基于crnn图像序列预测-pytorch代码实现——训练过程及常见错误

目录：
1、基于crnn图像序列预测-pytorch代码实现——加载自己的数据集
2、基于crnn图像序列预测-pytorch代码实现——模型介绍
3、基于crnn图像序列预测-pytorch代码实现——训练过程及常见错误

在这里以VGG_LSTM为例，优化算法选的是Adam，损失函数是CrossEntropyLoss()，详细训练代码如下：

if __name__ == "__main__":
    model = VGG_LSTM()
    print(model)
    if torch.cuda.is_available():
        model.cuda()
    optimizer = optim.Adam(model.parameters(), lr=learning_rate)
    loss_func = nn.CrossEntropyLoss()
    for epoch in range(100):
        print('epoch {}'.format(epoch + 1))
        train_loss = 0.
        train_acc = 0.
        for batch_x, batch_y in train_loader:
            # print(batch_x.size())
            batch_x, batch_y = Variable(batch_x).cuda(), Variable(batch_y).cuda()
            out = model(batch_x)
            # print(batch_x.size())
            loss = loss_func(out, batch_y)
            train_loss += loss.data[0]
            pred = torch.max(out, 1)[1]
            train_correct = (pred == batch_y).sum()
            train_acc += train_correct.data[0]
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
        print('Train Loss: {:.6f}, Acc: {:.6f}'.format(train_loss / (len(
            train_data)), train_acc / (len(train_data))))



        # -----------------------evaluation--------------------------------
        model.eval()
        eval_loss = 0.
        eval_acc = 0.
        for batch_x, batch_y in test_loader:
            batch_x, batch_y = Variable(batch_x, volatile=True).cuda(), Variable(batch_y, volatile=True).cuda()
            out = model(batch_x)
            loss = loss_func(out, batch_y)
            eval_loss +=