implement fix for #412; ordering of dictionaries

edward/inferences/hmc.py

@@ -5,6 +5,7 @@
 import six
 import tensorflow as tf
 
+from collections import OrderedDict
 from edward.inferences.monte_carlo import MonteCarlo
 from edward.models import Normal, RandomVariable, Uniform
 from edward.util import copy
@@ -65,9 +66,10 @@ def build_update(self):
     """
     old_sample = {z: tf.gather(qz.params, tf.maximum(self.t - 1, 0))
                   for z, qz in six.iteritems(self.latent_vars)}
+    old_sample = OrderedDict(old_sample)
 
     # Sample momentum.
-    old_r_sample = {}
+    old_r_sample = OrderedDict()
     for z, qz in six.iteritems(self.latent_vars):
       event_shape = qz.get_event_shape()
       normal = Normal(mu=tf.zeros(event_shape), sigma=tf.ones(event_shape))
@@ -153,8 +155,8 @@ def _log_joint(self, z_sample):
 
 
 def leapfrog(z_old, r_old, step_size, log_joint):
-  z_new = {}
-  r_new = {}
+  z_new = z_old.copy()
+  r_new = r_old.copy()
 
   grad_log_joint = tf.gradients(log_joint(z_old), list(six.itervalues(z_old)))
   for i, key in enumerate(six.iterkeys(z_old)):

edward/inferences/sgld.py

@@ -65,10 +65,8 @@ def build_update(self):
     grad_log_joint = tf.gradients(self._log_joint(old_sample),
                                   list(six.itervalues(old_sample)))
     sample = {}
-    for z, qz, grad_log_p in \
-        zip(six.iterkeys(self.latent_vars),
-            six.itervalues(self.latent_vars),
-            grad_log_joint):
+    for z, grad_log_p in zip(six.iterkeys(old_sample), grad_log_joint):
+      qz = self.latent_vars[z]
       event_shape = qz.get_event_shape()
       normal = Normal(mu=tf.zeros(event_shape),
                       sigma=learning_rate * tf.ones(event_shape))