The softmax function converts its inputs, known as logit or logit scores, to be between 0 and 1, and also normalizes the outputs so that they all sum up to 1. In other words, the softmax function turns your logits into probabilities. Mathematically, the softmax function is defined as follows:
In TensorFlow, the softmax function is implemented. It takes logits and returns softmax activations that have the same type and shape as input logits, as shown in the following image:
The following code is used to implement this: ...
