fixed the test that failed because of the fix to join

theano/tensor/basic.py

@@ -1943,6 +1943,7 @@ class Join(Op):
         # for the axis dimension.
         # All concatenated elements must also have the same broadcastable
         # dimensions.
+        orig = as_tensor_variable_args
         if isinstance(axis, int):
             bcasts = [x.type.broadcastable[0:axis] + \
                       x.type.broadcastable[axis + 1:] for x in as_tensor_variable_args]
@@ -1964,7 +1965,9 @@ class Join(Op):
 
         outputs = [tensor(dtype = out_dtype,
                           broadcastable = bcastable)]
-        return Apply(self, inputs, outputs)
+        node = Apply(self, inputs, outputs)
+        node.tag.shape_zero = None if any(not x.type.broadcastable[0] for x in orig) else len(orig)
+        return node
 
     def perform(self, node, axis_and_tensors, (out, )):
         axis, tensors = axis_and_tensors[0], axis_and_tensors[1:]
@@ -2004,13 +2007,10 @@ class Join(Op):
         assert isinstance(node.owner.op, Join)
         if node.ndim != 1:
             raise TypeError('argument must be symbolic vector')
-        inputs = node.owner.inputs
-        axis, tensors = inputs[0], inputs[1:]
-        # if v is a vector, then axis must be 0
-        # the question is whether all the inputs are broadcastable.
-        if all(i.broadcastable[0] for i in tensors):
-            return len(tensors)
-        raise ValueError("could not determine vector length")
+        if node.owner.tag.shape_zero is None:
+          raise ValueError("could not determine vector length")
+        else:
+          return node.owner.tag.shape_zero
 
 @_redefine_asRoutine(Join())
 def join(axis, *tensors):
@@ -2110,7 +2110,7 @@ def get_vector_length(v):
     if v.owner and isinstance(v.owner.op, Join):
         try:
             return join.vec_length(v)
-        except:
+        except ValueError:
             pass
     if v.owner and v.owner.op == shape:
         return v.owner.inputs[0].type.ndim