def test_crf_log_norm_zero_seq_length(dtype):
"""Test `crf_log_norm` when `sequence_lengths` contains one or more
zeros."""
inputs = tf.constant(np.ones([2, 10, 5], dtype=dtype))
transition_params = tf.constant(np.ones([5, 5], dtype=dtype))
sequence_lengths = tf.constant(np.zeros([2], dtype=np.int32))
expected_log_norm = np.zeros([2], dtype=dtype)
log_norm = text.crf_log_norm(inputs, sequence_lengths, transition_params)
test_utils.assert_allclose_according_to_type(log_norm, expected_log_norm)
def testCrfLogNormZeroSeqLength(self):
"""Test `crf_log_norm` when `sequence_lengths` contains one or more
zeros."""
inputs = tf.constant(np.ones([2, 10, 5], dtype=np.float32))
transition_params = tf.constant(np.ones([5, 5], dtype=np.float32))
sequence_lengths = tf.constant(np.zeros([2], dtype=np.int32))
expected_log_norm = np.zeros([2], dtype=np.float32)
log_norm = text.crf_log_norm(inputs, sequence_lengths,
transition_params)
tf_log_norm = self.evaluate(log_norm)
self.assertAllClose(tf_log_norm, expected_log_norm)
def testCrfLogNorm(self):
transition_params = np.array([[-3, 5, -2], [3, 4, 1], [1, 2, 1]],
dtype=np.float32)
# Test both the length-1 and regular cases.
sequence_lengths_list = [
np.array(3, dtype=np.int32),
np.array(1, dtype=np.int64),
]
inputs_list = [
np.array([[4, 5, -3], [3, -1, 3], [-1, 2, 1], [0, 0, 0]],
dtype=np.float32),
np.array([[3, -1, 3]], dtype=np.float32),
]
tag_indices_list = [
np.array([1, 2, 1, 0], dtype=np.int32),
np.array([2], dtype=np.int32),
]
for sequence_lengths, inputs, tag_indices in zip(
sequence_lengths_list, inputs_list, tag_indices_list):
num_words = inputs.shape[0]
num_tags = inputs.shape[1]
all_sequence_scores = []
# Compare the dynamic program with brute force computation.
for tag_indices in itertools.product(range(num_tags),
repeat=sequence_lengths):
tag_indices = list(tag_indices)
tag_indices.extend([0] * (num_words - sequence_lengths))
all_sequence_scores.append(
text.crf_sequence_score(
inputs=tf.expand_dims(inputs, 0),
tag_indices=tf.expand_dims(tag_indices, 0),
sequence_lengths=tf.expand_dims(sequence_lengths, 0),
transition_params=tf.constant(transition_params),
))
brute_force_log_norm = tf.reduce_logsumexp(all_sequence_scores)
log_norm = text.crf_log_norm(
inputs=tf.expand_dims(inputs, 0),
sequence_lengths=tf.expand_dims(sequence_lengths, 0),
transition_params=tf.constant(transition_params),
)
log_norm = tf.squeeze(log_norm, [0])
tf_brute_force_log_norm, tf_log_norm = self.evaluate(
[brute_force_log_norm, log_norm])
self.assertAllClose(tf_log_norm, tf_brute_force_log_norm)
