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| # Copyright 2017 The TensorFlow Authors. All Rights Reserved. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
| # ============================================================================== | ||
| """Tests for multinomial generation ops in the XLA JIT compiler.""" | ||
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| from __future__ import absolute_import | ||
| from __future__ import division | ||
| from __future__ import print_function | ||
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| import collections | ||
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| import numpy as np | ||
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| from tensorflow.compiler.tests.xla_test import XLATestCase | ||
| from tensorflow.python.framework import dtypes | ||
| from tensorflow.python.framework import random_seed | ||
| from tensorflow.python.ops import array_ops | ||
| from tensorflow.python.ops import random_ops | ||
| from tensorflow.python.platform import googletest | ||
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| # TODO(srvasude): Merge this with | ||
| # third_party/tensorflow/python/kernel_tests/random/multinomial_op_test.py. | ||
| class CategoricalTest(XLATestCase): | ||
| """Test cases for random-number generating operators.""" | ||
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| def _chi2(self, expected, actual): | ||
| """Returns Chi2 GOF statistic.""" | ||
| actual = np.asarray(actual) | ||
| expected = np.asarray(expected) | ||
| diff = actual - expected | ||
| chi2 = np.sum(diff * diff / expected) | ||
| return chi2 | ||
|
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| def _do_sampling(self, logits, num_samples): | ||
| """Categorical samples from given input. | ||
| Args: | ||
| logits: Numpy ndarray of shape [batch_size, num_classes]. | ||
| num_samples: Int; number of samples to draw. | ||
| Returns: | ||
| Frequencies from sampled classes; shape [batch_size, num_classes]. | ||
| """ | ||
| with self.test_session() as sess, self.test_scope(): | ||
| random_seed.set_random_seed(1618) | ||
| op = random_ops.multinomial(logits, num_samples) | ||
| d = sess.run(op) | ||
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| batch_size, num_classes = logits.shape | ||
| freqs_mat = [] | ||
| for i in range(batch_size): | ||
| cnts = dict(collections.Counter(d[i, :])) | ||
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| # Requires drawn class labels be in range. | ||
| self.assertLess(max(cnts.keys()), num_classes) | ||
| self.assertGreaterEqual(min(cnts.keys()), 0) | ||
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| freqs = [(cnts[k] * 1. / num_samples if k in cnts else 0) | ||
| for k in range(num_classes)] | ||
| freqs_mat.append(freqs) | ||
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| return freqs_mat | ||
|
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| def _testRngIsNotConstant(self, rng, dtype): | ||
| # Tests that 'rng' does not always return the same value. | ||
| with self.test_session() as sess: | ||
| with self.test_scope(): | ||
| x = rng(dtype) | ||
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| # The random-number generator, if working correctly, should produce the | ||
| # same output multiple times with low probability. | ||
| y = sess.run(x) | ||
| z = sess.run(x) | ||
| w = sess.run(x) | ||
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| # We use exact equality here. If the random-number generator is producing | ||
| # deterministic output, all three outputs will be bitwise identical. | ||
| self.assertTrue((not np.array_equal(y, z)) or | ||
| (not np.array_equal(z, w)) or | ||
| (not np.array_equal(y, w))) | ||
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| def testCategoricalIsNotConstant(self): | ||
| def rng(unused_dtype): | ||
| return random_ops.multinomial([[1., 1., 1.]], 10) | ||
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| dtype = dtypes.float32 | ||
| self._testRngIsNotConstant(rng, dtype) | ||
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| def testCategoricalIsInRange(self): | ||
| for dtype in [dtypes.float32, dtypes.float64]: | ||
| with self.test_session() as sess: | ||
| with self.test_scope(): | ||
| x = random_ops.multinomial( | ||
| array_ops.ones(shape=[1, 20], dtype=dtype), 1000) | ||
| y = sess.run(x) | ||
| self.assertTrue((y >= 0).sum() == 1000) | ||
| self.assertTrue((y < 20).sum() == 1000) | ||
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| def testSamplingCorrectness(self): | ||
| np.random.seed(1618) # Make it reproducible. | ||
| num_samples = 21000 | ||
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| rand_probs = np.random.dirichlet([1., 1., 2., 3.]) | ||
| rand_probs2 = np.random.dirichlet([1., 4., 5.], size=3) # batched | ||
| for probs in [[.5, .5], [.85, .05, .1], rand_probs, rand_probs2]: | ||
| probs = np.asarray(probs) | ||
| if len(probs.shape) == 1: | ||
| probs = probs.reshape(1, probs.size) # singleton batch | ||
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| logits = np.log(probs).astype(np.float32) | ||
| freqs = self._do_sampling(logits, num_samples) | ||
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| # the test here is similar to | ||
| # python/kernel_tests/random/multinomial_op_test.py | ||
| # Note that df >= 1 in all these cases. Choosing a cutoff of 1e-3 | ||
| # corresponds to an alpha value of 2.5% for df = 1, and smaller for larger | ||
| # df. | ||
| chi2 = self._chi2(probs, freqs) | ||
| self.assertLess(chi2, 1e-3) | ||
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| if __name__ == '__main__': | ||
| googletest.main() |
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