Need help with implementation of transformer-decoder model
📰 Reddit r/deeplearning
Improve your transformer-decoder model's performance by tweaking hyperparameters and training techniques
Action Steps
- Adjust the learning rate using a scheduler to adapt to the model's convergence
- Implement early stopping to prevent overfitting and stop training when the model's performance stops improving
- Try different optimizer algorithms, such as Adam or RMSProp, to see which one works best for the model
- Experiment with different batch sizes to find the optimal value for the model's training
- Use pre-trained models or transfer learning to leverage existing knowledge and improve convergence speed
Who Needs to Know This
Data scientists and ML engineers can benefit from this lesson to improve their model's performance and convergence speed
Key Insight
💡 Hyperparameter tuning and training techniques can significantly impact the model's convergence speed and performance
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🤖 Improve your transformer-decoder model's performance with these 5 tips! 🚀
Full Article
Hi, I'm a newbie to deep learning and as an exercise, I decided to implement the transformer-decoder model to make a little chatbot. However, while the training process has proven that the model can converge, it does so very very slowly, starting at: Validation Loss : 4.52899, Validation Accuracy: 0.14530, Perplexity: 92.665 , at epoch 20 it's: Epoch [20 / 20] Validation Loss : 2.98253, Validation Accuracy: 0.20009, Perplexity: 19
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