The Softmax Activation Function with Keras

The Softmax activation function is essential for neural networks dealing with multiclass classification. It converts logits, the outputs of the last layer of a neural network, into a discrete probability distribution over target classes. Softmax ensures probabilities are nonnegative and sum to 1. By learning to maximize logit outputs, models improve their accuracy in class predictions. This post explains Softmax's working, its importance in neural networks, and demonstrates its implementation in Keras.