make CrossentropySoftmax1HotWithBiasDx support uint* as class dtype.

theano/tensor/nnet/nnet.py

@@ -1012,7 +1012,7 @@ class CrossentropySoftmax1HotWithBiasDx (gof.Op):
         return [g_dy, g_sm, g_y_idx]
 
     def c_code_cache_version(self):
-        return (2,)
+        return (3,)
 
     def c_code(self, node, name, inp, out, sub):
         dnll, sm, y_idx = inp
@@ -1037,10 +1037,14 @@ class CrossentropySoftmax1HotWithBiasDx (gof.Op):
         if ((PyArray_DESCR(%(y_idx)s)->type_num != NPY_INT64)
             && (PyArray_DESCR(%(y_idx)s)->type_num != NPY_INT32)
             && (PyArray_DESCR(%(y_idx)s)->type_num != NPY_INT16)
-            && (PyArray_DESCR(%(y_idx)s)->type_num != NPY_INT8))
+            && (PyArray_DESCR(%(y_idx)s)->type_num != NPY_INT8)
+            && (PyArray_DESCR(%(y_idx)s)->type_num != NPY_UINT64)
+            && (PyArray_DESCR(%(y_idx)s)->type_num != NPY_UINT32)
+            && (PyArray_DESCR(%(y_idx)s)->type_num != NPY_UINT16)
+            && (PyArray_DESCR(%(y_idx)s)->type_num != NPY_UINT8))
         {
             PyErr_SetString(PyExc_TypeError,
-                 "y_idx not int8, int16, int32, or int64");
+                 "y_idx not [u]int8, [u]int16, [u]int32, or [u]int64");
             %(fail)s;
         }
         if ((PyArray_NDIM(%(dnll)s) != 1)

theano/tensor/nnet/tests/test_nnet.py

@@ -194,16 +194,20 @@ class T_CrossentropySoftmax1Hot(unittest.TestCase):
 class T_CrossentropySoftmax1HotWithBiasDx(utt.InferShapeTester):
 
     def test0(self):
+        def ff(class_dtype):
             def f(sm):
-            return (theano.tensor.nnet.crossentropy_softmax_1hot_with_bias_dx(
+               # Class indices
+               y = numpy.random.randint(low=0, high=5, size=10).astype(class_dtype)
+               return theano.tensor.nnet.crossentropy_softmax_1hot_with_bias_dx(
                    numpy.random.rand(10),  # Gradient w.r.t. NLL.
                    sm,                     # Softmax output.
-                numpy.random.randint(low=0,
-                high=5, size=10)))  # Class indices.
+                   y)
+            return f
         # Build a random softmax output whose rows sum to 1.
         softmax_output = numpy.random.rand(10, 5)
         softmax_output /= softmax_output.sum(axis=1).reshape(10, 1)
-        utt.verify_grad(f, [softmax_output])
+        for dtype in ['uint8', 'int8', 'uint64', 'int64']:
+            utt.verify_grad(ff(dtype), [softmax_output])
 
     def test1(self):
         rng = numpy.random.RandomState(utt.fetch_seed())