If somehow inputs of scan end up being numpy ndarrays cast them to constants

theano/scan_module/scan.py

@@ -411,9 +411,10 @@ def scan( fn
                 # the output of the lambda expression directly to replace
                 # the output of scan.
 
-                  # If not we need to use copies, that will be replaced at
-                  # each frame by the corresponding slice
-                nw_slice = seq['input'][0].type()
+                # If not we need to use copies, that will be replaced at
+                # each frame by the corresponding slice
+                _seq_val = tensor.as_tensor_variable(seq['input'])
+                nw_slice = _seq_val[0].type()
                 actual_slice = seq['input'][k-mintap]
 
 
@@ -574,7 +575,8 @@ def scan( fn
             for k in init_out['taps']:
                 # create a new slice
                 actual_nw_slice = init_out['initial'][k+mintap]
-                nw_slice = init_out['initial'][0].type()
+                _init_out_var = tensor.as_tensor_variable(init_out['initial'])
+                nw_slice = _init_out_var[0].type()
 
                 # give it a name or debugging and pretty printing
                 if getattr(init_out['initial'],'name', None) is not None:

theano/scan_module/scan_utils.py

@@ -49,6 +49,7 @@ def info(*msg):
 
 
 def safe_new(x):
+    x = tensor.as_tensor_variable(x)
     if cuda.cuda_available and isinstance(x.type, cuda.CudaNdarrayType):
         return tensor.TensorType(
             broadcastable = x.type.broadcastable
@@ -57,6 +58,7 @@ def safe_new(x):
         return x.type()
 
 def safe_to_cpu(x):
+    x = tensor.as_tensor_variable(x)
     if cuda.cuda_available and isinstance(x.type, cuda.CudaNdarrayType):
         return cuda.basic_ops.host_from_gpu(x)
     else: