I am using Tensorflow v1.14 for creating networks and training them. Everything works fine and I don't have any problem with code. I use the function tf.reduce_min()
in my loss function. For the gradients to flow, it is essential that the loss function is differentiable. But a min
operator is not differentiable as such. This link, gives the necessary explanation for the tf.reduce_min()
function but without references.
In general there are functions in Tensorflow (tf.cond
, tf.where
, among many more) that are inherently not differentiable by their definition. I want to know how these are made differentiable by defining "pseudo gradients" and the proper references to documentation. Thanks.