diff --git a/classes/Jimmy_Neuron/main_network.py b/classes/Jimmy_Neuron/main_network.py
index ce26604..282b63c 100644
--- a/classes/Jimmy_Neuron/main_network.py
+++ b/classes/Jimmy_Neuron/main_network.py
@@ -41,6 +41,25 @@ hidden_size = 135*64
 
 #żeby to skończyć kiedykolwiek i jak uda mi się to CUDA zrobić to zmieniam rozdziałke z 1024 na 128
 #Użycie konwolucyjnej warstwy dobiło mi z 85 do 90
+# model = torch.nn.Sequential(
+#     torch.nn.Conv2d(3, 6, 5),
+#     torch.nn.MaxPool2d(2, 2),
+#     torch.nn.Conv2d(6, 16, 5),
+#     torch.nn.Flatten(),
+#     torch.nn.Linear(53824, hidden_size),
+#     torch.nn.ReLU(),
+#     torch.nn.Linear(hidden_size, 32*32),
+#     torch.nn.ReLU(),
+#     torch.nn.Linear(32*32, 10),
+#     torch.nn.LogSoftmax(dim=-1)
+# ).to(device)
+
+# train(model, trainset) 
+
+# print(accuracy(model, testset))
+
+# torch.save(model.state_dict(), 'model.pt')
+
 model = torch.nn.Sequential(
     torch.nn.Conv2d(3, 6, 5),
     torch.nn.MaxPool2d(2, 2),
@@ -53,9 +72,4 @@ model = torch.nn.Sequential(
     torch.nn.Linear(32*32, 10),
     torch.nn.LogSoftmax(dim=-1)
 ).to(device)
-
-train(model, trainset) 
-
-print(accuracy(model, testset))
-
-# torch.save(model.state_dict(), 'model.pt')
+model.load_state_dict(torch.load('model.pt'))