Seq2Seq实现闲聊机器人

1. 准备训练数据

单轮次的聊天数据非常不好获取，所以这里从github上使用一些开放的数据集来训练我们的闲聊模型

数据地址：https://github.com/codemayq/chaotbot_corpus_Chinese

主要的数据有两个：

1.小黄鸡的聊天语料：噪声很大

2.微博的标题和评论：质量相对较高

2. 数据的处理和保存

由于数据中存到大量的噪声，可以对其进行基础的处理，然后分别把input和target使用两个文件保存，即input中的第N行尾问，target的第N行为答

后续可能会把单个字作为特征（存放在input_word.txt），也可能会把词语作为特征(input.txt)

2.1 小黄鸡的语料的处理

def format_xiaohuangji_corpus(word=False):"""处理小黄鸡的语料"""if word:corpus_path = "./chatbot/corpus/xiaohuangji50w_nofenci.conv"input_path = "./chatbot/corpus/input_word.txt"output_path = "./chatbot/corpus/output_word.txt"else:corpus_path = "./chatbot/corpus/xiaohuangji50w_nofenci.conv"input_path = "./chatbot/corpus/input.txt"output_path = "./chatbot/corpus/output.txt"f_input = open(input_path, "a")f_output = open(output_path, "a")pair = []for line in tqdm(open(corpus_path), ascii=True):if line.strip() == "E":if not pair:continueelse:assert len(pair) == 2, "长度必须是2"if len(pair[0].strip()) >= 1 and len(pair[1].strip()) >= 1:f_input.write(pair[0] + "\n")f_output.write(pair[1] + "\n")pair = []elif line.startswith("M"):line = line[1:]if word:pair.append(" ".join(list(line.strip())))else:pair.append(" ".join(jieba_cut(line.strip())))

2.2 微博语料的处理

def format_weibo(word=False):"""微博数据存在一些噪声，未处理:return:"""if word:origin_input = "./chatbot/corpus/stc_weibo_train_post"input_path = "./chatbot/corpus/input_word.txt"origin_output = "./chatbot/corpus/stc_weibo_train_response"output_path = "./chatbot/corpus/output_word.txt"else:origin_input = "./chatbot/corpus/stc_weibo_train_post"input_path = "./chatbot/corpus/input.txt"origin_output = "./chatbot/corpus/stc_weibo_train_response"output_path = "./chatbot/corpus/output.txt"f_input = open(input_path, "a")f_output = open(output_path, "a")with open(origin_input) as in_o, open(origin_output) as out_o:for _in, _out in tqdm(zip(in_o, out_o), ascii=True):_in = _in.strip()_out = _out.strip()if _in.endswith("）") or _in.endswith("」") or _in.endswith(")"):_in = re.sub("（.*）|「.*?」|\(.*?\)", " ", _in)_in = re.sub("我在.*?alink|alink|（.*?\d+x\d+.*?）|#|】|【|-+|_+|via.*?：*.*", " ", _in)_in = re.sub("\s+", " ", _in)if len(_in) < 1 or len(_out) < 1:continueif word:_in = re.sub("\s+", "", _in)  # 转化为一整行，不含空格_out = re.sub("\s+", "", _out)if len(_in) >= 1 and len(_out) >= 1:f_input.write(" ".join(list(_in)) + "\n")f_output.write(" ".join(list(_out)) + "\n")else:if len(_in) >= 1 and len(_out) >= 1:f_input.write(_in.strip() + "\n")f_output.write(_out.strip() + "\n")f_input.close()f_output.close()

2.3 处理后的结果

3. 构造文本序列化和反序列化方法

和之前的操作相同，需要把文本能转化为数字，同时还需实现方法把数字转化为文本

示例代码：

import config
import pickleclass Word2Sequence():UNK_TAG = "UNK"PAD_TAG = "PAD"SOS_TAG = "SOS"EOS_TAG = "EOS"UNK = 0PAD = 1SOS = 2EOS = 3def __init__(self):self.dict = {self.UNK_TAG: self.UNK,self.PAD_TAG: self.PAD,self.SOS_TAG: self.SOS,self.EOS_TAG: self.EOS}self.count = {}self.fited = Falsedef to_index(self, word):"""word -> index"""assert self.fited == True, "必须先进行fit操作"return self.dict.get(word, self.UNK)def to_word(self, index):"""index -> word"""assert self.fited, "必须先进行fit操作"if index in self.inversed_dict:return self.inversed_dict[index]return self.UNK_TAGdef __len__(self):return len(self.dict)def fit(self, sentence):""":param sentence:[word1,word2,word3]:param min_count: 最小出现的次数:param max_count: 最大出现的次数:param max_feature: 总词语的最大数量:return:"""for a in sentence:if a not in self.count:self.count[a] = 0self.count[a] += 1self.fited = Truedef build_vocab(self, min_count=1, max_count=None, max_feature=None):# 比最小的数量大和比最大的数量小的需要if min_count is not None:self.count = {k: v for k, v in self.count.items() if v >= min_count}if max_count is not None:self.count = {k: v for k, v in self.count.items() if v <= max_count}# 限制最大的数量if isinstance(max_feature, int):count = sorted(list(self.count.items()), key=lambda x: x[1])if max_feature is not None and len(count) > max_feature:count = count[-int(max_feature):]for w, _ in count:self.dict[w] = len(self.dict)else:for w in sorted(self.count.keys()):self.dict[w] = len(self.dict)# 准备一个index->word的字典self.inversed_dict = dict(zip(self.dict.values(), self.dict.keys()))def transform(self, sentence, max_len=None, add_eos=False):"""实现吧句子转化为数组（向量）:param sentence::param max_len::return:"""assert self.fited, "必须先进行fit操作"r = [self.to_index(i) for i in sentence]if max_len is not None:if max_len > len(sentence):if add_eos:r += [self.EOS] + [self.PAD for _ in range(max_len - len(sentence) - 1)]else:r += [self.PAD for _ in range(max_len - len(sentence))]else:if add_eos:r = r[:max_len - 1]r += [self.EOS]else:r = r[:max_len]else:if add_eos:r += [self.EOS]# print(len(r),r)return rdef inverse_transform(self, indices):"""实现从数组 转化为 向量:param indices: [1,2,3....]:return:[word1,word2.....]"""sentence = []for i in indices:word = self.to_word(i)sentence.append(word)return sentence# 之后导入该word_sequence使用
word_sequence = pickle.load(open("./pkl/ws.pkl", "rb")) if not config.use_word else pickle.load(open("./pkl/ws_word.pkl", "rb"))if __name__ == '__main__':from word_sequence import Word2Sequencefrom tqdm import tqdmimport pickleword_sequence = Word2Sequence()# 词语级别input_path = "../corpus/input.txt"target_path = "../corpus/output.txt"for line in tqdm(open(input_path).readlines()):word_sequence.fit(line.strip().split())for line in tqdm(open(target_path).readlines()):word_sequence.fit(line.strip().split())# 使用max_feature=5000个数据word_sequence.build_vocab(min_count=5, max_count=None, max_feature=5000)print(len(word_sequence))pickle.dump(word_sequence, open("./pkl/ws.pkl", "wb"))

word_sequence.py:

class WordSequence(object):PAD_TAG = 'PAD'  # 填充标记UNK_TAG = 'UNK'  # 未知词标记SOS_TAG = 'SOS'  # start of sequenceEOS_TAG = 'EOS'  # end of sequencePAD = 0UNK = 1SOS = 2EOS = 3def __init__(self):self.dict = {self.PAD_TAG: self.PAD,self.UNK_TAG: self.UNK,self.SOS_TAG: self.SOS,self.EOS_TAG: self.EOS}self.count = {}  # 保存词频词典self.fited = Falsedef to_index(self, word):"""word --> index:param word::return:"""assert self.fited == True, "必须先进行fit操作"return self.dict.get(word, self.UNK)def to_word(self, index):"""index -- > word:param index::return:"""assert self.fited, '必须先进行fit操作'if index in self.inverse_dict:return self.inverse_dict[index]return self.UNK_TAGdef fit(self, sentence):"""传入句子，统计词频:param sentence::return:"""for word in sentence:# 对word出现的频率进行统计，当word不在sentence时，返回值是0，当word在sentence中时，返回+1，以此进行累计计数# self.count[word] = self.dict.get(word, 0) + 1if word not in self.count:self.count[word] = 0self.count[word] += 1self.fited = Truedef build_vocab(self, min_count=2, max_count=None, max_features=None):"""构造词典:param min_count:最小词频:param max_count: 最大词频:param max_features: 词典中词的数量:return:"""# self.count.pop(key),和del self.count[key] 无法在遍历self.count的同时进行删除key.因此浅拷贝temp后对temp遍历并删除self.counttemp = self.count.copy()for key in temp:cur_count = self.count.get(key, 0)  # 当前词频if min_count is not None:if cur_count < min_count:del self.count[key]if max_count is not None:if cur_count > max_count:del self.count[key]if max_features is not None:self.count = dict(sorted(list(self.count.items()), key=lambda x: x[1], reversed=True)[:max_features])for key in self.count:self.dict[key] = len(self.dict)#  准备一个index-->word的字典self.inverse_dict = dict(zip(self.dict.values(), self.dict.keys()))def transforms(self, sentence, max_len=10, add_eos=False):"""把sentence转化为序列:param sentence: 传入的句子:param max_len: 句子的最大长度:param add_eos: 是否添加结束符add_eos : True时，输出句子长度为max_len + 1add_eos : False时，输出句子长度为max_len:return:"""assert self.fited, '必须先进行fit操作！'if len(sentence) > max_len:sentence = sentence[:max_len]#  提前计算句子长度，实现ass_eos后，句子长度统一sentence_len = len(sentence)#  sentence[1,3,4,5,UNK,EOS,PAD,....]if add_eos:sentence += [self.EOS_TAG]if sentence_len < max_len:#  句子长度不够，用PAD来填充sentence += (max_len - sentence_len) * [self.PAD_TAG]#  对于新出现的词采用特殊标记result = [self.dict.get(i, self.UNK) for i in sentence]return resultdef invert_transform(self, indices):"""序列转化为sentence:param indices::return:"""# return [self.inverse_dict.get(i, self.UNK_TAG) for i in indices]result = []for i in indices:if self.inverse_dict[i] == self.EOS_TAG:breakresult.append(self.inverse_dict.get(i, self.UNK_TAG))return resultdef __len__(self):return len(self.dict)if __name__ == '__main__':num_sequence = WordSequence()print(num_sequence.dict)str1 = ['中国', '您好', '我爱你', '中国', '我爱你', '北京']num_sequence.fit(str1)num_sequence.build_vocab()print(num_sequence.transforms(str1))print(num_sequence.dict)print(num_sequence.inverse_dict)print(num_sequence.invert_transform([5, 4]))  # 这儿要传列表

运行结果：

4. 构建Dataset和DataLoader

创建dataset.py 文件，准备数据集

import config
import torch
from torch.utils.data import Dataset, DataLoader
from word_sequence import WordSequenceclass ChatDataset(Dataset):def __init__(self):self.input_path = config.chatbot_input_pathself.target_path = config.chatbot_target_pathself.input_lines = open(self.input_path, encoding='utf-8').readlines()self.target_lines = open(self.target_path, encoding='utf-8').readlines()assert len(self.input_lines) == len(self.target_lines), 'input和target长度不一致'def __getitem__(self, item):input = self.input_lines[item].strip().split()target = self.target_lines[item].strip().split()if len(input) == 0 or len(target) == 0:input = self.input_lines[item + 1].strip().split()target = self.target_lines[item + 1].strip().split()# 此处句子的长度如果大于max_len，那么应该返回max_leninput_length = min(len(input), config.max_len)target_length = min(len(target), config.max_len)return input, target, input_length, target_lengthdef __len__(self):return len(self.input_lines)def collate_fn(batch):#  1.排序batch = sorted(batch, key=lambda x: x[2], reversed=True)input, target, input_length, target_length = zip(*batch)#  2.进行padding的操作input = torch.LongTensor([WordSequence.transform(i, max_len=config.max_len) for i in input])target = torch.LongTensor([WordSequence.transforms(i, max_len=config.max_len, add_eos=True) for i in target])input_length = torch.LongTensor(input_length)target_length = torch.LongTensor(target_length)return input, target, input_length, target_lengthdata_loader = DataLoader(dataset=ChatDataset(), batch_size=config.batch_size, shuffle=True, collate_fn=collate_fn,drop_last=True)if __name__ == '__main__':print(len(data_loader))for idx, (input, target, input_length, target_length) in enumerate(data_loader):print(idx)print(input)print(target)print(input_length)print(target_length)

5. 完成`encoder`编码器逻辑

encode.py:

import torch.nn as nn
import config
from torch.nn.utils.rnn import pad_packed_sequence, pack_padded_sequenceclass Encoder(nn.Module):def __init__(self):super(Encoder, self).__init__()#  torch.nn.Embedding(num_embeddings词典大小即不重复词数,embedding_dim单个词用多长向量表示)self.embedding = nn.Embedding(num_embeddings=len(config.word_sequence.dict),embedding_dim=config.embedding_dim,padding_idx=config.word_sequence.PAD)self.gru = nn.GRU(input_size=config.embedding_dim,num_layers=config.num_layer,hidden_size=config.hidden_size,bidirectional=False,batch_first=True)def forward(self, input, input_length):""":param input: [batch_size, max_len]:return:"""embedded = self.embedding(input)  # embedded [batch_size, max_len, embedding_dim]# 加速循环过程embedded = pack_padded_sequence(embedded, input_length, batch_first=True)  # 打包out, hidden = self.gru(embedded)out, out_length = pad_packed_sequence(out, batch_first=True, padding_value=config.num_sequence.PAD)  # 解包# hidden即h_n [num_layer*[1/2],batchsize, hidden_size]# out : [batch_size, seq_len/max_len, hidden_size]return out, hidden, out_lengthif __name__ == '__main__':from dataset import data_loaderencoder = Encoder()print(encoder)for input, target, input_length, target_length in data_loader:out, hidden, out_length = encoder(input, input_length)print(input.size())print(out.size())print(hidden.size())print(out_length)break

6. 完成`decoder`解码器的逻辑

decode.py:

import torch
import torch.nn as nn
import config
import torch.nn.functional as F
from word_sequence import WordSequenceclass Decode(nn.Module):def __init__(self):super().__init__()self.max_seq_len = config.max_lenself.vocab_size = len(WordSequence)self.embedding_dim = config.embedding_dimself.dropout = config.dropoutself.embedding = nn.Embedding(num_embeddings=self.vocab_size, embedding_dim=self.embedding_dim,padding_idx=WordSequence.PAD)self.gru = nn.GRU(input_size=self.embedding_dim, hidden_size=config.hidden_size, num_layers=1, batch_first=True,dropout=self.dropout)self.log_softmax = nn.LogSoftmax()self.fc = nn.Linear(config.hidden_size, self.vocab_size)def forward(self, encoder_hidden, target, target_length):# encoder_hidden [batch_size,hidden_size]# target [batch_size,seq-len]decoder_input = torch.LongTensor([[WordSequence.SOS]] * config.batch_size).to(config.device)decoder_outputs = torch.zeros(config.batch_size, config.max_len, self.vocab_size).to(config.device)  # [batch_size,seq_len,14]decoder_hidden = encoder_hidden  # [batch_size,hidden_size]for t in range(config.max_len):decoder_output_t, decoder_hidden = self.forward_step(decoder_input, decoder_hidden)decoder_outputs[:, t, :] = decoder_output_tvalue, index = torch.topk(decoder_output_t, 1)  # index [batch_size,1]decoder_input = indexreturn decoder_outputs, decoder_hiddendef forward_step(self, decoder_input, decoder_hidden):""":param decoder_input:[batch_size,1]:param decoder_hidden:[1,batch_size,hidden_size]:return:[batch_size,vocab_size],decoder_hidden:[1,batch_size,didden_size]"""embeded = self.embedding(decoder_input)  # embeded: [batch_size,1 , embedding_dim]out, decoder_hidden = self.gru(embeded, decoder_hidden)  # out [1, batch_size, hidden_size]out = out.squeeze(0)out = F.log_softmax(self.fc(out), dim=1)  # [batch_Size, vocab_size]out = out.squeeze(0)# print("out size:",out.size(),decoder_hidden.size())return out, decoder_hidden

关于 decoder_outputs[:,t,:] = decoder_output_t的演示

decoder_outputs 形状 [batch_size, seq_len, vocab_size]
decoder_output_t 形状[batch_size, vocab_size]

示例代码：

import torcha = torch.zeros((2, 3, 5))
print(a.size())
print(a)b = torch.randn((2, 5))
print(b.size())
print(b)a[:, 0, :] = b
print(a.size())
print(a)

运行结果：

关于torch.topk, torch.max(),torch.argmax()

value, index = torch.topk(decoder_output_t , k = 1)
decoder_output_t [batch_size, vocab_size]

示例代码：

import torcha = torch.randn((3, 5))
print(a.size())
print(a)values, index = torch.topk(a, k=1)
print(values)
print(index)
print(index.size())values, index = torch.max(a, dim=-1)
print(values)
print(index)
print(index.size())index = torch.argmax(a, dim=-1)
print(index)
print(index.size())index = a.argmax(dim=-1)
print(index)
print(index.size())

运行结果：

若使用teacher forcing ,将采用下次真实值作为下个time step的输入

# 注意unsqueeze 相当于浅拷贝,不会对原张量进行修改decoder_input = target[:,t].unsqueeze(-1)target 形状 [batch_size, seq_len]decoder_input 要求形状[batch_size, 1]

示例代码：

import torcha = torch.randn((3, 5))
print(a.size())
print(a)b = a[:, 3]
print(b.size())
print(b)
c = b.unsqueeze(-1)
print(c.size())
print(c)

运行结果：

7.完成seq2seq的模型

seq2seq.py:

import torch
import torch.nn as nnclass Seq2Seq(nn.Module):def __init__(self, encoder, decoder):super(Seq2Seq, self).__init__()self.encoder = encoderself.decoder = decoderdef forward(self, input, target, input_length, target_length):encoder_outputs, encoder_hidden = self.encoder(input, input_length)decoder_outputs, decoder_hidden = self.decoder(encoder_hidden, target, target_length)return decoder_outputs, decoder_hiddendef evaluation(self, inputs, input_length):encoder_outputs, encoder_hidden = self.encoder(inputs, input_length)decoded_sentence = self.decoder.evaluation(encoder_hidden)return decoded_sentence

8. 完成训练逻辑

为了加速训练，可以考虑在gpu上运行，那么在我们自顶一个所以的tensor和model都需要转化为CUDA支持的类型。

当前的数据量为500多万条，在GTX1070（8G显存）上训练，大概需要90分一个epoch，耐心的等待吧

train.py:

import torch
import config
from torch import optim
import torch.nn as nn
from encode import Encoder
from decode import Decoder
from seq2seq import Seq2Seq
from dataset import data_loader as train_dataloader
from word_sequence import WordSequenceencoder = Encoder()
decoder = Decoder()
model = Seq2Seq(encoder, decoder)# device在config文件中实现
model.to(config.device)print(model)model.load_state_dict(torch.load("model/seq2seq_model.pkl"))
optimizer = optim.Adam(model.parameters())
optimizer.load_state_dict(torch.load("model/seq2seq_optimizer.pkl"))
criterion = nn.NLLLoss(ignore_index=WordSequence.PAD, reduction="mean")def get_loss(decoder_outputs, target):target = target.view(-1)  # [batch_size*max_len]decoder_outputs = decoder_outputs.view(config.batch_size * config.max_len, -1)return criterion(decoder_outputs, target)def train(epoch):for idx, (input, target, input_length, target_len) in enumerate(train_dataloader):input = input.to(config.device)target = target.to(config.device)input_length = input_length.to(config.device)target_len = target_len.to(config.device)optimizer.zero_grad()##[seq_len,batch_size,vocab_size] [batch_size,seq_len]decoder_outputs, decoder_hidden = model(input, target, input_length, target_len)loss = get_loss(decoder_outputs, target)loss.backward()optimizer.step()print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(epoch, idx * len(input), len(train_dataloader.dataset),100. * idx / len(train_dataloader), loss.item()))torch.save(model.state_dict(), "model/seq2seq_model.pkl")torch.save(optimizer.state_dict(), 'model/seq2seq_optimizer.pkl')if __name__ == '__main__':for i in range(10):train(i)

训练10个epoch之后的效果如下,可以看出损失依然很高：

Train Epoch: 9 [2444544/4889919 (50%)]   Loss: 4.923604
Train Epoch: 9 [2444800/4889919 (50%)]  Loss: 4.364594
Train Epoch: 9 [2445056/4889919 (50%)]  Loss: 4.613254
Train Epoch: 9 [2445312/4889919 (50%)]  Loss: 4.143538
Train Epoch: 9 [2445568/4889919 (50%)]  Loss: 4.412729
Train Epoch: 9 [2445824/4889919 (50%)]  Loss: 4.516526
Train Epoch: 9 [2446080/4889919 (50%)]  Loss: 4.124945
Train Epoch: 9 [2446336/4889919 (50%)]  Loss: 4.777015
Train Epoch: 9 [2446592/4889919 (50%)]  Loss: 4.358538
Train Epoch: 9 [2446848/4889919 (50%)]  Loss: 4.513412
Train Epoch: 9 [2447104/4889919 (50%)]  Loss: 4.202757
Train Epoch: 9 [2447360/4889919 (50%)]  Loss: 4.589584

9.评估逻辑

decoder 中添加评估方法

def evaluate(self, encoder_hidden):"""评估, 和fowward逻辑类似:param encoder_hidden: encoder最后time step的隐藏状态 [1, batch_size, hidden_size]:return:"""batch_size = encoder_hidden.size(1)# 初始化一个[batch_size, 1]的SOS张量,作为第一个time step的输出decoder_input = torch.LongTensor([[config.target_ws.SOS]] * batch_size).to(config.device)# encoder_hidden 作为decoder第一个时间步的hidden [1, batch_size, hidden_size]decoder_hidden = encoder_hidden# 初始化[batch_size, seq_len, vocab_size]的outputs 拼接每个time step结果decoder_outputs = torch.zeros((batch_size, config.chatbot_target_max_len, self.vocab_size)).to(config.device)# 初始化一个空列表,存储每次的预测序列predict_result = []# 对每个时间步进行更新for t in range(config.chatbot_target_max_len):decoder_output_t, decoder_hidden = self.forward_step(decoder_input, decoder_hidden)# 拼接每个time step,decoder_output_t [batch_size, vocab_size]decoder_outputs[:, t, :] = decoder_output_t# 由于是评估,需要每次都获取预测值index = torch.argmax(decoder_output_t, dim = -1)# 更新下一时间步的输入decoder_input = index.unsqueeze(1)# 存储每个时间步的预测序列predict_result.append(index.cpu().detach().numpy()) # [[batch], [batch]...] ->[seq_len, vocab_size]# 结果转换为ndarry,每行是一个预测结果即单个字对应的索引, 所有行为seq_len长度predict_result = np.array(predict_result).transpose()  # (batch_size, seq_len)的arrayreturn decoder_outputs, predict_result

eval.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from dataset import get_dataloader
import config
import numpy as np
from Seq2Seq import Seq2SeqModel
import os
from tqdm import tqdmmodel = Seq2SeqModel().to(config.device)
if os.path.exists('./model/chatbot_model.pkl'):model.load_state_dict(torch.load('./model/chatbot_model.pkl'))def eval():model.eval()loss_list = []test_data_loader = get_dataloader(train = False)with torch.no_grad():bar = tqdm(test_data_loader, desc = 'testing', total = len(test_data_loader))for idx, (input, target, input_length, target_length) in enumerate(bar):input = input.to(config.device)target = target.to(config.device)input_length = input_length.to(config.device)target_length = target_length.to(config.device)# 获取模型的预测结果decoder_outputs, predict_result = model.evaluation(input, input_length)# 计算损失loss = F.nll_loss(decoder_outputs.view(-1, len(config.target_ws)), target.view(-1), ignore_index = config.target_ws.PAD)loss_list.append(loss.item())bar.set_description('idx{}:/{}, loss:{}'.format(idx, len(test_data_loader), np.mean(loss_list)))if __name__ == '__main__':eval()

interface.py:

from cut_sentence import cut
import torch
import config
from Seq2Seq import Seq2SeqModel
import os# 模拟聊天场景，对用户输入进来的话进行回答
def interface():# 加载训练集好的模型model = Seq2SeqModel().to(config.device)assert os.path.exists('./model/chatbot_model.pkl') , '请先对模型进行训练!'model.load_state_dict(torch.load('./model/chatbot_model.pkl'))model.eval()while True:# 输入进来的原始字符串,进行分词处理input_string = input('me>>:')if input_string == 'q':print('下次再聊')breakinput_cuted = cut(input_string, by_word = True)# 进行序列转换和tensor封装input_tensor = torch.LongTensor([config.input_ws.transfrom(input_cuted, max_len = config.chatbot_input_max_len)]).to(config.device)input_length_tensor = torch.LongTensor([len(input_cuted)]).to(config.device)# 获取预测结果outputs, predict = model.evaluation(input_tensor, input_length_tensor)# 进行序列转换文本result = config.target_ws.inverse_transform(predict[0])print('chatbot>>:', result)if __name__ == '__main__':interface()

config.py:

import torch
from word_sequence import WordSequencechatbot_input_path = './corpus/input.txt'
chatbot_target_path = './corpus/target.txt'word_sequence = WordSequence()
max_len = 9
batch_size = 128
embedding_dim = 100
num_layer = 1
hidden_size = 64
dropout = 0.1
model_save_path = './model.pkl'
optimizer_save_path = './optimizer.pkl'
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

cut.py:

"""
分词
"""
import jieba
import config1
import string
import jieba.posseg as psg  # 返回词性
from lib.stopwords import stopwords# 加载词典
jieba.load_userdict(config1.user_dict_path)
# 准备英文字符
letters = string.ascii_lowercase + '+'def cut_sentence_by_word(sentence):"""实现中英文分词"""temp = ''result = []for word in sentence:if word.lower() in letters:# 如果是英文字符,则进行拼接空字符串temp += wordelse:# 遇到汉字后,把英文先添加到结果中if temp != '':result.append(temp.lower())temp = ''result.append(word.strip())if temp != '':# 若英文出现在最后result.append(temp.lower())return resultdef cut(sentence, by_word=False, use_stopwords=True, with_sg=False):""":param sentence: 句子:param by_word: T根据单个字分词或者F句子:param use_stopwords: 是否使用停用词，默认False:param with_sg: 是否返回词性:return:"""if by_word:result = cut_sentence_by_word(sentence)else:result = psg.lcut(sentence)# psg 源码返回i.word,i.flag 即词,定义的词性result = [(i.word, i.flag) for i in result]# 是否返回词性if not with_sg:result = [i[0] for i in result]# 是否使用停用词if use_stopwords:result = [i for i in result if i not in stopwords]return result