提交__init__文件，和网络文件

.gitignore

@@ -1,4 +1,3 @@
 /dataset/
-/demo/cifar_net103.pth
-/demo/data/
 /.idea/
+/model/

net/__init__.py

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+"""
+食物分类网络模型包
+"""
+
+from .food_net import FoodCNN, create_food_cnn
+
+__all__ = ['FoodCNN', 'create_food_cnn']

net/food_net.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class FoodCNN(nn.Module):
+    """
+    食物分类CNN模型
+    基于CIFAR10结构，适配3分类任务
+    """
+    def __init__(self):
+        super(FoodCNN, self).__init__()
+        # 第一个卷积块
+        self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
+        self.conv2 = nn.Conv2d(32, 32, 3, padding=1)
+        self.pool1 = nn.MaxPool2d(2, 2)
+        self.dropout1 = nn.Dropout2d(0.25)
+        
+        # 第二个卷积块
+        self.conv3 = nn.Conv2d(32, 64, 3, padding=1)
+        self.conv4 = nn.Conv2d(64, 64, 3, padding=1)
+        self.pool2 = nn.MaxPool2d(2, 2)
+        self.dropout2 = nn.Dropout2d(0.25)
+        
+        # 第三个卷积块
+        self.conv5 = nn.Conv2d(64, 128, 3, padding=1)
+        self.conv6 = nn.Conv2d(128, 128, 3, padding=1)
+        self.pool3 = nn.MaxPool2d(2, 2)
+        self.dropout3 = nn.Dropout2d(0.25)
+        
+        # 全连接层
+        self.fc1 = nn.Linear(128 * 4 * 4, 512)
+        self.dropout4 = nn.Dropout(0.5)
+        self.fc2 = nn.Linear(512, 3)  # 3分类
+        
+    def forward(self, x):
+        # 第一个卷积块
+        x = F.relu(self.conv1(x))
+        x = F.relu(self.conv2(x))
+        x = self.pool1(x)
+        x = self.dropout1(x)
+        
+        # 第二个卷积块
+        x = F.relu(self.conv3(x))
+        x = F.relu(self.conv4(x))
+        x = self.pool2(x)
+        x = self.dropout2(x)
+        
+        # 第三个卷积块
+        x = F.relu(self.conv5(x))
+        x = F.relu(self.conv6(x))
+        x = self.pool3(x)
+        x = self.dropout3(x)
+        
+        # 展平
+        x = x.view(-1, 128 * 4 * 4)
+        
+        # 全连接层
+        x = F.relu(self.fc1(x))
+        x = self.dropout4(x)
+        x = self.fc2(x)
+        
+        return x
+
+
+def create_food_cnn():
+    """
+    创建食物分类CNN模型
+    
+    Returns:
+        FoodCNN: 网络模型实例
+    """
+    return FoodCNN()
+
+
+if __name__ == "__main__":
+    # 测试网络
+    model = create_food_cnn()
+    print(f"模型参数数量: {sum(p.numel() for p in model.parameters() if p.requires_grad)}")
+    
+    # 测试前向传播
+    dummy_input = torch.randn(1, 3, 32, 32)
+    output = model(dummy_input)
+    print(f"输出形状: {output.shape}")

settings/__init__.py

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+"""
+配置包
+"""
+
+from .settings import *