Learning rate selection in Computer Vision - Model Pipeline Trace

Model Pipeline - Learning rate selection

This pipeline shows how choosing different learning rates affects training a simple image classifier. The learning rate controls how big steps the model takes to learn from mistakes.

Data Flow - 5 Stages

1Input images

1000 images x 28 x 28 pixels x 1 channel→Load grayscale images of handwritten digits→1000 images x 28 x 28 pixels x 1 channel

Image of digit '7' as 28x28 pixel grayscale array

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2Preprocessing

1000 images x 28 x 28 x 1→Normalize pixel values to range 0-1→1000 images x 28 x 28 x 1

Pixel value 150 scaled to 0.59

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3Feature extraction

1000 images x 28 x 28 x 1→Flatten images to 784 features→1000 samples x 784 features

28x28 image converted to 1D array of 784 numbers

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4Model training

800 samples x 784 features→Train simple neural network with chosen learning rate→Trained model weights

Weights updated after each batch using learning rate 0.01

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5Evaluation

200 samples x 784 features→Calculate accuracy and loss on test set→Accuracy and loss values

Test accuracy 85%, loss 0.35

Training Trace - Epoch by Epoch

Loss
1.2 |*       
0.9 | **     
0.7 |  ***   
0.55|   **** 
0.45|    *****
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	0.45	High loss and low accuracy; model just started learning
2	0.9	0.60	Loss decreased, accuracy improved
3	0.7	0.72	Model learning well with chosen learning rate
4	0.55	0.80	Loss continues to decrease, accuracy rises
5	0.45	0.85	Training converging nicely

Prediction Trace - 4 Layers

Layer 1: Input image flattening

Layer 2: First dense layer with ReLU

Layer 3: Output layer with softmax

Layer 4: Prediction

Model Quiz - 3 Questions

What happens if the learning rate is too high?

A Model learns faster and perfectly

B Loss may bounce up and down without decreasing

C Loss decreases smoothly every epoch

D Model ignores the learning rate

Key Insight

Choosing the right learning rate helps the model learn steadily without jumping around or learning too slowly. Normalizing inputs and using proper activations ensure smooth training and accurate predictions.