Fine-tuning approach in TensorFlow - Model Pipeline Trace

Model Pipeline - Fine-tuning approach

Fine-tuning is a way to teach a model by starting with a model already trained on a big dataset. We then adjust it a little bit with our own smaller dataset to make it work better for our specific task.

Data Flow - 5 Stages

1Load Pretrained Model

None→Load a model trained on a large dataset (e.g., ImageNet)→Model with fixed weights except last layers

A model trained to recognize 1000 types of objects

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2Replace Last Layer

Model output: 1000 classes→Replace last layer to match new task classes (e.g., 5 classes)→Model output: 5 classes

Change output layer to predict 5 types of flowers

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3Freeze Base Layers

Model layers→Freeze weights of base layers to keep learned features→Frozen base layers + trainable last layers

Base layers do not change during initial training

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4Train Last Layers

Training data: 500 images x 224x224x3→Train only last layers on new data→Updated last layers weights

Train last layers to recognize 5 flower types

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5Unfreeze and Fine-tune

Model with trained last layers→Unfreeze some base layers and train with low learning rate→Fine-tuned model weights

Adjust base layers slightly to improve accuracy

Training Trace - Epoch by Epoch

Loss
1.2 |************
0.9 |*********
0.7 |*******
0.6 |******
0.55|*****
0.52|****
0.48|****
0.45|***
0.43|**
0.42|*

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	0.55	Initial training of last layers, loss high, accuracy moderate
2	0.9	0.68	Loss decreases, accuracy improves as model learns
3	0.7	0.75	Continued improvement, model starts to generalize
4	0.6	0.80	Loss decreases steadily, accuracy rises
5	0.55	0.83	Last layers trained well, ready to unfreeze base layers
6	0.52	0.85	Unfreeze some base layers, start fine-tuning with low learning rate
7	0.48	0.87	Fine-tuning improves feature extraction, accuracy rises
8	0.45	0.89	Model converges with better accuracy and lower loss
9	0.43	0.90	Stable training, no overfitting observed
10	0.42	0.91	Final fine-tuned model ready for prediction

Prediction Trace - 6 Layers

Layer 1: Input Image

Layer 2: Base Layers (Frozen)

Layer 3: Fine-tuned Base Layers

Layer 4: New Last Layer

Layer 5: Softmax Activation

Layer 6: Prediction

Model Quiz - 3 Questions

Why do we freeze the base layers during initial training?

A To keep the useful features learned from the big dataset

B To make the model train faster by skipping layers

C Because base layers are not important for new tasks

D To prevent the model from making any predictions

Key Insight

Fine-tuning lets us reuse a big model's knowledge and adjust it gently to new tasks. This saves time and data, while improving accuracy by carefully training only parts of the model first, then fine-tuning more layers.