Week 01 – MNIST Pretraining & Binary Classification
Dates: May 25 – June 1
Internship: AI/ML Intern at SynerSense Pvt. Ltd.
Mentor: Praveen Kulkarni Sir
Focus
This week focused on unsupervised pretraining with autoencoders and transfer learning on the MNIST dataset for binary classification (even vs. odd).
Goals for the Week
- Perform unsupervised pretraining on the MNIST dataset using an autoencoder
- Fine-tune the pretrained encoder for binary classification (even vs. odd digits)
- Evaluate both models and report their performance
Tasks Completed
| Task | Status | Notes |
|---|---|---|
Loaded MNIST dataset using torchvision.datasets | ✅ Completed | Used PyTorch DataLoader for efficient batching |
| Built encoder-decoder architecture for autoencoder pretraining | ✅ Completed | Applied ReLU activations and trained with MSE loss |
| Implemented early stopping during autoencoder training | ✅ Completed | Avoided overfitting using validation loss threshold |
| Saved pretrained encoder model | ✅ Completed | Model checkpoint saved in .pt format |
| Built binary classifier using pretrained encoder | ✅ Completed | Added fully connected layer for even/odd classification |
| Fine-tuned classifier using labeled MNIST | ✅ Completed | Used BCEWithLogitsLoss; improved performance with frozen encoder |
| Evaluated both models and generated classification report | ✅ Completed | Accuracy ~98%, Confusion matrix plotted |
| Documented use cases where pretraining outperforms direct training | ✅ Completed | Reported better feature extraction and convergence benefits |
Key Learnings
- Pretraining with an autoencoder helps extract more structured and meaningful representations.
- Freezing encoder layers during fine-tuning helped stabilize results and speed up training.
- Binary classification tasks benefit from unsupervised feature learning.
- Regularization techniques like early stopping and dropout were crucial.
Problems Faced & Solutions
| Problem | Solution |
|---|---|
| Overfitting during unsupervised pretraining | Applied early stopping and dropout layers |
| Poor accuracy in initial fine-tuning attempts | Froze encoder layers and adjusted learning rate |
| Difficulty in even/odd labeling from digit labels | Mapped digit labels programmatically (0,2,4,6,8 as 0; 1,3,5,7,9 as 1) |
| Unclear performance difference | Added visualizations, confusion matrix, and precision/recall scores |
📎 References
Goals for Next Week
- Begin exploring ComfyUI for synthetic biometric generation
- Implement visual transformations using masking and prompts
- Compare facial realism across tools like InvokeAI and Replicate
Screenshots (Optional)
Add encoder architecture diagrams, loss plots, or confusion matrices here if available.
“Week one showed me how powerful representation learning is. Pretraining builds intuition the model can trust.”
layout: default title: Week 01 parent: Weekly Logs nav_order: 1 —
Week 01 – MNIST Pretraining & Binary Classification
Dates: May 25 – June 1
Internship: AI/ML Intern at SynerSense Pvt. Ltd.
Mentor: Praveen Kulkarni Sir
Focus
This week focused on unsupervised pretraining with autoencoders and transfer learning on the MNIST dataset for binary classification (even vs. odd).
Goals for the Week
- Perform unsupervised pretraining on the MNIST dataset using an autoencoder
- Fine-tune the pretrained encoder for binary classification (even vs. odd digits)
- Evaluate both models and report their performance
Tasks Completed
| Task | Status | Notes |
|---|---|---|
Loaded MNIST dataset using torchvision.datasets | ✅ Completed | Used PyTorch DataLoader for efficient batching |
| Built encoder-decoder architecture for autoencoder pretraining | ✅ Completed | Applied ReLU activations and trained with MSE loss |
| Implemented early stopping during autoencoder training | ✅ Completed | Avoided overfitting using validation loss threshold |
| Saved pretrained encoder model | ✅ Completed | Model checkpoint saved in .pt format |
| Built binary classifier using pretrained encoder | ✅ Completed | Added fully connected layer for even/odd classification |
| Fine-tuned classifier using labeled MNIST | ✅ Completed | Used BCEWithLogitsLoss; improved performance with frozen encoder |
| Evaluated both models and generated classification report | ✅ Completed | Accuracy ~98%, Confusion matrix plotted |
| Documented use cases where pretraining outperforms direct training | ✅ Completed | Reported better feature extraction and convergence benefits |
Key Learnings
- Pretraining with an autoencoder helps extract more structured and meaningful representations.
- Freezing encoder layers during fine-tuning helped stabilize results and speed up training.
- Binary classification tasks benefit from unsupervised feature learning.
- Regularization techniques like early stopping and dropout were crucial.
Problems Faced & Solutions
| Problem | Solution |
|---|---|
| Overfitting during unsupervised pretraining | Applied early stopping and dropout layers |
| Poor accuracy in initial fine-tuning attempts | Froze encoder layers and adjusted learning rate |
| Difficulty in even/odd labeling from digit labels | Mapped digit labels programmatically (0,2,4,6,8 as 0; 1,3,5,7,9 as 1) |
| Unclear performance difference | Added visualizations, confusion matrix, and precision/recall scores |
📎 References
Goals for Next Week
- Begin exploring ComfyUI for synthetic biometric generation
- Implement visual transformations using masking and prompts
- Compare facial realism across tools like InvokeAI and Replicate
Screenshots (Optional)
Add encoder architecture diagrams, loss plots, or confusion matrices here if available.
“Week one showed me how powerful representation learning is. Pretraining builds intuition the model can trust.”