Pytorch 中非常高的验证损失/小火车损失，同时对 resnet 50 进行微调

如何解决Pytorch 中非常高的验证损失/小火车损失，同时对 resnet 50 进行微调

我正在训练模型来对 2 种类型的图像进行分类。我决定采用迁移学习方法，冻结 resnet50 和新层的每个部分并开始微调过程。我的数据集并不完全平衡，但我为此使用了权重。请查看验证损失与训练损失图。这似乎非常不一致。你能看看我的代码吗？我是 Pytorch 的新手，也许我的方法和代码有问题。在测试集上测试的最终准确率为 86%。谢谢！

        learning_rate = 1e-1
        num_epochs = 100
        patience = 10
        batch_size = 100
        weights = [4,1]    
        
        model = models.resnet50(pretrained=True)
        
        # Replace last layer       
        num_features = model.fc.in_features
        
        model.fc = nn.Sequential(
                nn.Linear(num_features,512),nn.ReLU(inplace=True),nn.Linear(512,64),nn.Dropout(0.5,inplace=True),nn.Linear(64,2))
    
        class_weights = torch.FloatTensor(weights).cuda()
        criterion = nn.CrossEntropyLoss(weight=class_weights)
        optimizer = torch.optim.SGD(model.parameters(),lr=learning_rate)
        running_loss = 0
        losses = []
        
            # To freeze the residual layers
        
            for param in model.parameters():
                param.requires_grad = False
            for param in model.fc.parameters():
                param.requires_grad = True
            
            # Find total parameters and trainable parameters
            total_params = sum(p.numel() for p in model.parameters())
            print(f'{total_params:,} total parameters.')
            total_trainable_params = sum(
                p.numel() for p in model.parameters() if p.requires_grad)
            print(f'{total_trainable_params:,} training parameters.')

总共 24,590,082 个参数。 1,082,050 个训练参数。

# initialize the early_stopping object
early_stopping = pytorchtools.EarlyStopping(patience=patience,verbose=True)
for epoch in range(num_epochs):
    ##########################    
    #######TRAIN MODEL########
    ##########################
    epochs_loss=0
    
    ##Switch to train mode
    model.train()
    for i,(images,labels) in enumerate(train_dl):
        # Move tensors to the configured device
        images = images.to(device)
        labels = labels.to(device)
        # Forward pass
       
        
        # Backprpagation and optimization
        optimizer.zero_grad()
        outputs = model(images).to(device)
        loss = criterion(outputs,labels)
        
        loss.backward()
        optimizer.step()
        #calculate train_loss
        train_losses.append(loss.item())
    
    ##########################    
    #####VALIDATE MODEL#######
    ##########################
    model.eval()
    for images,labels in val_dl:
        images = images.to(device)
        labels = labels.to(device)
        outputs = model(images).to(device)
        loss = criterion(outputs,labels)
        valid_losses.append(loss.item())
    
    # print training/validation statistics 
    # calculate average loss over an epoch
    train_loss = np.average(train_losses)
    valid_loss = np.average(valid_losses)
#     print(train_loss)
    avg_train_losses.append(train_loss)
    avg_valid_losses.append(valid_loss)
    
    print_msg = (f'train_loss: {train_loss:.5f} ' + f'valid_loss: {valid_loss:.5f}')
    
    print(print_msg)

    
    # clear lists to track next epoch
    train_losses = []
    valid_losses = []
    
    early_stopping(valid_loss,model)
    print(epoch)
        
    if early_stopping.early_stop:
        print("Early stopping")
        break