Computing KL divergence for many distributions

2024/10/12 19:15:34

I have a matrix of test probability distributions:

qs = np.array([[0.1, 0.6], [0.9, 0.4] ])

(sums up to 1 in each column) and "true" distribution:

p = np.array([0.5, 0.5])

I would like to calculate the KL divergence from p to every column of qs in TensorFlow. I know that there is a function tf.distributions.kl_divergence, but it takes just two distributions...

Answer

You could loop through the columns:

sess = tf.InteractiveSession()
A = tf.distributions.Categorical(probs=[0.5, 0.5])for i in range(2):B = tf.distributions.Categorical(probs=list(qs[:,i]))print(tf.distributions.kl_divergence(A, B).eval())

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