diff --git a/Deep Learning/submission_template04.py b/Deep Learning/submission_template04.py
index 498fa3e..5db2002 100644
--- a/Deep Learning/submission_template04.py	
+++ b/Deep Learning/submission_template04.py	
@@ -5,11 +5,28 @@
 
 class ConvNet(nn.Module):
     def __init__(self):
-        ...
+        import torch
+from torch import nn
+from torch.nn import functional as F  # 只需导入一次
+
+class ConvNet(nn.Module):
+    def __init__(self):
+        super().__init__()  # 修正：super().__init__() 末尾双下划线
+        # 定义卷积层和全连接层
+        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)  # 输入通道3，输出通道16
+        self.pool = nn.MaxPool2d(2, 2)                           # 池化层
+        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)
+        self.fc1 = nn.Linear(32 * 8 * 8, 256)                    # 修正：self.fc1 而非 fcl
+        self.fc2 = nn.Linear(256, 10)                            # 输出10个类别
 
-    
     def forward(self, x):
-        ...
+        # 前向传播流程
+        x = self.pool(F.relu(self.conv1(x)))   # 输出形状: [batch, 16, 16, 16]
+        x = self.pool(F.relu(self.conv2(x)))   # 输出形状: [batch, 32, 8, 8]
+        x = x.view(-1, 32 * 8 * 8)             # 展平为 [batch, 2048]
+        x = F.relu(self.fc1(x))                # 全连接层1 → [batch, 256]
+        x = self.fc2(x)                        # 全连接层2 → [batch, 10]
+        return x
 
 def create_model():
     return ConvNet()