Added helper class for getting tf variables from loss function (#184)

* Added helper class for getting tf variables from loss function

* Updated usage and documentation

* Removed try-catches

* Added futures

* Added documentation

* fixes and tests

* more tests

* install tensorflow in travis

lib/python/ray/experimental/__init__.py

@@ -3,3 +3,4 @@ from __future__ import division
 from __future__ import print_function
 
 from .utils import copy_directory
+from .tfutils import TensorFlowVariables

lib/python/ray/experimental/tfutils.py

@@ -0,0 +1,37 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+class TensorFlowVariables(object):
+  """An object used to extract variables from a loss function, and provide 
+     methods for getting and setting the weights of said variables.
+
+  Attributes:
+    sess (tf.Session): The tensorflow session used to run assignment.
+    loss: The loss function passed in by the user.
+    variables (List[tf.Variable]): Extracted variables from the loss.
+    assignment_placeholders (List[tf.placeholders]): The nodes that weights get passed to.
+    assignment_nodes (List[tf.Tensor]): The nodes that assign the weights.
+  """
+  def __init__(self, loss, sess):
+    """Creates a TensorFlowVariables instance."""
+    import tensorflow as tf
+    self.sess = sess
+    self.loss = loss
+    variable_names = [op.node_def.name for op in loss.graph.get_operations() if op.node_def.op == "Variable"]
+    self.variables = [v for v in tf.trainable_variables() if v.op.node_def.name in variable_names]
+    self.assignment_placeholders = dict()
+    self.assignment_nodes = []
+    
+    # Create new placeholders to put in custom weights.
+    for var in self.variables:
+      self.assignment_placeholders[var.op.node_def.name] = tf.placeholder(var.value().dtype, var.get_shape().as_list())
+      self.assignment_nodes.append(var.assign(self.assignment_placeholders[var.op.node_def.name]))
+
+  def get_weights(self):
+    """Returns the weights of the variables of the loss function in a list."""
+    return {v.op.node_def.name: v.eval(session=self.sess) for v in self.variables}
+
+  def set_weights(self, new_weights):
+    """Sets the weights to new_weights."""
+    self.sess.run(self.assignment_nodes, feed_dict={self.assignment_placeholders[name]: value for (name, value) in new_weights.items()})