Learning rate for fine-tuning in TensorFlow - Model Pipeline Trace

Model Pipeline - Learning rate for fine-tuning

This pipeline shows how a pre-trained model is fine-tuned on new data using a smaller learning rate. Fine-tuning helps the model adjust gently to new information without forgetting what it already learned.

Data Flow - 5 Stages

1Load pre-trained model

224 rows x 224 columns x 3 channels→Load a model trained on a large dataset (e.g., ImageNet)→224 rows x 224 columns x 3 channels

An image of a cat with size 224x224 pixels and 3 color channels

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2Freeze base layers

224 rows x 224 columns x 3 channels→Freeze weights of early layers to keep learned features→224 rows x 224 columns x 3 channels (frozen layers)

Early layers detecting edges and colors remain unchanged

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3Add new classification head

Output from frozen layers→Add new layers for the specific task (e.g., 5 classes)→5 classes output

New layers classify images into 5 categories

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4Compile model with small learning rate

Model with frozen base and new head→Set optimizer with a small learning rate (e.g., 0.0001)→Compiled model ready for fine-tuning

Adam optimizer with learning rate 0.0001

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5Train on new data

1000 images x 224 x 224 x 3→Fine-tune model weights on new dataset→1000 images x 5 class predictions

Model adjusts weights slightly to new categories

Training Trace - Epoch by Epoch

Loss:
0.9 |************
0.8 |**********  
0.7 |********   
0.6 |*******    
0.5 |*****      
0.4 |****       
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.85	0.60	Initial fine-tuning with small learning rate starts improving accuracy
2	0.65	0.72	Loss decreases steadily, accuracy improves
3	0.50	0.80	Model learns new task well without forgetting old features
4	0.45	0.83	Fine-tuning stabilizes, small improvements
5	0.42	0.85	Training converges with good accuracy

Prediction Trace - 5 Layers

Layer 1: Input image

Layer 2: Frozen base layers

Layer 3: New classification head

Layer 4: Softmax activation

Layer 5: Prediction output

Model Quiz - 3 Questions

Why do we use a smaller learning rate during fine-tuning?

A To freeze all layers permanently

B To speed up training drastically

C To adjust the model weights gently without losing previous knowledge

D To increase the model size

Key Insight

Using a smaller learning rate during fine-tuning helps the model adapt to new data carefully, preserving useful features learned before. This leads to steady improvement in accuracy and stable training.