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Livello di output
The output layer is the last layer of a neural network, the one that produces the model's final result in the format required by the task at hand: a probability for each class in a classification problem, a continuous numerical value in a regression problem, or a probability distribution over the entire vocabulary in the case of a language model predicting the next token.
The structure of the output layer depends closely on the task: in a language model it is typically a linear projection that transforms the representation from the last hidden layer into a vector with one component per vocabulary token, followed by a softmax function that converts these values into probabilities. In a classifier, it instead has a number of components equal to the number of possible categories.
It is present in every neural network, regardless of the task, and is the point where the abstract internal representation built by the previous layers is translated into an interpretable, usable result: an answer, a label, a prediction. In modern language models, the output layer is often shared, in whole or in part, with the initial embedding layer.
The term simply describes its position and function: it is the layer that generates the output, meaning the final result, as opposed to the input layer that receives raw data and the intermediate hidden layers that process its representation.
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