#-*- coding:utf-8 -*-
import generate_chat
import seq2seq_model
import tensorflow as tf
import numpy as np
import logging
import logging.handlers
if __name__ == '__main__':
_, _, source_vocab_size = generate_chat.get_vocabs(
generate_chat.vocab_encode_file)
_, _, target_vocab_size = generate_chat.get_vocabs(
generate_chat.vocab_decode_file)
train_set = generate_chat.read_data(generate_chat.train_encode_vec_file,
generate_chat.train_decode_vec_file)
test_set = generate_chat.read_data(generate_chat.test_encode_vec_file,
generate_chat.test_decode_vec_file)
train_bucket_sizes = [
len(train_set[i]) for i in range(len(generate_chat._buckets))
]
train_total_size = float(sum(train_bucket_sizes))
train_buckets_scale = [
sum(train_bucket_sizes[:i + 1]) / train_total_size
for i in range(len(train_bucket_sizes))
]
with tf.Session() as sess:
model = seq2seq_model.Seq2SeqModel(
source_vocab_size,
target_vocab_size,
generate_chat._buckets,
#-*- coding:utf-8 -*-
import generate_chat
import seq2seq_model
import tensorflow as tf
import numpy as np
import sys
if __name__ == '__main__':
source_id_to_word, source_word_to_id, source_vocab_size = generate_chat.get_vocabs(
generate_chat.vocab_encode_file)
target_id_to_word, target_word_to_id, target_vocab_size = generate_chat.get_vocabs(
generate_chat.vocab_decode_file)
to_id = lambda word: source_word_to_id.get(word, generate_chat.UNK_ID)
with tf.Session() as sess:
model = seq2seq_model.Seq2SeqModel(
source_vocab_size,
target_vocab_size,
generate_chat._buckets,
generate_chat.units_num,
generate_chat.num_layers,
generate_chat.max_gradient_norm,
1,
generate_chat.learning_rate,
generate_chat.learning_rate_decay_factor,
forward_only=True,
use_lstm=True)
model.saver.restore(sess, "chatbot.ckpt-317000")
while True:
sys.stdout.write("ask > ")
sys.stdout.flush()
sentence = sys.stdin.readline().strip('\n')
