Softmax Activation Function
In neural network models that predict a multinomial probability distribution, the softmax activation function is utilised as the activation function in the output layer. Softmax is used as the activation function for multi-class classification problems requiring class membership on more than two labels. The softmax activation will output one value for each node in the output layer by definition. The output numbers will be probabilities (or can be interpreted as such), and the values will add to 1.0. The data must be prepared before modelling a multi-class classification problem. The class labels in the target variable are first label encoded, which means that each class label is assigned an integer from 0 to N-1, where N is the number of class labels. The target variables that have been label encoded (or integer encoded) are then one-hot encoded. This, like the softmax output, is a probabilistic representation of the class label. Each class label and its position are given a position