使用交叉熵损失函数和Adam优化器来训练bigru加注意力预测模型的代码示例

以下是一个基于Pytorch实现的BiGRU+Attention预测模型训练的代码示例：

import torch.optim as optim

# 定义模型
model = BiGRU_Attention(vocab_size, embedding_dim, hidden_dim, output_dim, dropout).to(device)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters())

# 训练模型
def train(model, iterator, optimizer, criterion):
    model.train()
    epoch_loss = 0

    for batch in iterator:
        optimizer.zero_grad()
        text, label = batch.text.to(device), batch.label.to(device)
        output = model(text)
        loss = criterion(output, label)
        loss.backward()
        optimizer.step()
        epoch_loss += loss.item()

    return epoch_loss / len(iterator)

# 测试模型
def evaluate(model, iterator, criterion):
    model.eval()
    epoch_loss = 0

    with torch.no_grad():
        for batch in iterator:
            text, label = batch.text.to(device), batch.label.to(device)
            output = model(text)
            loss = criterion(output, label)
            epoch_loss += loss.item()

    return epoch_loss / len(iterator)

# 训练模型
N_EPOCHS = 10

best_valid_loss = float('inf')

for epoch in range(N_EPOCHS):
    train_loss = train(model, train_iterator, optimizer, criterion)
    valid_loss = evaluate(model, valid_iterator, criterion)

    if valid_loss < best_valid_loss:
        best_valid_loss = valid_loss
        torch.save(model.state_dict(), 'model.pt')

    print(f'Epoch: {epoch+1:02} | Train Loss: {train_loss:.3f} | Val. Loss: {valid_loss:.3f}')

# 加载最好的模型
model.load_state_dict(torch.load('model.pt'))

# 测试模型
test_loss = evaluate(model, test_iterator, criterion)
print(f'Test Loss: {test_loss:.3f}')

在训练过程中，先将模型设置为训练状态（model.train()），然后对每个batch进行训练，计算损失函数并进行反向传播和参数更新。训练完一个epoch后，将模型设置为评估状态（model.eval()），然后对验证集进行评估。如果当前的验证集损失函数比之前的最佳损失函数还要小，就保存当前模型的参数（torch.save(model.state_dict(), 'model.pt')）。最后，加载最好的模型并对测试集进行评估。