What is the purpose of the “softmax” function in neural networks?
a) To convert raw model outputs into probabilities
b) To update weights in the network
c) To reduce the dimensionality of the input
d) To cluster data points
Answer:
a) To convert raw model outputs into probabilities
Explanation:
The softmax function is commonly used in the output layer of a neural network for classification tasks. It converts the raw output of the network into probabilities, where each class is assigned a probability between 0 and 1, and the sum of all probabilities is equal to 1.
This allows the network to make predictions by selecting the class with the highest probability. The softmax function is especially useful for multi-class classification problems where the model needs to assign probabilities to multiple categories.
Using softmax in the output layer ensures that the predictions are interpretable as probabilities, which is critical for tasks like image recognition, language modeling, and other classification problems.