文本情感分析
在本文中介绍如何使用神经网络实现情感分析任务，主要内容为：
加载预训练的词向量介绍如何处理情感分析数据集使用循环神经网络模型训练使用一维卷积神经网络模型训练
参考：动手学深度学习
1、加载Glove预训练的词向量
下面创建TokenEmbedding类来加载并使用预训练的词向量。
import torch
import os
import collections
from torch import nn
from d2l import torch as d2l
from torch.utils.data import TensorDataset,DataLoader
'''
加载并使用Glove预训练的词向量
'''
class TokenEmbedding:
def __init__(self, embedding_name):
self.idx_to_token, self.idx_to_vec = self._load_embedding(
embedding_name)
self.unknown_idx = 0
self.token_to_idx = {token: idx for idx, token in
enumerate(self.idx_to_token)}
def _load_embedding(self, embedding_name):
#用于保存id-token和id-特征向量
idx_to_token, idx_to_vec = ['
data_dir = 'F:/论文数据集/glove.6B'
with open(os.path.join(data_dir, embedding_name + '.txt'), 'r',encoding='UTF-8') as f:
for line in f:
#用空格将token和词向量分开
elems = line.rstrip().split(' ')
token, elems = elems[0], [float(elem) for elem in elems[1:]]
# 跳过标题信息
if len(elems) > 1:
idx_to_token.append(token)
idx_to_vec.append(elems)
#对idx_to_vec的前面加上
idx_to_vec = [[0] * len(idx_to_vec[0])] + idx_to_vec
return idx_to_token, torch.tensor(idx_to_vec)
def __getitem__(self, tokens):
#参数为所有的词元，然后获得预训练词向量的索引
indices = [self.token_to_idx.get(token, self.unknown_idx)
for token in tokens]
#返回词向量索引所对应的词向量。
vecs = self.idx_to_vec[torch.tensor(indices)]
return vecs
def __len__(self):
return len(self.idx_to_token)
glove_6b50d = TokenEmbedding('glove.6b.50d')
len(glove_6b50d.idx_to_token),len(glove_6b50d.idx_to_vec)
(400002, 400002)
2、处理情感分析数据集
情感分析的数据集有很多，本文使用大型电影评论数据集进行情感分析。由于原数据为文本和标签，因此需要对其进行处理才能用于模型的输入。
def read_imdb(data_dir,is_train):
data,labels = [],[]
for label in ('pos','neg'):
folder_name = os.path.join(data_dir, 'train' if is_train else 'test',label)
#遍历folder_name文件夹下所有内容
for file in os.listdir(folder_name):
with open(os.path.join(folder_name, file), 'rb') as f:
#保存文本
review = f.read().decode('utf-8').replace('\n', '')
#保存标签
data.append(review)
labels.append(1 if label == 'pos' else 0)
#返回文本和标签内容
return data, labels
下面加载训练集测试上述方法
data_dir = 'F:/论文数据集/aclImdb'
train_data = read_imdb(data_dir, is_train=True)
print('训练集数目：', len(train_data[0]))
for x, y in zip(train_data[0][:3], train_data[1][:3]):
print('标签：', y, 'review:', x[0:60])
训练集数目： 25000
标签： 1 review: Bromwell High is a cartoon comedy. It ran at the same time a
标签： 1 review: Homelessness (or Houselessness as George Carlin stated) has
标签： 1 review: Brilliant over-acting by Lesley Ann Warren. Best dramatic ho
下面创建tokenize函数用于将文本序列拆分为词元列表。
#将文本拆分为单词或者字符词元
def tokenize(lines, token = 'word'):
#拆分为单词
if token == 'word':
return [line.split() for line in lines]
#拆分为字符
elif token == 'char':
return [list(line) for line in lines]
else:
print('错误:未知词元类型：'+token)
创建Vocab类用于生成词表，生成每个词与索引的一一对应。
#统计词元的频率，返回每个词元及其出现的次数，以一个字典形式返回。
def count_corpus(tokens):
#这里的tokens是一个1D列表或者是2D列表
if len(tokens) == 0 or isinstance(tokens[0], list):
#将词元列表展平为一个列表
tokens = [token for line in tokens for token in line]
#该方法用于统计某序列中每个元素出现的次数，以键值对的方式存在字典中。
return collections.Counter(tokens)
#文本词表
class Vocab:
def __init__(self,tokens = None, min_freq = 0, reserved_tokens = None):
if tokens is None:
tokens = []
if reserved_tokens is None:
reserved_tokens = []
#按照单词出现频率排序
counter = count_corpus(tokens)
#counter.items():为一个字典
#lambda x:x[1]:对第二个字段进行排序
#reverse = True:降序
self._token_freqs = sorted(counter.items(),key = lambda x:x[1],reverse = True)
#未知单词的索引为0
#idx_to_token用于保存所有未重复的词元
self.idx_to_token = ['
#token_to_idx:是一个字典，保存词元和其对应的索引
self.token_to_idx = {token:idx for idx,token in enumerate(self.idx_to_token)}
for token, freq in self._token_freqs:
#min_freq为最小出现的次数，如果小于这个数，这个单词被抛弃
if freq < min_freq:
break
#如果这个词元未出现在词表中，将其添加进词表
if token not in self.token_to_idx:
self.idx_to_token.append(token)
#因为第一个位置被位置单词占据
self.token_to_idx[token] = len(self.idx_to_token) - 1
#返回词表的长度
def __len__(self):
return len(self.idx_to_token)
#获取要查询词元的索引，支持list，tuple查询多个词元的索引
def __getitem__(self, tokens):
if not isinstance(tokens,(list,tuple)):
#self.unk：如果查询不到返回0
return self.token_to_idx.get(tokens,self.unk)
return [self.__getitem__(token) for token in tokens]
# 根据索引查询词元，支持list，tuple查询多个索引对应的词元
def to_tokens(self,indices):
if not isinstance(indices,(list,tuple)):
return self.idx_to_token[indices]
return [self.idx_to_token[index] for index in indices]
@property
def unk(self):
return 0
@property
def token_freqs(self):
return self._token_freqs
创建load_array函数用于创建数据迭代器。
def load_array(data_arrays,batch_size,is_train=True):
#构造一个Pytorch数据迭代器
dataset = TensorDataset(*data_arrays)
return DataLoader(dataset,batch_size,shuffle=is_train)
创建truncate_pad函数用于将序列截断或者填充为指定长度。
def truncate_pad(line,num_steps,padding_token):
if len(line) > num_steps:
return line[:num_steps]
return line + [padding_token] * (num_steps - len(line))
最后整合上述函数，将其封装到load_data_imdb函数中，返回训练和测试数据集以及IMDb评论集的词表。
'''
返回数据迭代器和IMDb评论数据集的词表
'''
def load_data_imdb(batch_size, num_steps=500):
data_dir = 'F:/论文数据集/aclImdb'
train_data = read_imdb(data_dir, True)
test_data = read_imdb(data_dir, False)
#对句子进行分词
train_tokens = tokenize(train_data[0], token='word')
test_tokens = tokenize(test_data[0], token='word')
#构建词表，这里感觉应该将train_tokens和test_tokens一起构建词表？？
vocab = Vocab(train_tokens, min_freq=5)
#将每个词元转为id，并填充截断为统一长度500
train_features = torch.tensor([truncate_pad(
vocab[line], num_steps, vocab['
test_features = torch.tensor([truncate_pad(
vocab[line], num_steps, vocab['
train_iter = load_array((train_features, torch.tensor(train_data[1])),
batch_size)
test_iter = load_array((test_features, torch.tensor(test_data[1])),
batch_size,
is_train=False)
return train_iter, test_iter, vocab
3、使用循环神经网络模型训练
下面搭建一个循环神经网络，并使用上面介绍的数据集对其进行训练。
首先搭建模型，使用一个两层的双向LSTM模型。
class BiRNN(nn.Module):
def __init__(self, vocab_size, embed_size, num_hiddens,
num_layers, **kwargs):
super(BiRNN, self).__init__(**kwargs)
#self.embedding = nn.Embedding.from_pretrained(torch.tensor(embedding_matrix, dtype=torch.float), freeze=False)
self.embedding = nn.Embedding(vocab_size, embed_size)
# 将bidirectional设置为True以获取双向循环神经网络
self.encoder = nn.LSTM(embed_size, num_hiddens, num_layers=num_layers, bidirectional=True,batch_first = True)
self.decoder = nn.Linear(4 * num_hiddens, 2)
def forward(self, inputs):
# inputs的形状是（批量大小，时间步数）
# 输出形状为（时间步数，批量大小，词向量维度）
embeddings = self.embedding(inputs)
self.encoder.flatten_parameters()
# 返回上一个隐藏层在不同时间步的隐状态，
# outputs的形状是（时间步数，批量大小，2*隐藏单元数）
outputs, _ = self.encoder(embeddings)
# 连结初始和最终时间步的隐状态，作为全连接层的输入，
# 其形状为（批量大小，4*隐藏单元数）
encoding = torch.cat((outputs[:,0,:], outputs[:,-1,:]), dim=1)
outs = self.decoder(encoding)
return outs
加载上一节介绍的数据集。
batch_size = 64
train_iter, test_iter, vocab = load_data_imdb(batch_size)
下面为词表中的单词加载预训练的100维Glove嵌入，得到每个词元所对应的词嵌入。
glove_embedding = TokenEmbedding('glove.6b.100d')
embeds = glove_embedding[vocab.idx_to_token]
embeds.shape
torch.Size([49346, 100])
def try_all_gpus():
devices=[torch.device(f'cuda:{i}') for i in range(torch.cuda.device_count())]
return devices if devices else [torch.device('cpu')]
'''
计算准确率
'''
def accuracy(y_hat,y):
#计算预测正确的数量
if len(y_hat.shape)>1 and y_hat.shape[1]>1:
y_hat=y_hat.argmax(axis=1)
cmp=y_hat.type(y.dtype)==y
return float(cmp.type(y.dtype).sum())
'''
GPU上计算准确率
'''
def evaluate_accuracy_gpu(net, data_iter, device=None):
if isinstance(net, nn.Module):
net.eval() # Set the model to evaluation mode
if not device:
device = next(iter(net.parameters())).device
# No. of correct predictions, no. of predictions
metric = d2l.Accumulator(2)
with torch.no_grad():
for X, y in data_iter:
if isinstance(X, list):
# Required for BERT Fine-tuning (to be covered later)
X = [x.to(device) for x in X]
else:
X = X.to(device)
y = y.to(device)
metric.add(accuracy(net(X), y), d2l.size(y))
return metric[0] / metric[1]
'''
用多GPU进行小批量训练
'''
def train_batch(net, X, y, loss, trainer, devices):
if isinstance(X, list):
X = [x.to(devices[0]) for x in X]
else:
X = X.to(devices[0])
y = y.to(devices[0])
net.train()
trainer.zero_grad()
pred = net(X)
l = loss(pred, y)
l.sum().backward()
trainer.step()
scheduler.step()
train_loss_sum = l.sum()
train_acc_sum = accuracy(pred, y)
return train_loss_sum, train_acc_sum
'''
用多GPU进行模型训练
'''
def train(net, train_iter, test_iter, loss, trainer, num_epochs,
devices = try_all_gpus()):
timer, num_batches = d2l.Timer(), len(train_iter)
animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs], ylim=[0, 1],
legend=['train loss', 'train acc', 'test acc'])
net = nn.DataParallel(net, device_ids=devices).to(devices[0])
for epoch in range(num_epochs):
# 4个维度：储存训练损失，训练准确度，实例数，特点数
metric = d2l.Accumulator(4)
for i, (features, labels) in enumerate(train_iter):
timer.start()
l, acc = train_batch(
net, features, labels, loss, trainer, devices)
metric.add(l, acc, labels.shape[0], labels.numel())
timer.stop()
if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1:
animator.add(epoch + (i + 1) / num_batches,
(metric[0] / metric[2], metric[1] / metric[3],
None))
test_acc = evaluate_accuracy_gpu(net, test_iter)
animator.add(epoch + 1, (None, None, test_acc))
print(f'loss {metric[0] / metric[2]:.3f}, train acc '
f'{metric[1] / metric[3]:.3f}, test acc {test_acc:.3f}')
print(f'{metric[2] * num_epochs / timer.sum():.1f} examples/sec on '
f'{str(devices)}')
embed_size, num_hiddens, num_layers = 100, 100, 2
devices = try_all_gpus()
net = BiRNN(len(vocab), embed_size, num_hiddens, num_layers)
net.embedding.weight.data.copy_(embeds)
net.embedding.weight.requires_grad = False
#初始化模型参数
def init_weights(m):
if type(m) == nn.Linear:
nn.init.xavier_uniform_(m.weight)
if type(m) == nn.LSTM:
for param in m._flat_weights_names:
if "weight" in param:
nn.init.xavier_uniform_(m._parameters[param])
net.apply(init_weights);
lr, num_epochs = 0.01, 5
#params = filter(lambda p: p.requires_grad, net.parameters())
trainer = torch.optim.Adam(net.parameters(), lr=lr)
loss = nn.CrossEntropyLoss(reduction="none")
train(net, train_iter, test_iter, loss, trainer, num_epochs,
devices)
loss 0.276, train acc 0.884, test acc 0.839
505.9 examples/sec on [device(type='cuda', index=0)]
4、使用一维卷积神经网络模型训练
先看看一维卷积是如何工作的。下图是基于互相关运算的二维卷积的特例。
搭建一维时间卷积模型
class TextCNN(nn.Module):
def __init__(self, vocab_size, embed_size, kernel_sizes, num_channels,embedding_matrix,
**kwargs):
super(TextCNN, self).__init__(**kwargs)
self.embedding = nn.Embedding.from_pretrained(torch.tensor(embedding_matrix, dtype=torch.float))
self.constant_embedding = nn.Embedding.from_pretrained(torch.tensor(embedding_matrix, dtype=torch.float), freeze=False)
# self.embedding = nn.Embedding(vocab_size, embed_size)
# 这个嵌入层不需要训练
# self.constant_embedding = nn.Embedding(vocab_size, embed_size)
self.dropout = nn.Dropout(0.5)
self.decoder = nn.Linear(sum(num_channels), 2)
# 池化层
#对于一个输入(B C L)的tensor进行一维的pool，变为(B,C,1)
self.pool = nn.AdaptiveAvgPool1d(1)
self.relu = nn.ReLU()
# 创建多个一维卷积层
self.convs = nn.ModuleList()
for c, k in zip(num_channels, kernel_sizes):
self.convs.append(nn.Conv1d(2 * embed_size, c, k))
def forward(self, inputs):
# 沿着向量维度将两个嵌入层连结起来，
# 每个嵌入层的输出形状都是（批量大小，词元数量，词元向量维度）连结起来
embeddings = torch.cat((
self.embedding(inputs), self.constant_embedding(inputs)), dim=2)
#print(embeddings.shape)
# 根据一维卷积层的输入格式，重新排列张量，以便通道作为第2维
embeddings = embeddings.permute(0, 2, 1)
# 每个一维卷积层在最大时间汇聚层合并后，获得的张量形状是（批量大小，通道数，1）
# 删除最后一个维度并沿通道维度连结
#单独使用三个ConvD1，将最后结构拼在一起
encoding = torch.cat([torch.squeeze(self.relu(self.pool(conv(embeddings))), dim = -1) for conv in self.convs], dim = 1)
#print(encoding.shape)
outputs = self.decoder(self.dropout(encoding))
return outputs
定义相关参数，对模型进行训练。
embed_size, kernel_sizes, nums_channels = 100, [3, 4, 5], [100, 100, 100]
devices = d2l.try_all_gpus()
net = TextCNN(len(vocab), embed_size, kernel_sizes, nums_channels,embeds)
def init_weights(m):
if type(m) in (nn.Linear, nn.Conv1d):
nn.init.xavier_uniform_(m.weight)
net.apply(init_weights);
#net.embedding.weight.data.copy_(embeds)
#net.constant_embedding.weight.data.copy_(embeds)
#net.constant_embedding.weight.requires_grad = False
lr, num_epochs = 0.001, 5
params = filter(lambda p: p.requires_grad, net.parameters())
trainer = torch.optim.Adam(params, lr=lr)
loss = nn.CrossEntropyLoss(reduction="none")
#自己调整学习率
#scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, 10)
train(net, train_iter, test_iter, loss, trainer, num_epochs,
devices)
loss 0.127, train acc 0.954, test acc 0.875
1072.4 examples/sec on [device(type='cuda', index=0)]
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