FastPrepImplement Cross-Entropy Loss
Problem · Array

Implement Cross-Entropy Loss

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Problem statement

You are given a batch of model probability distributions probabilities and the correct class index for each example in labels.

Return the mean multiclass cross-entropy loss:

-(1 / n) * sum(log(probabilities[i][labels[i]])).

The problem statement continues
Pro

Examples

Example 1

probabilities = [[0.7,0.2,0.1],[0.1,0.5,0.4]]labels = [0,2]return = 0.6364828379

The mean loss is (-log(0.7) - log(0.4)) / 2.

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Reported in 1 OpenAI interview this week

Unlock this recently reported problem

FastPrep Pro gives you full access to interview problems reported within the last week.

  • Full problem statement and constraints
  • 1 more worked example, explained
  • Guided hints and editorial
  • Run your code on real test cases
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$99 billed yearly — or $19 month-to-month. Cancel anytime.

Free plan — 2 of 2 free unlocks used this week