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Style fix for loss functions (#91)

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* MLE and L1 loss functions

* logsoftmax change and tests

* subtract max logit for numerical stability

* l1 name change

* cross entropy reduction + unit tests

* docstrings

* l1 test name change

* old loss impl + default none

* style
This commit is contained in:
Kai Ma
2023-12-09 00:11:56 -05:00
committed by GitHub
parent 641d316484
commit cb9e585b8e
2 changed files with 15 additions and 11 deletions
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7
python/tests/test_nn.py
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@@ -10,6 +10,7 @@ import mlx_tests
import numpy as np
from mlx.utils import tree_flatten, tree_map, tree_unflatten
class TestNN(mlx_tests.MLXTestCase):
def test_linear(self):
inputs = mx.zeros((10, 4))
@@ -22,17 +23,17 @@ class TestNN(mlx_tests.MLXTestCase):
targets = mx.array([0, 1])
# Test with reduction 'none'
losses_none = nn.losses.cross_entropy(logits, targets, reduction='none')
losses_none = nn.losses.cross_entropy(logits, targets, reduction="none")
expected_none = mx.array([0.0, 0.0])
self.assertTrue(mx.array_equal(losses_none, expected_none))
# Test with reduction 'mean'
losses_mean = nn.losses.cross_entropy(logits, targets, reduction='mean')
losses_mean = nn.losses.cross_entropy(logits, targets, reduction="mean")
expected_mean = mx.mean(expected_none)
self.assertEqual(losses_mean, expected_mean)
# Test with reduction 'sum'
losses_sum = nn.losses.cross_entropy(logits, targets, reduction='sum')
losses_sum = nn.losses.cross_entropy(logits, targets, reduction="sum")
expected_sum = mx.sum(expected_none)
self.assertEqual(losses_sum, expected_sum)