In the following TensorFlow function, we must feed the activation of artificial neurons in the final layer. That I understand. But I don't understand why it is called logits? Isn't that a mathematical function?
loss_function = tf.nn.softmax_cross_entropy_with_logits(
logits = last_layer,
labels = target_output
)
Logits is an overloaded term which can mean many different things:
In Math, Logit is a function that maps probabilities ([0, 1]
) to R ((-inf, inf)
)
Probability of 0.5 corresponds to a logit of 0. Negative logit correspond to probabilities less than 0.5, positive to > 0.5.
In ML, it can be
the vector of raw (non-normalized) predictions that a classification model generates, which is ordinarily then passed to a normalization function. If the model is solving a multi-class classification problem, logits typically become an input to the softmax function. The softmax function then generates a vector of (normalized) probabilities with one value for each possible class.
Logits also sometimes refer to the element-wise inverse of the sigmoid function.