bf734b0eec
* Add torch backend - random * Add Random Unit Test * Adds Random Unit Test * Random Test * Adds Random Test
64 lines
2.8 KiB
Python
64 lines
2.8 KiB
Python
import numpy as np
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from absl.testing import parameterized
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from keras_core import testing
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from keras_core.operations import numpy as knp
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from keras_core.random import random
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class RandomTest(testing.TestCase, parameterized.TestCase):
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@parameterized.parameters(
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{"seed": 10, "shape": (5,), "mean": 0, "stddev": 1},
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{"seed": 10, "shape": (2, 3), "mean": 0, "stddev": 1},
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{"seed": 10, "shape": (2, 3, 4), "mean": 0, "stddev": 1},
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{"seed": 10, "shape": (2, 3), "mean": 10, "stddev": 1},
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{"seed": 10, "shape": (2, 3), "mean": 10, "stddev": 3},
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)
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def test_normal(self, seed, shape, mean, stddev):
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np.random.seed(seed)
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np_res = np.random.normal(loc=mean, scale=stddev, size=shape)
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res = random.normal(shape, mean=mean, stddev=stddev, seed=seed)
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self.assertEqual(res.shape, shape)
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self.assertEqual(res.shape, np_res.shape)
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@parameterized.parameters(
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{"seed": 10, "shape": (5,), "minval": 0, "maxval": 1},
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{"seed": 10, "shape": (2, 3), "minval": 0, "maxval": 1},
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{"seed": 10, "shape": (2, 3, 4), "minval": 0, "maxval": 2},
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{"seed": 10, "shape": (2, 3), "minval": -1, "maxval": 1},
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{"seed": 10, "shape": (2, 3), "minval": 1, "maxval": 3},
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)
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def test_uniform(self, seed, shape, minval, maxval):
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np.random.seed(seed)
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np_res = np.random.uniform(low=minval, high=maxval, size=shape)
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res = random.uniform(shape, minval=minval, maxval=maxval, seed=seed)
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self.assertEqual(res.shape, shape)
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self.assertEqual(res.shape, np_res.shape)
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self.assertLessEqual(knp.max(res), maxval)
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self.assertGreaterEqual(knp.max(res), minval)
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@parameterized.parameters(
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{"seed": 10, "shape": (5,), "mean": 0, "stddev": 1},
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{"seed": 10, "shape": (2, 3), "mean": 0, "stddev": 1},
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{"seed": 10, "shape": (2, 3, 4), "mean": 0, "stddev": 1},
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{"seed": 10, "shape": (2, 3), "mean": 10, "stddev": 1},
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{"seed": 10, "shape": (2, 3), "mean": 10, "stddev": 3},
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)
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def test_truncated_normal(self, seed, shape, mean, stddev):
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np.random.seed(seed)
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np_res = np.random.normal(loc=mean, scale=stddev, size=shape)
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res = random.truncated_normal(
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shape, mean=mean, stddev=stddev, seed=seed
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)
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self.assertEqual(res.shape, shape)
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self.assertEqual(res.shape, np_res.shape)
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self.assertLessEqual(knp.max(res), mean + 2 * stddev)
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self.assertGreaterEqual(knp.max(res), mean - 2 * stddev)
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def test_dropout(self):
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x = knp.ones((3, 5))
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self.assertAllClose(random.dropout(x, rate=0, seed=0), x)
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x_res = random.dropout(x, rate=0.8, seed=0)
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self.assertGreater(knp.max(x_res), knp.max(x))
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self.assertGreater(knp.sum(x_res == 0), 2)
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