BERT+textCNN+aclImdb文本情感分析（改）_bert+textcnn情感分析模型设计图

本次的内容没有大改动，只是可以不用配置torchtext，也不import d2l，那就意味着可以使用任意版本的pyrtoch。

相关的环境配置也只需要 pip install transformers 就可以了。

目录

训练网络

import相关包

导入bert

读取数据

数据预处理

定义网络

训练网络

模型保存

输入文字进行测试

模型读取与测试

---

训练网络

import相关包

import os
from tqdm import tqdm
import random
import time
import torch
from torch import nn
import torch.utils.data as Data
import  torch.nn.functional as F
import sys
sys.path.append("..") 
 
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
DATA_ROOT = "../../data"
print(torch.__version__, device)

导入bert

from transformers import BertModel, BertTokenizer
# 这里我们调用bert-base模型，同时模型的词典经过小写处理
model_name = 'bert-base-uncased'
# 读取模型对应的tokenizer
tokenizer = BertTokenizer.from_pretrained(model_name, cache_dir='./transformers/bert-base-uncased/')
# 载入模型
model = BertModel.from_pretrained(model_name, cache_dir='./transformers/bert-base-uncased/')

model.to(device)
model.eval()

读取数据

def read_imdb(folder='train', data_root="/Datasets/aclImdb"): 
    data = []
    for label in ['pos', 'neg']:
        folder_name = os.path.join(data_root, folder, label)
        for file in tqdm(os.listdir(folder_name)):
            with open(os.path.join(folder_name, file), 'rb') as f:
                review = f.read().decode('utf-8').replace('\n', '').lower()
                data.append([review, 1 if label == 'pos' else 0])
    random.shuffle(data)
    return data

train_data = read_imdb('train', data_root=os.path.join(DATA_ROOT, "aclImdb"))
test_data = read_imdb('test', data_root=os.path.join(DATA_ROOT, "aclImdb"))

数据预处理

def pretreatment(original_data):
    i = 0
    for element in original_data:
        temporary = []
        original_data[i][0] = torch.tensor(tokenizer.encode(element[0], add_special_tokens=True))
        if (original_data[i][0].shape)[0] > 500:
            original_data[i][0] = original_data[i][0][:500]
            original_data[i][0][499] = 102
        elif (original_data[i][0].shape)[0] < 500:
            n = torch.zeros(500)
            n[: (original_data[i][0].shape)[0]-1] = original_data[i][0][:(original_data[i][0].shape)[0]-1]
            original_data[i][0] = n
            original_data[i][0][499] = 102
        temporary.append(element[1])
        original_data[i][1] = torch.tensor(temporary)
        i = i+1
    features =  torch.cat([original_data[i][0].unsqueeze(0).long() for i in range(len(test_data))])
    labels =  torch.cat( [original_data[i][1] for i in range(len(test_data))], 0)
    return features, labels

train_set = Data.TensorDataset(*(pretreatment(train_data)))
test_set = Data.TensorDataset(*(pretreatment(test_data)))

batch_size = 2
train_iter = Data.DataLoader(train_set, batch_size, shuffle=True)
test_iter = Data.DataLoader(test_set, batch_size, shuffle=True)

del(train_data)
del(test_data)
del(train_set)
del(test_set)

定义网络

class GlobalMaxPool1d(nn.Module):
    def __init__(self):
        super(GlobalMaxPool1d, self).__init__()
    def forward(self, x):
         # x shape: (batch_size, channel, seq_len)
        return F.max_pool1d(x, kernel_size=x.shape[2]) # shape: (batch_size, channel, 1)

class TextCNN(nn.Module):
    def __init__(self, embed_size, kernel_sizes, num_channels):
        super(TextCNN, self).__init__()
        # self.embedding = nn.Embedding(len(vocab), embed_size)
        # 不参与训练的嵌入层
        # self.constant_embedding = nn.Embedding(len(vocab), embed_size)
        self.dropout = nn.Dropout(0.5)
        self.decoder = nn.Linear(sum(num_channels), 2)
        # 时序最大池化层没有权重，所以可以共用一个实例
        self.pool = GlobalMaxPool1d()
        self.convs = nn.ModuleList()  # 创建多个一维卷积层
        
        for c, k in zip(num_channels, kernel_sizes):
            self.convs.append(nn.Conv1d(in_channels = 2*embed_size, 
                                        out_channels = c, 
                                        kernel_size = k))
 
    def forward(self, inputs):
        outputs = model(inputs)[0] #shape（500， 768）
        embeddings = torch.cat((
            outputs, 
            outputs), dim=2) # (batch, seq_len, 2*embed_size)
        # 根据Conv1D要求的输入格式，将词向量维，即一维卷积层的通道维(即词向量那一维)，变换到前一维
        embeddings = embeddings.permute(0, 2, 1) # 交换维度的函数
        # 对于每个一维卷积层，在时序最大池化后会得到一个形状为(批量大小, 通道大小, 1)的
        # Tensor。使用flatten函数去掉最后一维，然后在通道维上连结
        encoding = torch.cat([self.pool(F.relu(conv(embeddings))).squeeze(-1) for conv in self.convs], dim=1)
        # 应用丢弃法后使用全连接层得到输出
        outputs = self.decoder(self.dropout(encoding))
        return outputs

训练网络

embed_size, kernel_sizes, nums_channels = 768, [3, 4, 5], [100, 100, 100]
net = TextCNN(embed_size, kernel_sizes, nums_channels)

def evaluate_accuracy(data_iter, net, device=None):
    if device is None and isinstance(net, torch.nn.Module):
        # 如果没指定device就使用net的device
        device = list(net.parameters())[0].device 
    acc_sum, n = 0.0, 0
    with torch.no_grad():
        for X, y in data_iter:
            if isinstance(net, torch.nn.Module):
                net.eval() # 评估模式, 这会关闭dropout
                acc_sum += (net(X.to(device)).argmax(dim=1) == y.to(device)).float().sum().cpu().item()
                net.train() # 改回训练模式
            else: # 自定义的模型, 3.13节之后不会用到, 不考虑GPU
                if('is_training' in net.__code__.co_varnames): # 如果有is_training这个参数
                    # 将is_training设置成False
                    acc_sum += (net(X, is_training=False).argmax(dim=1) == y).float().sum().item() 
                else:
                    acc_sum += (net(X).argmax(dim=1) == y).float().sum().item() 
            n += y.shape[0]
    return acc_sum / n

def train(train_iter, test_iter, net, loss, optimizer, device, num_epochs):
    net = net.to(device)
    print("training on ", device)
    batch_count = 0
    for epoch in range(num_epochs):
        train_l_sum, train_acc_sum, n, start = 0.0, 0.0, 0, time.time()
        for X, y in train_iter:
            X = X.to(device)
            y = y.to(device)
            y_hat = net(X)
            l = loss(y_hat, y) 
            optimizer.zero_grad()
            l.backward()
            optimizer.step()
            train_l_sum += l.cpu().item()
            train_acc_sum += (y_hat.argmax(dim=1) == y).sum().cpu().item()
            n += y.shape[0]
            batch_count += 1
        test_acc = evaluate_accuracy(test_iter, net)
        print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f, time %.1f sec'
              % (epoch + 1, train_l_sum / batch_count, train_acc_sum / n, test_acc, time.time() - start))

lr, num_epochs = 0.001, 1
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, net.parameters()), lr=lr)
loss = nn.CrossEntropyLoss()
train(train_iter, test_iter, net, loss, optimizer, device, num_epochs)

模型保存

PATH = "./SaveModel/BertBaseEpoch1.pth"
torch.save(net, PATH) 

输入文字进行测试

def predict_sentiment(net, sentence):
    """sentence是词语的列表"""
    device = list(net.parameters())[0].device
    sentence = torch.tensor(tokenizer.encode(s, add_special_tokens=True), device=device)
    label = torch.argmax(net(sentence.view((1, -1))), dim=1)
    return 'positive' if label.item() == 1 else 'negative'

print("请输入一句评价电影的英文：")
s = input() 
# 需不需要填充到500
print(predict_sentiment(net, s))

模型读取与测试

import torch
from torch import nn
import  torch.nn.functional as F
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

from transformers import BertModel, BertTokenizer
# 这里我们调用bert-base模型，同时模型的词典经过小写处理
model_name = 'bert-base-uncased'
# 读取模型对应的tokenizer
tokenizer = BertTokenizer.from_pretrained(model_name, cache_dir='./transformers/bert-base-uncased/')
# 载入模型
model = BertModel.from_pretrained(model_name, cache_dir='./transformers/bert-base-uncased/')

model.to(device)
model.eval()

class GlobalMaxPool1d(nn.Module):
    def __init__(self):
        super(GlobalMaxPool1d, self).__init__()
    def forward(self, x):
         # x shape: (batch_size, channel, seq_len)
        return F.max_pool1d(x, kernel_size=x.shape[2]) # shape: (batch_size, channel, 1)

class TextCNN(nn.Module):
    def __init__(self, embed_size, kernel_sizes, num_channels):
        super(TextCNN, self).__init__()
        # self.embedding = nn.Embedding(len(vocab), embed_size)
        # 不参与训练的嵌入层
        # self.constant_embedding = nn.Embedding(len(vocab), embed_size)
        self.dropout = nn.Dropout(0.5)
        self.decoder = nn.Linear(sum(num_channels), 2)
        # 时序最大池化层没有权重，所以可以共用一个实例
        self.pool = GlobalMaxPool1d()
        self.convs = nn.ModuleList()  # 创建多个一维卷积层
        
        for c, k in zip(num_channels, kernel_sizes):
            self.convs.append(nn.Conv1d(in_channels = 2*embed_size, 
                                        out_channels = c, 
                                        kernel_size = k))
 
    def forward(self, inputs):
        outputs = model(inputs)[0] #shape（500， 768）
        embeddings = torch.cat((
            outputs, 
            outputs), dim=2) # (batch, seq_len, 2*embed_size)
        # 根据Conv1D要求的输入格式，将词向量维，即一维卷积层的通道维(即词向量那一维)，变换到前一维
        embeddings = embeddings.permute(0, 2, 1) # 交换维度的函数
        # 对于每个一维卷积层，在时序最大池化后会得到一个形状为(批量大小, 通道大小, 1)的
        # Tensor。使用flatten函数去掉最后一维，然后在通道维上连结
        encoding = torch.cat([self.pool(F.relu(conv(embeddings))).squeeze(-1) for conv in self.convs], dim=1)
        # 应用丢弃法后使用全连接层得到输出
        outputs = self.decoder(self.dropout(encoding))
        return outputs

PATH = "./SaveModel/BertBaseEpoch1.pth"
net = torch.load(PATH)

net.to(device)

def predict_sentiment(net, sentence):
    """sentence是词语的列表"""
    device = list(net.parameters())[0].device
    sentence = torch.tensor(tokenizer.encode(s, add_special_tokens=True), device=device)
    label = torch.argmax(net(sentence.view((1, -1))), dim=1)
    return 'positive' if label.item() == 1 else 'negative'

print("请输入一句评价电影的英文：")
s = input() 
# 需不需要填充到500
print(predict_sentiment(net, s))