Keras NLP不平衡分类任务中class_weight导致准确率骤降的原因排查

Let’s walk through the potential issues here—both in your code and how class weights interact with imbalanced datasets:

1. You’re Wasting Your Pre-Split Validation Set #

Looking at your training code:

history = model.fit(train_X, train_y, batch_size=64, epochs = 30, validation_split = 0.1,verbose = 1)

You already split your data into train_X/val_X and train_y/val_y using train_test_split, but then you’re using validation_split=0.1 in model.fit(). This means:

• Your model is training on 90% of train_X, not the full training dataset you prepared.
• The validation data used during training is a 10% slice of train_X, not your independent, pre-split val_X.

When you added class_weight, this split creates a mismatch between your expected validation data and what the model is actually evaluating against. Worse, the small validation slice from train_X might have an even more skewed class distribution, making the accuracy metric look far worse than it would on your original val_X.

Fix: Replace validation_split=0.1 with validation_data=(val_X, val_y) to use your pre-split validation set consistently.

2. Accuracy Is a Misleading Metric for Imbalanced Data #

Your initial 70% accuracy was likely driven by the model just predicting the majority class(es) most of the time. When you add class weights, the model starts prioritizing minority classes to minimize the weighted loss. This means it will make more "wrong" predictions on the majority class (which makes up most of your data), causing overall accuracy to drop—even though the model is actually performing better at generalizing to all classes.

Don’t judge by accuracy alone: Check per-class metrics like precision, recall, and F1-score (use sklearn.metrics.classification_report), or look at a confusion matrix. You’ll probably see minority class performance improved, even if overall accuracy is lower.

3. Class Weight Calculation Might Be Misaligned #

Double-check that your class_weight_dict maps correctly to your model’s class indices:

• le.fit_transform(df['CHAPTER']) encodes classes based on the order of unique CHAPTER values in your full dataset.
• np.unique(train_y) returns sorted class indices. Since train_y uses the same encoding, this should match, but confirm with:

  print("Label encoder classes:", le.classes_)
  print("Class weight keys:", list(class_weight_dict.keys()))
  print("Class weight values:", list(class_weight_dict.values()))
  

If a minority class has an extremely large weight (e.g., 100x higher than the majority class), it could cause the model to overfit to that small subset, ruining performance on the majority class. Consider capping weights or using data augmentation (like SMOTE) instead if weights are too extreme.

4. Loss Function & Label Format Mismatch #

Class weights work seamlessly with sparse_categorical_crossentropy (for integer labels like your train_y). But if you’re using categorical_crossentropy (with one-hot encoded labels), class_weight won’t behave as expected—you’d need to use sample_weight instead.

Confirm your loss function: If your model’s output layer uses Dense(classes, activation='softmax'), make sure your loss is set to sparse_categorical_crossentropy (since your labels are integers).

5. Training Dynamics Changed (Epochs May Need Adjustment) #

Class weights alter how the model prioritizes error during training. Your original 30 epochs might now be:

• Too few: The model hasn’t had time to adjust to the new loss weighting and balance performance across classes.
• Too many: The model overfits to minority classes, leading to poor majority class predictions.

Check training curves: Plot training/validation loss and accuracy over epochs. If validation loss starts increasing after a certain point, you’re overfitting—stop early with EarlyStopping. If loss is still decreasing, extend the epoch count.

内容的提问来源于stack exchange，提问作者Deshwal