fixed bug where grad raised error for undefined grad not on path to wrt

theano/gradient.py

@@ -554,9 +554,6 @@ def grad(cost, wrt, g_cost = None, consider_constant = None, warn_type = 'ignore
                 for i in xrange(len(term_dict[node])):
                     if term_dict[node][i] is None:
                         term_dict[node][i] = tensor.zeros_like(node.inputs[i])
-                    if isinstance(term_dict[node][i].type,NaNType):
-                        raise TypeError("tensor.grad encountered a NaN. "+\
-                                term_dict[node][i].type.why_nan)
             return term_dict[node]
 
 
@@ -576,7 +573,13 @@ def grad(cost, wrt, g_cost = None, consider_constant = None, warn_type = 'ignore
                     terms = []
                     for child in var._children.keys():
                         idx = var._children[child]
-                        terms.append( access_term_cache(child)[idx])
+                        term = access_term_cache(child)[idx]
+
+                        if isinstance(term.type,NaNType):
+                            raise TypeError("tensor.grad encountered a NaN. "+\
+                                    term.type.why_nan)
+
+                        terms.append( term)
                     grad_dict[var] = nonempty_sum(terms)
                     if cost.name is not None and var.name is not None:
                         grad_dict[var].name = '(d%s/d%s)' % (cost.name, var.name)
@@ -671,9 +674,6 @@ def grad_sources_inputs(sources, graph_inputs, warn_type = 'ignored'):
                 for i in xrange(len(term_dict[node])):
                     if term_dict[node][i] is None:
                         term_dict[node][i] = tensor.zeros_like(node.inputs[i])
-                    if isinstance(term_dict[node][i].type,NaNType):
-                        raise TypeError("tensor.grad encountered a NaN. "+\
-                                term_dict[node][i].type.why_nan)
             return term_dict[node]
 
 
@@ -693,7 +693,13 @@ def grad_sources_inputs(sources, graph_inputs, warn_type = 'ignored'):
                     terms = []
                     for child in var._children.keys():
                         idx = var._children[child]
-                        terms.append( access_term_cache(child)[idx])
+                        term = access_term_cache(child)[idx]
+
+                        if isinstance(term.type,NaNType):
+                            raise TypeError("tensor.grad encountered a NaN. "+\
+                                    term.type.why_nan)
+
+                        terms.append( term)
                     grad_dict[var] = nonempty_sum(terms)
                 else:
                     #this variable is not connected to the cost in the computational