Specifically, I suppose I wonder about this statement:
Future major versions of TensorFlow will allow gradients to flow into the labels input on backprop by default.
Which is shown when I use
tf.nn.softmax_cross_entropy_with_logits. In the same message it urges me to have a look at
tf.nn.softmax_cross_entropy_with_logits_v2. I looked through the documentation but it only states that for
Backpropagation will happen into both logits and labels. To disallow backpropagation into labels, pass label tensors through a stop_gradients before feeding it to this function.
as opposed to,
Backpropagation will happen only into logits.
Being very new to the subject (I'm trying to make my way through some basic tutorials) those statements are not very clear. I have a shallow understanding of backpropagation but what does the previous statement actually mean? How are backpropagation and the labels connected? And how does this change how I work with
tf.nn.softmax_cross_entropy_with_logits_v2 as opposed to the original?