added some connection_pattern methods

theano/sparse/basic.py

@@ -782,6 +782,9 @@ class CSM(gof.Op):
                                               indptr.copy()), shape.copy(),
                                              copy=False)
 
+    def connection_pattern(self, node):
+        return [[True],[False],[False],[False]]
+
     def grad(self, (x_data, x_indices, x_indptr, x_shape), (g_out,)):
         g_data, g_indices, g_indptr, g_shape = csm_properties(g_out)
         # unpack the data vector and wrap it as a 1d TensorType

theano/tensor/nnet/nnet.py

@@ -712,6 +712,12 @@ class CrossentropySoftmaxArgmax1HotWithBias(gof.Op):
         am_shp = idx_shp
         return [nll_shp, sm_shp, am_shp]
 
+    def connection_pattern(self, node):
+
+        return [[True,True,True],#x
+                [True,True,True],#b
+                [True,True,False]]#y_idx
+
     def grad(self, inp, grads):
         x, b, y_idx = inp
         g_nll, g_sm, g_am = grads