print("going to restore checkpoint")
#sess.run(tf.global_variables_initializer())
input_ids_p = tf.placeholder(tf.int32, [batch_size, FLAGS.max_seq_length],
name="input_ids")
input_mask_p = tf.placeholder(tf.int32, [batch_size, FLAGS.max_seq_length],
name="input_mask")
label_ids_p = tf.placeholder(tf.int32, [batch_size, FLAGS.max_seq_length],
name="label_ids")
segment_ids_p = tf.placeholder(tf.int32,
[batch_size, FLAGS.max_seq_length],
name="segment_ids")
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
(total_loss, logits, trans,
pred_ids) = create_model(bert_config, is_training, input_ids_p,
input_mask_p, segment_ids_p, label_ids_p,
num_labels, use_one_hot_embeddings)
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint(FLAGS.output_dir))
tokenizer = tokenization.FullTokenizer(vocab_file=FLAGS.vocab_file,
do_lower_case=FLAGS.do_lower_case)
def predict_online():
"""
do online prediction. each time make prediction for one instance.
you can change to a batch if you want.
:param line: a list. element is: [dummy_label,text_a,text_b]
id2label = {value: key for key, value in label2id.items()}
with codecs.open(os.path.join(model_dir, 'label_list.pkl'), 'rb') as rf:
label_list = pickle.load(rf)
num_labels = len(label_list) + 1
graph = tf.get_default_graph()
with graph.as_default():
print("going to restore checkpoint")
#sess.run(tf.global_variables_initializer())
input_ids_p = tf.placeholder(tf.int32, [batch_size, FLAGS.max_seq_length], name="input_ids")
label_ids_p = tf.placeholder(tf.int32, [batch_size, FLAGS.max_seq_length], name="label_ids")
input_mask_p = tf.placeholder(tf.int32, [batch_size, FLAGS.max_seq_length], name="input_mask")
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
(total_loss, logits, trans, pred_ids) = create_model(
bert_config, is_training, input_ids_p, input_mask_p, label_ids_p, num_labels)
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint(model_dir))
def predict_terminal():
"""
do online prediction. each time make prediction for one instance.
you can change to a batch if you want.
:param line: a list. element is: [dummy_label,text_a,text_b]
:return:
"""
def convert(line):
feature = convert_single_example1(0, line, label_list, FLAGS.max_seq_length, tokenizer, 'p')
input_ids = np.reshape([feature.input_ids],(batch_size, FLAGS.max_seq_length))