python使用TensorFlow基于seq2seq实现人工智能中文聊天机器人代码
代码语言:python
所属分类:人工智能
代码描述:python使用TensorFlow基于seq2seq实现人工智能中文聊天机器人代码,首先对问答的数据进行训练,然后根据生成的模型进行预测问答。
代码标签: python TensorFlow seq2seq 人工 智能 中文 聊天 机器人 代码
下面为部分代码预览,完整代码请点击下载或在bfwstudio webide中打开
# coding:utf-8
import sys
import numpy as np
import tensorflow as tf
from tensorflow.contrib.legacy_seq2seq.python.ops import seq2seq
from numpy import unicode
import jieba
import random
class WordToken(object):
def __init__(self):
# 最小起始id号, 保留的用于表示特殊标记
self.START_ID = 4
self.word2id_dict = {}
self.id2word_dict = {}
def load_file_list(self, file_list, min_freq):
"""
加载样本文件列表,全部切词后统计词频,按词频由高到低排序后顺次编号
并存到self.word2id_dict和self.id2word_dict中
"""
words_count = {}
for file in file_list:
with open(file, 'r',encoding='utf-8') as file_object:
for line in file_object.readlines():
line = line.strip()
seg_list = jieba.cut(line)
for str in seg_list:
if str in words_count:
words_count[str] = words_count[str] + 1
else:
words_count[str] = 1
sorted_list = [[v[1], v[0]] for v in words_count.items()]
sorted_list.sort(reverse=True)
for index, item in enumerate(sorted_list):
word = item[1]
if item[0] < min_freq:
break
self.word2id_dict[word] = self.START_ID + index
self.id2word_dict[self.START_ID + index] = word
return index
def word2id(self, word):
if not isinstance(word, unicode):
print ("Exception: error word not unicode")
sys.exit(1)
if word in self.word2id_dict:
return self.word2id_dict[word]
else:
return None
def id2word(self, id):
id = int(id)
if id in self.id2word_dict:
return self.id2word_dict[id]
else:
return None
# 输入序列长度
input_seq_len = 5
# 输出序列长度
output_seq_len = 5
# 空值填充0
PAD_ID = 0
# 输出序列起始标记
GO_ID = 1
# 结尾标记
EOS_ID = 2
# LSTM神经元size
size = 8
# 初始学习率
init_learning_rate = 1
# 在样本中出现频率超过这个值才会进入词表
min_freq = 1
wordToken = WordToken()
# 放在全局的位置,为了动态算出num_encoder_symbols和num_decoder_symbols
max_token_id = wordToken.load_file_list(['/data/wwwroot/default/dataset/ask/question', '/data/wwwroot/default/dataset/ask/answer'], min_freq)
num_encoder_symbols = max_token_id + 5
num_decoder_symbols = max_token_id + 5
def get_id_list_from(sentence):
sentence_id_list = []
seg_list = jieba.cut(sentence)
print(seg_list)
for str in seg_list:
id = wordToken.word2id(str)
if id:
sentence_id_list.append(wordToken.word2id(str))
return sentence_id_list
def get_train_set():
global num_encoder_symbols, num_decoder_symbols
train_set = []
with open('/data/wwwroot/default/dataset/ask/question', 'r', encoding='utf-8') as question_file:
with open('/data/wwwroot/default/dataset/ask/answer', 'r', encoding='utf-8') as answer_file:
while True:
question = question_file.readline()
answer = answer_file.readline()
if question and answer:
question = question.strip()
answer = answer.strip()
question_id_list = get_id_list_from(question)
answer_id_list = get_id_list_from(answer)
if len(question_id_list) > 0 and len(answer_id_list) > 0:
answer_id_list.append(EOS_ID)
train_set.append([question_id_list, answer_id_list])
else:
break
return train_set
def get_samples(train_set, batch_num):
raw_encoder_input = []
raw_decoder_input = []
if batch_num >= len(train_set):
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