Padding and stride in TensorFlow - Model Pipeline Trace

Model Pipeline - Padding and stride

This pipeline shows how padding and stride affect the size of images as they pass through a convolutional layer in a neural network. Padding adds extra pixels around the image edges, and stride controls how the filter moves over the image.

Data Flow - 3 Stages

1Input Image

1 image x 28 height x 28 width x 1 channel→Original grayscale image→1 image x 28 height x 28 width x 1 channel

A 28x28 pixel handwritten digit image

↓

2Apply Padding

1 image x 28 height x 28 width x 1 channel→Add 1 pixel zero padding around all edges→1 image x 30 height x 30 width x 1 channel

Image now has a 1-pixel black border around it

↓

3Convolution with stride

1 image x 30 height x 30 width x 1 channel→Apply 3x3 filter with stride 2→1 image x 14 height x 14 width x 1 channel

Filter moves 2 pixels at a time, reducing size

Training Trace - Epoch by Epoch

Loss
0.7 |****
0.6 |*** 
0.5 |**  
0.4 |*   
0.3 |    
0.2 |    
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.60	Model starts learning, loss is high, accuracy low
2	0.48	0.75	Loss decreases, accuracy improves as model learns features
3	0.35	0.85	Model captures patterns well, loss drops further
4	0.28	0.90	Good convergence, accuracy nearing 90%
5	0.22	0.93	Model stabilizes with low loss and high accuracy

Prediction Trace - 3 Layers

Layer 1: Input Image

Layer 2: Padding

Layer 3: Convolution with stride 2

Model Quiz - 3 Questions

What effect does adding padding have on the image size before convolution?

A Decreases image size by cropping edges

B Does not change image size

C Increases image size by adding pixels around edges

D Changes image colors

Key Insight

Padding helps keep the image size from shrinking too much during convolution, preserving edge details. Stride controls how much the filter moves, affecting output size and computation. Together, they balance detail retention and efficiency in feature extraction.