循环神经网络五-使用LSTM完成文本情感分类

在前面使用word embedding去实现了文本情感分类。那么现在在这个模型中添加上LSTM层，为了达到更好的效果，做一下修改

1.MAX_LEN=200

2.构建dataset的过程，将数据转化为2分类，前面是十分类。pos类为1，neg为0，因为25000个要本做十分类数据量太小了

3.在实例化LSTM的时候，使用dropout=0.5，在模型评估过程中，dropout自动会为0

一.修改模型

python 复制代码

"""构建模型model.py文件"""
import torch.nn as nn
import torch
import config
import torch.nn.functional as F

class IMDBLstmmodel(nn.Module):
    def __init__(self):
        super(IMDBLstmmodel,self).__init__()
        self.hidden_size = 64   # 设置隐藏个数
        self.embedding_dim = 200  # 设置一个词语用多大的向量表示
        self.num_layer = 2   # 设置隐藏层数量
        self.bidriectional = True  # 是否使用双向LSTM
        self.bi_num = 2 if self.bidriectional else 1
        self.dropout = 0.5 
        #以上部分为超参数，可以自行修改

        self.embedding = nn.Embedding(len(config.ws),self.embedding_dim,padding_idx=config.ws.PAD) #实例化embedding[N,300]
        self.lstm = nn.LSTM(self.embedding_dim,self.hidden_size,self.num_layer,bidirectional=True,dropout=self.dropout)  # 实例化lstm
        #使用两个全连接层，中间使用relu激活函数
        self.fc = nn.Linear(self.hidden_size*self.bi_num,20)  
        self.fc2 = nn.Linear(20,2)


    def forward(self, x):
        x = self.embedding(x)
        x = x.permute(1,0,2) #进行轴交换
        h_0,c_0 = self.init_hidden_state(x.size(1))
        _,(h_n,c_n) = self.lstm(x,(h_0,c_0))

        #只要最后一个lstm单元处理的结果，这里多去的hidden state
        out = torch.cat([h_n[-2, :, :], h_n[-1, :, :]], dim=-1)
        out = self.fc(out)
        out = F.relu(out)
        out = self.fc2(out)
        return F.log_softmax(out,dim=-1)

    def init_hidden_state(self,batch_size):
        h_0 = torch.rand(self.num_layer * self.bi_num, batch_size, self.hidden_size).to(device)
        c_0 = torch.rand(self.num_layer * self.bi_num, batch_size, self.hidden_size).to(device)
        return h_0,c_0

二.完成训练和测试代码

python 复制代码

train_batch_size = 64
test_batch_size = 5000
# imdb_model = IMDBModel(MAX_LEN) #基础model
imdb_model = IMDBLstmmodel().to(device) #在gpu上运行，提高运行速度
# imdb_model.load_state_dict(torch.load("model/mnist_net.pkl"))
optimizer = optim.Adam(imdb_model.parameters())
criterion = nn.CrossEntropyLoss()

def train(epoch):
    mode = True
    imdb_model.train(mode)
    train_dataloader =get_dataloader(mode,train_batch_size)
    for idx,(target,input,input_lenght) in enumerate(train_dataloader):
        target = target.to(device)
        input = input.to(device)
        optimizer.zero_grad()
        output = imdb_model(input)
        loss = F.nll_loss(output,target) #traget需要是[0,9]，不能是[1-10]
        loss.backward()
        optimizer.step()
        if idx %10 == 0:
            pred = torch.max(output, dim=-1, keepdim=False)[-1]
            acc = pred.eq(target.data).cpu().numpy().mean()*100.

            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}\t ACC: {:.6f}'.format(epoch, idx * len(input), len(train_dataloader.dataset),
                       100. * idx / len(train_dataloader), loss.item(),acc))

            torch.save(imdb_model.state_dict(), "model/mnist_net.pkl")
            torch.save(optimizer.state_dict(), 'model/mnist_optimizer.pkl')
            
 def test():
    mode = False
    imdb_model.eval()
    test_dataloader = get_dataloader(mode, test_batch_size)
    with torch.no_grad():
        for idx,(target, input, input_lenght) in enumerate(test_dataloader):
            target = target.to(device)
            input = input.to(device)
            output = imdb_model(input)
            test_loss  = F.nll_loss(output, target,reduction="mean")
            pred = torch.max(output,dim=-1,keepdim=False)[-1]
            correct = pred.eq(target.data).sum()
            acc = 100. * pred.eq(target.data).cpu().numpy().mean()
            print('idx: {} Test set: Avg. loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\n'.format(idx,test_loss, correct, target.size(0),acc))
            
 if __name__ == "__main__":
    test()
    for i in range(10):
        train(i)
        test()