Made an error message more explicit, and implemented MakeVector.grad

theano/tensor/opt.py

@@ -317,8 +317,10 @@ class MakeVector(T.Op):
         inputs = map(T.as_tensor_variable, inputs)
         if not all(a.type == inputs[0].type for a in inputs) or (len(inputs)>0 and inputs[0].dtype != self.dtype):
             dtype=theano.scalar.upcast(self.dtype,*[i.dtype for i in inputs])
-            #upcast the input to the determined dtype, but don't upcast downcast anything
-            assert dtype==self.dtype, "Upcast the input of MakeVector to dtype gived in init without precissino loss only."
+            #upcast the input to the determined dtype, but don't downcast anything
+            assert dtype==self.dtype, (
+                    "The upcast of the inputs to MakeVector should match the "
+                    "dtype given in __init__.")
             if not all(self.dtype == T.cast(i,dtype=dtype).dtype for a in inputs):
                 raise TypeError("MakeVector.make_node expected inputs upcastable to %s. got %s"%(
                         self.dtype,
@@ -348,6 +350,9 @@ class MakeVector(T.Op):
             # assume that out has correct dtype.  there is no cheap way to check
             out[0][...] = inputs
 
+    def grad(self, inputs, output_gradients):
+        return [output_gradients[0][i] for i in xrange(len(inputs))]
+
 make_vector = MakeVector()
 
 class MakeVectorPrinter: