simplify placeholder if/else logic (#486)

edward/inferences/gan_inference.py

@@ -158,9 +158,8 @@ def update(self, feed_dict=None, variables=None):
       feed_dict = {}
 
     for key, value in six.iteritems(self.data):
-      if isinstance(key, tf.Tensor):
-        if "Placeholder" in key.op.type:
-          feed_dict[key] = value
+      if isinstance(key, tf.Tensor) and "Placeholder" in key.op.type:
+        feed_dict[key] = value
 
     sess = get_session()
     if variables is None:

edward/inferences/inference.py

@@ -249,9 +249,8 @@ def run(self, variables=None, use_coordinator=True, *args, **kwargs):
     # Feed placeholders in case initialization depends on them.
     feed_dict = {}
     for key, value in six.iteritems(self.data):
-      if isinstance(key, tf.Tensor):
-        if "Placeholder" in key.op.type:
-          feed_dict[key] = value
+      if isinstance(key, tf.Tensor) and "Placeholder" in key.op.type:
+        feed_dict[key] = value
 
     init.run(feed_dict)
 
@@ -375,9 +374,8 @@ def update(self, feed_dict=None):
       feed_dict = {}
 
     for key, value in six.iteritems(self.data):
-      if isinstance(key, tf.Tensor):
-        if "Placeholder" in key.op.type:
-          feed_dict[key] = value
+      if isinstance(key, tf.Tensor) and "Placeholder" in key.op.type:
+        feed_dict[key] = value
 
     sess = get_session()
     t = sess.run(self.increment_t)

edward/inferences/monte_carlo.py

@@ -126,9 +126,8 @@ def update(self, feed_dict=None):
       feed_dict = {}
 
     for key, value in six.iteritems(self.data):
-      if isinstance(key, tf.Tensor):
-        if "Placeholder" in key.op.type:
-          feed_dict[key] = value
+      if isinstance(key, tf.Tensor) and "Placeholder" in key.op.type:
+        feed_dict[key] = value
 
     sess = get_session()
     _, accept_rate = sess.run([self.train, self.n_accept_over_t], feed_dict)

edward/inferences/variational_inference.py

@@ -131,9 +131,8 @@ def update(self, feed_dict=None):
       feed_dict = {}
 
     for key, value in six.iteritems(self.data):
-      if isinstance(key, tf.Tensor):
-        if "Placeholder" in key.op.type:
-          feed_dict[key] = value
+      if isinstance(key, tf.Tensor) and "Placeholder" in key.op.type:
+        feed_dict[key] = value
 
     sess = get_session()
     _, t, loss = sess.run([self.train, self.increment_t, self.loss], feed_dict)

edward/util/random_variables.py

@@ -144,9 +144,9 @@ def copy(org_instance, dict_swap=None, scope="copied",
     return graph.get_tensor_by_name(variables[org_instance.name].name)
 
   # Do the same for placeholders. Determine via its op's type.
-  if isinstance(org_instance, tf.Tensor):
-    if "Placeholder" in org_instance.op.type:
-      return org_instance
+  if isinstance(org_instance, tf.Tensor) and \
+          "Placeholder" in org_instance.op.type:
+    return org_instance
 
   if isinstance(org_instance, RandomVariable):
     rv = org_instance