Hey there, let's dig into why your loss is spiking wildly after the 1500th iteration—even with a learning rate that seems reasonable at 0.0075. Here are the most common causes and actionable fixes to test out:

• Missing Learning Rate Decay
  Even a "low" fixed learning rate can become too aggressive once your model gets close to a local minimum. After 1500 iterations, the model might start overshooting the optimal parameters, leading to extreme oscillations.
  Fix: Implement learning rate scheduling. Try ReduceLROnPlateau to automatically lower LR when validation loss plateaus, or a step decay (e.g., multiply LR by 0.5 every 500 iterations).

• Too Small Batch Size
  Tiny batches mean noisy gradient estimates. Early in training, this noise gets drowned out by large parameter updates, but once the model is near convergence, that noise pushes parameters back and forth violently.
  Fix: Increase your batch size if GPU memory allows. If not, use gradient accumulation—sum gradients over 2-4 small batches before updating parameters to mimic a larger batch.

• Data Distribution Drift
  It’s possible that after 1500 iterations, your training data batches start deviating from the early-stage distribution (e.g., a bug in the data loader, or overly random data augmentation creating outlier batches).
  Fix: Audit your data pipeline—check if later batches have abnormal samples (visualize a few!) and ensure consistent preprocessing across all iterations.

• Hidden Gradient Instability
  A low LR doesn’t prevent gradient explosions or vanishing later in training. For example, if you’re using ReLU, dead neurons (zero activation) can build up over time, leading to stalled gradients that cause the model to make drastic, erratic updates when it finally gets a usable gradient.
  Fix: Add gradient clipping (e.g., torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0) to cap gradient magnitudes). Swap ReLU for LeakyReLU to avoid dead neurons, and log gradient norms at each iteration to spot sudden spikes at the 1500 mark.

• Loss Function Numerical Instability
  If your loss involves operations like log or division, extreme model outputs (e.g., probabilities approaching 0 or 1) can cause numerical underflow/overflow, leading to erratic loss values.
  Fix: Stabilize your loss calculations—add a small epsilon (like 1e-8) to log inputs, or use numerically stable loss implementations (many frameworks have built-in versions, e.g., nn.CrossEntropyLoss instead of manual softmax + log loss).

• Hardware/Environment Glitches
  Rare but possible: A temporary GPU memory issue, overheating, or driver glitch at the 1500th iteration could corrupt gradient calculations.
  Fix: Monitor GPU usage and temperature during training. Restart the training run to see if the oscillation happens at the exact same iteration—if not, it’s likely a one-off hardware blip.

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