def predict(self, seq_lengths, word_ids, char_for_ids, char_rev_ids,
word_lengths, cap_ids, pos_ids):
feed_dict = dict()
feed_dict[self.seq_lengths] = seq_lengths
feed_dict[self.dropout_keep_prob] = 1
if self.word_dim:
feed_dict[self.word_ids] = word_ids
if self.char_dim:
feed_dict[self.char_for_ids] = char_for_ids
feed_dict[self.word_lengths] = word_lengths
if self.char_bidirect:
feed_dict[self.char_rev_ids] = char_rev_ids
if self.cap_dim:
feed_dict[self.cap_ids] = cap_ids
if self.pos_dim:
feed_dict[self.pos_ids] = pos_ids
tag_scores, transitions = self.session.run(
[self.tag_scores, self.transitions], feed_dict)
batch_viterbi_sequence = []
batch_score = []
for tag_score_, seq_length_ in zip(tag_scores, seq_lengths):
# Remove padding from scores and tag sequence.
tag_score_ = tag_score_[:seq_length_]
# Compute the highest scoring sequence.
viterbi_sequence, score = crf.viterbi_decode(
tag_score_, transitions)
batch_viterbi_sequence.append(viterbi_sequence)
batch_score.append(score)
return batch_viterbi_sequence, batch_score
def run_evaluation(batches, extra_text=''):
predictions = []
batches_with_mask = []
for batch in batches:
token_batch, label_batch, shape_batch, char_batch, seq_len_batch, tok_len_batch, label_mask_batch = batch
batch_seq_len, mask_batch, seq_len_batch = mask(batch)
batches_with_mask.append(batch + (mask_batch, ))
char_embedding_feed = char_feed(token_batch, char_batch, tok_len_batch)
lstm_feed = {
model.input_x1: token_batch,
model.input_x2: shape_batch,
model.input_y: label_batch,
model.input_mask: mask_batch,
model.sequence_lengths: seq_len_batch,
model.max_seq_len: batch_seq_len,
model.batch_size: batch_size,
}
lstm_feed.update(char_embedding_feed)
if viterbi:
run_list = [model.predictions, model.transition_params]
preds, transition_params = sess.run(run_list, feed_dict=lstm_feed)
viterbi_repad = np.empty((batch_size, batch_seq_len))
for i, (unary_scores,
sequence_lens) in enumerate(zip(preds, seq_len_batch)):
viterbi_sequence, _ = crf.viterbi_decode(
unary_scores, transition_params)
viterbi_repad[i] = viterbi_sequence
predictions.append(viterbi_repad)
else:
run_list = [model.predictions, model.unflat_scores]
preds, scores = sess.run(run_list, feed_dict=lstm_feed)
inv_label_map = dp.inv_label_map()
f1_micro, precision = evaluation.segment_eval(
batches_with_mask,
predictions,
type_set,
type_int_int_map,
inv_label_map,
dp.inv_token_map(),
outside_idx=map(
lambda t: type_set[t]
if t in type_set else type_set['O'], outside_set),
pad_width=pad_width,
start_end=False,
extra_text='Segment evaluation %s:' % extra_text)
print('')
return f1_micro, precision