From a60cd58a0be30bd6181c3ea92ede3a87ea8a2925 Mon Sep 17 00:00:00 2001
From: zhanghuan <1262329256@qq.com>
Date: Wed, 3 Sep 2025 09:57:11 +0800
Subject: [PATCH] =?UTF-8?q?=E4=BC=98=E5=8C=96=E6=9B=B2=E7=BA=BF=E7=9A=84?=
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---
 food_classifier.py | 19 ++++++++++++-------
 1 file changed, 12 insertions(+), 7 deletions(-)

diff --git a/food_classifier.py b/food_classifier.py
index fc62041..f7335bd 100644
--- a/food_classifier.py
+++ b/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(val_losses, label='验证损失')
-    plt.title('损失曲线')
+    plt.plot(train_losses, label='Train Loss')
+    plt.plot(val_losses, label='Val Loss')
+    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(val_accuracies, label='验证准确率')
-    plt.title('准确率曲线')
+    plt.plot(train_accuracies, label='Train Accuracy')
+    plt.plot(val_accuracies, label='Val Accuracy')
+    plt.title('Accuracy Curve')
     plt.xlabel('Epoch')
     plt.ylabel('Accuracy (%)')
     plt.legend()