Softmax Cross Entropy损失爆炸求助：像素级分类CNN训练异常

Hey Devin, let's troubleshoot why your pixel-wise classification CNN's loss won't budge when using Softmax + cross-entropy—frustrating stuff, I know. You've already tried adjusting learning rates, epsilon, and switching optimizers, so let's dive into some less obvious checks that might fix this:

• Pixel-wise classification relies on per-pixel cross-entropy, so make sure you're not using a standard classification cross-entropy that averages over the entire batch incorrectly.
• Critical note: Frameworks like PyTorch's CrossEntropyLoss or TensorFlow's SparseCategoricalCrossentropy already include a Softmax layer under the hood. If you added a standalone Softmax layer at the end of your model AND use these loss functions, you're effectively applying Softmax twice—this causes massive numerical instability and will absolutely stall your loss. Drop the explicit Softmax if you're using these built-in loss functions, or use NLLLoss (PyTorch) with log_softmax for more stable calculations.
• Double-check label dimensions: If your model outputs (batch_size, height, width, num_classes), your labels need to match—either as one-hot encoded tensors of the same shape, or as integer index tensors (for sparse cross-entropy, which is more memory-efficient).

• Label integrity: Are your pixel labels correctly aligned with your input images? It's easy to mix up class indices (e.g., swapping background and target classes) or have invalid values (like -1 for unlabeled pixels) that aren't being masked out. Even a small percentage of misaligned labels can stop learning cold.
• Input normalization: Are your images scaled to a range your model expects? Feeding raw 0-255 pixel values into a CNN without normalizing to 0-1 or -1 to 1 can cause gradient explosion/vanishing, making the optimizer unable to update weights effectively.
• Class imbalance: If your dataset is dominated by one class (e.g., 95% background pixels), the model will quickly learn to predict that class to minimize loss, giving you a stagnant low loss that's actually fake convergence. Try adding class weights to your loss function or switching to Focal Loss to prioritize hard-to-learn pixels.

• Forward pass sanity check: Run a single batch through your model and inspect the Softmax outputs. Do all predictions collapse to a single class? If yes, your model initialization might be off (e.g., weights set too large/small) or your first layer is squashing all inputs to the same value.
• Gradient inspection: Check the gradients of your model's weights after a backward pass. If gradients are 0 or NaN, you've got a dead neuron problem (common with ReLU layers—try LeakyReLU instead) or numerical overflow from Softmax. Using log_softmax instead of standard Softmax can mitigate overflow issues.
• Adam hyperparameter deep dive: You adjusted learning rate and epsilon, but what about beta1 and beta2? The default 0.9 for beta1 can slow down early learning—try lowering it to 0.8 to make momentum updates faster. Also, don't sleep on weight decay: adding a small value (like 1e-4) can prevent overfitting, but too much will freeze learning entirely.

• Strip your model down to the basics: Use a tiny dataset (10-20 images), a simple CNN (2 conv layers + 1 dense layer), and see if loss drops. If this minimal model works, gradually reintroduce your original model's components (e.g., skip connections, multi-scale features) to pinpoint which part is causing the stall. If the minimal model still fails, you've got a fundamental code bug (like incorrect loss calculation or data loading) to fix first.

内容的提问来源于stack exchange，提问作者Devin Haslam