diff --git a/tests/icosagon/test_declayer.py b/tests/icosagon/test_declayer.py
index cd903f1..0a3c617 100644
--- a/tests/icosagon/test_declayer.py
+++ b/tests/icosagon/test_declayer.py
@@ -5,12 +5,14 @@
 
 
 from icosagon.input import OneHotInputLayer
+from icosagon.convolve import DropoutGraphConvActivation
 from icosagon.convlayer import DecagonLayer
 from icosagon.declayer import DecodeLayer, \
     Predictions, \
     RelationFamilyPredictions, \
     RelationPredictions
-from icosagon.decode import DEDICOMDecoder
+from icosagon.decode import DEDICOMDecoder, \
+    InnerProductDecoder
 from icosagon.data import Data
 from icosagon.trainprep import prepare_training, \
     TrainValTest
@@ -116,3 +118,109 @@ def test_decode_layer_04():
 
     assert isinstance(pred, Predictions)
     assert len(pred.relation_families) == 0
+
+
+def test_decode_layer_05():
+    d = Data()
+    d.add_node_type('Dummy', 10)
+    mat = torch.rand((10, 10))
+    mat = (mat + mat.transpose(0, 1)) / 2
+    mat = mat.round()
+    fam = d.add_relation_family('Dummy-Dummy', 0, 0, True,
+        decoder_class=InnerProductDecoder)
+    fam.add_relation_type('Dummy Rel', mat.to_sparse())
+    prep_d = prepare_training(d, TrainValTest(1., 0., 0.))
+
+    in_layer = OneHotInputLayer(d)
+    conv_layer = DecagonLayer(in_layer.output_dim, 32, prep_d,
+        rel_activation=lambda x: x, layer_activation=lambda x: x)
+    dec_layer = DecodeLayer(conv_layer.output_dim, prep_d,
+        keep_prob=1., activation=lambda x: x)
+    seq = torch.nn.Sequential(in_layer, conv_layer, dec_layer)
+
+    pred = seq(None)
+    rel_pred = pred.relation_families[0].relation_types[0]
+
+    for edge_type in ['edges_pos', 'edges_neg', 'edges_back_pos', 'edges_back_neg']:
+        edge_pred = getattr(rel_pred, edge_type)
+        assert isinstance(edge_pred, TrainValTest)
+        for part_type in ['train', 'val', 'test']:
+            part_pred = getattr(edge_pred, part_type)
+            assert isinstance(part_pred, torch.Tensor)
+            assert len(part_pred.shape) == 1
+            print(edge_type, part_type, part_pred.shape)
+            if (edge_type, part_type) not in [('edges_pos', 'train'), ('edges_neg', 'train')]:
+                assert part_pred.shape[0] == 0
+            else:
+                assert part_pred.shape[0] > 0
+
+    prep_rel = prep_d.relation_families[0].relation_types[0]
+    assert len(rel_pred.edges_pos.train) == len(prep_rel.edges_pos.train)
+    assert len(rel_pred.edges_neg.train) == len(prep_rel.edges_neg.train)
+
+    assert len(prep_rel.edges_pos.train) == torch.sum(mat)
+
+    # print('Predictions for positive edges:')
+    # print(rel_pred.edges_pos.train)
+    # print('Predictions for negative edges:')
+    # print(rel_pred.edges_neg.train)
+
+    repr_in = in_layer(None)
+    assert isinstance(repr_in, list)
+    assert len(repr_in) == 1
+    assert isinstance(repr_in[0], torch.Tensor)
+    assert torch.all(repr_in[0].to_dense() == torch.eye(10))
+
+    assert len(conv_layer.next_layer_repr[0]) == 1
+    assert len(conv_layer.next_layer_repr[0][0].convolutions) == 1
+    assert conv_layer.rel_activation(0) == 0
+    assert conv_layer.rel_activation(1) == 1
+    assert conv_layer.rel_activation(-1) == -1
+    assert conv_layer.layer_activation(0) == 0
+    assert conv_layer.layer_activation(1) == 1
+    assert conv_layer.layer_activation(-1) == -1
+
+    graph_conv = conv_layer.next_layer_repr[0][0].convolutions[0]
+    assert isinstance(graph_conv, DropoutGraphConvActivation)
+    assert graph_conv.activation(0) == 0
+    assert graph_conv.activation(1) == 1
+    assert graph_conv.activation(-1) == -1
+    weight = graph_conv.graph_conv.weight
+    adj_mat = prep_d.relation_families[0].relation_types[0].adjacency_matrix
+    repr_conv = torch.sparse.mm(repr_in[0], weight)
+    repr_conv = torch.mm(adj_mat, repr_conv)
+    repr_conv = torch.nn.functional.normalize(repr_conv, p=2, dim=1)
+    repr_conv_expect = conv_layer(repr_in)[0]
+    print('repr_conv:\n', repr_conv)
+    # print(repr_conv_expect)
+    assert torch.all(repr_conv == repr_conv_expect)
+    assert repr_conv.shape[1] == 32
+
+    dec = InnerProductDecoder(32, 1, keep_prob=1., activation=lambda x: x)
+    x, y = torch.meshgrid(torch.arange(0, 10), torch.arange(0, 10))
+    x = x.flatten()
+    y = y.flatten()
+    repr_dec_expect = dec(repr_conv[x], repr_conv[y], 0)
+    repr_dec_expect = repr_dec_expect.view(10, 10)
+
+
+    repr_dec = torch.mm(repr_conv, torch.transpose(repr_conv, 0, 1))
+    # repr_dec = torch.flatten(repr_dec)
+    # repr_dec -= torch.eye(10)
+    #repr_dec_expect = torch.zeros((10, 10))
+    #x = prep_d.relation_families[0].relation_types[0].edges_pos.train
+    #repr_dec_expect[x[:, 0], x[:, 1]] = pred.relation_families[0].relation_types[0].edges_pos.train
+    #x = prep_d.relation_families[0].relation_types[0].edges_neg.train
+    #repr_dec_expect[x[:, 0], x[:, 1]] = pred.relation_families[0].relation_types[0].edges_neg.train
+    print(repr_dec)
+    print(repr_dec_expect)
+    assert torch.all(torch.abs(repr_dec - repr_dec_expect) < 0.000001)
+
+    #print(prep_rel.edges_pos.train)
+    #print(prep_rel.edges_neg.train)
+
+    # assert isinstance(edge_pred.train)
+    # assert isinstance(rel_pred.edges_pos, TrainValTest)
+    # assert isinstance(rel_pred.edges_neg, TrainValTest)
+    # assert isinstance(rel_pred.edges_back_pos, TrainValTest)
+    # assert isinstance(rel_pred.edges_back_neg, TrainValTest)