What is difference between tf.truncated_normal and tf.random_normal?

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The documentation says it all:
For truncated normal distribution:

The values are drawn from a normal distribution with specified mean and standard deviation, discarding and re-drawing any samples that are more than two standard deviations from the mean.

Most probably it is easy to understand the difference by plotting the graph for yourself (%magic is because I use jupyter notebook):

import tensorflow as tf
import matplotlib.pyplot as plt

%matplotlib inline  

n = 500000
A = tf.truncated_normal((n,))
B = tf.random_normal((n,))
with tf.Session() as sess:
    a, b = sess.run([A, B])

And now

plt.hist(a, 100, (-4.2, 4.2));
plt.hist(b, 100, (-4.2, 4.2));

enter image description here

The point for using truncated normal is to overcome saturation of tome functions like sigmoid (where if the value is too big/small, the neuron stops learning).

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