handle case where all outputs are int

theano/tensor/elemwise.py

@@ -15,6 +15,7 @@ from theano.printing import min_informative_str, pprint
 from theano.gof.python25 import all, any
 from theano.tensor.utils import hash_from_dict
 from theano.gradient import DisconnectedType
+from theano.gof.null_type import NullType
 
 config = theano.config
 
@@ -638,9 +639,42 @@ class Elemwise(Op):
 
     def grad(self, inputs, ograds):
 
+
+        outs = self(*inputs)
+        if not isinstance(outs, (list,tuple)):
+            outs = [ outs ]
+
+
         #compute grad with respect to broadcasted input
         rval = self._bgrad(inputs, ograds)
 
+        # TODO: make sure that zeros are clearly identifiable
+        # to the gradient.grad method when the outputs have
+        # some integer and some floating point outputs
+        if False in [str(out.type.dtype).find('int') == -1
+                for out in outs]:
+            # For integer output, return value may
+            # only be zero or undefined
+            # We don't bother with trying to check
+            # that the scalar ops correctly
+            # returned something that evaluates to 0,
+            # we just make the return
+            # value obviously zero so that gradient.grad
+            # can tell this op did
+            # the right thing.
+            new_rval = []
+            for elem, ipt in zip(rval, inputs):
+                if isinstance(elem.type, (NullType, DisconnectedType)):
+                    new_rval.append(elem)
+                else:
+                    elem = ipt.zeros_like()
+                    if str(elem.type.dtype).find('int') != -1:
+                        elem = elem.astype(theano.config.floatX)
+                    assert str(elem.type.dtype).find('int') == -1
+                    new_rval.append(elem)
+            return new_rval
+
+
         #sum out the broadcasted dimensions
         for i, ipt in enumerate(inputs):
             if rval[i] is None: