diff --git a/tests/test_feed_forward.py b/tests/test_feed_forward.py
index ea0fb94..ff6cb71 100644
--- a/tests/test_feed_forward.py
+++ b/tests/test_feed_forward.py
@@ -8,17 +8,15 @@ class TestFeedForward:
         return FeedForward(emb_size=512)
 
     def test_initialization(self, ff_layer):
-        assert isinstance(ff_layer.net, torch.nn.Sequential)
-        assert len(ff_layer.net) == 4
-        assert isinstance(ff_layer.net[0], torch.nn.Linear)
-        assert isinstance(ff_layer.net[1], torch.nn.ReLU)
-        assert isinstance(ff_layer.net[2], torch.nn.Linear)
-        assert isinstance(ff_layer.net[3], torch.nn.Dropout)
+        assert isinstance(ff_layer._layer1, torch.nn.Linear)
+        assert isinstance(ff_layer._layer2, torch.nn.Linear)
+        assert isinstance(ff_layer._relu, torch.nn.ReLU)
+        assert isinstance(ff_layer._dropout, torch.nn.Dropout)
         
-        assert ff_layer.net[0].in_features == 512
-        assert ff_layer.net[0].out_features == 2048
-        assert ff_layer.net[2].in_features == 2048
-        assert ff_layer.net[2].out_features == 512
+        assert ff_layer._layer1.in_features == 512
+        assert ff_layer._layer1.out_features == 2048
+        assert ff_layer._layer2.in_features == 2048
+        assert ff_layer._layer2.out_features == 512
 
     def test_forward_pass_shape(self, ff_layer):
         batch_size = 4
@@ -35,9 +33,7 @@ class TestFeedForward:
         output = ff_layer(x)
         
         # Проверяем, что dropout действительно работает в режиме обучения
-        layers = ff_layer.net
-        no_dropout = layers[2](layers[1](layers[0](x)))
-        assert not torch.allclose(output, no_dropout)
+        assert not torch.allclose(output, ff_layer._layer2(ff_layer._relu(ff_layer._layer1(x))))
 
     def test_dropout_eval(self):
         ff_layer = FeedForward(512, dropout=0.5)
@@ -46,8 +42,7 @@ class TestFeedForward:
         output = ff_layer(x)
         
         # В eval режиме dropout не должен работать
-        layers = ff_layer.net
-        expected = layers[2](layers[1](layers[0](x)))
+        expected = ff_layer._layer2(ff_layer._relu(ff_layer._layer1(x)))
         assert torch.allclose(output, expected)
 
     def test_dtype_preservation(self, ff_layer):