Merge pull request #7 from jaberg/small_stuff

theano/compile/function_module.py

@@ -476,6 +476,16 @@ class Function(object):
         self._value = ValueAttribute()
         self._container = ContainerAttribute()
 
+        # Compute self.n_returned_outputs.
+        # This is used only when fn.need_update_inputs is False
+        # because we're using one of the VM objects and it is
+        # putting updates back into the input containers all by itself.
+        assert len(self.maker.expanded_inputs) == len(self.input_storage)
+        self.n_returned_outputs = len(self.output_storage)
+        for input in self.maker.expanded_inputs:
+            if input.update is not None:
+                self.n_returned_outputs -= 1
+
     def __contains__(self, item):
         return self.value.__contains__(item)
 
@@ -636,6 +646,8 @@ class Function(object):
             for input, storage in reversed(zip(self.maker.expanded_inputs, self.input_storage)):
                 if input.update is not None:
                     storage.data = outputs.pop()
+        else:
+            outputs = outputs[:self.n_returned_outputs]
 
         # Put default values back in the storage
         for i, (required, refeed, value) in enumerate(self.defaults):

theano/tensor/basic.py

@@ -1270,9 +1270,12 @@ class _tensor_py_operators:
                 break
 
         if advanced:
-            if len(args) == 1 and isinstance(args[0],
-                                             (list, TensorVariable,
-                                              theano.tensor.sharedvar.TensorSharedVariable)):
+            if (len(args) == 1
+                    and isinstance(args[0], (
+                        list,
+                        TensorVariable,
+                        TensorConstant,
+                        theano.tensor.sharedvar.TensorSharedVariable))):
                 return advanced_subtensor1(self, *args)
             else:
                 return AdvancedSubtensor(args)(self, *args)
@@ -4863,10 +4866,6 @@ class AdvancedSubtensor1(Op):
             raise TypeError('index must be vector')
         if x_.type.ndim == 0:
             raise TypeError('cannot index into a scalar')
-        if x_.type.broadcastable[0]:
-            # the caller should have made a copy of x len(ilist) times
-            raise TypeError('cannot index into a broadcastable dimension')
-
         return Apply(self, [x_, ilist_], [x_.type()])
 
     def perform(self, node, inp, out_):