Pytorch 实战RNN

一、简单实例

# coding:utf8
import torch as t
from torch import nn
from torch.autograd import Variable

#输入词用10维词向量表示
#隐藏层用20维向量表示
#两层LSTM
rnn =nn.LSTM(10,20,2)

# 输入每句话有5个词
# 每个词由10维的词向量表示
#总共有3句话（batch size）
input =Variable(t.randn(5,3,10))

# 隐藏元的初始值（num_layers, batch_size,hidden_size）
h0 = Variable(t.zeros(2,3,20))
c0 = Variable(t.zeros(2,3,20))

# output 是最后一层所有所有隐藏元的值
# hn 和cn 是所有层（这里是两层）的最后一个隐藏元的值
output,(hn,cn) =rnn(input,(h0,c0))

print('output--->',output.size())
print('hn--->',hn.size())
print('cn--->',cn.size())

注意： output的形状与LSTM的层数无关，只和序列长度有关，而hn和cn则只和层数有关，和序列长度无关。

二、 Pytorcn 做诗

2.1 数据预处理

原始数据：

由于原始数据为.json文件，前包含很多的脏数据在其中，需要对其进行预处理
原始数据来源：https://github.com/chinese-poetry/chinese-poetry

#coding:utf-8
import sys
import os
import json
import re

def parseRawData(author = None, constrain = None,filePath=None):
 
    def sentenceParse(para):
        # para = "-181-村橋路不端，數里就迴湍。積壤連涇脉，高林上笋竿。早嘗甘蔗淡，生摘琵琶酸。（「琵琶」，嚴壽澄校《張祜詩集》云：疑「枇杷」之誤。）好是去塵俗，煙花長一欄。"
        #subn统计sub替换次数
        #清洗数据，去掉诗词中（）、《》、{}中的内容
        result, number = re.subn("（.*）", "", para)
        result, number = re.subn("（.*）", "", para)
        result, number = re.subn("{.*}", "", result)
        result, number = re.subn("《.*》", "", result)
        result, number = re.subn("《.*》", "", result)
        result, number = re.subn("[\]\[]", "", result)
        r = ""
        for s in result:
            
            if s not in ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '-']:
                r += s;
        r, number = re.subn("。。", "。", r)
        return r

    def handleJson(file):
        """
        获取json文件中的诗句
        """
        rst = []
        data = json.loads(open(file,encoding='utf8').read())
        for poetry in data:
            pdata = ""
            if (author!=None and poetry.get("author")!=author):
                continue
            p = poetry.get("paragraphs")
            flag = False
            for s in p:
                sp = re.split("[，！。]", s)
                for tr in sp:
                    if constrain != None and len(tr) != constrain and len(tr)!=0:
                        flag = True
                        break
                    if flag:
                        break
            if flag:
                continue
            for sentence in poetry.get("paragraphs"):
                pdata += sentence
            pdata = sentenceParse(pdata)
            if pdata!="":
                rst.append(pdata)
        return rst
    # print sentenceParse("")
    data = []
    for filename in os.listdir(filePath):
        if filename.startswith("poet.tang"):
            data.extend(handleJson(src+filename))
    return data

def pad_sequences(sequences,
                  maxlen=None,
                  dtype='int32',
                  padding='pre',
                  truncating='pre',
                  value=0.):
 
    if not hasattr(sequences, '__len__'):
        raise ValueError('`sequences` must be iterable.')
    lengths = []
    for x in sequences:
        if not hasattr(x, '__len__'):
            raise ValueError('`sequences` must be a list of iterables. '
                             'Found non-iterable: ' + str(x))
        #存放每首诗的长度
        lengths.append(len(x))

    #诗的数量
    num_samples = len(sequences)
    if maxlen is None:
        maxlen = np.max(lengths)

    # take the sample shape from the first non empty sequence
    # checking for consistency in the main loop below.
    sample_shape = tuple()
    
    #for s in sequences:
        
#         if len(s) > 0:  # pylint: disable=g-explicit-length-test
#             sample_shape = np.asarray(s).shape[1:]
#             break

    #构造大小为（诗的数量，诗最长长度）的元组
    x = (np.ones((num_samples, maxlen) + sample_shape) * value).astype(dtype)
    #print(x.shape)
    for idx, s in enumerate(sequences):
        if not len(s):  # pylint: disable=g-explicit-length-test
            continue  # empty list/array was found
        
         #截取方式：从后边截取还是从前边截取
        if truncating == 'pre':
            trunc = s[-maxlen:]  # pylint: disable=invalid-unary-operand-type
            
        elif truncating == 'post':
            trunc = s[:maxlen]
            #print(trunc)
        else:
            raise ValueError('Truncating type "%s" not understood' % truncating)

        # check `trunc` has expected shape
        trunc = np.asarray(trunc, dtype=dtype)
        if trunc.shape[1:] != sample_shape:
            raise ValueError(
                'Shape of sample %s of sequence at position %s is different from '
                'expected shape %s'
                % (trunc.shape[1:], idx, sample_shape))

        #填充方式，从后边填充还是从前边填充
        if padding == 'post':
            x[idx, :len(trunc)] = trunc
        elif padding == 'pre':
            x[idx, -len(trunc):] = trunc
        else:
            raise ValueError('Padding type "%s" not understood' % padding)
    return x

def get_data(opt):
    """
    @param opt 配置选项 Config对象
    @return word2ix: dict,每个字对应的序号，形如u'月'->100
    @return ix2word: dict,每个序号对应的字，形如'100'->u'月'
    @return data: numpy数组，每一行是一首诗对应的字的下标
    """
    if os.path.exists(opt.pickle_path):
        data = np.load(opt.pickle_path, allow_pickle=True)
        data, word2ix, ix2word = data['data'], data['word2ix'].item(), data['ix2word'].item()
        return data, word2ix, ix2word

    # 如果没有处理好的二进制文件，则处理原始的json文件
    data = parseRawData(filePath=opt.data_path)
    words = {_word for _sentence in data for _word in _sentence}
    word2ix = {_word: _ix for _ix, _word in enumerate(words)}
    word2ix[''] = len(word2ix)  # 终止标识符
    word2ix[''] = len(word2ix)  # 起始标识符
    word2ix[''] = len(word2ix)  # 空格
    ix2word = {_ix: _word for _word, _ix in list(word2ix.items())}

    # 为每首诗歌加上起始符和终止符
    for i in range(len(data)):
        data[i] = [""] + list(data[i]) + [""]
    # 将每首诗歌保存的内容由‘字’变成‘数’
    # 形如[春,江,花,月,夜]变成[1,2,3,4,5]
    new_data = [[word2ix[_word] for _word in _sentence]
                for _sentence in data]
    
    # 诗歌长度不够opt.maxlen的在前面补空格，超过的，删除末尾的
    pad_data = pad_sequences(new_data,
                             maxlen=opt.maxlen,
                             padding='pre',
                             truncating='post',
                             value=len(word2ix) - 1)

    # 保存成二进制文件
    print(pad_data)
    np.savez_compressed(opt.pickle_path,
                        data=pad_data,
                        word2ix=word2ix,
                        ix2word=ix2word)
    return pad_data, word2ix, ix2word

涉及到的配置文件

class Config(object):
 data_path = 'data/'  # 诗歌的文本文件存放路径
 pickle_path = 'data/tang.npz'  # 预处理好的二进制文件
 author = None  # 只学习某位作者的诗歌
 constrain = None  # 长度限制
 lr = 1e-3
 weight_decay = 1e-4
 DEVICE = torch.device('cuda'if torch.cuda.is_available() else 'cpu')
 epoch = 20
 batch_size = 128
 maxlen = 125  # 超过这个长度的之后字被丢弃，小于这个长度的在前面补空格
 seq_len= 48 #由于唐诗主要是五言绝句和七言绝句，各自加上一个标点符号为 6和8,选择一个公约数48，这样刚好凑够48
 plot_every = 20  # 每20个batch 可视化一次
 # use_env = True # 是否使用visodm
 env = 'poetry'  # visdom env
 max_gen_len = 200  # 生成诗歌最长长度
 debug_file = '/tmp/debugp'
 model_path = None  # 预训练模型路径
 prefix_words = '细雨鱼儿出,微风燕子斜。'  # 不是诗歌的组成部分，用来控制生成诗歌的意境
 start_words = '闲云潭影日悠悠'  # 诗歌开始
 acrostic = False  # 是否是藏头诗
 model_prefix = 'checkpoints/tang'  # 模型保存路径
 num_layers=2

opt = Config()

2.2 构建数据集

class PoemDataSet(Dataset):
 
 def __init__(self,opt):
     self.seq_len =opt.seq_len #诗的长度
     self.opt =opt #配置
     self.poem_data,self.word2ix,self.ix2word=self.get_raw_data() #数据集
     self.no_space_data = self.filter_space()
     
 def __getitem__(self,item): #获取每一行数据
     
     #每首诗的下一个字为上一个字的目标值
     inputs = self.no_space_data[item*self.seq_len:(item+1)*self.seq_len]
     labels = self.no_space_data[item*self.seq_len+1:(item+1)*self.seq_len+1]
     inputs = t.from_numpy(np.array(inputs))
     labels= t.from_numpy(np.array(labels))
     return inputs,labels
 
 def __len__(self): 
     return int(len(self.no_space_data)/self.seq_len)
 
 def filter_space(self):#将空格的数据过滤掉，并将原始数据平整到一维
     t_data =t.from_numpy(self.poem_data).view(-1)
     flat_data =t_data.numpy()
     no_space_data =[]
     for i in flat_data:
         if (i!=8292):
             no_space_data.append(i)
     return no_space_data
 
 def get_raw_data(self): #获取数据
     pad_data,word2ix,ix2word=get_data(self.opt)
     return pad_data,word2ix,ix2word

#获取数据
dataset=PoemDataSet(opt)
print(len(dataset))
data= next(iter(dataset))

print('*'*15+'inputs'+'*'*15)
print(data[0])
print([dataset.ix2word[d.item()] for d in data[0]])
print('*'*15+'labels'+'*'*15)
print(data[1])
print([dataset.ix2word[d.item()] for d in data[1]])

2.3 定义模型

class PoetryModel(nn.Module):
    def __init__(self,vocab_size,embedding_dim,hidden_dim):
        super(PoetryModel,self).__init__()
        self.hidden_dim=hidden_dim
        # 词向量层，词表大小 * 向量维度
        self.embeddings=nn.Embedding(vocab_size,embedding_dim)
        # 网络主要结构
        self.lstm = nn.LSTM(embedding_dim,hidden_dim,num_layers=opt.num_layers,batch_first=True)
        # 进行分类
        self.linear =nn.Linear(hidden_dim,vocab_size)
        
    
    def forward(self,input,hidden=None):
        batch_size,seq_len = input.size()
        
        if hidden is None:
            #h_0: (num_layers*bidirectional,batch_size,hidden_dim)
            #c_0: (num_layers*bidirectional,batch_size,hidden_dim)
            h_0 = input.data.new(opt.num_layers,batch_size,self.hidden_dim).fill_(0).float()
            c_0 = input.data.new(opt.num_layers,batch_size,self.hidden_dim).fill_(0).float()
        else:
            h_0,c_0 = hidden
            
        # 输入 序列长度 * batch(每个汉字是一个数字下标)，
        # 输出 序列长度 * batch * 向量维度
        embeds = self.embeddings(input)
        
        # output=batch_size * seq_len *  (num_directions*hidden_size)   
        # hn =(num_layers*num_directions) * batch_size * hidden_size
        output,hidden =self.lstm(embeds,(h_0,c_0))
        #print(output.size())
        output = self.linear(output.contiguous().view(batch_size*seq_len, -1))
        return output,hidden

2.4 训练

#模型定义
model =PoetryModel(len(dataset.word2ix),128,256)

if opt.model_path:
    model.load_state_dict(t.load(opt.model_path))
model.to(opt.DEVICE)

#损失函数
loss = nn.CrossEntropyLoss()
# loss = nn.BCELoss()  #二分类交叉熵损失函数
# loss = nn.BCEWithLogitsLoss() #二分类交叉熵损失函数 带log loss
# loss = nn.MSELoss()

#优化器
optim = t.optim.Adam(model.parameters(),lr=opt.lr)
#也可以选择SGD优化方法
# optimizer = torch.optim.SGD(model.parameters(),lr=1e-2)
 
#scheduler =StepLR(optim,step_size=10)

#获取数据
dataloader =DataLoader(dataset,batch_size=opt.batch_size,shuffle=True,num_workers=0)

#模型训练
def train(model,dataloader,ix2word,word2ix,device,optim,loss,epoch):
    
    model.train()
    train_loss = 0.0
    train_losses = []
    for epoch in range(epoch):
        for batch_idx, data in enumerate(dataloader):

            #如果模型没有设置 batch_first=True(将批次维度放到第一位)的话，需要将0维和1维互换位置。
            #transpose()函数的作用就是调换数组的行列值的索引值，类似于求矩阵的转置：
            # data = data.long().transpose(1,0).contiguous()
            # input, target = data[:-1, :], data[1:, :]
            optim.zero_grad()
            #inputs: batch_size * seq_len
            #targets: batch_size * seq_len
            inputs,targets =data[0].long().to(device),data[1].long().to(device)

            #模型训练
            outputs,_ = model(inputs)
            #print(output)
            los = loss(outputs,targets.view(-1))
            los.backward()
            optim.step()
            train_loss +=los.item()

            if (batch_idx+1)%200 ==0:
                print('train epoch: {} [{}/{} ({:.0f}%)]\tloss: {:.6f}'.format(epoch, batch_idx * len(data[0]), len(dataloader.dataset),100. * batch_idx / len(dataloader), los.item()))

        train_loss *= opt.batch_size
        train_loss /=len(dataloader.dataset)
        print('\ntrain epoch: {}\t average loss: {:.6f}\n'.format(epoch,train_loss))
        train_losses.append(train_loss)
    
        #保存模型
        t.save(model.state_dict(), '%s_%s.pth' % (opt.model_prefix, epoch))

2.5 测试

model.load_state_dict(torch.load(opt.model_prefix+'_17.pth'))  # 模型加载

def generate(model,start_words,ix2word,word2ix,max_gen_len,prefix_words=None):
    #读取唐诗的第一句
    start_words =list(start_words)
    start_word_len = len(start_words)
    results=[]
    
    #设置第一个词为
    input = torch.Tensor([word2ix['']]).view(1,1).long()
    input =input.to(opt.DEVICE)
    model = model.to(opt.DEVICE)
    model.eval()
    
    hidden =None
    index =0 #指示生成了多少句
    pred_word ='' #上一个词
    
    #控制意境
    if prefix_words:
        for word in prefix_words:
            output,hidden = model(input,hidden)
            input = Variable(input.data.new([word2ix[word]])).view(1,1)
            
    #生成藏头诗
    for i in range(max_gen_len): #诗的长度
        output,hidden = model(input,hidden)
        #获取生成字的index
        top_index = output.data[0].topk(1)[1][0].item()
        w = ix2word[top_index]
        #诗的第一个字必须是输入关键词的第一个
        if i==0:
            w= start_words[index]
            index+=1
            input = input.data.new([word2ix[w]]).view(1,1) 
        #如果遇到标志一句话的结尾，喂入下一个“头”
        if pred_word in {'。','！','，'}:
            #如果生成的诗已经包含全部“头”，则介绍
            if index == start_word_len:
                break
            #把‘头’作为输入喂入模型
            else:
                w= start_words[index]
                index +=1
                input = input.data.new([word2ix[w]]).view(1,1)
        
        #否则，把上一次预测作为下一个词的输入
        else: 
             input = input.data.new([word2ix[w]]).view(1,1)
        results.append(w)
        pred_word=w
        # 结束标志
        if w =='':
            del results[-1]
            break
    return results

result=generate(model,'深度学习',dataset.ix2word,dataset.word2ix,48)
print(result)