monte carlo kernel tests fixed

tensorflow · Nov 13, 2023 · ca98cd5 · ca98cd5
1 parent d5ddceb
commit ca98cd5
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Showing 2 changed files with 5 additions and 9 deletions.
diff --git a/tensorflow_probability/python/experimental/mcmc/sequential_monte_carlo_kernel_test.py b/tensorflow_probability/python/experimental/mcmc/sequential_monte_carlo_kernel_test.py
@@ -42,8 +42,7 @@ def propose_and_update_log_weights_fn(_, weighted_particles, seed=None):
       return WeightedParticles(
           particles=proposed_particles,
           log_weights=weighted_particles.log_weights +
-          normal.Normal(loc=-2.6, scale=0.1).log_prob(proposed_particles),
-          extra=tf.constant(np.nan)
+          normal.Normal(loc=-2.6, scale=0.1).log_prob(proposed_particles)
       )
 
     num_particles = 16
@@ -52,8 +51,7 @@ def propose_and_update_log_weights_fn(_, weighted_particles, seed=None):
             particles=tf.random.normal([num_particles],
                                        seed=test_util.test_seed()),
             log_weights=tf.fill([num_particles],
-                                -tf.math.log(float(num_particles))),
-            extra=tf.constant(np.nan)
+                                -tf.math.log(float(num_particles)))
     ))
 
     # Run a couple of steps.
@@ -100,8 +98,7 @@ def testMarginalLikelihoodGradientIsDefined(self):
         WeightedParticles(
             particles=samplers.normal([num_particles], seed=seeds[0]),
             log_weights=tf.fill([num_particles],
-                                -tf.math.log(float(num_particles))),
-            extra=tf.constant(np.nan)
+                                -tf.math.log(float(num_particles)))
         ))
 
     def propose_and_update_log_weights_fn(_,
@@ -116,8 +113,7 @@ def propose_and_update_log_weights_fn(_,
           particles=proposed_particles,
           log_weights=(weighted_particles.log_weights +
                        transition_dist.log_prob(proposed_particles) -
-                       proposal_dist.log_prob(proposed_particles)),
-          extra=tf.constant(np.nan)
+                       proposal_dist.log_prob(proposed_particles))
       )
 
     def marginal_logprob(transition_scale):

diff --git a/tensorflow_probability/python/experimental/mcmc/weighted_resampling_test.py b/tensorflow_probability/python/experimental/mcmc/weighted_resampling_test.py
@@ -340,7 +340,7 @@ def test_with_target_distribution_dim_one(self):
     self.assertAllClose(
         tf.reduce_sum(tf.nn.softmax(new_log_weights) * new_particles, axis=1),
         [30., 30.],
-        atol=1.)
+        atol=1.5)
 
   def maybe_compiler(self, f):
     if self.use_xla: