NLP - Sequence Models for NLP
After training an RNN model, you notice the training accuracy is very high but validation accuracy is low. Which of the following is the best way to fix this?
After training an RNN model, you notice the training accuracy is very high but validation accuracy is low. Which of the following is the best way to fix this?
medium📝 Debug Q7 of 15
Step-by-Step Solution
Solution:
Step 1: Understand high train but low validation accuracy
This indicates overfitting, where model memorizes training data but fails to generalize.Step 2: Apply regularization technique
Adding dropout randomly disables neurons during training, helping model generalize better.Final Answer:
Add dropout layers to reduce overfitting -> Option CQuick Check:
Overfitting fix = Add dropout [OK]
Quick Trick: Dropout helps prevent overfitting in RNNs [OK]
Common Mistakes:
MISTAKES- Increasing learning rate can worsen overfitting
- Removing embedding layer loses word meaning
- Reducing training samples reduces learning
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