Is the SoftMax layer always at the end of a neural network?

 The softmax layer is typically used as the last layer of a neural network, particularly when the network is used for multi-class classification tasks. The softmax function is a generalization of the logistic function that can handle multiple classes. It converts the output of the final layer of the network, which is typically a set of real numbers, into a probability distribution over the possible classes. This allows the network to output a prediction for the class with the highest probability.

However, the softmax layer doesn't always have to be at the end of a neural network. In some cases, it can be used in an intermediate layer of the network, such as in the case of a Hierarchical Softmax, which is used to speed up the training of the network in problems with a large number of classes.

Additionally, other activation functions such as Sigmoid, ReLU, etc can also be used in the last layer depending on the task and requirements of the model. For example, in a binary classification problem, the sigmoid function might be more suitable than softmax.

In summary, the softmax layer is commonly used as the last layer of a neural network for multi-class classification tasks, but it can also be used in an intermediate layer or replaced by other activation functions in some cases.