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label_smoothing in sparse_cat_crossentropy (#3568)

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* label_smoothing in sparse_cat_crossentropy

* test multiple values, assert
This commit is contained in:
David Hou
2024-03-01 17:02:46 -08:00
committed by GitHub
parent 6b29c70b3d
commit b3cdc11a58
2 changed files with 8 additions and 7 deletions
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8
test/test_nn.py
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@@ -13,13 +13,13 @@ class TestNN(unittest.TestCase):
# create in tinygrad
input = Tensor.randn(3, 5)
target = Tensor.randint((3,), low=0, high=4)
loss = input.sparse_categorical_crossentropy(target)
torch_input = torch.tensor(input.numpy())
torch_target = torch.tensor(target.numpy(), dtype=torch.long)
torch_loss = torch.nn.CrossEntropyLoss(reduction='mean')(torch_input, torch_target)
np.testing.assert_allclose(loss.numpy(), torch_loss.detach().numpy(), atol=1e-5, rtol=1e-6)
for smoothing in [0.0, 0.1, 0.5, 1.0]:
loss = input.sparse_categorical_crossentropy(target, label_smoothing=smoothing)
torch_loss = torch.nn.CrossEntropyLoss(reduction='mean', label_smoothing=smoothing)(torch_input, torch_target)
np.testing.assert_allclose(loss.numpy(), torch_loss.detach().numpy(), atol=1e-5, rtol=1e-6)
def test_batchnorm2d(self, training=False):
with Tensor.train(training):