def main(_):
FLAGS.start_string = FLAGS.start_string
converter = TextConverter(filename=FLAGS.converter_path)
if os.path.isdir(FLAGS.checkpoint_path):
FLAGS.checkpoint_path =\
tf.train.latest_checkpoint(FLAGS.checkpoint_path)
model = CharRNN(converter.vocab_size, sampling=True,
lstm_size=FLAGS.lstm_size, num_layers=FLAGS.num_layers,
use_embedding=FLAGS.use_embedding,
embedding_size=FLAGS.embedding_size)
model.load(FLAGS.checkpoint_path)
start = converter.text_to_arr(FLAGS.start_string)
arr = model.predict(FLAGS.max_length, start, converter.vocab_size, 10)
for c, p in arr:
prediction = converter.arr_to_text(c)
prediction = remove_return(prediction)
# 如果有中文字生成，请将 {1:^14} 改为 {1:{4}^14} 以修复对齐问题。
# {1:^14}中的 14 随着生成的字符数量而定，一般可以设为字符数+4
print("{0} -> {1:^14} {2} {3}".format(FLAGS.start_string, prediction, "probability:", p, chr(12288)))
from model import CharRNN
import preprocess as pre
import numpy as np
from model import *
if __name__ == '__main__':
classes, classDict = pre.build_class()
model = CharRNN()
model.load_model()
sent = "The mobile phase consisted of methanol, acetonitrile and dichloromethane (42:42:16) with a flow rate of 1.0 mL/min at 30°C."
words = sent.split(" ")
sample = np.array(pre.words2sample(words), dtype=int)
labels = model.predict(sample)
tags = [classes[label] for label in labels]
size = len(words)
for i in range(size):
print(words[i], ":::", tags[i])
