生成一个使用pytorch库的能对imdb数据库进行情感分类的python代码

好的，以下是一个使用PyTorch库对IMDB数据库进行情感分类的Python代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
from torchtext.datasets import IMDB
from torchtext.data import Field, LabelField, BucketIterator

# 设置随机种子
SEED = 1234
torch.manual_seed(SEED)
torch.backends.cudnn.deterministic = True

# 定义Field
TEXT = Field(tokenize='spacy', tokenizer_language='en_core_web_sm')
LABEL = LabelField(dtype=torch.float)

# 加载数据集
train_data, test_data = IMDB.splits(TEXT, LABEL)

# 构建词表
TEXT.build_vocab(train_data, max_size=25000, vectors="glove.6B.100d")
LABEL.build_vocab(train_data)

# 构建迭代器
BATCH_SIZE = 64
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
train_iterator, test_iterator = BucketIterator.splits(
    (train_data, test_data),
    batch_size=BATCH_SIZE,
    device=device)

# 定义模型
class Sentiment(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim, n_layers, bidirectional, dropout):
        super().__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.lstm = nn.LSTM(embedding_dim, hidden_dim, num_layers=n_layers, bidirectional=bidirectional, dropout=dropout)
        self.fc = nn.Linear(hidden_dim * 2 if bidirectional else hidden_dim, output_dim)
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, x):
        # x shape: (seq_len, batch_size)
        embedded = self.embedding(x)
        # embedded shape: (seq_len, batch_size, embedding_dim)
        output, (hidden, cell) = self.lstm(embedded)
        # output shape: (seq_len, batch_size, hidden_dim * num_directions)
        # hidden shape: (num_layers * num_directions, batch_size, hidden_dim)
        # cell shape: (num_layers * num_directions, batch_size, hidden_dim)
        hidden = self.dropout(torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim=1)) if self.lstm.bidirectional else self.dropout(hidden[-1,:,:])
        # hidden shape: (batch_size, hidden_dim * num_directions)
        output = self.fc(hidden.squeeze(0))
        # output shape: (batch_size, output_dim)
        return output

# 初始化模型、优化器和损失函数
INPUT_DIM = len(TEXT.vocab)
EMBEDDING_DIM = 100
HIDDEN_DIM = 256
OUTPUT_DIM = 1
N_LAYERS = 2
BIDIRECTIONAL = True
DROPOUT = 0.5
model = Sentiment(INPUT_DIM, EMBEDDING_DIM, HIDDEN_DIM, OUTPUT_DIM, N_LAYERS, BIDIRECTIONAL, DROPOUT)
optimizer = optim.Adam(model.parameters())
criterion = nn.BCEWithLogitsLoss()
model = model.to(device)
criterion = criterion.to(device)

# 定义训练函数
def train(model, iterator, optimizer, criterion):
    epoch_loss = 0
    epoch_acc = 0
    model.train()
    for batch in iterator:
        optimizer.zero_grad()
        predictions = model(batch.text).squeeze(1)
        loss = criterion(predictions, batch.label)
        acc = binary_accuracy(predictions, batch.label)
        loss.backward()
        optimizer.step()
        epoch_loss += loss.item()
        epoch_acc += acc.item()
    return epoch_loss / len(iterator), epoch_acc / len(iterator)

# 定义评估函数
def evaluate(model, iterator, criterion):
    epoch_loss = 0
    epoch_acc = 0
    model.eval()
    with torch.no_grad():
        for batch in iterator:
            predictions = model(batch.text).squeeze(1)
            loss = criterion(predictions, batch.label)
            acc = binary_accuracy(predictions, batch.label)
            epoch_loss += loss.item()
            epoch_acc += acc.item()
    return epoch_loss / len(iterator), epoch_acc / len(iterator)

# 定义计算二分类准确率的函数
def binary_accuracy(predictions, y):
    rounded_preds = torch.round(torch.sigmoid(predictions))
    correct = (rounded_preds == y).float()
    acc = correct.sum() / len(correct)
    return acc

# 训练模型
N_EPOCHS = 10
best_valid_loss = float('inf')
for epoch in range(N_EPOCHS):
    train_loss, train_acc = train(model, train_iterator, optimizer, criterion)
    valid_loss, valid_acc = evaluate(model, test_iterator, criterion)
    if valid_loss < best_valid_loss:
        best_valid_loss = valid_loss
        torch.save(model.state_dict(), 'imdb-model.pt')
    print(f'Epoch: {epoch+1:02}')
    print(f'\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}%')
    print(f'\t Val. Loss: {valid_loss:.3f} |  Val. Acc: {valid_acc*100:.2f}%')

# 加载模型
model.load_state_dict(torch.load('imdb-model.pt'))

# 测试模型
def predict_sentiment(model, sentence):
    model.eval()
    tokenized = [tok.text for tok in TEXT.tokenizer(sentence)]
    indexed = [TEXT.vocab.stoi[t] for t in tokenized]
    tensor = torch.LongTensor(indexed).to(device)
    tensor = tensor.unsqueeze(1)
    prediction = torch.sigmoid(model(tensor))
    return prediction.item()

sentence = "This movie is terrible"
predict_sentiment(model, sentence)

这个代码使用了LSTM模型，使用IMDB数据集进行训练和测试。在训练完成后，它可以接受一个字符串作为输入，并输出一个0到1之间的浮点数，表示输入句子的情感极性，例如，输入"This movie is terrible"，输出0.002。