Do a copy when NumPy didn't allocate new memory.

theano/tensor/basic.py

@@ -4011,9 +4011,18 @@ class Tile(Op):
     def perform(self, node, inp, out_):
         x, reps = inp
         out, = out_
-        out[0] = numpy.tile(x, reps)
-        if len(out[0].shape) != self.ndim:
-            raise ValueError('Tile.perform produced incorrect shape')
+        res = numpy.tile(x, reps)
+        if res.ndim != self.ndim:
+            raise ValueError(
+                'Tile.perform produced incorrect number of dimensions')
+
+        if (numpy.asarray(reps) == 1).all():
+            # In that case, some NumPy version return a view!  As this
+            # op isn't declared as inplace, we need to check that and
+            # copy the data.
+            if numpy.may_share_memory(res, x):
+                res = res.copy()
+        out[0] = res
 
     def infer_shape(self, node, in_shapes):
         # Note: in contrast with numpy, it is assumed that x.shape and reps

theano/tensor/tests/test_opt.py

@@ -2894,6 +2894,7 @@ class T_Tile(unittest.TestCase):
                 topo = f.maker.fgraph.toposort()
                 assert len(topo) == 1
                 assert isinstance(topo[0].op, compile.DeepCopyOp)
+                f(data)
 
         # If the repeat parameter is longer then v.ndim, we must
         # replace it with a DimShuffle to add the extra parameter.