Merge pull request #6156 from botev/master

theano/gradient.py

@@ -160,7 +160,8 @@ disconnected_type = DisconnectedType()
 ########################
 
 
-def Rop(f, wrt, eval_points):
+def Rop(f, wrt, eval_points, disconnected_outputs="raise",
+        return_disconnected="zero"):
     """
     Computes the R operation on `f` wrt to `wrt` evaluated at points given
     in `eval_points`. Mathematically this stands for the jacobian of `f` wrt
@@ -174,6 +175,22 @@ def Rop(f, wrt, eval_points):
                described by `f`
     :type eval_points: Variable or list of Variables
                        evalutation points for each of the variables in `wrt`
+    :type disconnected_outputs: str
+        Defines the behaviour if some of the variables in `f` are
+        have no dependency on any of the variable in `wrt` (or if
+        all links are non-differentiable). The possible values are:
+
+        - 'ignore': considers that the gradient on these parameters is zero.
+        - 'warn': consider the gradient zero, and print a warning.
+        - 'raise': raise DisconnectedInputError.
+
+    :type return_disconnected : {'zero', 'None', 'Disconnected'}
+        - 'zero' : If wrt[i] is disconnected, return value i will be
+                   wrt[i].zeros_like()
+        - 'None' : If wrt[i] is disconnected, return value i will be
+                   None
+        - 'Disconnected' : returns variables of type DisconnectedType
+
     :rtype: :class:`~theano.gof.Variable` or list/tuple of Variables depending on type of f
     :return: symbolic expression such that
         R_op[i] = sum_j ( d f[i] / d wrt[j]) eval_point[j]
@@ -296,9 +313,33 @@ def Rop(f, wrt, eval_points):
     for out in f:
         if out in wrt:
             rval.append(eval_points[wrt.index(out)])
-        elif seen_nodes[out.owner][out.owner.outputs.index(out)] is None:
-            raise ValueError(('The function is not differentiable with '
-                              'respect to the provided inputs !'))
+        elif seen_nodes.get(out.owner, None) is None or \
+                seen_nodes[out.owner][out.owner.outputs.index(out)] is None:
+            message = ("Rop method was asked to compute the gradient "
+                       "with respect to a variable that is not part of "
+                       "the computational graph of variables in wrt, or is "
+                       "used only by a non-differentiable operator: %s" % out)
+            if disconnected_outputs == 'ignore':
+                pass
+            elif disconnected_outputs == 'warn':
+                warnings.warn(message, stacklevel=2)
+            elif disconnected_outputs == 'raise':
+                message = utils.get_variable_trace_string(out)
+                raise DisconnectedInputError(message)
+            else:
+                raise ValueError("Invalid value for keyword "
+                                 "'disconnected_inputs', valid values are "
+                                 "'ignore', 'warn' and 'raise'.")
+            if return_disconnected.lower() == "zero":
+                rval.append(tensor.zeros_like(out))
+            elif return_disconnected.lower() == "none":
+                rval.append(None)
+            elif return_disconnected.lower() == "disconnected":
+                rval.append(disconnected_type())
+            else:
+                raise ValueError("Invalid value for keyword "
+                                 "'return_disconnected', valid values are "
+                                 "'zero', 'None' and 'Disconnected'.")
         else:
             rval.append(seen_nodes[out.owner][out.owner.outputs.index(out)])