AI & MACHINE LEARNING PROGRAM • LEVEL 07 — LOGISTIC CLASSIFICATION

Calculate Binary Cross-entropy with Python

Learn calculate binary cross-entropy with python with a short, executable Python example.

IntermediateBinary cross-entropyLoss

PROBLEM UNDERSTANDING

Input and expected output

Sample input

No input required

Sample output

0.2231

COMPLETE PYTHON PROGRAM

Complete Python implementation

from math import log
y, probability = 1, 0.8
loss = -(y*log(probability) + (1-y)*log(1-probability))
print(round(loss, 4))

CURRENT STEP

Select Start to walk through the important lines.

SELECTED LINE

No line selected

EXPECTED OUTPUT FOR THE SAMPLE

0.2231

0%Step 0 of 0

PROGRAM EXPLANATION

Algorithm and explanation

Import the required standard-library tools and prepare the sample data for calculate binary cross-entropy.
Apply Binary cross-entropy and Loss to compute the required result.
Display the result for calculate binary cross-entropy and compare it with the documented sample output.

This example of calculate binary cross-entropy computes the result directly from the prepared sample data. It demonstrates Binary cross-entropy and Loss and prints a deterministic result that can be checked against the sample output.

EFFICIENCY

Time and space complexity

Time complexity

O(1)

Auxiliary space

O(1)

DEBUGGING CHECKLIST

Common mistakes

Check this

For calculate binary cross-entropy, keep the data shape and value types consistent with Binary cross-entropy.

Check this

Apply Binary cross-entropy in the same order shown by the algorithm; changing the order can change the result.

Check this

Check denominators, numeric ranges and rounding before comparing the calculated value.

Try it yourself

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