使用Caffe训练AlexNet：训练损失下降但验证损失上升求助

Hey there! Let's figure out why your AlexNet training is going great (loss dropping nicely) but your validation loss keeps climbing—this is a super common issue, and we can break down the likely culprits based on your setup.

1. You might be over-augmenting your validation set #

Wait a second—you applied all those heavy augmentations (rotation, crop, flip, blur) to your validation set too? That's a big mistake. Validation sets are supposed to mimic the real-world data your model will actually predict on, not be tweaked like training data.

If you're modifying validation images, you're making the model evaluate on data that doesn't represent its intended use case. Even worse, since your validation set is just 700 original images blown up to 70k via augmentation, the model might not be seeing the "real" validation samples it needs to generalize to.

Fix: Use the original 700 validation images without any augmentation for evaluation. Keep the center cropping (since you did that for training to focus on the target), but skip all the random rotations, flips, etc. This will give you an accurate measure of how well the model generalizes.

2. Your model is overfitting to the limited original data #

Even with 300k augmented training samples, they're all derived from just 3000 unique original images. AlexNet is a deep model with plenty of capacity, so it's probably memorizing every single variation of those 3000 images instead of learning generalizable features that work on new data.

Fixes to try:

• Crank up regularization:
  • Check your solver file for weight_decay (L2 regularization). If it's set to something low like 0.0001, bump it to 0.001 or 0.0005 to penalize large weights and prevent overfitting.
  • Ensure your AlexNet has dropout layers in the fully connected blocks (AlexNet typically uses 0.5 dropout). If you don't have dropout, add it—if you do, try increasing the rate to 0.6 or 0.7.
• Use transfer learning: Instead of training from scratch, initialize your AlexNet with pre-trained weights from ImageNet. Then fine-tune only the top 1-2 fully connected layers (or all layers with a very small learning rate, like 0.0001). This gives the model a base of general visual features, so it doesn't have to learn everything from your small dataset.

3. Train/validation data distribution mismatch #

Make sure your training and validation sets are processed exactly the same way (except for augmentation):

• Did you crop the center of validation images the same way as training? If validation images have their targets outside the center crop, you're feeding the model empty or irrelevant regions, which will make validation loss skyrocket.
• Check class balance: If your original 3000 training images have a skewed class distribution (e.g., one class makes up 70% of samples) but your validation set has a different balance, the model will perform poorly on underrepresented classes in validation, driving up overall loss.

4. Solver hyperparameter tweaks might help #

Since you mentioned your solver file, here are common settings to check:

• Learning rate: If your learning rate is too high, the model might be bouncing around the optimal weights instead of converging. Try reducing it (e.g., from 0.01 to 0.001) or adding a learning rate scheduler (like step decay) that lowers the rate every few thousand iterations.
• Early stopping: Stop training when your validation loss stops improving for, say, 1000 iterations. Continuing to train after the model starts overfitting will only make validation loss keep rising.
• Batch size: If your batch size is tiny, the model updates will be noisy, leading to unstable validation loss. Increase it if your GPU memory can handle it (AlexNet typically uses batch sizes of 256 or 128).

Start with the validation set fix first—it's the quickest check, and it's often the root cause here. Then move on to regularization or transfer learning if needed.

内容的提问来源于stack exchange，提问作者Marwa Said