PyTorch输出不符合预期：CNN模型对已正确分类数据预测精度未达100%

Hey there, sorry this has been bugging you for days—let’s break down why you’re not hitting that 100% accuracy on your correctly classified data. Here are the most common culprits to check in your PyTorch 0.3.1.post2 code:

1. Your Model Isn’t in Evaluation Mode #

This is the #1 mistake when inference accuracy drops unexpectedly. Layers like Dropout and BatchNorm behave differently during training vs. inference: Dropout randomly deactivates neurons, and BatchNorm uses batch-specific stats instead of precomputed running averages. If you don’t switch to evaluation mode before predicting, these layers will still act like they’re in training, leading to inconsistent results even on known data.

Fix: Add this line right before making predictions:

model.eval()

If you plan to resume training later, remember to call model.train() again to reactivate training-mode behavior.

2. Inconsistent Data Preprocessing #

Even tiny differences in how you prepare data for training vs. prediction can throw off results. For example:

• Did you apply the exact same normalization (mean/std values) to the prediction data as you did during training?
• Are you accidentally applying data augmentation (like random cropping/flipping) during inference?
• Did you resize or format tensors the same way (e.g., channel order, data type like float32)?

Check: Compare your training data loader’s preprocessing pipeline to your prediction pipeline line by line. Ensure every step matches exactly.

3. BatchNorm Running Stats Aren’t Properly Updated #

In PyTorch 0.3.1, BatchNorm relies on running mean and variance calculated during training. If your training loop didn’t properly update these stats (e.g., you forgot to use model.train() during training, or your batch size was too small to get stable stats), the running averages might be inaccurate. Even in eval mode, incorrect stats can lead to wrong predictions.

Check: Verify that during training, you had model.train() enabled, and that you trained for enough epochs to stabilize the running stats.

4. Model Weight Loading Issues #

If you saved your model after training and reloaded it for prediction, there might be a bug in how you loaded the weights:

• Did you save the entire model instead of just the state dict? Saving the state dict is more reliable: torch.save(model.state_dict(), 'model.pth')
• Did you load the state dict correctly? Use model.load_state_dict(torch.load('model.pth')) instead of loading the entire model object.
• Did you accidentally load an older, untrained version of the weights?

Fix: Double-check your save/load code to ensure you’re using the latest, fully trained weights.

5. Bug in Filtering Correctly Classified Data #

It’s possible the "correctly classified" data you’re using isn’t actually 100% accurate. Maybe there’s an error in how you filtered samples where the model’s prediction matched the true label:

• Did you mix up indices when selecting correct samples?
• Did you compare logits (raw model outputs) directly to labels instead of using softmax to get class probabilities?

Check: Print a handful of samples from your filtered dataset to confirm each sample’s true label matches the model’s training-time prediction.

Quick Test to Isolate the Problem #

To narrow things down quickly:

1. Take one sample that was correctly classified during training.
2. Run the model on it once in train mode, once in eval mode.
3. If outputs differ, you forgot to enable eval mode.
4. If outputs are the same but still wrong, check preprocessing or weight loading.

If you can share snippets of your inference loop, preprocessing code, or model save/load logic, we can dig even deeper!

内容的提问来源于stack exchange，提问作者joe