Discover the correct loss function in Convolutional Neural Networks (CNNs) for your certification exam. Learn why Cross Entropy is the right choice among Sigmoid, ReLU, and others.
Table of Contents
Question
Which of the following is a loss function?
A. Sigmoid function
B. Cross entropy
C. ReLu
D. All of the above
Answer
B. Cross entropy
Explanation
A. Sigmoid function: This is an activation function, not a loss function. It transforms input values into a range between 0 and 1, making it useful for binary classification tasks, but it does not measure error or loss.
B. Cross entropy: This is indeed a loss function commonly used in classification problems, particularly in neural networks, including Convolutional Neural Networks (CNNs). Cross entropy quantifies the difference between the predicted probability distribution produced by the model and the actual distribution of the labels. The goal during training is to minimize this loss, thus improving model accuracy.
C. ReLU (Rectified Linear Unit): Similar to the sigmoid function, ReLU is also an activation function. It introduces non-linearity into the model and helps in faster convergence during training by allowing only positive values to pass through while zeroing out negative values.
D. All of the above: Since only cross entropy qualifies as a loss function, this option is incorrect.
Importance of Loss Functions
Loss functions are critical in training neural networks as they provide a measure of how well the model’s predictions align with actual outcomes. The choice of loss function can significantly affect how effectively a model learns during training. In classification tasks, cross-entropy loss is favored because it provides a clear gradient for optimization, encouraging models to output probabilities that closely match true labels.
Conclusion
In summary, among the options provided, Cross entropy stands out as the only valid loss function used in CNNs and other neural network architectures for classification tasks. Understanding its role and how it compares to activation functions like Sigmoid and ReLU is essential for mastering concepts related to CNNs and preparing for certification exams in this field.
Convolutional Neural Network CNN certification exam assessment practice question and answer (Q&A) dump including multiple choice questions (MCQ) and objective type questions, with detail explanation and reference available free, helpful to pass the Convolutional Neural Network CNN exam and earn Convolutional Neural Network CNN certification.