def testCrfSequenceScore(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.int32)
]
inputs_list = [
np.array([[4, 5, -3], [3, -1, 3], [-1, 2, 1], [0, 0, 0]],
dtype=np.float32),
np.array([[4, 5, -3]], dtype=np.float32),
]
tag_indices_list = [
np.array([1, 2, 1, 0], dtype=np.int32),
np.array([1], dtype=np.int32)
]
for sequence_lengths, inputs, tag_indices in zip(
sequence_lengths_list, inputs_list, tag_indices_list):
with self.test_session() as sess:
sequence_score = crf.crf_sequence_score(
inputs=array_ops.expand_dims(inputs, 0),
tag_indices=array_ops.expand_dims(tag_indices, 0),
sequence_lengths=array_ops.expand_dims(
sequence_lengths, 0),
transition_params=constant_op.constant(transition_params))
sequence_score = array_ops.squeeze(sequence_score, [0])
tf_sequence_score = sess.run(sequence_score)
expected_unary_score = sum(inputs[i][tag_indices[i]]
for i in range(sequence_lengths))
expected_binary_score = sum(
transition_params[tag_indices[i], tag_indices[i + 1]]
for i in range(sequence_lengths - 1))
expected_sequence_score = expected_unary_score + expected_binary_score
self.assertAllClose(tf_sequence_score, expected_sequence_score)
def testCrfSequenceScore(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.int32)
]
inputs_list = [
np.array([[4, 5, -3], [3, -1, 3], [-1, 2, 1], [0, 0, 0]],
dtype=np.float32),
np.array([[4, 5, -3]],
dtype=np.float32),
]
tag_indices_list = [
np.array([1, 2, 1, 0], dtype=np.int32),
np.array([1], dtype=np.int32)
]
for sequence_lengths, inputs, tag_indices in zip(sequence_lengths_list,
inputs_list,
tag_indices_list):
with self.test_session() as sess:
sequence_score = crf.crf_sequence_score(
inputs=array_ops.expand_dims(inputs, 0),
tag_indices=array_ops.expand_dims(tag_indices, 0),
sequence_lengths=array_ops.expand_dims(sequence_lengths, 0),
transition_params=constant_op.constant(transition_params))
sequence_score = array_ops.squeeze(sequence_score, [0])
tf_sequence_score = sess.run(sequence_score)
expected_sequence_score = self.calculateSequenceScore(
inputs, transition_params, tag_indices, sequence_lengths)
self.assertAllClose(tf_sequence_score, expected_sequence_score)
def testCrfLogNorm(self):
inputs = np.array(
[[4, 5, -3], [3, -1, 3], [-1, 2, 1], [0, 0, 0]], dtype=np.float32)
transition_params = np.array(
[[-3, 5, -2], [3, 4, 1], [1, 2, 1]], dtype=np.float32)
num_words = inputs.shape[0]
num_tags = inputs.shape[1]
sequence_lengths = np.array(3, dtype=np.int32)
with self.test_session() as sess:
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(
crf.crf_sequence_score(
inputs=array_ops.expand_dims(inputs, 0),
tag_indices=array_ops.expand_dims(tag_indices, 0),
sequence_lengths=array_ops.expand_dims(sequence_lengths, 0),
transition_params=constant_op.constant(transition_params)))
brute_force_log_norm = math_ops.reduce_logsumexp(all_sequence_scores)
log_norm = crf.crf_log_norm(
inputs=array_ops.expand_dims(inputs, 0),
sequence_lengths=array_ops.expand_dims(sequence_lengths, 0),
transition_params=constant_op.constant(transition_params))
log_norm = array_ops.squeeze(log_norm, [0])
tf_brute_force_log_norm, tf_log_norm = sess.run(
[brute_force_log_norm, log_norm])
self.assertAllClose(tf_log_norm, tf_brute_force_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.int32)
]
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]
with self.test_session() as sess:
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(
crf.crf_sequence_score(
inputs=array_ops.expand_dims(inputs, 0),
tag_indices=array_ops.expand_dims(tag_indices, 0),
sequence_lengths=array_ops.expand_dims(
sequence_lengths, 0),
transition_params=constant_op.constant(
transition_params)))
brute_force_log_norm = math_ops.reduce_logsumexp(
all_sequence_scores)
log_norm = crf.crf_log_norm(
inputs=array_ops.expand_dims(inputs, 0),
sequence_lengths=array_ops.expand_dims(
sequence_lengths, 0),
transition_params=constant_op.constant(transition_params))
log_norm = array_ops.squeeze(log_norm, [0])
tf_brute_force_log_norm, tf_log_norm = sess.run(
[brute_force_log_norm, log_norm])
self.assertAllClose(tf_log_norm, tf_brute_force_log_norm)
def testCrfDecode(self):
inputs = np.array([[4, 5, -3], [3, -1, 3], [-1, 2, 1], [0, 0, 0]],
dtype=np.float32)
transition_params = np.array([[-3, 5, -2], [3, 4, 1], [1, 2, 1]],
dtype=np.float32)
sequence_lengths = np.array(3, dtype=np.int32)
num_words = inputs.shape[0]
num_tags = inputs.shape[1]
with self.test_session() as sess:
all_sequence_scores = []
all_sequences = []
# 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_sequences.append(tag_indices)
sequence_score = crf.crf_sequence_score(
inputs=array_ops.expand_dims(inputs, 0),
tag_indices=array_ops.expand_dims(tag_indices, 0),
sequence_lengths=array_ops.expand_dims(
sequence_lengths, 0),
transition_params=constant_op.constant(transition_params))
sequence_score = array_ops.squeeze(sequence_score, [0])
all_sequence_scores.append(sequence_score)
tf_all_sequence_scores = sess.run(all_sequence_scores)
expected_max_sequence_index = np.argmax(tf_all_sequence_scores)
expected_max_sequence = all_sequences[expected_max_sequence_index]
expected_max_score = tf_all_sequence_scores[
expected_max_sequence_index]
actual_max_sequence, actual_max_score = crf.crf_decode(
array_ops.expand_dims(inputs, 0),
constant_op.constant(transition_params),
array_ops.expand_dims(sequence_lengths, 0))
actual_max_sequence = array_ops.squeeze(actual_max_sequence, [0])
actual_max_score = array_ops.squeeze(actual_max_score, [0])
tf_actual_max_sequence, tf_actual_max_score = sess.run(
[actual_max_sequence, actual_max_score])
self.assertAllClose(tf_actual_max_score, expected_max_score)
self.assertEqual(list(tf_actual_max_sequence[:sequence_lengths]),
expected_max_sequence[:sequence_lengths])
def testCrfDecode(self):
inputs = np.array(
[[4, 5, -3], [3, -1, 3], [-1, 2, 1], [0, 0, 0]], dtype=np.float32)
transition_params = np.array(
[[-3, 5, -2], [3, 4, 1], [1, 2, 1]], dtype=np.float32)
sequence_lengths = np.array(3, dtype=np.int32)
num_words = inputs.shape[0]
num_tags = inputs.shape[1]
with self.test_session() as sess:
all_sequence_scores = []
all_sequences = []
# 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_sequences.append(tag_indices)
sequence_score = crf.crf_sequence_score(
inputs=array_ops.expand_dims(inputs, 0),
tag_indices=array_ops.expand_dims(tag_indices, 0),
sequence_lengths=array_ops.expand_dims(sequence_lengths, 0),
transition_params=constant_op.constant(transition_params))
sequence_score = array_ops.squeeze(sequence_score, [0])
all_sequence_scores.append(sequence_score)
tf_all_sequence_scores = sess.run(all_sequence_scores)
expected_max_sequence_index = np.argmax(tf_all_sequence_scores)
expected_max_sequence = all_sequences[expected_max_sequence_index]
expected_max_score = tf_all_sequence_scores[expected_max_sequence_index]
actual_max_sequence, actual_max_score = crf.crf_decode(
array_ops.expand_dims(inputs, 0),
constant_op.constant(transition_params),
array_ops.expand_dims(sequence_lengths, 0))
actual_max_sequence = array_ops.squeeze(actual_max_sequence, [0])
actual_max_score = array_ops.squeeze(actual_max_score, [0])
tf_actual_max_sequence, tf_actual_max_score = sess.run(
[actual_max_sequence, actual_max_score])
self.assertAllClose(tf_actual_max_score, expected_max_score)
self.assertEqual(list(tf_actual_max_sequence[:sequence_lengths]),
expected_max_sequence[:sequence_lengths])
def testCrfSequenceScore(self):
inputs = np.array(
[[4, 5, -3], [3, -1, 3], [-1, 2, 1], [0, 0, 0]], dtype=np.float32)
tag_indices = np.array([1, 2, 1, 0], dtype=np.int32)
transition_params = np.array(
[[-3, 5, -2], [3, 4, 1], [1, 2, 1]], dtype=np.float32)
sequence_lengths = np.array(3, dtype=np.int32)
with self.test_session() as sess:
sequence_score = crf.crf_sequence_score(
inputs=array_ops.expand_dims(inputs, 0),
tag_indices=array_ops.expand_dims(tag_indices, 0),
sequence_lengths=array_ops.expand_dims(sequence_lengths, 0),
transition_params=constant_op.constant(transition_params))
sequence_score = array_ops.squeeze(sequence_score, [0])
tf_sequence_score = sess.run(sequence_score)
expected_unary_score = sum(inputs[i][tag_indices[i]]
for i in range(sequence_lengths))
expected_binary_score = sum(
transition_params[tag_indices[i], tag_indices[i + 1]]
for i in range(sequence_lengths - 1))
expected_sequence_score = expected_unary_score + expected_binary_score
self.assertAllClose(tf_sequence_score, expected_sequence_score)
