diff --git a/main.py b/main.py
index 434fcf2..5825c88 100644
--- a/main.py
+++ b/main.py
@@ -13,9 +13,9 @@ class Net(torch.nn.Module):
         self.fc3: torch.nn.Linear = torch.nn.Linear(64, 64)
         self.fc4: torch.nn.Linear = torch.nn.Linear(64, 10)
     def forward(self, x: torch.Tensor):
-        x = torch.nn.functional.relu(self.fc1(x))
-        x = torch.nn.functional.relu(self.fc2(x))
-        x = torch.nn.functional.relu(self.fc3(x))
+        x = torch.nn.functional.rrelu(self.fc1(x))
+        x = torch.nn.functional.rrelu(self.fc2(x))
+        x = torch.nn.functional.rrelu(self.fc3(x))
         x = torch.nn.functional.log_softmax(self.fc4(x), dim=1)
         return x
 
@@ -39,13 +39,16 @@ def evaluate(test_data: DataLoader[Image], net: Net) -> float:
 def main():
     print("=== START ===")
     torch.set_default_device(torch.device(type='cpu', index=0))
+    torch.set_default_dtype(torch.float16)
     train_data = get_data_loader(is_train=True)
+    print('train data size:',len(train_data))
     test_data = get_data_loader(is_train=False)
+    print('test data size: ', len(test_data))
     net = Net()
 
     print('initial accuracy:', evaluate(test_data, net))
     optimizer = torch.optim.Adam(net.parameters(), lr=0.001) # type: ignore
-    for epoch in range(1, 7, 1):
+    for epoch in range(1, 3, 1):
         for (x, y) in train_data:
             net.zero_grad()
             output = net.forward(x.view(-1, 28 * 28))
@@ -54,14 +57,9 @@ def main():
             optimizer.step()
         print('epoch', epoch, 'accuracy', evaluate(test_data, net))
 
-    # test
-    for (n, (x, _)) in enumerate(test_data):
-        if(n > 15):
-            break
-        predict = torch.argmax(net.forward(x[0].view(-1, 28 * 28)))
-        plt.figure(n)
-        plt.title('prediction: ' + str(int(predict)))
-        plt.imsave(str(n) + '_predict-' + str(predict) + '.png', x[0].view(28, 28))
+    model_name = 'model.pth'
+    print('saving model to: ', model_name)
+    torch.save(net.state_dict(), model_name)
     print('=== DONE ===')
 if __name__ == '__main__':
-    main()
\ No newline at end of file
+    main()
diff --git a/model.pth b/model.pth
new file mode 100644
index 0000000..b71e826
Binary files /dev/null and b/model.pth differ
diff --git a/test.py b/test.py
index 9b071b4..e2ae983 100644
--- a/test.py
+++ b/test.py
@@ -1,2 +1,38 @@
-def __main__():
-    print('hello')
\ No newline at end of file
+from matplotlib import pyplot as plt
+import torch
+import os
+import time
+from PIL.Image import Image
+from torchvision import transforms
+from torchvision.datasets import MNIST
+from torch.utils.data import DataLoader
+
+from main import Net
+
+
+def get_test_loader() -> DataLoader[Image]:
+    to_tensor = transforms.Compose([transforms.ToTensor()])
+    data_set = MNIST("", False, transform=to_tensor, download=True)
+    return DataLoader(data_set, batch_size=15, shuffle=True)
+
+def main():
+    path = '.result'
+    try:
+        os.mkdir(path)
+    except:
+        pass
+    net = Net()
+    model = torch.load('model.pth', weights_only=True)
+    net.load_state_dict(model)
+
+    test_data = get_test_loader()
+    for (n, (x, _)) in enumerate(test_data):
+        if(n > 15):
+            break
+        predict = torch.argmax(net.forward(x[0].view(-1, 28 * 28)))
+        plt.figure(n)
+        plt.title('prediction: ' + str(int(predict)))
+        plt.imsave(path + '/' + str(time.time_ns()) + '_predict-' + str(predict) + '.png', x[0].view(28, 28))
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file