'simplified following recode review comment that was wrong as the code was too complex.'

theano/tensor/basic.py

@@ -3372,14 +3372,14 @@ def stack(*tensors):
         raise Exception('theano.tensor.stack(*tensors) must have at least one parameter')
     # If all tensors are scalars of the same type, call make_vector.
     # It makes the graph simpler, by not adding DimShuffles and Rebroadcasts
-    if isinstance(tensors[0], (numpy.number, float, int, python_complex)):
-        tensors=list(tensors)
-        tensors[0]=as_tensor_variable(tensors[0])
-    if numpy.all([isinstance(t, (numpy.number, float, int, python_complex))#in case their is direct int
-                  or (isinstance(t, Variable) and
+
+    # Why this is not an optimization?
+    # This make the graph less canonicalized(more type need to be understood by all optimization)
+    # DebugMode can't detect error in this code as it is not in an optimization.
+    if numpy.all([isinstance(t, (numpy.number, float, int, python_complex)) or #in case their is direct int
+                  (isinstance(t, Variable) and
                       isinstance(t.type, TensorType) and
-                      t.ndim==0 and
-                      t.type.__class__==tensors[0].type.__class__)
+                   t.ndim==0)
                   for t in tensors]):
         tensors = map(as_tensor_variable,tensors)#in case their is direct int
         dtype = scal.upcast(*[i.dtype for i in tensors])