What is the softmax function and how is it used in neural networks?

What is the softmax function and how is it used in neural networks?

The softmax function is a mathematical function that converts a vector of raw scores (logits) into probabilities. It is commonly used in the output layer of neural networks for multi-class classification tasks.

Can you explain how the softmax function works mathematically?

Sure! The softmax function takes a vector of real numbers and exponentiates each element, then normalizes these values by dividing by the sum of all exponentiated values. This ensures that the output probabilities sum to 1.

What are some advantages of using softmax over other activation functions?

One advantage of softmax is that it provides a clear probabilistic interpretation of the output, making it suitable for multi-class classification. It also helps in gradient-based optimization by providing smooth gradients.

Are there any limitations to using the softmax function?

Yes, one limitation is that softmax can be sensitive to outliers in the input data, which can lead to poor performance if not handled properly. Additionally, it assumes that classes are mutually exclusive.