Added functionality for retrieving variables from control dependencies (#220)

* Added test for retriving variables from an optimizer

* Added comments to test

* Addressed comments

* Fixed travis bug

* Added fix to circular controls

* Added set for explored operations and duplicate prefix stripping

* Removed embeded ipython

* Removed prefix, use seperate graph for each network

* Removed redundant imports

* Addressed comments and added separate graph to initializer

* fix typos

* get rid of prefix in documentation

python/ray/experimental/tfutils.py

@@ -28,28 +28,41 @@ class TensorFlowVariables(object):
     assignment_placeholders (List[tf.placeholders]): The nodes that weights get
       passed to.
     assignment_nodes (List[tf.Tensor]): The nodes that assign the weights.
-    prefix (Bool): Boolean for if there is a prefix on the variable names.
   """
-  def __init__(self, loss, sess=None, prefix=False):
+  def __init__(self, loss, sess=None):
     """Creates a TensorFlowVariables instance."""
     import tensorflow as tf
     self.sess = sess
     self.loss = loss
-    self.prefix = prefix
     queue = deque([loss])
     variable_names = []
+    explored_inputs = set([loss])
 
     # We do a BFS on the dependency graph of the input function to find 
     # the variables.
     while len(queue) != 0:
-      op = queue.popleft().op
-      queue.extend(op.inputs)
-      if op.node_def.op == "Variable":
-        variable_names.append(op.node_def.name)
+      tf_obj = queue.popleft()
+     
+      # The object put into the queue is not necessarily an operation, so we
+      # want the op attribute to get the operation underlying the object.
+      # Only operations contain the inputs that we can explore.
+      if hasattr(tf_obj, "op"):
+         tf_obj = tf_obj.op
+      for input_op in tf_obj.inputs:
+        if input_op not in explored_inputs:
+          queue.append(input_op)
+          explored_inputs.add(input_op)
+      # Tensorflow control inputs can be circular, so we keep track of
+      # explored operations.
+      for control in tf_obj.control_inputs:
+        if control not in explored_inputs:
+          queue.append(control)
+          explored_inputs.add(control)
+      if tf_obj.node_def.op == "Variable":
+        variable_names.append(tf_obj.node_def.name)
     self.variables = OrderedDict()
     for v in [v for v in tf.global_variables() if v.op.node_def.name in variable_names]:
-      name = v.op.node_def.name.split("/", 1 if prefix else 0)[-1]
-      self.variables[name] = v
+      self.variables[v.op.node_def.name] = v
     self.assignment_placeholders = dict()
     self.assignment_nodes = []