def testNegativeLogPerplexityMasked(self):
predictions = np.random.randint(4, size=(12, 12, 12, 1))
targets = np.random.randint(4, size=(12, 12, 12, 1))
features = {'targets_mask': tf.to_float(tf.ones([12, 12]))}
with self.test_session() as session:
scores, _ = metrics.padded_neg_log_perplexity_with_masking(
tf.one_hot(predictions, depth=4, dtype=tf.float32),
tf.constant(targets, dtype=tf.int32), features)
a = tf.reduce_mean(scores)
session.run(tf.global_variables_initializer())
actual = session.run(a)
self.assertEqual(actual.shape, ())
def testNegativeLogPerplexityMaskedAssert(self):
predictions = np.random.randint(4, size=(12, 12, 12, 1))
targets = np.random.randint(4, size=(12, 12, 12, 1))
features = {}
with self.assertRaisesRegexp(
ValueError,
'masked_neg_log_perplexity requires targets_mask feature'):
with self.test_session() as session:
scores, _ = metrics.padded_neg_log_perplexity_with_masking(
tf.one_hot(predictions, depth=4, dtype=tf.float32),
tf.constant(targets, dtype=tf.int32), features)
a = tf.reduce_mean(scores)
session.run(tf.global_variables_initializer())
_ = session.run(a)
def testNegativeLogPerplexityMasked(self):
predictions = np.random.randint(4, size=(12, 12, 12, 1))
targets = np.random.randint(4, size=(12, 12, 12, 1))
features = {
'targets_mask': tf.to_float(tf.not_equal(targets, 0))
}
with self.test_session() as session:
scores, _ = metrics.padded_neg_log_perplexity_with_masking(
tf.one_hot(predictions, depth=4, dtype=tf.float32),
tf.constant(targets, dtype=tf.int32),
features)
a = tf.reduce_mean(scores)
session.run(tf.global_variables_initializer())
actual = session.run(a)
self.assertEqual(actual.shape, ())
def testNegativeLogPerplexityMaskedAssert(self):
predictions = np.random.randint(4, size=(12, 12, 12, 1))
targets = np.random.randint(4, size=(12, 12, 12, 1))
features = {}
with self.assertRaisesRegexp(
ValueError,
'masked_neg_log_perplexity requires targets_mask feature'):
with self.test_session() as session:
scores, _ = metrics.padded_neg_log_perplexity_with_masking(
tf.one_hot(predictions, depth=4, dtype=tf.float32),
tf.constant(targets, dtype=tf.int32),
features)
a = tf.reduce_mean(scores)
session.run(tf.global_variables_initializer())
_ = session.run(a)
