Small code refactor to remove duplicate code. This make sure the new cast is…

theano/tensor/opt.py

@@ -5710,12 +5710,11 @@ def local_opt_alloc(node):
         if node_inps.owner and isinstance(node_inps.owner.op, T.Alloc):
             input = node_inps.owner.inputs[0]
             shapes = node_inps.owner.inputs[1:]
-            if (node.op.axis is None or
-                    node.op.axis == tuple(range(input.ndim))):
             try:
                 val = get_scalar_constant_value(input,
                                                 only_process_constants=True)
                 assert val.size == 1
+                val = val.reshape(1)[0]
                 # check which type of op
                 size = T.mul(*shapes)
                 if input.dtype == "float32":
@@ -5723,10 +5722,12 @@ def local_opt_alloc(node):
                     # We don't want to have a float64 upcast here
                     # if input is a float32.
                     size = size.astype(input.dtype)
+                if (node.op.axis is None or
+                        node.op.axis == tuple(range(input.ndim))):
                     if isinstance(node.op, T.Sum):
-                        val = val.reshape(1)[0] * size
+                        val = val * size
                     else:
-                        val = val.reshape(1)[0] ** size
+                        val = val ** size
                     # Sum can change the input dtype (upcast or bool
                     # -> float32) by default or by user request.
                     # We can ignore the acc_dtype, as there is only 1
@@ -5736,15 +5737,6 @@ def local_opt_alloc(node):
                     # dtype.
                     val = val.astype(node.outputs[0].dtype)
                     return [val]
-
-                except NotScalarConstantError:
-                    pass
-            else:
-                try:
-                    val = get_scalar_constant_value(input,
-                                                    only_process_constants=True)
-                    assert val.size == 1
-                    val = val.reshape(1)[0]
                 to_prod = [shapes[i] for i in xrange(len(shapes))
                            if i in node.op.axis]
                 if to_prod:
@@ -5753,6 +5745,7 @@ def local_opt_alloc(node):
                         val *= size
                     else:
                         val = val ** size
+                # See comments above.
                 val = val.astype(node.outputs[0].dtype)
                 return [T.alloc(val,
                                 *[shapes[i] for i in xrange(len(shapes))