利用tensorflow制作一个简单的聊天机器人

         现在很多卖货公司都使用聊天机器人充当客服人员，许多科技巨头也纷纷推出各自的聊天助手，如苹果Siri、Google Now、Amazon Alexa、微软小冰等等。前不久有一个视频

比较了Google Now和Siri 哪个更智能，貌似Google Now更智能。

       本帖使用TensorFlow制作一个简单的聊天机器人。这个聊天机器人使用中文对话数据集进行训练（使用什么数据集训练决定了对话类型）。使用的模型为RNN(seq2seq)，和前文的《RNN生成古诗词》《RNN生成音乐》类似。

       本次博客使用的数据集：影视对白数据集；

       下载数据集后，解压提取dgk_shooter_min.conv文件；

         1）数据预处理：#coding=utf-8
import os
import random
from io import open
conv_path = 'dgk_shooter_min.conv'
#判断数据集是否存在？
if not os.path.exists(conv_path):
print('数据集不存在')
exit()

# 数据集格式
"""
E
M 畹/华/吾/侄/
M 你/接/到/这/封/信/的/时/候/
M 不/知/道/大/伯/还/在/不/在/人/世/了/
E
M 咱/们/梅/家/从/你/爷/爷/起/
M 就/一/直/小/心/翼/翼/地/唱/戏/
M 侍/奉/宫/廷/侍/奉/百/姓/
M 从/来/不/曾/遭/此/大/祸/
M 太/后/的/万/寿/节/谁/敢/不/穿/红/
M 就/你/胆/儿/大/
M 唉/这/我/舅/母/出/殡/
M 我/不/敢/穿/红/啊/
M 唉/呦/唉/呦/爷/
M 您/打/得/好/我/该/打/
M 就/因/为/没/穿/红/让/人/赏/咱/一/纸/枷/锁/
M 爷/您/别/给/我/戴/这/纸/枷/锁/呀/
E
M 您/多/打/我/几/下/不/就/得/了/吗/
M 走/
M 这/是/哪/一/出/啊/…/ / /这/是/
M 撕/破/一/点/就/弄/死/你/
M 唉/
M 记/着/唱/戏/的/再/红/
M 还/是/让/人/瞧/不/起/
M 大/伯/不/想/让/你/挨/了/打/
M 还/得/跟/人/家/说/打/得/好/
M 大/伯/不/想/让/你/再/戴/上/那/纸/枷/锁/
M 畹/华/开/开/门/哪/
E
...
"""

# 我首先使用文本编辑器sublime把dgk_shooter_min.conv文件编码转为UTF-8，一下子省了不少麻烦
convs = [] # 对话集合
with open(conv_path, encoding="utf8") as f:
one_conv = [] # 一次完整对话
for line in f:
line = line.strip('\n').replace('/', '')#将分隔符去掉
if line == '':
continue
if line[0] == 'E':
if one_conv:
convs.append(one_conv)
one_conv = []
elif line[0] == 'M':
one_conv.append(line.split(' ')[1])
#将对话转成utf-8格式，并将其保存在dgk_shooter_min.conv文件中

"""
print(convs[:3]) # 个人感觉对白数据集有点不给力啊
[ ['畹华吾侄', '你接到这封信的时候', '不知道大伯还在不在人世了'],
['咱们梅家从你爷爷起', '就一直小心翼翼地唱戏', '侍奉宫廷侍奉百姓', '从来不曾遭此大祸', '太后的万寿节谁敢不穿红', '就你胆儿大', '唉这我舅母出殡', '我不敢穿红啊', '唉呦唉呦爷', '您打得好我该打', '就因为没穿红让人赏咱一纸枷锁', '爷您别给我戴这纸枷锁呀'],
['您多打我几下不就得了吗', '走', '这是哪一出啊 ', '撕破一点就弄死你', '唉', '记着唱戏的再红', '还是让人瞧不起', '大伯不想让你挨了打', '还得跟人家说打得好', '大伯不想让你再戴上那纸枷锁', '畹华开开门哪'], ....]
"""

# 把对话分成问与答
ask = [] # 问
response = [] # 答
for conv in convs:
if len(conv) == 1:
continue
if len(conv) % 2 != 0: # 奇数对话数, 转为偶数对话
conv = conv[:-1]
for i in range(len(conv)):
if i % 2 == 0:
ask.append(conv[i])#偶数对，填写问题
else:
response.append(conv[i])#回答

"""
print(len(ask), len(response))
print(ask[:3])
print(response[:3])
['畹华吾侄', '咱们梅家从你爷爷起', '侍奉宫廷侍奉百姓']
['你接到这封信的时候', '就一直小心翼翼地唱戏', '从来不曾遭此大祸']
"""

def convert_seq2seq_files(questions, answers, TESTSET_SIZE=8000):
# 创建文件
train_enc = open('train.enc', 'w') # 问
train_dec = open('train.dec', 'w') # 答
test_enc = open('test.enc', 'w') # 问
test_dec = open('test.dec', 'w') # 答

# 选择8000数据作为测试数据
test_index = random.sample([i for i in range(len(questions))], TESTSET_SIZE)

for i in range(len(questions)):
if i in test_index:#创建测试文件
test_enc.write(questions[i] + '\n')
test_dec.write(answers[i] + '\n')
else:#创建训练文件
train_enc.write(questions[i] + '\n')
train_dec.write(answers[i] + '\n')
if i % 1000 == 0:#表示处理了多少个i
print(len(range(len(questions))), '处理进度:', i)

train_enc.close()
train_dec.close()
test_enc.close()
test_dec.close()

convert_seq2seq_files(ask, response)
# 生成的*.enc文件保存了问题
# 生成的*.dec文件保存了回答           2）创建词汇表
   #coding=utf-8
# 前一步生成的问答文件路径
train_encode_file = 'train.enc'
train_decode_file = 'train.dec'
test_encode_file = 'test.enc'
test_decode_file = 'test.dec'

print('开始创建词汇表...')
# 特殊标记，用来填充标记对话
PAD = "__PAD__"
GO = "__GO__"
EOS = "__EOS__" # 对话结束
UNK = "__UNK__" # 标记未出现在词汇表中的字符
START_VOCABULART = [PAD, GO, EOS, UNK]
PAD_ID = 0
GO_ID = 1
EOS_ID = 2
UNK_ID = 3
# 参看tensorflow.models.rnn.translate.data_utils

vocabulary_size = 5000

# 生成词汇表文件
def gen_vocabulary_file(input_file, output_file):
vocabulary = {}
with open(input_file) as f:
counter = 0
for line in f:
counter += 1
tokens = [word for word in line.strip()]
for word in tokens:
if word in vocabulary:
vocabulary[word] += 1
else:
vocabulary[word] = 1
vocabulary_list = START_VOCABULART + sorted(vocabulary, key=vocabulary.get, reverse=True)
# 取前5000个常用汉字, 应该差不多够用了(额, 好多无用字符, 最好整理一下. 我就不整理了)
if len(vocabulary_list) > 5000:
vocabulary_list = vocabulary_list[:5000]
print(input_file + " 词汇表大小:", len(vocabulary_list))
with open(output_file, "w") as ff:
for word in vocabulary_list:
ff.write(word + "\n")

gen_vocabulary_file(train_encode_file, "train_encode_vocabulary")
gen_vocabulary_file(train_decode_file, "train_decode_vocabulary")

train_encode_vocabulary_file = 'train_encode_vocabulary'
train_decode_vocabulary_file = 'train_decode_vocabulary'

print("对话转向量...")

# 把对话字符串转为向量形式
def convert_to_vector(input_file, vocabulary_file, output_file):
tmp_vocab = []
with open(vocabulary_file, "r") as f:
tmp_vocab.extend(f.readlines())
tmp_vocab = [line.strip() for line in tmp_vocab]
vocab = dict([(x, y) for (y, x) in enumerate(tmp_vocab)])
# {'硕': 3142, 'v': 577, 'Ｉ': 4789, '\ue796': 4515, '拖': 1333, '疤': 2201 ...}
output_f = open(output_file, 'w')
with open(input_file, 'r') as f:
for line in f:
line_vec = []
for words in line.strip():
line_vec.append(vocab.get(words, UNK_ID))
output_f.write(" ".join([str(num) for num in line_vec]) + "\n")
output_f.close()

convert_to_vector(train_encode_file, train_encode_vocabulary_file, 'train_encode.vec')
convert_to_vector(train_decode_file, train_decode_vocabulary_file, 'train_decode.vec')

convert_to_vector(test_encode_file, train_encode_vocabulary_file, 'test_encode.vec')
convert_to_vector(test_decode_file, train_decode_vocabulary_file, 'test_decode.vec')生成的train_encode.vec和train_decode.vec用于训练，对应的词汇表是train_encode_vocabulary和train_decode_vocabulary。
        3)训练

#coding=utf-8
import tensorflow as tf # 0.12
from tensorflow.models.rnn.translate import seq2seq_model
import os
import numpy as np
import math
#导入文件
PAD_ID = 0
GO_ID = 1
EOS_ID = 2
UNK_ID = 3

train_encode_vec = 'train_encode.vec'
train_decode_vec = 'train_decode.vec'
test_encode_vec = 'test_encode.vec'
test_decode_vec = 'test_decode.vec'

# 词汇表大小5000
vocabulary_encode_size = 5000
vocabulary_decode_size = 5000

buckets = [(5, 10), (10, 15), (20, 25), (40, 50)]
layer_size = 256 # 每层大小
num_layers = 3 # 层数
batch_size = 64

# 读取*dencode.vec和*decode.vec数据（数据还不算太多, 一次读人到内存）
def read_data(source_path, target_path, max_size=None):
data_set = [[] for _ in buckets]#生成了[[],[],[],[]],即当值与参数不一样
with tf.gfile.GFile(source_path, mode="r") as source_file:#以读格式打开源文件（source_file）
with tf.gfile.GFile(target_path, mode="r") as target_file:#以读格式打开目标文件
source, target = source_file.readline(), target_file.readline()#只读取一行
counter = 0#计数器为0
while source and target and ( not max_size or counter < max_size):#当读入的还存在时
counter += 1
source_ids = [int(x) for x in source.split()]#source的目标序列号，默认分隔符为空格，组成了一个源序列
target_ids = [int(x) for x in target.split()]#target组成一个目标序列，为目标序列
target_ids.append(EOS_ID)#加上结束标记的序列号
for bucket_id, (source_size, target_size) in enumerate(buckets):#enumerate()遍历序列中的元素和其下标
if len(source_ids) < source_size and len(target_ids) < target_size:#判断是否超越了最大长度
data_set[bucket_id].append([source_ids, target_ids])#读取到数据集文件中区
break#一次即可，跳出当前循环
source, target = source_file.readline(), target_file.readline()#读取了下一行
return data_set

model = seq2seq_model.Seq2SeqModel(source_vocab_size=vocabulary_encode_size, target_vocab_size=vocabulary_decode_size,
buckets=buckets, size=layer_size, num_layers=num_layers, max_gradient_norm=5.0,
batch_size=batch_size, learning_rate=0.5, learning_rate_decay_factor=0.97,
forward_only=False)

config = tf.ConfigProto()
config.gpu_options.allocator_type = 'BFC' # 防止 out of memory

with tf.Session(config=config) as sess:
# 恢复前一次训练
ckpt = tf.train.get_checkpoint_state('.')
if ckpt != None:
print(ckpt.model_checkpoint_path)
model.saver.restore(sess, ckpt.model_checkpoint_path)
else:
sess.run(tf.global_variables_initializer())

train_set = read_data(train_encode_vec, train_decode_vec)
test_set = read_data(test_encode_vec, test_decode_vec)

train_bucket_sizes = [len(train_set[b]) for b in range(len(buckets))]#分别计算出训练集中的长度【1,2,3,4】
train_total_size = float(sum(train_bucket_sizes))#训练实例总数
train_buckets_scale = [sum(train_bucket_sizes[:i + 1]) / train_total_size for i in range(len(train_bucket_sizes))]#计算了之前所有的数的首战百分比

loss = 0.0#损失置位0
total_step = 0
previous_losses = []
# 一直训练，每过一段时间保存一次模型
while True:
random_number_01 = np.random.random_sample()#每一次循环结果不一样
#选出最小的大于随机采样的值的索引号
bucket_id = min([i for i in range(len(train_buckets_scale)) if train_buckets_scale[i] > random_number_01])

encoder_inputs, decoder_inputs, target_weights = model.get_batch(train_set, bucket_id)
#get_batch()函数首先获取bucket的encoder_size与decoder_size
_, step_loss, _ = model.step(sess, encoder_inputs, decoder_inputs, target_weights, bucket_id, False)#损失

loss += step_loss / 500
total_step += 1

print(total_step)
if total_step % 500 == 0:
print(model.global_step.eval(), model.learning_rate.eval(), loss)

# 如果模型没有得到提升，减小learning rate
if len(previous_losses) > 2 and loss > max(previous_losses[-3:]):#即损失比以前的大则降低学习率
sess.run(model.learning_rate_decay_op)
previous_losses.append(loss)
# 保存模型
checkpoint_path = "chatbot_seq2seq.ckpt"
model.saver.save(sess, checkpoint_path, global_step=model.global_step)
#返回路径checkpoint_file = "%s-%s" % (save_path, "{:08d}".format(global_step))
loss = 0.0#置当前损失为0
# 使用测试数据评估模型
for bucket_id in range(len(buckets)):
if len(test_set[bucket_id]) == 0:
continue
#获取当前bucket的encoder_inputs, decoder_inputs, target_weights
encoder_inputs, decoder_inputs, target_weights = model.get_batch(test_set, bucket_id)
#计算bucket_id的损失权重
_, eval_loss, _ = model.step(sess, encoder_inputs, decoder_inputs, target_weights, bucket_id, True)
eval_ppx = math.exp(eval_loss) if eval_loss < 300 else float('inf')
print(bucket_id, eval_ppx)#输出的是bucket_id与eval_ppx

   这个阶段最好要用GPU运行，不然需要很长时间。
          4）使用训练好的模型

                  #coding=utf-8
import tensorflow as tf # 0.12
from tensorflow.models.rnn.translate import seq2seq_model#在翻译模型中，引入seq2seq_model
import os
import numpy as np

PAD_ID = 0
GO_ID = 1
EOS_ID = 2
UNK_ID = 3
#词汇表路径path
train_encode_vocabulary = 'train_encode_vocabulary'
train_decode_vocabulary = 'train_decode_vocabulary'

#读取词汇表
def read_vocabulary(input_file):
tmp_vocab = []
with open(input_file, "r") as f:
tmp_vocab.extend(f.readlines())#打开的文件全部读入input_file中
tmp_vocab = [line.strip() for line in tmp_vocab]#转换成列表
vocab = dict([(x, y) for (y, x) in enumerate(tmp_vocab)])
return vocab, tmp_vocab#返回字典，列表

vocab_en, _, = read_vocabulary(train_encode_vocabulary)#得到词汇字典
_, vocab_de, = read_vocabulary(train_decode_vocabulary)#得到词汇列表

# 词汇表大小5000
vocabulary_encode_size = 5000
vocabulary_decode_size = 5000

buckets = [(5, 10), (10, 15), (20, 25), (40, 50)]
layer_size = 256 # 每层大小
num_layers = 3 # 层数
batch_size = 1

model = seq2seq_model.Seq2SeqModel(source_vocab_size=vocabulary_encode_size, target_vocab_size=vocabulary_decode_size,
buckets=buckets, size=layer_size, num_layers=num_layers, max_gradient_norm=5.0,
batch_size=batch_size, learning_rate=0.5, learning_rate_decay_factor=0.99,
forward_only=True)
#模型说明：源,目标词汇尺寸=vocabulary_encode(decode)_size;batch_size:训练期间使用的批次的大小;#forward_only:仅前向不传递误差

model.batch_size = 1#batch_size=1

with tf.Session() as sess:#打开作为一次会话
# 恢复前一次训练
ckpt = tf.train.get_checkpoint_state('.')#从检查点文件中返回一个状态(ckpt)
#如果ckpt存在，输出模型路径
if ckpt != None:
print(ckpt.model_checkpoint_path)
model.saver.restore(sess, ckpt.model_checkpoint_path)#储存模型参数
else:
print("没找到模型")
#测试该模型的能力
while True:
input_string = input('me > ')
# 退出
if input_string == 'quit':
exit()

input_string_vec = []#输入字符串向量化
for words in input_string.strip():
input_string_vec.append(vocab_en.get(words, UNK_ID))#get()函数：如果words在词表中，返回索引号；否则，返回UNK_ID
bucket_id = min([b for b in range(len(buckets)) if buckets[b][0] > len(input_string_vec)])#保留最小的大于输入的bucket的id
encoder_inputs, decoder_inputs, target_weights = model.get_batch({bucket_id: [(input_string_vec, [])]}, bucket_id)
#get_batch(A,B):两个参数，A为大小为len(buckets)的元组，返回了指定bucket_id的encoder_inputs,decoder_inputs,target_weights
_, _, output_logits = model.step(sess, encoder_inputs, decoder_inputs, target_weights, bucket_id, True)
#得到其输出
outputs = [int(np.argmax(logit, axis=1)) for logit in output_logits]#求得最大的预测范围列表
if EOS_ID in outputs:#如果EOS_ID在输出内部，则输出列表为[,,,,:End]
outputs = outputs[:outputs.index(EOS_ID)]

response = "".join([tf.compat.as_str(vocab_de[output]) for output in outputs])#转为解码词汇分别添加到回复中
print('AI > ' + response)#输出回复结果：



转载地址： http://blog.topspeedsnail.com/archives/10735