优化曲线的绘制，避免中文不显示。

food_classifier.py

@@ -5,10 +5,15 @@ import torch.nn.functional as F
 from torch.utils.data import DataLoader
 from torchvision import datasets, transforms
 import matplotlib.pyplot as plt
+import matplotlib
 import numpy as np
 from tqdm import tqdm
 import os
 
+# 设置matplotlib支持中文显示
+plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']  # 指定默认字体
+plt.rcParams['axes.unicode_minus'] = False  # 解决保存图像是负号'-'显示为方块的问题
+
 # 设置设备
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 print(f"使用设备: {device}")
@@ -71,7 +76,7 @@ class FoodCNN(nn.Module):
 
 # 数据预处理
 transform_train = transforms.Compose([
-    transforms.Resize((32, 32)),  # 调整为32x32以匹配CIFAR10结构
+    transforms.Resize((32, 32)),
     transforms.RandomHorizontalFlip(p=0.5),
     transforms.RandomRotation(10),
     transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1),
@@ -254,18 +259,18 @@ if __name__ == '__main__':
     plt.figure(figsize=(12, 4))
 
     plt.subplot(1, 2, 1)
-    plt.plot(train_losses, label='训练损失')
+    plt.plot(train_losses, label='Train Loss')
-    plt.plot(val_losses, label='验证损失')
+    plt.plot(val_losses, label='Val Loss')
-    plt.title('损失曲线')
+    plt.title('Loss Curve')
     plt.xlabel('Epoch')
     plt.ylabel('Loss')
     plt.legend()
     plt.grid(True)
 
     plt.subplot(1, 2, 2)
-    plt.plot(train_accuracies, label='训练准确率')
+    plt.plot(train_accuracies, label='Train Accuracy')
-    plt.plot(val_accuracies, label='验证准确率')
+    plt.plot(val_accuracies, label='Val Accuracy')
-    plt.title('准确率曲线')
+    plt.title('Accuracy Curve')
     plt.xlabel('Epoch')
     plt.ylabel('Accuracy (%)')
     plt.legend()