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Seq2Seq的PyTorch实现

June 30, 2020 • Read: 100 • Deep Learning

本文介绍一下如何使用 PyTorch 复现 Seq2Seq,实现简单的机器翻译应用,请先简单阅读论文Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation(2014),了解清楚Seq2Seq结构是什么样的,之后再阅读本篇文章,可达到事半功倍的效果

我看了很多Seq2Seq网络结构图,感觉PyTorch官方提供的这个图是最好理解的

首先,从上面的图可以很明显的看出,Seq2Seq需要对三个变量进行操作,这和之前我接触到的所有网络结构都不一样。我们把Encoder的输入称为 enc_input,Decoder的输入称为 dec_input, Decoder的输出称为 dec_output。下面以一个具体的例子来说明整个Seq2Seq的工作流程

下图是一个由LSTM组成的Encoder结构,输入的是"go away"中的每个字母(包括空格),我们只需要最后一个时刻隐藏状态的信息,即$h_t$和$c_t$

然后将Encoder输出的$h_t$和$c_t$作为Decoder初始时刻隐藏状态的输入$h_0$、$c_0$,如下图所示。同时Decoder初始时刻输入层输入的是代表一个句子开始的标志(由用户定义,"<SOS>","\t","S"等均可,这里以"\t"为例),之后得到输出"m",以及新的隐藏状态$h_1$和$c_1$

再将$h_1$、$c_1$和"m"作为输入,得到输入"a",以及新的隐藏状态$h_2$和$c_2$

重复上述步骤,直到最终输出句子的结束标志(由用户定义,"<EOS>","\n","E"等均可,这里以"\n"为例)

在Decoder部分,大家可能会有以下几个问题,我做下解答

  • 训练过程中,如果Decoder停不下来怎么办?即一直不输出句子的终止标志

    • 首先,训练过程中Decoder应该要输出多长的句子,这个是已知的,假设当前时刻已经到了句子长度的最后一个字符了,并且预测的不是终止标志,那也没有关系,就此打住,计算loss即可
  • 测试过程中,如果Decoder停不下来怎么办?例如预测得到"wasd s w \n sdsw \n..........(一直输出下去)"

    • 不会停不下来的,因为测试过程中,Decoder也会有输入,只不过这个输入是很多个没有意义的占位符,例如很多个"<pad>"。由于Decoder有有限长度的输入,所以Decoder一定会有有限长度的输出。那么只需要获取第一个终止标志之前的所有字符即可,对于上面的例子,最终的预测结果为"wasd s w"
  • Decoder的输入和输出,即 dec_inputdec_output 有什么关系?

    • 在训练阶段,不论当前时刻Decoder输出什么字符,下一时刻Decoder都按照原来的"计划"进行输入。举个例子,假设 dec_input="\twasted",首先输入"\t"之后,Decoder输出的是"m"这个字母,记录下来就行了,并不会影响到下一时刻Decoder继续输入"w"这个字母
    • 在验证或者测试阶段,Decoder每一时刻的输出是会影响到输入的,因为在验证或者测试时,网络是看不到结果的,所以它只能循环的进行下去。举个例子,我现在要将英语"wasted"翻译为德语"verschwenden"。那么Decoder一开始输入"\t",得到一个输出,假如是"m",下一时刻Decoder会输入"m",得到输出,假如是"a",之后会将"a"作为输入,得到输出......如此循环往复,直到最终时刻

这里说句题外话,其实我个人觉得Seq2Seq与AutoEncoder非常相似

下面开始代码讲解

首先导库,这里我用'S'作为开始标志,'E'作为结束标志,如果输入或者输入过短,我使用'?'进行填充

# code by Tae Hwan Jung(Jeff Jung) @graykode, modify by wmathor
import torch
import numpy as np
import torch.nn as nn
import torch.utils.data as Data

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# S: Symbol that shows starting of decoding input
# E: Symbol that shows starting of decoding output
# ?: Symbol that will fill in blank sequence if current batch data size is short than n_step

定义数据集以及参数,这里数据集我设定的非常简单,可以看作是翻译任务,只不过是将英语翻译成英语罢了。

n_step 保存的是最长单词的长度,其它所有不够这个长度的单词,都会在其后用'?'填充

letter = [c for c in 'SE?abcdefghijklmnopqrstuvwxyz']
letter2idx = {n: i for i, n in enumerate(letter)}

seq_data = [['man', 'women'], ['black', 'white'], ['king', 'queen'], ['girl', 'boy'], ['up', 'down'], ['high', 'low']]

# Seq2Seq Parameter
n_step = max([max(len(i), len(j)) for i, j in seq_data]) # max_len(=5)
n_hidden = 128
n_class = len(letter2idx) # classfication problem
batch_size = 3

下面是对数据进行处理,主要做的是,首先对单词长度不够的,用'?'进行填充;然后将Deocder的输入数据末尾添加终止标志'E',Decoder的输入数据开头添加开始标志'S',Decoder的输出数据末尾添加结束标志'E',其实也就如下图所示

def make_data(seq_data):
    enc_input_all, dec_input_all, dec_output_all = [], [], []

    for seq in seq_data:
        for i in range(2):
            seq[i] = seq[i] + '?' * (n_step - len(seq[i])) # 'man??', 'women'

        enc_input = [letter2idx[n] for n in seq[0]] # ['m', 'a', 'n', '?', '?', 'E']
        dec_input = [letter2idx[n] for n in ('S' + seq[1])] # ['S', 'w', 'o', 'm', 'e', 'n']
        dec_output = [letter2idx[n] for n in (seq[1] + 'E')] # ['w', 'o', 'm', 'e', 'n', 'E']

        enc_input_all.append(np.eye(n_class)[enc_input])
        dec_input_all.append(np.eye(n_class)[dec_input])
        dec_output_all.append(dec_output) # not one-hot

    # make tensor
    return torch.Tensor(enc_input_all), torch.Tensor(dec_input_all), torch.LongTensor(dec_output_all)

'''
enc_input_all: [6, n_step+1 (because of 'E'), n_class]
dec_input_all: [6, n_step+1 (because of 'S'), n_class]
dec_output_all: [6, n_step+1 (because of 'E')]
'''
enc_input_all, dec_input_all, dec_output_all = make_data(seq_data)

由于这里有三个数据要返回,所以需要自定义DataSet,具体来说就是继承torch.utils.data.Dataset类,然后实现里面的__len__以及__getitem__方法

class TranslateDataSet(Data.Dataset):
    def __init__(self, enc_input_all, dec_input_all, dec_output_all):
        self.enc_input_all = enc_input_all
        self.dec_input_all = dec_input_all
        self.dec_output_all = dec_output_all
    
    def __len__(self): # return dataset size
        return len(self.enc_input_all)
    
    def __getitem__(self, idx):
        return self.enc_input_all[idx], self.dec_input_all[idx], self.dec_output_all[idx]

loader = Data.DataLoader(TranslateDataSet(enc_input_all, dec_input_all, dec_output_all), batch_size, True)

下面定义Seq2Seq模型,我用的是简单的RNN作为编码器和解码器。如果你对RNN比较了解的话,定义网络结构的部分其实没什么说的,注释我也写的很清楚了,包括数据维度的变化

# Model
class Seq2Seq(nn.Module):
    def __init__(self):
        super(Seq2Seq, self).__init__()
        self.encoder = nn.RNN(input_size=n_class, hidden_size=n_hidden, dropout=0.5) # encoder
        self.decoder = nn.RNN(input_size=n_class, hidden_size=n_hidden, dropout=0.5) # decoder
        self.fc = nn.Linear(n_hidden, n_class)

    def forward(self, enc_input, enc_hidden, dec_input):
        # enc_input(=input_batch): [batch_size, n_step+1, n_class]
        # dec_inpu(=output_batch): [batch_size, n_step+1, n_class]
        enc_input = enc_input.transpose(0, 1) # enc_input: [n_step+1, batch_size, n_class]
        dec_input = dec_input.transpose(0, 1) # dec_input: [n_step+1, batch_size, n_class]

        # h_t : [num_layers(=1) * num_directions(=1), batch_size, n_hidden]
        _, h_t = self.encoder(enc_input, enc_hidden)
        # outputs : [n_step+1, batch_size, num_directions(=1) * n_hidden(=128)]
        outputs, _ = self.decoder(dec_input, h_t)

        model = self.fc(outputs) # model : [n_step+1, batch_size, n_class]
        return model

model = Seq2Seq().to(device)
criterion = nn.CrossEntropyLoss().to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

下面是训练,由于输出的pred是个三维的数据,所以计算loss需要每个样本单独计算,因此就有了下面for循环的代码

for epoch in range(5000):
  for enc_input_batch, dec_input_batch, dec_output_batch in loader:
      # make hidden shape [num_layers * num_directions, batch_size, n_hidden]
      h_0 = torch.zeros(1, batch_size, n_hidden).to(device)

      (enc_input_batch, dec_intput_batch, dec_output_batch) = (enc_input_batch.to(device), dec_input_batch.to(device), dec_output_batch.to(device))
      # enc_input_batch : [batch_size, n_step+1, n_class]
      # dec_intput_batch : [batch_size, n_step+1, n_class]
      # dec_output_batch : [batch_size, n_step+1], not one-hot
      pred = model(enc_input_batch, h_0, dec_intput_batch)
      # pred : [n_step+1, batch_size, n_class]
      pred = pred.transpose(0, 1) # [batch_size, n_step+1(=6), n_class]
      loss = 0
      for i in range(len(dec_output_batch)):
          # pred[i] : [n_step+1, n_class]
          # dec_output_batch[i] : [n_step+1]
          loss += criterion(pred[i], dec_output_batch[i])
      if (epoch + 1) % 1000 == 0:
          print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss))
          
      optimizer.zero_grad()
      loss.backward()
      optimizer.step()

从下面测试的代码可以看出,在测试过程中,Decoder的input是没有意义占位符,所占位置的长度即最大长度 n_step 。并且在输出中找到第一个终止符的位置,截取在此之前的所有字符

# Test
def translate(word):
    enc_input, dec_input, _ = make_data([[word, '?' * n_step]])
    enc_input, dec_input = enc_input.to(device), dec_input.to(device)
    # make hidden shape [num_layers * num_directions, batch_size, n_hidden]
    hidden = torch.zeros(1, 1, n_hidden).to(device)
    output = model(enc_input, hidden, dec_input)
    # output : [n_step+1, batch_size, n_class]

    predict = output.data.max(2, keepdim=True)[1] # select n_class dimension
    decoded = [letter[i] for i in predict]
    translated = ''.join(decoded[:decoded.index('E')])

    return translated.replace('?', '')

print('test')
print('man ->', translate('man'))
print('mans ->', translate('mans'))
print('king ->', translate('king'))
print('black ->', translate('black'))
print('up ->', translate('up'))

完整代码如下

# code by Tae Hwan Jung(Jeff Jung) @graykode, modify by wmathor
import torch
import numpy as np
import torch.nn as nn
import torch.utils.data as Data

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# S: Symbol that shows starting of decoding input
# E: Symbol that shows starting of decoding output
# ?: Symbol that will fill in blank sequence if current batch data size is short than n_step

letter = [c for c in 'SE?abcdefghijklmnopqrstuvwxyz']
letter2idx = {n: i for i, n in enumerate(letter)}

seq_data = [['man', 'women'], ['black', 'white'], ['king', 'queen'], ['girl', 'boy'], ['up', 'down'], ['high', 'low']]

# Seq2Seq Parameter
n_step = max([max(len(i), len(j)) for i, j in seq_data]) # max_len(=5)
n_hidden = 128
n_class = len(letter2idx) # classfication problem
batch_size = 3

def make_data(seq_data):
    enc_input_all, dec_input_all, dec_output_all = [], [], []

    for seq in seq_data:
        for i in range(2):
            seq[i] = seq[i] + '?' * (n_step - len(seq[i])) # 'man??', 'women'

        enc_input = [letter2idx[n] for n in seq[0]] # ['m', 'a', 'n', '?', '?', 'E']
        dec_input = [letter2idx[n] for n in ('S' + seq[1])] # ['S', 'w', 'o', 'm', 'e', 'n']
        dec_output = [letter2idx[n] for n in (seq[1] + 'E')] # ['w', 'o', 'm', 'e', 'n', 'E']

        enc_input_all.append(np.eye(n_class)[enc_input])
        dec_input_all.append(np.eye(n_class)[dec_input])
        dec_output_all.append(dec_output) # not one-hot

    # make tensor
    return torch.Tensor(enc_input_all), torch.Tensor(dec_input_all), torch.LongTensor(dec_output_all)

'''
enc_input_all: [6, n_step+1 (because of 'E'), n_class]
dec_input_all: [6, n_step+1 (because of 'S'), n_class]
dec_output_all: [6, n_step+1 (because of 'E')]
'''
enc_input_all, dec_input_all, dec_output_all = make_data(seq_data)

class TranslateDataSet(Data.Dataset):
    def __init__(self, enc_input_all, dec_input_all, dec_output_all):
        self.enc_input_all = enc_input_all
        self.dec_input_all = dec_input_all
        self.dec_output_all = dec_output_all
    
    def __len__(self): # return dataset size
        return len(self.enc_input_all)
    
    def __getitem__(self, idx):
        return self.enc_input_all[idx], self.dec_input_all[idx], self.dec_output_all[idx]

loader = Data.DataLoader(TranslateDataSet(enc_input_all, dec_input_all, dec_output_all), batch_size, True)

# Model
class Seq2Seq(nn.Module):
    def __init__(self):
        super(Seq2Seq, self).__init__()
        self.encoder = nn.RNN(input_size=n_class, hidden_size=n_hidden, dropout=0.5) # encoder
        self.decoder = nn.RNN(input_size=n_class, hidden_size=n_hidden, dropout=0.5) # decoder
        self.fc = nn.Linear(n_hidden, n_class)

    def forward(self, enc_input, enc_hidden, dec_input):
        # enc_input(=input_batch): [batch_size, n_step+1, n_class]
        # dec_inpu(=output_batch): [batch_size, n_step+1, n_class]
        enc_input = enc_input.transpose(0, 1) # enc_input: [n_step+1, batch_size, n_class]
        dec_input = dec_input.transpose(0, 1) # dec_input: [n_step+1, batch_size, n_class]

        # h_t : [num_layers(=1) * num_directions(=1), batch_size, n_hidden]
        _, h_t = self.encoder(enc_input, enc_hidden)
        # outputs : [n_step+1, batch_size, num_directions(=1) * n_hidden(=128)]
        outputs, _ = self.decoder(dec_input, h_t)

        model = self.fc(outputs) # model : [n_step+1, batch_size, n_class]
        return model

model = Seq2Seq().to(device)
criterion = nn.CrossEntropyLoss().to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

for epoch in range(5000):
  for enc_input_batch, dec_input_batch, dec_output_batch in loader:
      # make hidden shape [num_layers * num_directions, batch_size, n_hidden]
      h_0 = torch.zeros(1, batch_size, n_hidden).to(device)

      (enc_input_batch, dec_intput_batch, dec_output_batch) = (enc_input_batch.to(device), dec_input_batch.to(device), dec_output_batch.to(device))
      # enc_input_batch : [batch_size, n_step+1, n_class]
      # dec_intput_batch : [batch_size, n_step+1, n_class]
      # dec_output_batch : [batch_size, n_step+1], not one-hot
      pred = model(enc_input_batch, h_0, dec_intput_batch)
      # pred : [n_step+1, batch_size, n_class]
      pred = pred.transpose(0, 1) # [batch_size, n_step+1(=6), n_class]
      loss = 0
      for i in range(len(dec_output_batch)):
          # pred[i] : [n_step+1, n_class]
          # dec_output_batch[i] : [n_step+1]
          loss += criterion(pred[i], dec_output_batch[i])
      if (epoch + 1) % 1000 == 0:
          print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss))
          
      optimizer.zero_grad()
      loss.backward()
      optimizer.step()
    
# Test
def translate(word):
    enc_input, dec_input, _ = make_data([[word, '?' * n_step]])
    enc_input, dec_input = enc_input.to(device), dec_input.to(device)
    # make hidden shape [num_layers * num_directions, batch_size, n_hidden]
    hidden = torch.zeros(1, 1, n_hidden).to(device)
    output = model(enc_input, hidden, dec_input)
    # output : [n_step+1, batch_size, n_class]

    predict = output.data.max(2, keepdim=True)[1] # select n_class dimension
    decoded = [letter[i] for i in predict]
    translated = ''.join(decoded[:decoded.index('E')])

    return translated.replace('?', '')

print('test')
print('man ->', translate('man'))
print('mans ->', translate('mans'))
print('king ->', translate('king'))
print('black ->', translate('black'))
print('up ->', translate('up'))
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已有 7 条评论
  1. Wang Siwen Wang Siwen

    您好,关于这个 loss那个部分 loss += criterion(pred[i], dec_output_batch[i]),我应该是这个地方报错了,想知道 两个不同维度的 可以这样loss吗?

    1. mathor mathor

      @Wang Siwen可以的,比方说crossentropyloss,传入的一个参数是[batch_size, n_class],另一个参数是[batch_size],可以计算loss,不会有错

  2. Wang Siwen Wang Siwen

    C:\python36\lib\site-packages\torch\nn\modules\rnn.py:51: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.5 and num_layers=1
    "num_layers={}".format(dropout, num_layers))
    Traceback (most recent call last):
    File "D:/pycharmproject/SOME_folder/demo2.py", line 96, in
    loss += criterion(pred[i],dec_output_batch[i])
    File "C:\python36\lib\site-packages\torch\nn\modules\module.py", line 541, in __call__
    result = self.forward(*input, **kwargs)
    File "C:\python36\lib\site-packages\torch\nn\modules\loss.py", line 916, in forward
    ignore_index=self.ignore_index, reduction=self.reduction)
    File "C:\python36\lib\site-packages\torch\nn\functional.py", line 2009, in cross_entropy
    return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)
    File "C:\python36\lib\site-packages\torch\nn\functional.py", line 1838, in nll_loss
    ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
    RuntimeError: Expected object of scalar type Long but got scalar type Float for argument #2 'target' in call to _thnn_nll_loss_forward
    我运行的时候报了这个错误,不知道是为什么,博主可以帮我看看吗@(泪)@(泪)@(泪)

    1. mathor mathor

      @Wang Siwencriterion(a, b),你把b改为LongTensor类型

    2. Wang Siwen Wang Siwen

      @mathor之后loss也会变成tensor, 那最后 loss.backword() 就会报错 'Tensor' object has no attribute 'backword'

    3. mathor mathor

      @Wang Siwen...那真是奇怪了,我运行都没有问题

    4. Wang Siwen Wang Siwen

      @mathor神奇的是 我直接复制您的程序 运行 就可以!但是我打出来的就是不行 但是 肉眼 真的看不出来和您的任何区别@(汗)太神奇@(心碎)