2025-06-22 18:56:53.192710: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-06-22 18:56:54.136866: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-06-22 18:56:56,467 - INFO - 加载并增强数据集: augmented_data
2025-06-22 18:56:56,560 - INFO - 原始数据集: 150 张图片, 5 个类别
2025-06-22 18:56:56,565 - INFO - 类别 book: 30 张原始图像
2025-06-22 18:56:56,989 - INFO - 类别 cup: 30 张原始图像
2025-06-22 18:56:57,403 - INFO - 类别 glasses: 30 张原始图像
2025-06-22 18:56:57,820 - INFO - 类别 phone: 30 张原始图像
2025-06-22 18:56:58,248 - INFO - 类别 shoe: 30 张原始图像
2025-06-22 18:56:58,859 - INFO - 增强后数据集: 450 张图片
2025-06-22 18:56:58,954 - INFO - 构建优化的迁移学习模型...
2025-06-22 18:56:58.959007: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: SSE3 SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
Model: "functional"
┌─────────────────────┬───────────────────┬────────────┬───────────────────┐
│ Layer (type) │ Output Shape │ Param # │ Connected to │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ input_layer_1 │ (None, 224, 224, │ 0 │ - │
│ (InputLayer) │ 3) │ │ │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ efficientnetb0 │ (None, 7, 7, │ 4,049,571 │ input_layer_1[0]… │
│ (Functional) │ 1280) │ │ │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ global_average_poo… │ (None, 1280) │ 0 │ efficientnetb0[0… │
│ (GlobalAveragePool… │ │ │ │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ dense (Dense) │ (None, 512) │ 655,872 │ global_average_p… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ dense_1 (Dense) │ (None, 1280) │ 656,640 │ dense[0][0] │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ multiply (Multiply) │ (None, 1280) │ 0 │ global_average_p… │
│ │ │ │ dense_1[0][0] │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ dense_2 (Dense) │ (None, 512) │ 655,872 │ multiply[0][0] │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ batch_normalization │ (None, 512) │ 2,048 │ dense_2[0][0] │
│ (BatchNormalizatio… │ │ │ │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ dropout (Dropout) │ (None, 512) │ 0 │ batch_normalizat… │
├─────────────────────┼───────────────────┼────────────┼───────────────────┤
│ dense_3 (Dense) │ (None, 5) │ 2,565 │ dropout[0][0] │
└─────────────────────┴───────────────────┴────────────┴───────────────────┘
Total params: 6,022,568 (22.97 MB)
Trainable params: 1,971,973 (7.52 MB)
Non-trainable params: 4,050,595 (15.45 MB)
2025-06-22 18:56:59,882 - INFO - 开始高级训练策略...
2025-06-22 18:56:59,882 - INFO - 阶段1: 冻结基础模型训练
Epoch 1/50
23/23 ━━━━━━━━━━━━━━━━━━━━ 16s 442ms/step - accuracy: 0.2001 - loss: 1.7270 - val_accuracy: 0.2000 - val_loss: 1.6104 - learning_rate: 1.0000e-04
Epoch 2/50
23/23 ━━━━━━━━━━━━━━━━━━━━ 8s 335ms/step - accuracy: 0.1486 - loss: 1.7114 - val_accuracy: 0.2000 - val_loss: 1.6100 - learning_rate: 1.0000e-04
Epoch 3/50
23/23 ━━━━━━━━━━━━━━━━━━━━ 8s 332ms/step - accuracy: 0.2337 - loss: 1.7239 - val_accuracy: 0.2000 - val_loss: 1.6117 - learning_rate: 1.0000e-04
Epoch 4/50
23/23 ━━━━━━━━━━━━━━━━━━━━ 8s 335ms/step - accuracy: 0.2558 - loss: 1.6466 - val_accuracy: 0.2000 - val_loss: 1.6104 - learning_rate: 1.0000e-04
Epoch 5/50
23/23 ━━━━━━━━━━━━━━━━━━━━ 0s 270ms/step - accuracy: 0.2281 - loss: 1.6503
Epoch 5: ReduceLROnPlateau reducing learning rate to 4.999999873689376e-05.
23/23 ━━━━━━━━━━━━━━━━━━━━ 8s 367ms/step - accuracy: 0.2271 - loss: 1.6513 - val_accuracy: 0.2111 - val_loss: 1.6118 - learning_rate: 1.0000e-04
Epoch 6/50
23/23 ━━━━━━━━━━━━━━━━━━━━ 8s 333ms/step - accuracy: 0.1899 - loss: 1.6756 - val_accuracy: 0.2000 - val_loss: 1.6112 - learning_rate: 5.0000e-05
Epoch 7/50
23/23 ━━━━━━━━━━━━━━━━━━━━ 8s 333ms/step - accuracy: 0.2394 - loss: 1.6269 - val_accuracy: 0.2000 - val_loss: 1.6128 - learning_rate: 5.0000e-05
Epoch 8/50
23/23 ━━━━━━━━━━━━━━━━━━━━ 0s 266ms/step - accuracy: 0.2041 - loss: 1.7332
Epoch 8: ReduceLROnPlateau reducing learning rate to 2.499999936844688e-05.
23/23 ━━━━━━━━━━━━━━━━━━━━ 8s 333ms/step - accuracy: 0.2042 - loss: 1.7319 - val_accuracy: 0.2000 - val_loss: 1.6103 - learning_rate: 5.0000e-05
Epoch 9/50
23/23 ━━━━━━━━━━━━━━━━━━━━ 8s 355ms/step - accuracy: 0.1765 - loss: 1.6814 - val_accuracy: 0.3333 - val_loss: 1.6107 - learning_rate: 2.5000e-05
2025-06-22 18:58:18,867 - INFO - 阶段2: 微调部分层
Epoch 1/25
23/23 ━━━━━━━━━━━━━━━━━━━━ 18s 460ms/step - accuracy: 0.2374 - loss: 2.4082 - val_accuracy: 0.2000 - val_loss: 1.6107 - learning_rate: 1.0000e-05
Epoch 2/25
23/23 ━━━━━━━━━━━━━━━━━━━━ 9s 390ms/step - accuracy: 0.2021 - loss: 2.2585 - val_accuracy: 0.2000 - val_loss: 1.6112 - learning_rate: 1.0000e-05
Epoch 3/25
23/23 ━━━━━━━━━━━━━━━━━━━━ 9s 375ms/step - accuracy: 0.2259 - loss: 2.3548 - val_accuracy: 0.2111 - val_loss: 1.6121 - learning_rate: 1.0000e-05
Epoch 4/25
23/23 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step - accuracy: 0.2416 - loss: 2.0942
Epoch 4: ReduceLROnPlateau reducing learning rate to 4.999999873689376e-06.
23/23 ━━━━━━━━━━━━━━━━━━━━ 9s 374ms/step - accuracy: 0.2405 - loss: 2.1006 - val_accuracy: 0.2000 - val_loss: 1.6127 - learning_rate: 1.0000e-05
Epoch 5/25
23/23 ━━━━━━━━━━━━━━━━━━━━ 9s 377ms/step - accuracy: 0.2053 - loss: 2.1248 - val_accuracy: 0.2000 - val_loss: 1.6136 - learning_rate: 5.0000e-06
Epoch 6/25
23/23 ━━━━━━━━━━━━━━━━━━━━ 9s 382ms/step - accuracy: 0.1995 - loss: 2.2549 - val_accuracy: 0.2000 - val_loss: 1.6150 - learning_rate: 5.0000e-06
Epoch 7/25
23/23 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step - accuracy: 0.1949 - loss: 2.1615
Epoch 7: ReduceLROnPlateau reducing learning rate to 2.499999936844688e-06.
23/23 ━━━━━━━━━━━━━━━━━━━━ 9s 373ms/step - accuracy: 0.1962 - loss: 2.1615 - val_accuracy: 0.2000 - val_loss: 1.6165 - learning_rate: 5.0000e-06
Epoch 8/25
23/23 ━━━━━━━━━━━━━━━━━━━━ 9s 375ms/step - accuracy: 0.2320 - loss: 2.1199 - val_accuracy: 0.2000 - val_loss: 1.6186 - learning_rate: 2.5000e-06
Epoch 9/25
23/23 ━━━━━━━━━━━━━━━━━━━━ 9s 378ms/step - accuracy: 0.2379 - loss: 2.1694 - val_accuracy: 0.2000 - val_loss: 1.6204 - learning_rate: 2.5000e-06
2025-06-22 18:59:46,920 - INFO - 训练完成
2025-06-22 18:59:46,920 - INFO - 评估模型...
2025-06-22 18:59:48,600 - INFO - 测试准确率: 20.00%
E:\pycharm\study\计算机视觉\物品识别系统.py:313: UserWarning: Glyph 20934 (\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from font(s) DejaVu Sans.
plt.tight_layout()
E:\pycharm\study\计算机视觉\物品识别系统.py:313: UserWarning: Glyph 30830 (\N{CJK UNIFIED IDEOGRAPH-786E}) missing from font(s) DejaVu Sans.
plt.tight_layout()
E:\pycharm\study\计算机视觉\物品识别系统.py:313: UserWarning: Glyph 29575 (\N{CJK UNIFIED IDEOGRAPH-7387}) missing from font(s) DejaVu Sans.
plt.tight_layout()
E:\pycharm\study\计算机视觉\物品识别系统.py:313: UserWarning: Glyph 35757 (\N{CJK UNIFIED IDEOGRAPH-8BAD}) missing from font(s) DejaVu Sans.
plt.tight_layout()
E:\pycharm\study\计算机视觉\物品识别系统.py:313: UserWarning: Glyph 32451 (\N{CJK UNIFIED IDEOGRAPH-7EC3}) missing from font(s) DejaVu Sans.
plt.tight_layout()
E:\pycharm\study\计算机视觉\物品识别系统.py:313: UserWarning: Glyph 21644 (\N{CJK UNIFIED IDEOGRAPH-548C}) missing from font(s) DejaVu Sans.
plt.tight_layout()
E:\pycharm\study\计算机视觉\物品识别系统.py:313: UserWarning: Glyph 39564 (\N{CJK UNIFIED IDEOGRAPH-9A8C}) missing from font(s) DejaVu Sans.
plt.tight_layout()
E:\pycharm\study\计算机视觉\物品识别系统.py:313: UserWarning: Glyph 35777 (\N{CJK UNIFIED IDEOGRAPH-8BC1}) missing from font(s) DejaVu Sans.
plt.tight_layout()
E:\pycharm\study\计算机视觉\物品识别系统.py:313: UserWarning: Glyph 25439 (\N{CJK UNIFIED IDEOGRAPH-635F}) missing from font(s) DejaVu Sans.
plt.tight_layout()
E:\pycharm\study\计算机视觉\物品识别系统.py:313: UserWarning: Glyph 22833 (\N{CJK UNIFIED IDEOGRAPH-5931}) missing from font(s) DejaVu Sans.
plt.tight_layout()
E:\pycharm\study\计算机视觉\物品识别系统.py:314: UserWarning: Glyph 20934 (\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from font(s) DejaVu Sans.
plt.savefig('training_history.png')
E:\pycharm\study\计算机视觉\物品识别系统.py:314: UserWarning: Glyph 30830 (\N{CJK UNIFIED IDEOGRAPH-786E}) missing from font(s) DejaVu Sans.
plt.savefig('training_history.png')
E:\pycharm\study\计算机视觉\物品识别系统.py:314: UserWarning: Glyph 29575 (\N{CJK UNIFIED IDEOGRAPH-7387}) missing from font(s) DejaVu Sans.
plt.savefig('training_history.png')
E:\pycharm\study\计算机视觉\物品识别系统.py:314: UserWarning: Glyph 35757 (\N{CJK UNIFIED IDEOGRAPH-8BAD}) missing from font(s) DejaVu Sans.
plt.savefig('training_history.png')
E:\pycharm\study\计算机视觉\物品识别系统.py:314: UserWarning: Glyph 32451 (\N{CJK UNIFIED IDEOGRAPH-7EC3}) missing from font(s) DejaVu Sans.
plt.savefig('training_history.png')
E:\pycharm\study\计算机视觉\物品识别系统.py:314: UserWarning: Glyph 21644 (\N{CJK UNIFIED IDEOGRAPH-548C}) missing from font(s) DejaVu Sans.
plt.savefig('training_history.png')
E:\pycharm\study\计算机视觉\物品识别系统.py:314: UserWarning: Glyph 39564 (\N{CJK UNIFIED IDEOGRAPH-9A8C}) missing from font(s) DejaVu Sans.
plt.savefig('training_history.png')
E:\pycharm\study\计算机视觉\物品识别系统.py:314: UserWarning: Glyph 35777 (\N{CJK UNIFIED IDEOGRAPH-8BC1}) missing from font(s) DejaVu Sans.
plt.savefig('training_history.png')
E:\pycharm\study\计算机视觉\物品识别系统.py:314: UserWarning: Glyph 25439 (\N{CJK UNIFIED IDEOGRAPH-635F}) missing from font(s) DejaVu Sans.
plt.savefig('training_history.png')
E:\pycharm\study\计算机视觉\物品识别系统.py:314: UserWarning: Glyph 22833 (\N{CJK UNIFIED IDEOGRAPH-5931}) missing from font(s) DejaVu Sans.
plt.savefig('training_history.png')
2025-06-22 18:59:48,905 - INFO - 训练历史图表已保存到 training_history.png
2025-06-22 18:59:49,390 - INFO - 模型已保存到: optimized_model.keras
2025-06-22 18:59:49,390 - INFO - 执行内存清理...
WARNING:tensorflow:From E:\python3.9.13\lib\site-packages\keras\src\backend\common\global_state.py:82: The name tf.reset_default_graph is deprecated. Please use tf.compat.v1.reset_default_graph instead.
2025-06-22 18:59:50,195 - WARNING - From E:\python3.9.13\lib\site-packages\keras\src\backend\common\global_state.py:82: The name tf.reset_default_graph is deprecated. Please use tf.compat.v1.reset_default_graph instead.
2025-06-22 18:59:50,743 - INFO - 内存清理完成
E:\pycharm\study\计算机视觉\物品识别系统.py:355: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
ax2.set_xticklabels(self.class_labels, rotation=45)
E:\pycharm\study\计算机视觉\物品识别系统.py:364: UserWarning: Glyph 39044 (\N{CJK UNIFIED IDEOGRAPH-9884}) missing from font(s) DejaVu Sans.
plt.tight_layout()
E:\pycharm\study\计算机视觉\物品识别系统.py:364: UserWarning: Glyph 27979 (\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from font(s) DejaVu Sans.
plt.tight_layout()
E:\pycharm\study\计算机视觉\物品识别系统.py:364: UserWarning: Glyph 27010 (\N{CJK UNIFIED IDEOGRAPH-6982}) missing from font(s) DejaVu Sans.
plt.tight_layout()
E:\pycharm\study\计算机视觉\物品识别系统.py:364: UserWarning: Glyph 29575 (\N{CJK UNIFIED IDEOGRAPH-7387}) missing from font(s) DejaVu Sans.
plt.tight_layout()
E:\pycharm\study\计算机视觉\物品识别系统.py:364: UserWarning: Glyph 31867 (\N{CJK UNIFIED IDEOGRAPH-7C7B}) missing from font(s) DejaVu Sans.
plt.tight_layout()
E:\pycharm\study\计算机视觉\物品识别系统.py:364: UserWarning: Glyph 21035 (\N{CJK UNIFIED IDEOGRAPH-522B}) missing from font(s) DejaVu Sans.
plt.tight_layout()
E:\pycharm\study\计算机视觉\物品识别系统.py:364: UserWarning: Glyph 20998 (\N{CJK UNIFIED IDEOGRAPH-5206}) missing from font(s) DejaVu Sans.
plt.tight_layout()
E:\pycharm\study\计算机视觉\物品识别系统.py:364: UserWarning: Glyph 24067 (\N{CJK UNIFIED IDEOGRAPH-5E03}) missing from font(s) DejaVu Sans.
plt.tight_layout()
E:\pycharm\study\计算机视觉\物品识别系统.py:365: UserWarning: Glyph 39044 (\N{CJK UNIFIED IDEOGRAPH-9884}) missing from font(s) DejaVu Sans.
plt.savefig(output_path)
E:\pycharm\study\计算机视觉\物品识别系统.py:365: UserWarning: Glyph 27979 (\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from font(s) DejaVu Sans.
plt.savefig(output_path)
E:\pycharm\study\计算机视觉\物品识别系统.py:365: UserWarning: Glyph 27010 (\N{CJK UNIFIED IDEOGRAPH-6982}) missing from font(s) DejaVu Sans.
plt.savefig(output_path)
E:\pycharm\study\计算机视觉\物品识别系统.py:365: UserWarning: Glyph 29575 (\N{CJK UNIFIED IDEOGRAPH-7387}) missing from font(s) DejaVu Sans.
plt.savefig(output_path)
E:\pycharm\study\计算机视觉\物品识别系统.py:365: UserWarning: Glyph 31867 (\N{CJK UNIFIED IDEOGRAPH-7C7B}) missing from font(s) DejaVu Sans.
plt.savefig(output_path)
E:\pycharm\study\计算机视觉\物品识别系统.py:365: UserWarning: Glyph 21035 (\N{CJK UNIFIED IDEOGRAPH-522B}) missing from font(s) DejaVu Sans.
plt.savefig(output_path)
E:\pycharm\study\计算机视觉\物品识别系统.py:365: UserWarning: Glyph 20998 (\N{CJK UNIFIED IDEOGRAPH-5206}) missing from font(s) DejaVu Sans.
plt.savefig(output_path)
E:\pycharm\study\计算机视觉\物品识别系统.py:365: UserWarning: Glyph 24067 (\N{CJK UNIFIED IDEOGRAPH-5E03}) missing from font(s) DejaVu Sans.
plt.savefig(output_path)
2025-06-22 18:59:52,251 - INFO - 预测结果已保存到 prediction_result.png
2025-06-22 18:59:52,252 - INFO - 真实类别: cup