tf KL divergence

tf_KL_divergence.py

import tensorflow as tf
import numpy as np

m1 = np.array([[[0.0], [1.1]], [[0.1], [1.2]], [[0.2], [1.3]]])
s1 = np.array([[[1.1]], [[1.2]], [[1.3]]])
m2 = np.array([[[1.0]], [[1.1]], [[1.2]]])
s2 = np.array([[[0.1]], [[0.2]], [[0.3]]])
print(m1.shape)

nd_1 = tf.distributions.Normal(loc=m1, scale=s1)
nd_2 = tf.distributions.Normal(loc=m2, scale=s2)
KL = tf.distributions.kl_divergence(nd_1, nd_2)   # to be added to loss function, try to minimize KL
with tf.Session() as sess:
    print(sess.run(KL))