AI & MACHINE LEARNING PROGRAM • LEVEL 18 — COMPUTER VISION

Apply Max Pooling with Python

Learn apply max pooling with python with a short, executable Python example.

IntermediateMax poolingFeature map

PROBLEM UNDERSTANDING

Input and expected output

Sample input

No input required

Sample output

[[5, 8], [6, 9]]

COMPLETE PYTHON PROGRAM

Complete Python implementation

pixels=[[1,4,2,3],[5,2,8,1],[0,6,3,7],[4,2,9,5]]
pooled=[[max(pixels[r+i][c+j] for i in range(2) for j in range(2)) for c in (0,2)] for r in (0,2)]
print(pooled)

CURRENT STEP

Select Start to walk through the important lines.

SELECTED LINE

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EXPECTED OUTPUT FOR THE SAMPLE

[[5, 8], [6, 9]]

0%Step 0 of 0

PROGRAM EXPLANATION

Algorithm and explanation

Initialize the sample values used to apply max pooling.
Process the data step by step using Max pooling and Feature map.
Display the result for apply max pooling and compare it with the documented sample output.

This example of apply max pooling processes the sample values in a controlled iteration. It demonstrates Max pooling and Feature map and prints a deterministic result that can be checked against the sample output.

EFFICIENCY

Time and space complexity

Time complexity

O(n)

Auxiliary space

O(n²)

DEBUGGING CHECKLIST

Common mistakes

Check this

For apply max pooling, keep the data shape and value types consistent with Max pooling.

Check this

Keep every dependent statement inside the correct indented Python block.

Check this

Verify the final Max pooling and Feature map result against the sample before trying new data.

Try it yourself

Practice: Run the program with the sample input, predict its output, and then test one boundary case of your own.