Differentiable round function in Tensorflow?

Question

So the output of my network is a list of propabilities, which I then round using tf.round() to be either 0 or 1, this is crucial for this project. I then found out that tf.roun

Answer 1

You can use the fact that tf.maximum() and tf.minimum() are differentiable, and the inputs are probabilities from 0 to 1

# round numbers less than 0.5 to zero;
# by making them negative and taking the maximum with 0
differentiable_round = tf.maximum(x-0.499,0)
# scale the remaining numbers (0 to 0.5) to greater than 1
# the other half (zeros) is not affected by multiplication
differentiable_round = differentiable_round * 10000
# take the minimum with 1
differentiable_round = tf.minimum(differentiable_round, 1)

Example:

[0.1,       0.5,     0.7]
[-0.0989, 0.001, 0.20099] # x - 0.499
[0,       0.001, 0.20099] # max(x-0.499, 0)
[0,          10,  2009.9] # max(x-0.499, 0) * 10000
[0,         1.0,     1.0] # min(max(x-0.499, 0) * 10000, 1)