The AI Interview - Master AI/ML Interviews

Implement a function that computes the average cross-entropy loss for a batch of predictions in a multi-class classification task. Your function should take in a batch of predicted probabilities and one-hot encoded true labels, then return the average cross-entropy loss. Ensure that you handle numerical stability by clipping probabilities by epsilon.

Examples

Example 1:

Input:

predicted_probs = [[0.7, 0.2, 0.1], [0.3, 0.6, 0.1]]
true_labels = [[1, 0, 0], [0, 1, 0]]

Output: 0.4338

Explanation: The predicted probabilities for the correct classes are 0.7 and 0.6. The cross-entropy is computed as -mean(log(0.7), log(0.6)), resulting in approximately 0.4463.

Starter Code

import numpy as np

def compute_cross_entropy_loss(predicted_probs: np.ndarray, true_labels: np.ndarray, epsilon = 1e-15) -> float:
    # Your code here
    pass

Compute Multi-class Cross-Entropy Loss

Examples

Starter Code