AI & MACHINE LEARNING PROGRAM • LEVEL 21 — TRANSFORMERS & LLMS

Compute an Attention Output with Python

Learn compute an attention output with python with a short, executable Python example.

IntermediateSelf-attentionWeighted sum

PROBLEM UNDERSTANDING

Input and expected output

Sample input

No input required

Sample output

[5.0, 7.0]

COMPLETE PYTHON PROGRAM

Complete Python implementation

weights=[0.25,0.75]
values=[[2,4],[6,8]]
output=[sum(weights[i]*values[i][j] for i in range(2)) for j in range(2)]
print(output)

CURRENT STEP

Select Start to walk through the important lines.

SELECTED LINE

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EXPECTED OUTPUT FOR THE SAMPLE

[5.0, 7.0]

0%Step 0 of 0

PROGRAM EXPLANATION

Algorithm and explanation

Initialize the sample values used to compute an attention output.
Process the data step by step using Self-attention and Weighted sum.
Display the result for compute an attention output and compare it with the documented sample output.

This example of compute an attention output processes the sample values in a controlled iteration. It demonstrates Self-attention and Weighted sum 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(n)

DEBUGGING CHECKLIST

Common mistakes

Check this

For compute an attention output, keep the data shape and value types consistent with Self-attention.

Check this

Keep every dependent statement inside the correct indented Python block.

Check this

Verify the final Self-attention and Weighted sum 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.