KLqp with Beta/Bernoulli (Coin Flip example)

Hi all !
I am very much learning the ropes and just trying out very simple things. For a coin flip model, while using the KLqp inference, the model does not seem to converge. Am I missing/misusing something? Or is there a general reason for the algorithm instability? I am mostly curious from the educational perspective.

NOTE: Using the PointMass+MAP works fine and produces correct result (0.8 in this case)

import tensorflow as tf
import edward as ed
import numpy as np

# Data
data = np.array([1,1,1,1,1,1,1,0,0,1])

# Model
p = ed.models.Beta([2.0], [1.0])
x = ed.models.Bernoulli(probs = tf.ones(10) * p)

# Inference
qp_a = tf.nn.softmax(tf.Variable(tf.random_normal([1])))
qp_b = tf.nn.softmax(tf.Variable(tf.random_normal([1])))
qp = ed.models.Beta(qp_a, qp_b)

inference = ed.KLqp({p: qp}, data={x: data})
inference.run(n_iter = 1000)

print("\nLearnt probability: ", p.eval())
print("\nLearnt (a): ", qp_a.eval())
print("\nLearnt (b): ", qp_b.eval())


1000/1000 [100%] ██████████████████████████████ Elapsed: 9s | Loss: 9.311

Learnt probability:  [0.62244993]

Learnt (a):  [1.]

Learnt (b):  [1.]

You have a typo. tf.nn.softmax will is 1 for every tensor of size [1]. You need to use tf.nn.softplus.