When a gradient is zero because the variable has no influence on the result, now…

theano/tensor/basic.py

@@ -3452,8 +3452,7 @@ def grad(cost, wrt, g_cost=None, consider_constant=[], warn_type=False):
     :return: symbolic expression of gradient of `cost` with respect to `wrt`.
     If `wrt` is a list, then return a list containing the gradient of `cost` wrt
     each element of the list.  If an element of `wrt` is not differentiable
-    with respect to the output, then a `TensorConstant` with an appropriate
-    kind of zero is returned.
+    with respect to the output, then a zero variable is returned.
 
     This function is a wrapper around a the more general function
     `theano.gradient.grad_sources_inputs``.
@@ -3473,21 +3472,13 @@ def grad(cost, wrt, g_cost=None, consider_constant=[], warn_type=False):
     gmap = gradient.grad_sources_inputs([(cost, g_cost)], inputs + consider_constant,
             warn_type=warn_type)
 
-    def zero(p):
-        return TensorConstant(
-                TensorType(dtype = p.type.dtype, broadcastable = []),
-                theano._asarray(0, dtype=p.type.dtype))
-
-    #try:
-        #it = iter(wrt)
-    #except:
-        #it = None
-
-    #if it: #hasattr(wrt, '__iter__'): # isinstance(wrt, (list, tuple)):
+    # Note that it is important to use `zeros_like` when there is no gradient,
+    # instead of returning a scalar constant equal to zero. Otherwise we lose
+    # the guarantee that the gradient has same shape as `wrt`.
     if isinstance(wrt, (list, tuple)):
-        return [gmap.get(p, zero(p)) for p in wrt]
+        return [gmap.get(p, zeros_like(p)) for p in wrt]
     else:
-        return gmap.get(wrt, zero(wrt))
+        return gmap.get(wrt, zeros_like(wrt))
 
 class numeric_grad:
     """WRITEME"""