PyTorch单层LSTM中Dropout降低测试与训练精度问题咨询

Hey there! Let's walk through what's going on with your model and whether your setup is correct—spoiler: most of what you're seeing is totally expected behavior.

First: Your Initial Setup is Correct #

You're right about PyTorch's nn.LSTM dropout parameter: it only applies to intermediate layers of multi-layer LSTMs, so it does nothing for a single-layer LSTM. Adding a Dropout layer before your fully connected (FC) layer is a perfectly valid workaround to add regularization here. No mistake there!

Why the Accuracy Changes Happen #

Let's break down the three accuracy numbers you saw:

1. 97.75% (no Dropout): Without regularization, your model is likely overfitting to the MNIST training data a bit. It's memorizing patterns specific to the training set that don't generalize perfectly, but still gets a high test score because MNIST is a relatively simple dataset.
2. 95.36% (Dropout added, no eval() mode): When you don't switch to eval() mode during testing, the Dropout layer is still active—meaning it's randomly zeroing out neurons in your FC layer during inference. This effectively uses a "partial" version of your model, which naturally leads to lower accuracy because the model isn't using all its learned weights.
3. 96.44% (Dropout added, with eval() mode): This is the real test accuracy of your regularized model. Calling model.eval() disables Dropout (and other training-only layers like BatchNorm), so your model uses all its learned weights during inference. The drop from the no-Dropout score is a tradeoff: Dropout reduces overfitting, which can lower peak test accuracy slightly but makes the model more robust to unseen data.

Tips to Optimize Your Model #

• Always pair eval() with torch.no_grad() during testing: While eval() turns off Dropout, torch.no_grad() disables gradient computation, saving memory and speeding up inference. Your test code should look something like this:

  model.eval()
  with torch.no_grad():
      # Run test data through model and calculate accuracy
  

• Tune the Dropout probability: 0.5 is a common default, but you might get better results with a lower value (like 0.3) if you want to balance regularization and accuracy. Experiment with different values to see what works best for your model.
• Check training vs. validation accuracy: If your no-Dropout model has a huge gap between training and validation accuracy, that's a clear sign of overfitting—Dropout is doing its job by reducing that gap, even if the final test accuracy is a bit lower.
• Double-check your layer order: Make sure your Dropout layer is placed after the LSTM output and before the FC layer, like this:

  def forward(self, x):
      lstm_out, _ = self.lstm(x)
      # Grab the last time step's output for classification
      final_out = lstm_out[:, -1, :]
      final_out = self.dropout(final_out)
      logits = self.fc(final_out)
      return logits
  

Final Takeaway #

Your operations are totally correct—this is just how Dropout regularization works. The accuracy drop when enabling Dropout (and recovering some when switching to eval()) is a normal part of using regularization to build more generalizable models.

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