Seq2Seq的PyTorch实现
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_input和dec_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
- 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
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'))本文参与 腾讯云自媒体分享计划，分享自作者个人站点/博客。
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