def infer(self, sess, batch, id2word, isBatch=True, beam_szie=5):
def predict_ids_to_seq(predict_ids, id2word, beam_szie):
'''
将beam_search返回的结果转化为字符串
:param predict_ids: 列表，长度为batch_size，每个元素都是decode_len*beam_size的数组
:param id2word: vocab字典
:return:
'''
for single_predict in predict_ids:
for i in range(beam_szie):
predict_list = np.ndarray.tolist(single_predict[:, :, i])
predict_seq = [id2word[idx] for idx in predict_list[0]]
return " ".join(predict_seq)
if isBatch == False:
batch = sentence2enco(inputData, self.word_to_idx, self.lang)
#infer阶段只需要运行最后的结果，不需要计算loss，所以feed_dict只需要传入encoder_input相应的数据即可
feed_dict = {
self.encoder_inputs: batch.encoder_inputs,
self.encoder_inputs_length: batch.encoder_inputs_length,
self.keep_prob_placeholder: 1.0,
self.batch_size: len(batch.encoder_inputs)
}
predict = sess.run([self.decoder_predict_decode], feed_dict=feed_dict)
return predict_ids_to_seq(predict, id2word, beam_szie)
def predict():
with tf.Session() as sess:
model = Seq2SeqModel(flags,mode='predict',beam_search=True)
ckpt = tf.train.get_checkpoint_state(flags.model_dir)
if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):
print('Reloading model parameters...')
model.saver.restore(sess,ckpt.model_checkpoint_path)
else:
raise ValueError('No such file:[{}]'.format(flags.model_dir))
sys.stdout.write(">")
sys.stdout.flush()
sentence = sys.stdin.readline()
while sentence:
batch = sentence2enco(sentence,model.word2id)
predict_ids = model.infer(sess,batch)
predict_ids_seq(predict_ids,model.id2word,model.beam_size)
print(">")
sys.stdout.flush()
sentence = sys.stdin.readline()
# tf.train.get_checkpoint_state函数通过checkpoint文件找到模型文件名
ckpt = tf.train.get_checkpoint_state(model_dir)
# 如果模型存在
if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):
print('Reloading model parameters..')
# 使用saver.restore()方法恢复变量
model.saver.restore(sess, ckpt.model_checkpoint_path)
else: # 如果模型不存在
raise ValueError('No such file:[{}]'.format(model_dir)) # 报错
# 打印一'>'，提示用户输入句子
# sys.stdout.write()的功能大概是不带回车'\n'的print()
sys.stdout.write("> ")
# sys.stdout带有缓冲区，使用sys.stdout.flush()使其立即输出
sys.stdout.flush()
# sys.stdin.readline()用于读取一行输入
sentence = sys.stdin.readline()
while sentence: # 只要还在输入，就持续运行
# 将用户输入的句子切词、转换成id并放入一个batch中，具体细节进入data_helpers.py查看
batch = sentence2enco(sentence, word_to_id)
# 通过这一句，预测下一句
predicted_ids = model.infer(batch)
# 将beam_search返回的结果转化为字符串
predict_ids_to_seq(predicted_ids, id_to_word, beam_size)
# 再次输出'>'，提示用户输入下一句子
print("> ")
# 使用sys.stdout.flush()使其立即输出
sys.stdout.flush()
# sys.stdin.readline()再次读取一行输入
sentence = sys.stdin.readline()
FLAGS.num_layers,
FLAGS.embedding_size,
FLAGS.learning_rate,
word2id,
mode='decode',
use_attention=True,
beam_search=True,
beam_size=5,
max_gradient_norm=5.0)
ckpt = tf.train.get_checkpoint_state(FLAGS.model_dir)
if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):
print('Reloading model parameters..')
model.saver.restore(sess, ckpt.model_checkpoint_path)
else:
raise ValueError('No such file:[{}]'.format(FLAGS.model_dir))
sys.stdout.write("> ")
sys.stdout.flush()
sentence = sys.stdin.readline()
while sentence:
batch = sentence2enco(sentence, word2id)
predicted_ids = model.infer(sess, batch)
# print(predicted_ids)
predict_ids_to_seq(predicted_ids, id2word, 5)
print("> ", "")
sys.stdout.flush()
sentence = sys.stdin.readline()
:return:
'''
for single_predict in predict_ids:
for i in range(beam_szie):
predict_list = np.ndarray.tolist(single_predict[:, :, i])
predict_seq = [id2word[idx] for idx in predict_list[0]]
print(" ".join(predict_seq))
with tf.Session() as sess:
model = Seq2SeqModel(FLAGS.rnn_size, FLAGS.num_layers, FLAGS.embedding_size, FLAGS.learning_rate, word2id,
mode='decode', use_attention=True, beam_search=True, beam_size=5, max_gradient_norm=5.0)
ckpt = tf.train.get_checkpoint_state(FLAGS.model_dir)
if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):
print('Reloading model parameters..')
model.saver.restore(sess, ckpt.model_checkpoint_path)
else:
raise ValueError('No such file:[{}]'.format(FLAGS.model_dir))
sys.stdout.write("> ")
sys.stdout.flush()
sentence = sys.stdin.readline()
while sentence:
batch = sentence2enco(sentence, word2id)
# 获得预测的id
predicted_ids = model.infer(sess, batch)
# print(predicted_ids)
# 将预测的id转换成汉字
predict_ids_to_seq(predicted_ids, id2word, 5)
print("> ", "")
sys.stdout.flush()
sentence = sys.stdin.readline()