Why does TensorFlow's documentation call a softmax's input "logits"? : <p><a href="https://www.tensorflow.org/api_docs/python/tf/nn/softmax_cross_entropy_with_logits" rel="noreferrer">TensorFlow calls</a> each of the inputs to a softmax a logit. They go on to define the softmax's inputs/logits as: "Unscaled log probabilities."</p>

<p><a href="https://en.wikipedia.org/wiki/Logit" rel="noreferrer">Wikipedia</a> and other sources say that a logit is the log of the odds, and the inverse of the sigmoid/logistic function. I.e., if sigmoid(x) = p(x), then logit( p(x) ) = log( p(x) / (1-p(x)) ) = x. </p>

<p>Is there a mathematical or conventional reason for TensorFlow to call a softmax's inputs "logits"? Shouldn't they just be called "unscaled log probabilities"? </p>

<p>Perhaps TensorFlow just wanted to keep the same variable name for binary logistic regression (where it makes sense to use the term logit) and categorical logistic regression...</p>

<p>This question was <a href="https://stackoverflow.com/questions/34240703/difference-between-tensorflow-tf-nn-softmax-and-tf-nn-softmax-cross-entropy-with">covered a little bit here</a>, but no one seemed bothered by the use of the word "logit" to mean "unscaled log probability".</p>
