完成实验2

Lab2/code/3.py

@@ -74,9 +74,37 @@ class Model_3_3(nn.Module):
         x = self.fc3(x)
         x = self.activate_fn(x)
         return x
+    
+
+class Model_3_4(nn.Module):
+    def __init__(self, num_classes):
+        super().__init__()
+        self.flatten = nn.Flatten()
+        self.fc1 = nn.Linear(in_features=28 * 28, out_features=1024)
+        self.fc2 = nn.Linear(in_features=1024, out_features=256)
+        self.fc3 = nn.Linear(in_features=256, out_features=num_classes)
+        self.activate_fn = leaky_relu
+
+    def forward(self, x: torch.Tensor):
+        x = self.flatten(x)
+        x = self.fc1(x)
+        x = self.activate_fn(x)
+
+        x = self.fc2(x)
+        x = self.activate_fn(x)
+
+        x = self.fc3(x)
+        x = self.activate_fn(x)
+        return x
 
 
 if __name__ == "__main__":
-    train_MNIST_CLS(Model=Model_3_1)
-    train_MNIST_CLS(Model=Model_3_2)
-    train_MNIST_CLS(Model=Model_3_3)
+    print("模型1开始训练，激活函数为relu：")
+    train_loss_3_1, test_acc_3_1 = train_MNIST_CLS(Model=Model_3_1) # 激活函数为relu
+    print("模型2开始训练，激活函数为sigmoid：")
+    train_loss_3_2, test_acc_3_2 = train_MNIST_CLS(Model=Model_3_2) # 激活函数为sigmoid
+    print("模型3开始训练，激活函数为tanh：")
+    train_loss_3_3, test_acc_3_3 = train_MNIST_CLS(Model=Model_3_3) # 激活函数为tanh
+    print("模型4开始训练，激活函数为leaky_relu：")
+    train_loss_3_4, test_acc_3_4 = train_MNIST_CLS(Model=Model_3_4) # 激活函数为leaky_relu
+