基于Numpy实现的猫狗分类模型过拟合问题求助

Hey there! Let's tackle that overfitting issue you're facing—training loss dropping nicely but validation loss climbing is a classic sign your model's memorizing training data instead of learning generalizable features. Here are practical, actionable fixes tailored to your setup:

1. Crank Up Regularization (Or Try Different Types) #

You’ve tested λ=0.01 and 0.03, but those might be too weak for your model size. Try scaling up the L2 regularization strength to 0.1, 0.5, or even 1.0—start with bigger jumps to see if validation loss starts stabilizing.

• Don’t forget to apply regularization to all weight layers, including the output layer (easy to miss!).
• If L2 alone isn’t cutting it, experiment with L1 regularization (which drives small weights to zero) or an elastic net combo of L1+L2.

2. Implement Early Stopping #

Right now you’re training for a fixed 600 iterations, but your model likely stops learning useful features long before that. Add early stopping:

• Track both training and validation loss after each iteration.
• Stop training when the validation loss hasn’t improved (or keeps rising) for 10-20 consecutive iterations. Save the weights from the iteration where validation loss was lowest—this prevents over-training on noise.

3. Add Data Augmentation #

Kaggle’s cat-dog dataset isn’t huge, so your model can easily memorize every training image. Manual data augmentation with NumPy will boost diversity:

• Random horizontal flips (super effective for animal images—cats/dogs look natural flipped).
• Random cropping (take a 200x200 crop from a 224x224 image, for example).
• Minor brightness/contrast adjustments (shift pixel values within a small range).
  Apply these only to training data—keep validation data untouched to get an honest performance measure.

4. Shrink Your Model Size #

Your two hidden layers (125 and 50 neurons) might have more capacity than needed for this task. Smaller models are less prone to overfitting:

• Try reducing to 64 and 32 neurons, or even a single hidden layer with 64 neurons.
• Test different sizes incrementally to find the sweet spot where training loss still drops but validation loss stays low.

5. Add Dropout Layers #

Dropout is a powerful trick to reduce neuron dependency. Here’s how to implement it in NumPy:

• During training, for each hidden layer, generate a binary mask (same shape as the layer’s output) where each element is 0 with a dropout probability (try 0.2-0.5) and 1 otherwise. Multiply the layer’s output by this mask.
• During inference (validation/testing), multiply the layer’s output by (1 - dropout_probability) to scale the activations back up (since no neurons are dropped).
  Add dropout after each hidden layer—this forces the model to learn redundant features instead of relying on specific neurons.

6. Tweak Your Learning Rate #

A learning rate of 0.075 might be too high, causing the model to overshoot optimal weights and lock onto training noise. Try:

• Lowering the learning rate to 0.01 or 0.005—smaller steps help the model converge to more generalizable weights.
• Adding learning rate decay: reduce the rate by 10-20% every 100 iterations (e.g., lr = lr * 0.9 after each 100 steps) to stabilize training in later stages.

7. Double-Check Data Preprocessing #

Make sure your data is set up correctly:

• Normalize pixel values: Scale all image pixels to the range [0, 1] or [-1, 1] (divide by 255, or (pixel - 127.5)/127.5). This helps the model train more smoothly and avoids large weight updates from raw pixel values.
• Verify train/validation split: Ensure your validation set is a random, stratified sample (same ratio of cats/dogs as training set) so it’s representative of the overall data.

Start with early stopping and data augmentation—these are usually the quickest wins for overfitting. Then combine them with regularization or model size adjustments to fine-tune.

内容的提问来源于stack exchange，提问作者user9761720