add fix also for MH

edward/inferences/metropolis_hastings.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 RandomVariable, Uniform
 from edward.util import copy
@@ -67,6 +68,7 @@ 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)
 
     # Form dictionary in order to replace conditioning on prior or
     # observed variable with conditioning on a specific value.
@@ -85,7 +87,7 @@ def build_update(self):
     scope_new = 'inference_' + str(id(self)) + '/new'
 
     # Draw proposed sample and calculate acceptance ratio.
-    new_sample = {}
+    new_sample = old_sample.copy()  # copy to ensure same order
     ratio = 0.0
     for z, proposal_z in six.iteritems(self.proposal_vars):
       # Build proposal g(znew | zold).

tests/test-inferences/test_bayesian_nn.py

@@ -19,7 +19,7 @@ def four_layer_nn(x, W_1, W_2, W_3, b_1, b_2):
 
 class test_inference_bayesian_nn_class(tf.test.TestCase):
 
-  def test_hmc_sgld(self):
+  def test_monte_carlo(self):
     ed.set_seed(42)
 
     # DATA
@@ -47,6 +47,9 @@ def test_hmc_sgld(self):
     qb_1 = Empirical(params=tf.Variable(tf.random_normal([T, 20])))
     qb_2 = Empirical(params=tf.Variable(tf.random_normal([T, 15])))
 
+    # note ideally these would be separate test methods; there's an
+    # issue with the tensorflow graph when re-running the above
+    # unfortunately
     inference = ed.HMC(
         {W_1: qW_1, b_1: qb_1, W_2: qW_2, b_2: qb_2, W_3: qW_3},
         data={y: y_train, x_ph: X_train})
@@ -57,5 +60,11 @@ def test_hmc_sgld(self):
         data={y: y_train, x_ph: X_train})
     inference.run()
 
+    inference = ed.MetropolisHastings(
+        {W_1: qW_1, b_1: qb_1, W_2: qW_2, b_2: qb_2, W_3: qW_3},
+        {W_1: W_1, b_1: b_1, W_2: W_2, b_2: b_2, W_3: W_3},
+        data={y: y_train, x_ph: X_train})
+    inference.run()
+
 if __name__ == '__main__':
   tf.test.main()