lstm文本分类pytorch

您好！要实现LSTM文本分类，您可以使用PyTorch库。下面是一个简单的示例代码：

首先，导入所需的库和模块：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchtext.datasets import AG_NEWS
from torchtext.data.utils import get_tokenizer
from torchtext.vocab import build_vocab_from_iterator

接下来，定义一个LSTM模型类：

class LSTMModel(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim, num_layers):
        super(LSTMModel, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.lstm = nn.LSTM(embedding_dim, hidden_dim, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_dim, output_dim)

    def forward(self, x):
        embedded = self.embedding(x)
        output, (hidden, cell) = self.lstm(embedded)
        hidden = hidden[-1]
        return self.fc(hidden)

然后，加载数据集并进行预处理：

train_iter = AG_NEWS(split='train')
tokenizer = get_tokenizer('basic_english')
train_iter = [tokenizer(item[1]) for item in train_iter]

vocab = build_vocab_from_iterator(train_iter)
vocab.set_default_index(vocab["<unk>"])
vocab_size = len(vocab)

train_dataset = [[vocab[token] for token in tokens] for tokens in train_iter]

接下来，创建一个自定义的collate函数用于将数据打包成batch：

def collate(batch):
    labels = torch.tensor([entry[0] for entry in batch])
    text = [entry[1:] for entry in batch]
    text = nn.utils.rnn.pad_sequence(text, padding_value=vocab["<pad>"], batch_first=True)
    return labels, text

然后，设置模型的超参数并创建数据加载器：

embedding_dim = 100
hidden_dim = 256
output_dim = 4
num_layers = 2
batch_size = 64
epochs = 10

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, collate_fn=collate)

接下来，实例化模型并定义损失函数和优化器：

model = LSTMModel(vocab_size, embedding_dim, hidden_dim, output_dim, num_layers)
model.to(device)

criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters())

然后，进行训练和评估：

for epoch in range(epochs):
    model.train()
    for labels, text in train_dataloader:
        labels = labels.to(device)
        text = text.to(device)

        optimizer.zero_grad()
        outputs = model(text)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

    model.eval()
    # 在验证集上评估模型性能

这是一个基本的LSTM文本分类的示例，您可以根据您的数据集和任务需求进行适当的修改和调整。希望对您有帮助！如果有任何问题，请随时提问。