47 lines
1.5 KiB
Python
47 lines
1.5 KiB
Python
import numpy as np
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from keras_core import constraints
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from keras_core import testing
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def get_example_array():
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np.random.seed(3537)
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example_array = np.random.random((100, 100)) * 100.0 - 50.0
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example_array[0, 0] = 0.0 # Possible edge case
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return example_array
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class ConstraintsTest(testing.TestCase):
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def test_max_norm(self):
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constraint_fn = constraints.MaxNorm(2.0)
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x = np.array([[0, 0, 0], [1.0, 0, 0], [3, 0, 0], [3, 3, 3]]).T
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target = np.array(
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[
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[0, 0, 0],
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[1.0, 0, 0],
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[2.0, 0, 0],
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[2.0 / np.sqrt(3), 2.0 / np.sqrt(3), 2.0 / np.sqrt(3)],
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]
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).T
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output = constraint_fn(x)
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self.assertAllClose(target, output)
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def test_non_neg(self):
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constraint_fn = constraints.NonNeg()
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output = constraint_fn(get_example_array())
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output = np.array(output)
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self.assertTrue((np.min(output, axis=1) >= 0.0).all())
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def test_unit_norm(self):
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constraint_fn = constraints.UnitNorm()
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output = constraint_fn(get_example_array())
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l2 = np.sqrt(np.sum(np.square(output), axis=0))
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self.assertAllClose(l2, 1.0)
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def test_min_max_norm(self):
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constraint_fn = constraints.MinMaxNorm(min_value=0.2, max_value=0.5)
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output = constraint_fn(get_example_array())
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l2 = np.sqrt(np.sum(np.square(output), axis=0))
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self.assertFalse(l2[l2 < 0.2])
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self.assertFalse(l2[l2 > 0.5 + 1e-6])
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