What is the meaning of the word logits in TensorFlow?

Question

I can understand that we have to feed the activation of artificial neurons in the last layer of the TensorFlow function given below, but why we call it logits? I think it's a mathematical function: loss_function = tf.nn.softmax_cross_entropy_with_logits( logits = last_layer, labels = target_output ) Can anyone answer this? Select the correct answer from above options

Answer 1

Logits are values that are used as input to softmax. To understand this better click here this is official by tensorflow. Hence, Logits are used to map probabilities [0,1] to R [-inf, inf] L=ln(p/1-p) p=1/1+e^-L From limits we conclude that the probability approaching to 0.5 corresponds to a logits of 0. Therefore, +ive logits correspond to probability of greater than 0.5 and negative corresponds to a probability value of less than 0.5. Sometimes they are also refer to inverse of sigmoid function. It's mainly used in Statistics. Become Master of Tenserflow by going through this online Artificial Intelligence Course.