def main(_):
start_time = time.time()
model_path = os.path.join('model', FLAGS.name)
if os.path.exists(model_path) is False:
os.makedirs(model_path)
with open(FLAGS.input_file, 'r') as f:
text = f.read()
converter = TextConverter(text, FLAGS.max_vocab)
converter.save_to_file(os.path.join(model_path, 'converter.pkl'))
arr = converter.text_to_arr(text)
g = batch_generator(arr, FLAGS.num_seqs, FLAGS.num_steps)
print(converter.vocab_size)
model = CharRNN(converter.vocab_size,
num_seqs=FLAGS.num_seqs,
num_steps=FLAGS.num_steps,
lstm_size=FLAGS.lstm_size,
num_layers=FLAGS.num_layers,
learning_rate=FLAGS.learning_rate,
train_keep_prob=FLAGS.train_keep_prob,
use_embedding=FLAGS.use_embedding,
embedding_size=FLAGS.embedding_size)
model.train(
g,
FLAGS.max_steps,
model_path,
FLAGS.save_every_n,
FLAGS.log_every_n,
)
print("Timing cost is --- %s ---second(s)" % (time.time() - start_time))
def main(_):
converter = TextConverter(filename=FLAGS.converter_path)
if os.path.isdir(FLAGS.checkpoint_path):
FLAGS.checkpoint_path = \
tf.train.latest_checkpoint(FLAGS.checkpoint_path)
model = BilstmNer(converter.vocab_size,
converter.num_classes,
lstm_size=FLAGS.lstm_size,
embedding_size=FLAGS.embedding_size)
print("[*] Success to read {}".format(FLAGS.checkpoint_path))
model.load(FLAGS.checkpoint_path)
demo_sent = "京剧研究院就美日联合事件讨论"
tag = model.demo([(converter.text_to_arr(demo_sent), [0] * len(demo_sent))
])
print(tag)