Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
Gradient descent minimizes cross-entropy by adjusting model parameters to increase the likelihood of the correct class predictions.
B
Gradient descent works by maximizing the cross-entropy loss, thus leading to poorer model performance.
C
The softmax function is unaffected by changes in model parameters during gradient descent.
D
Cross-entropy loss is only applicable for binary classification problems.
Understanding the Answer
Let's break down why this is correct
Answer
In a softmax‑cross‑entropy setting the gradient of the loss with respect to each weight is the difference between the predicted probability and the true label, multiplied by the input feature. Gradient descent therefore moves each weight in the opposite direction of this gradient, which reduces the loss. Because the loss is convex in the logits, this update always decreases the loss until it reaches a minimum. For example, if a model predicts 0. 7 for class 1 when the true label is 1, the gradient for that weight is 0.
Detailed Explanation
Gradient descent lowers the cross‑entropy loss by changing the model weights. Other options are incorrect because The idea that gradient descent increases the loss is a common mistake; Softmax does change when weights change.
Key Concepts
Cross-entropy loss
Softmax function
Gradient descent
Topic
Multi-class Loss Functions
Difficulty
hard level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1In a multi-class classification problem, how does the choice of loss function impact the gradient descent optimization process?
mediumComputer-science
Practice
2
Question 2In a multi-class classification problem, given a model that outputs the probabilities of each class using softmax, how is the cross-entropy loss calculated when using one-hot encoding for the true labels, and how does this relate to precision and recall in evaluating the model's performance?
hardComputer-science
Practice
3
Question 3Order the following multi-class loss functions based on their typical application from least to most suitable for optimizing a multi-class classification model: A. Hinge Loss → B. Logistic Loss → C. Neyman-Pearson Loss → D. Cross-Entropy Loss
easyComputer-science
Practice
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