pytorch logits

In PyTorch, logits are the outputs of the last layer of a neural network before applying a softmax activation function. Logits are real-valued numbers that represent the unnormalized probabilities of different classes. These values can be positive or negative and can be interpreted as a measure of confidence in the model's prediction for a given input. Logits are typically used in classification problems, where the goal is to assign a label or category to an input based on its features. The softmax function is then applied to the logits to convert them into probabilities that sum up to one, which can be interpreted as the model's predicted probability distribution over the different classes.