The prediction step in a multi-class neural network utilizes the same procedure as the softmax function 1 pointTrue False

Answer

The statement is True.

The prediction step in a multi-class neural network frequently employs the softmax function to output class probabilities. The softmax function converts raw scores (logits) from the final layer of the neural network into probabilities that sum to 1. This is crucial for multi-class classification tasks where the model needs to predict which class an input belongs to among multiple classes.

In practical terms, if a neural network outputs a vector of scores $z = [z_1, z_2, \dots, z_n]$ for $n$ classes, the softmax function is defined as:

$P(y = j | z) = \frac{e^{z_j}}{\sum_{k=1}^{n} e^{z_k}}$

for each class $j$. Thus, the final predicted class is often the one with the highest probability, making the use of the softmax function essential in converting unbounded logits into a normalized probability distribution across the multiple classes the network is trying to classify.