RepeatOp.{grad,connection_pattern}

theano/tensor/extra_ops.py

@@ -5,6 +5,7 @@ import theano
 import basic
 from theano import gof, scalar
 import basic as tensor
+from theano.gradient import DisconnectedType
 
 
 class DiffOp(theano.Op):
@@ -258,6 +259,10 @@ class RepeatOp(theano.Op):
         z = output_storage[0]
         z[0] = np.repeat(x, repeats=repeats, axis=self.axis)
 
+    def connection_pattern(self, node):
+
+        return [ [True], [False] ]
+
     def grad(self, (x, repeats), (gz, )):
         if repeats.ndim == 0:
             if self.axis is None:
@@ -271,7 +276,8 @@ class RepeatOp(theano.Op):
             shape = [x.shape[k] for k in range(x.ndim)]
             shape.insert(axis, repeats)
 
-            return [gz.reshape(shape, x.ndim + 1).sum(axis=axis), None]
+            return [gz.reshape(shape, x.ndim + 1).sum(axis=axis),
+                    DisconnectedType()() ]
         elif repeats.ndim == 1:
             # For this implementation, we would need to specify the length
             # of repeats in order to split gz in the right way to sum