Edward Shape Problem

shape-problem.py

import numpy as np
import tensorflow as tf
import edward as ed
# specific modules
from edward.models import Normal

def sample_model(model, n_samples):
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        samples = sess.run(model.sample([n_samples]))
    return samples

# want to fit, so need to use variables
mean = tf.Variable(3.0)
std = tf.Variable(1.0)
N = 10
x = Normal(loc=mean, scale=std)
samples = sample_model(x, N)

print("x is a {0} with shape {1}".format(type(x), x.get_shape()))
print("\nsamples is a {0} with shape {1}".format(type(samples), samples.shape))

# fails as x and samples don't have the same shape
#mle = ed.MAP({}, data={x: samples})

# Alternative
x = Normal(loc=mean*tf.ones(N), scale=std*tf.ones(N))
samples = sample_model(x, N)

print("\nx is a {0} with shape {1}".format(type(x), x.get_shape()))
print("\nsamples is a {0} with shape {1}".format(type(samples), samples.shape))

# fails as x and samples don't have the same shape
#mle = ed.MAP({}, data={x: samples})
# works but is hugely inefficient, as only using 1 row of a N x N tensor
mle = ed.MAP({}, data={x: samples[0]})
mle.run()

sess = ed.get_session()
print(sess.run(mean))

# As ed.models.Normal inherits from tf.contrib.distributions.Normal the results are the same with pure TF
x = tf.contrib.distributions.Normal(loc=mean*tf.ones(N), scale=std*tf.ones(N))
samples = sample_model(x, N)

print("\nx is a {0} with shape {1}".format(type(x), x.event_shape))
print("samples is a {0} with shape {1}".format(type(samples), samples.shape))