_none))
self.assertTrue(mx.allclose(losses_none, expected_none))
# Test with reduction 'mean'
losses_mean = nn.losses.mse_loss(predictions, targets, reduction="mean")
@@ -82,23 +82,23 @@ class TestNN(mlx_tests.MLXTestCase):
self.assertEqual(losses_sum, expected_sum)
def test_kl_div_loss(self):
p_logits = mx.array([[1.0, 2.0], [0.5, 1.0]])
q_logits = mx.array([[0.8, 1.5], [0.4, 1.2]])
p_logits = mx.log(mx.array([[0.5, 0.5], [0.8, 0.2]]))
q_logits = mx.log(mx.array([[0.5, 0.5], [0.2, 0.8]]))
# Test with reduction 'none'
losses_none = nn.losses.kl_div_loss(p_logits, q_logits, reduction="none")
expected_none = mx.array([0.22314353, 0.09966799])
self.assertTrue(mx.array_equal(losses_none, expected_none))
expected_none = mx.array([0.0, 0.831777])
self.assertTrue(mx.allclose(losses_none, expected_none))
# Test with reduction 'mean'
losses_mean = nn.losses.kl_div_loss(p_logits, q_logits, reduction="mean")
expected_mean = mx.mean(expected_none)
self.assertEqual(losses_mean, expected_mean)
self.assertTrue(mx.allclose(losses_mean, expected_mean))
# Test with reduction 'sum'
losses_sum = nn.losses.kl_div_loss(p_logits, q_logits, reduction="sum")
expected_sum = mx.sum(expected_none)
self.assertEqual(losses_sum, expected_sum)
self.assertTrue(mx.allclose(losses_sum, expected_sum))
def test_gelu(self):
inputs = [1.15286231, -0.81037411, 0.35816911, 0.77484438, 0.66276414]