def testSampledCandidates(self):
with self.cached_session():
true_classes = constant_op.constant(
[[1, 2], [0, 4], [3, 3]], dtype=dtypes.int64)
sampled_candidates, _, _ = candidate_sampling_ops.all_candidate_sampler(
true_classes, self.NUM_TRUE, self.NUM_SAMPLED, True)
result = self.evaluate(sampled_candidates)
expected_ids = [0, 1, 2, 3, 4]
self.assertAllEqual(result, expected_ids)
self.assertEqual(sampled_candidates.get_shape(), [self.NUM_SAMPLED])
def testSampledLogExpectedCount(self):
with self.cached_session():
true_classes = constant_op.constant(
[[1, 2], [0, 4], [3, 3]], dtype=dtypes.int64)
_, _, sampled_expected_count = candidate_sampling_ops.all_candidate_sampler( # pylint: disable=line-too-long
true_classes, self.NUM_TRUE, self.NUM_SAMPLED, True)
sampled_log_expected_count = math_ops.log(sampled_expected_count)
result = self.evaluate(sampled_log_expected_count)
self.assertAllEqual(result, [0.0] * self.NUM_SAMPLED)
self.assertEqual(sampled_expected_count.get_shape(), [self.NUM_SAMPLED])
self.assertEqual(sampled_log_expected_count.get_shape(), [self.NUM_SAMPLED])
def testTrueLogExpectedCount(self):
with self.cached_session():
true_classes = constant_op.constant(
[[1, 2], [0, 4], [3, 3]], dtype=dtypes.int64)
_, true_expected_count, _ = candidate_sampling_ops.all_candidate_sampler(
true_classes, self.NUM_TRUE, self.NUM_SAMPLED, True)
true_log_expected_count = math_ops.log(true_expected_count)
result = self.evaluate(true_log_expected_count)
self.assertAllEqual(result, [[0.0] * self.NUM_TRUE] * self.BATCH_SIZE)
self.assertEqual(true_expected_count.get_shape(),
[self.BATCH_SIZE, self.NUM_TRUE])
self.assertEqual(true_log_expected_count.get_shape(),
[self.BATCH_SIZE, self.NUM_TRUE])
def testAccidentalHits(self):
with self.cached_session() as sess:
true_classes = constant_op.constant(
[[1, 2], [0, 4], [3, 3]], dtype=dtypes.int64)
sampled_candidates, _, _ = candidate_sampling_ops.all_candidate_sampler(
true_classes, self.NUM_TRUE, self.NUM_SAMPLED, True)
accidental_hits = candidate_sampling_ops.compute_accidental_hits(
true_classes, sampled_candidates, self.NUM_TRUE)
indices, ids, weights = self.evaluate(accidental_hits)
self.assertEqual(1, accidental_hits[0].get_shape().ndims)
self.assertEqual(1, accidental_hits[1].get_shape().ndims)
self.assertEqual(1, accidental_hits[2].get_shape().ndims)
for index, id_, weight in zip(indices, ids, weights):
self.assertTrue(id_ in self.TRUE_LABELS[index])
self.assertLess(weight, -1.0e37)
