added support for warn_type

theano/gradient.py

@@ -444,7 +444,7 @@ def Lop(f, wrt, eval_points, consider_constant=None, warn_type=False,
 # Gradient
 #########################
 
-def grad(cost, wrt, g_cost = None, consider_constant = None, warn_type = 'ignored',
+def grad(cost, wrt, g_cost = None, consider_constant = None, warn_type = False,
         disconnected_inputs = 'raise'):
     global tensor
     if tensor is None:
@@ -558,8 +558,18 @@ def grad(cost, wrt, g_cost = None, consider_constant = None, warn_type = 'ignore
             term_dict[node] = list(input_grads)
 
             for i in xrange(len(term_dict[node])):
+
                 if term_dict[node][i] is None:
                     term_dict[node][i] = node.inputs[i].zeros_like()
+
+                if warn_type:
+                    g_r_type = term_dict[node][i].type
+                    r_type = inputs[i].type
+                    if g_r_type != r_type:
+                        _logger.warning('%s.grad returned a different type (%s) '
+                            'for input %i of type (%s)',
+                            node.op, g_r_type, i, r_type)
+
         return term_dict[node]
 
 
@@ -612,7 +622,7 @@ def grad(cost, wrt, g_cost = None, consider_constant = None, warn_type = 'ignore
 
 
 
-def grad_sources_inputs(sources, graph_inputs, warn_type = 'ignored'):
+def grad_sources_inputs(sources, graph_inputs, warn_type = True):
 
     outputs, output_grads = zip(*sources)