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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.

learner
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