diff --git a/src/icosagon/dropout.py b/src/icosagon/dropout.py
index 74bdd57..63cfb58 100644
--- a/src/icosagon/dropout.py
+++ b/src/icosagon/dropout.py
@@ -24,7 +24,8 @@ def dropout_sparse(x, keep_prob):
 
 
 def dropout_dense(x, keep_prob):
-    x = x.clone().detach()
+    # print('dropout_dense()')
+    x = x.clone()
     i = torch.nonzero(x)
 
     n = keep_prob + torch.rand(len(i))
diff --git a/tests/icosagon/test_trainloop.py b/tests/icosagon/test_trainloop.py
index ca77edd..192cdf9 100644
--- a/tests/icosagon/test_trainloop.py
+++ b/tests/icosagon/test_trainloop.py
@@ -1,4 +1,5 @@
-from icosagon.data import Data
+from icosagon.data import Data, \
+    _equal
 from icosagon.trainprep import prepare_training, \
     TrainValTest
 from icosagon.model import Model
@@ -78,6 +79,56 @@ def test_train_loop_03():
     loop.run_epoch()
 
 
+def test_train_loop_04():
+    adj_mat = torch.rand(10, 10).round()
+
+    d = Data()
+    d.add_node_type('Dummy', 10)
+    fam = d.add_relation_family('Dummy-Dummy', 0, 0, False)
+    fam.add_relation_type('Dummy Rel', adj_mat)
+
+    prep_d = prepare_training(d, TrainValTest(.8, .1, .1))
+
+    m = Model(prep_d)
+
+    old_values = []
+    for prm in m.parameters():
+        old_values.append(prm.clone().detach())
+
+    loop = TrainLoop(m)
+
+    loop.run_epoch()
+
+    for i, prm in enumerate(m.parameters()):
+        assert not prm.requires_grad or \
+            not torch.all(_equal(prm, old_values[i]))
+
+
+def test_train_loop_05():
+    adj_mat = torch.rand(10, 10).round().to_sparse()
+
+    d = Data()
+    d.add_node_type('Dummy', 10)
+    fam = d.add_relation_family('Dummy-Dummy', 0, 0, False)
+    fam.add_relation_type('Dummy Rel', adj_mat)
+
+    prep_d = prepare_training(d, TrainValTest(.8, .1, .1))
+
+    m = Model(prep_d)
+
+    old_values = []
+    for prm in m.parameters():
+        old_values.append(prm.clone().detach())
+
+    loop = TrainLoop(m)
+
+    loop.run_epoch()
+
+    for i, prm in enumerate(m.parameters()):
+        assert not prm.requires_grad or \
+            not torch.all(_equal(prm, old_values[i]))
+
+
 def test_timing_01():
     adj_mat = (torch.rand(2000, 2000) < .001).to(torch.float32).to_sparse()
     rep = torch.eye(2000).requires_grad_(True)