How softmax is used in neural network?
The softmax function takes as input a vector of real-valued numbers and normalizes it into a probability distribution consisting of values between 0 and 1 that sum up to 1. Specifically, the output of the softmax function is given by:
softmax(z_i) = exp(z_i) / sum(exp(z_j))
In a neural network, the softmax function is typically used in the output layer to compute the probabilities of each class given an input. The output of the softmax function can then be used to make a prediction by selecting the class with the highest probability.
Softmax is particularly useful in multiclass classification tasks, where the goal is to predict one of several possible classes for a given input. By using the softmax function in the output layer, the neural network can produce a probability distribution over all classes, which can be used to make a more informed prediction.