What is softmax loss? Why can't it be used in hidden layers?

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Softmax loss is a function used in classification tasks to calculate the probability distribution of classes. It cannot be used in hidden layers due to its non-differentiability.

Softmax loss is used in the output layer of neural networks for multi-class classification tasks.
It calculates the probability distribution of classes by normalizing the output scores using the softmax function.
Softmax loss is not used in hidden layers because it is non-differentiable, which makes backpropagation difficult.
Instead, activation functions like ReLU or sigmoid are used in hidden layers for differentiability.

What is softmax loss? Why can't it be used in hidden layers?

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