Epoch 1/10 2023-07-22 21:56:00.836220: W tensorflow/core/framework/op_kernel.cc:1807] OP_REQUIRES failed at cast_op.cc:121 : UNIMPLEMENTED: Cast string to int64 is not supported Traceback (most recent call last): File "d:\AI\1.py", line 37, in <module> model.fit(images, labels, epochs=10, validation_split=0.2) File "D:\AI\env\lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler raise e.with_traceback(filtered_tb) from None File "D:\AI\env\lib\site-packages\tensorflow\python\eager\execute.py", line 52, in quick_execute tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name, tensorflow.python.framework.errors_impl.UnimplementedError: Graph execution error: Detected at node 'sparse_categorical_crossentropy/Cast' defined at (most recent call last): File "d:\AI\1.py", line 37, in <module> model.fit(images, labels, epochs=10, validation_split=0.2) File "D:\AI\env\lib\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler return fn(*args, **kwargs) File "D:\AI\env\lib\site-packages\keras\engine\training.py", line 1685, in fit tmp_logs = self.train_function(iterator) File "D:\AI\env\lib\site-packages\keras\engine\training.py", line 1284, in train_function return step_function(self, iterator) File "D:\AI\env\lib\site-packages\keras\engine\training.py", line 1268, in step_function outputs = model.distribute_strategy.run(run_step, args=(data,)) File "D:\AI\env\lib\site-packages\keras\engine\training.py", line 1249, in run_step outputs = model.train_step(data) File "D:\AI\env\lib\site-packages\keras\engine\training.py", line 1051, in train_step loss = self.compute_loss(x, y, y_pred, sample_weight) File "D:\AI\env\lib\site-packages\keras\engine\training.py", line 1109, in compute_loss return self.compiled_loss( File "D:\AI\env\lib\site-packages\keras\engine\compile_utils.py", line 265, in __call__ loss_value = loss_obj(y_t, y_p, sample_weight=sw) File "D:\AI\env\lib\site-packages\keras\losses.py", line 142, in __call__ losses = call_fn(y_true, y_pred) File "D:\AI\env\lib\site-packages\keras\losses.py", line 268, in call return ag_fn(y_true, y_pred, **self._fn_kwargs) File "D:\AI\env\lib\site-packages\keras\losses.py", line 2078, in sparse_categorical_crossentropy return backend.sparse_categorical_crossentropy( File "D:\AI\env\lib\site-packages\keras\backend.py", line 5610, in sparse_categorical_crossentropy target = cast(target, "int64") File "D:\AI\env\lib\site-packages\keras\backend.py", line 2304, in cast return tf.cast(x, dtype) Node: 'sparse_categorical_crossentropy/Cast' Cast string to int64 is not supported [[{{node sparse_categorical_crossentropy/Cast}}]] [Op:__inference_train_function_1010]

时间: 2023-08-05 21:07:14 浏览: 269
这个错误是由于标签 `labels` 中的数据类型不正确导致的。`labels` 中的数据应该是整数类型，但是在您的代码中，它们被解析为字符串类型。为了解决这个问题，您需要将标签转换为整数类型。 请尝试在加载数据集时将标签转换为整数类型。修改以下部分的代码： ```python for file in os.listdir(image_dir): img_path = os.path.join(image_dir, file) img = Image.open(img_path).convert('L') # 转换为灰度图像 img = img.resize((100, 100)) img = np.array(img, dtype=np.float32) images.append(img) labels.append(file.split('.')[0]) ``` 修改为： ```python for file in os.listdir(image_dir): img_path = os.path.join(image_dir, file) img = Image.open(img_path).convert('L') # 转换为灰度图像 img = img.resize((100, 100)) img = np.array(img, dtype=np.float32) images.append(img) labels.append(int(file.split('.')[0])) ``` 这样修改后，将标签转换为整数类型后，应该能够解决 `Cast string to int64 is not supported` 的问题。
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Epoch 1/20 98/98 ━━━━━━━━━━━━━━━━━━━━ 0s 552ms/step - dense_3_accuracy: 0.0282 - dense_3_loss: 3.5552 - dense_4_accuracy: 0.0313 - dense_4_loss: 3.5419 - dense_5_accuracy: 0.0259 - dense_5_loss: 3.5605 - dense_6_accuracy: 0.0296 - dense_6_loss: 3.5452 - loss: 14.2645 Epoch 0 - 验证集全匹配准确率: 0.0000 98/98 ━━━━━━━━━━━━━━━━━━━━ 62s 575ms/step - dense_3_accuracy: 0.0283 - dense_3_loss: 3.5550 - dense_4_accuracy: 0.0314 - dense_4_loss: 3.5417 - dense_5_accuracy: 0.0259 - dense_5_loss: 3.5602 - dense_6_accuracy: 0.0296 - dense_6_loss: 3.5450 - loss: 14.2641 - val_dense_3_accuracy: 0.0333 - val_dense_3_loss: 3.5351 - val_dense_4_accuracy: 0.0333 - val_dense_4_loss: 3.5258 - val_dense_5_accuracy: 0.0400 - val_dense_5_loss: 3.5363 - val_dense_6_accuracy: 0.0333 - val_dense_6_loss: 3.5032 - val_loss: 14.2117 - learning_rate: 0.0010 - val_full_match_accuracy: 0.0000e+00 Epoch 2/20 98/98 ━━━━━━━━━━━━━━━━━━━━ 0s 547ms/step - dense_3_accuracy: 0.0283 - dense_3_loss: 3.5009 - dense_4_accuracy: 0.0306 - dense_4_loss: 3.4942 - dense_5_accuracy: 0.0378 - dense_5_loss: 3.4964 - dense_6_accuracy: 0.0378 - dense_6_loss: 3.4986 - loss: 14.0956 Epoch 1 - 验证集全匹配准确率: 0.0000 98/98 ━━━━━━━━━━━━━━━━━━━━ 54s 552ms/step - dense_3_accuracy: 0.0283 - dense_3_loss: 3.5009 - dense_4_accuracy: 0.0306 - dense_4_loss: 3.4942 - dense_5_accuracy: 0.0377 - dense_5_loss: 3.4964 - dense_6_accuracy: 0.0378 - dense_6_loss: 3.4986 - loss: 14.0954 - val_dense_3_accuracy: 0.0267 - val_dense_3_loss: 3.5849 - val_dense_4_accuracy: 0.0133 - val_dense_4_loss: 3.5698 - val_dense_5_accuracy: 0.0067 - val_dense_5_loss: 3.6506 - val_dense_6_accuracy: 0.0267 - val_dense_6_loss: 3.5626 - val_loss: 14.4254 - learning_rate: 0.0010 - val_full_match_accuracy: 0.0000e+00 Epoch 3/20 98/98 ━━━━━━━━━━━━━━━━━━━━ 0s 554ms/step - dense_3_accuracy: 0.0324 - dense_3_loss: 3.4974 - dense_4_accuracy: 0.0315 - dense_4_loss: 3.4956 - dense_5_accuracy: 0.0398 - dense_5_loss: 3.4950 - dense_6_accuracy: 0.0274 - dense_6_loss: 3.4961 - loss: 14.0531 Epoch 2 - 验证集全匹配准确率: 0.0000 98/98 ━━━━━━━━━━━━━━━━━━━━ 55s 559ms/step - dense_3_accuracy: 0.0324 - dense_3_loss: 3.4974 - dense_4_accuracy: 0.0315 - dense_4_loss: 3.4956 - dense_5_accuracy: 0.0398 - dense_5_loss: 3.4950 - dense_6_accuracy: 0.0274 - dense_6_loss: 3.4961 - loss: 14.0530 - val_dense_3_accuracy: 0.0067 - val_dense_3_loss: 3.5379 - val_dense_4_accuracy: 0.0400 - val_dense_4_loss: 3.5454 - val_dense_5_accuracy: 0.0067 - val_dense_5_loss: 3.6164 - val_dense_6_accuracy: 0.0400 - val_dense_6_loss: 3.5071 - val_loss: 14.2812 - learning_rate: 0.0010 - val_full_match_accuracy: 0.0000e+00

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ubuntu22.04的pycharm中： taskset -c 0,1,2,3 python src/synthesize.py --lot_size 100 --sgd_sigma 2.1 --sgd_epoch 15 --pca_sigma 10 --db credit --alg p3gm --n_iter 20 2025-03-28 20:52:46.026681: I tensorflow/core/util/port.cc:113] 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. 2025-03-28 20:52:46.437269: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered 2025-03-28 20:52:46.437987: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2025-03-28 20:52:46.501582: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered 2025-03-28 20:52:46.614687: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. 2025-03-28 20:52:47.488836: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT Traceback (most recent call last): File "/home/s/PycharmProjects/PythonProject1/P3GM/src/synthesize.py", line 8, in <module> from p3gm import P3GM File "/home/s/PycharmProjects/PythonProject1/P3GM/src/p3gm.py", line 8, in <module> from tensorflow_privacy.privacy.analysis.rdp_accountant import compute_rdp, get_privacy_spent File "/home/s/PycharmProjects/PythonProject1/P3GM/privacy/tensorflow_privacy/__init__.py", line 56, in <module> from tens

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D:\PyCharm\stock_predict_with_LSTM-master\venv\Scripts\python.exe D:/PyCharm/stock_predict_with_LSTM-master/main.py 2025-06-10 23:48:11.036396: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found 2025-06-10 23:48:11.036609: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine. [ 2025/06/10 23:48:13 ] Config: 'add_train': False 'batch_size': 64 'continue_flag': '' 'cur_time': '2025_06_10_23_48_13' 'debug_mode': False 'debug_num': 500 'do_continue_train': False 'do_figure_save': False 'do_log_print_to_screen': True 'do_log_save_to_file': True 'do_predict': True 'do_train': True 'do_train_visualized': False 'dropout_rate': 0.2 'epoch': 20 'feature_columns': [2, 3, 4, 5, 6, 7, 8] 'figure_save_path': './figure/' 'hidden_size': 128 'input_size': 7 'label_columns': [4, 5] 'label_in_feature_index': [2, 3] 'learning_rate': 0.001 'log_save_path': './log/2025_06_10_23_48_13_tensorflow/' 'lstm_layers': 2 'model_name': 'model_tensorflow.h5' 'model_postfix': {'pytorch': '.pth' 'keras': '.h5' 'tensorflow': '.h5'} 'model_save_path': './checkpoint/tensorflow/' 'output_size': 2 'patience': 5 'predict_day': 1 'random_seed': 42 'shuffle_train_data': True 'time_step': 20 'train_data_path': './data/stock_data.csv' 'train_data_rate': 0.95 'use_cuda': False 'used_frame': 'tensorflow' 'valid_data_rate': 0.15 2025-06-10 23:48:13.116524: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'nvcuda.dll'; dlerror: nvcuda.dll not found 2025-06-10 23:48:13.116622: W tensorflow/stream_executor/cuda/cuda_driver.cc:263] failed call to cuInit: UNKNOWN ERROR (303) 2025-06-10 23:48:13.119797: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: LAPTOP-VTR0417G 2025-06-10 23:48:13.119968: I tensorflow/stream_executor/cuda/cuda_diagnost

用户似乎已经查阅过站内引用[^1]提到的解决方案，但那个方案针对的是较老的CUDA10版本（cudart64_10.dll），而当前报错的是CUDA11版本（cudart64_110.dll）。直接下载dll文件放到System32可能带来系统不稳定风险，更...

Epoch 1/10 2025-05-19 20:56:13.073449: I tensorflow/stream_executor/cuda/cuda_blas.cc:1614] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once. 1/1 [==============================] - 1s 1s/step - loss: 12.2124 - accuracy: 0.0000e+00 Epoch 2/10 1/1 [==============================] - 0s 11ms/step - loss: 12.2109 - accuracy: 0.0000e+00 Epoch 3/10 1/1 [==============================] - 0s 10ms/step - loss: 12.2095 - accuracy: 0.0312 Epoch 4/10 1/1 [==============================] - 0s 5ms/step - loss: 12.2084 - accuracy: 0.0312 Epoch 5/10 1/1 [==============================] - 0s 5ms/step - loss: 12.2073 - accuracy: 0.0312 Epoch 6/10 1/1 [==============================] - 0s 5ms/step - loss: 12.2064 - accuracy: 0.0625 Epoch 7/10 1/1 [==============================] - 0s 10ms/step - loss: 12.2057 - accuracy: 0.0938 Epoch 8/10 1/1 [==============================] - 0s 7ms/step - loss: 12.2050 - accuracy: 0.1562 Epoch 9/10 1/1 [==============================] - 0s 5ms/step - loss: 12.2046 - accuracy: 0.1562 Epoch 10/10 1/1 [==============================] - 0s 9ms/step - loss: 12.2043 - accuracy: 0.1875 <keras.callbacks.History object at 0x000001972B1BF610>

#### 方法 1: 检查输入数据和目标数据 确保输入数据和目标数据的分布合理，并且目标数据是正确的标签格式。以下是检查数据的代码示例： python import tensorflow as tf # 假设 train_dataset 和 train_labels ...

2025-06-22 18:44:59.015180: 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 18:44:59.965132: 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 18:45:02,357 - INFO - 加载并增强数据集: augmented_data 2025-06-22 18:45:02,443 - INFO - 原始数据集: 150 张图片, 5 个类别 2025-06-22 18:45:02,446 - INFO - 类别 book: 30 张原始图像 2025-06-22 18:45:02,856 - INFO - 类别 cup: 30 张原始图像 2025-06-22 18:45:03,259 - INFO - 类别 glasses: 30 张原始图像 2025-06-22 18:45:03,667 - INFO - 类别 phone: 30 张原始图像 2025-06-22 18:45:04,084 - INFO - 类别 shoe: 30 张原始图像 2025-06-22 18:45:04,632 - INFO - 增强后数据集: 450 张图片 2025-06-22 18:45:04,704 - INFO - 构建优化的迁移学习模型... 2025-06-22 18:45:04,704 - ERROR - 发生错误: EfficientNetB3() got an unexpected keyword argument 'drop_connect_rate' 2025-06-22 18:45:04,704 - 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 18:45:05,488 - 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 18:45:05,681 - INFO - 内存清理完成

解决方案：1.检查当前TensorFlow版本中EfficientNet模型的可用参数。2.使用替代方法实现drop_connect（随机深度）正则化，或者如果当前版本不支持，则移除该参数。在TensorFlow2.x中，EfficientNet模型通常使用...

给我代码一的运行结果说明和分析。 C:\Users\蓝喲\PycharmProjects\pythonProject4\venv\Scripts\python.exe C:/Users/蓝喲/PycharmProjects/pythonProject4/main.py 2025-05-28 22:54:04.331335: 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. 2025-05-28 22:54:05.325903: 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. C:\Users\蓝喲\PycharmProjects\pythonProject4\venv\lib\site-packages\keras\src\layers\reshaping\flatten.py:37: UserWarning: Do not pass an input_shape/input_dim argument to a layer. When using Sequential models, prefer using an Input(shape) object as the first layer in the model instead. super().__init__(**kwargs) IDX格式数据验证: 训练集形状: (60000, 28, 28) (60000,) 测试集形状: (10000, 28, 28) (10000,) 2025-05-28 22:54:08.284734: 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 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. Epoch 1/5 1875/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 1ms/step - accuracy: 0.8817 - loss: 0.4224 - val_accuracy: 0.9586 - val_loss: 0.1406 Epoch 2/5 1875/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9653 - loss: 0.1182 - val_accuracy: 0.9693 - val_loss: 0.0975 Epoch 3/5 1875/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 1ms/step - accuracy: 0.9770 - loss: 0.0769 - val_accuracy: 0.9744 - val_loss: 0.0767 Epoch 4/5 1875/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 1ms/step - accuracy: 0.9833 - loss: 0.0569 - val_accuracy: 0.9734 - val_loss: 0.0840 Epoch 5/5 1875/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9857 - loss: 0.0442 - val_accuracy: 0.9706 - val_loss: 0.0930 进程已结束,退出代码0

训练损失从0.4224（Epoch 1）快速下降至0.0442（Epoch 5），验证损失则呈现先降后升的趋势（0.1406 → 0.0930）。这种典型的分叉现象表明： - **Epoch 1-3**：模型同时优化训练集和验证集，处于有效学习阶段 - ...

ppocr INFO: epoch: [600/600], global_step: 600, lr: 0.000519, dila_dbloss_Student_Teacher: 0.150282, dila_dbloss_Student2_Teacher: 0.109894, loss: 13.357243, dml_thrink_maps_0: 0.007054, db_Student_loss_shrink_maps: 4.822710, db_Student_loss_threshold_maps: 0.787166, db_Student_loss_binary_maps: 0.964863, db_Student2_loss_shrink_maps: 4.823677, db_Student2_loss_threshold_maps: 0.740454, db_Student2_loss_binary_maps: 0.964886, avg_reader_cost: 1.23998 s, avg_batch_cost: 2.55029 s, avg_samples: 8.0, ips: 3.13690 samples/s, eta: 0:00:02 [2025/07/18 16:17:44] ppocr INFO: save model in F:/PaddleOCR-release-2.6/output/ch_PP-OCR_v2_det/latest [2025/07/18 16:17:44] ppocr INFO: save model in F:/PaddleOCR-release-2.6/output/ch_PP-OCR_v2_det/iter_epoch_600 [2025/07/18 16:17:44] ppocr INFO: best metric, hmean: 0, is_float16: False

我们正在分析PPOCR训练日志，特别是第600个epoch的损失解释和模型保存信息。首先，我们需要理解PPOCR训练日志的一般结构。通常，训练日志会包含每个epoch的训练损失、验证损失、准确率等指标，以及模型保存的相关...

60/60 [==============================] - 19s 89ms/step - loss: 229.5776 - accuracy: 0.7818 - val_loss: 75.8205 - val_accuracy: 0.2848 Epoch 2/50 60/60 [==============================] - 5s 78ms/step - loss: 59.5195 - accuracy: 0.8323 - val_loss: 52.4355 - val_accuracy: 0.7152 Epoch 3/50 60/60 [==============================] - 5s 77ms/step - loss: 47.9256 - accuracy: 0.8453 - val_loss: 47.9466 - val_accuracy: 0.2848 Epoch 4/50 60/60 [==============================] - 5s 77ms/step - loss: 41.7355 - accuracy: 0.8521 - val_loss: 37.7279 - val_accuracy: 0.2848 Epoch 5/50 60/60 [==============================] - 5s 76ms/step - loss: 40.1783 - accuracy: 0.8505 - val_loss: 40.2293 - val_accuracy: 0.7152 Epoch 6/50 60/60 [==============================] - 5s 76ms/step - loss: 37.8785 - accuracy: 0.8781 - val_loss: 38.5298 - val_accuracy: 0.2848 Epoch 7/50 60/60 [==============================] - 5s 77ms/step - loss: 37.1490 - accuracy: 0.8786 - val_loss: 37.1918 - val_accuracy: 0.2848 Epoch 8/50 60/60 [==============================] - 5s 78ms/step - loss: 34.6709 - accuracy: 0.9156 - val_loss: 34.0621 - val_accuracy: 0.2765 Epoch 9/50 60/60 [==============================] - 5s 76ms/step - loss: 35.7891 - accuracy: 0.8849 - val_loss: 37.8741 - val_accuracy: 0.7152 Epoch 10/50 60/60 [==============================] - 5s 76ms/step - loss: 34.5359 - accuracy: 0.9141 - val_loss: 35.2664 - val_accuracy: 0.7152 Epoch 11/50 60/60 [==============================] - 5s 76ms/step - loss: 34.6172 - accuracy: 0.9016 - val_loss: 34.5135 - val_accuracy: 0.6258 Epoch 12/50 60/60 [==============================] - 5s 76ms/step - loss: 34.2331 - accuracy: 0.9083 - val_loss: 34.0945 - val_accuracy: 0.9168 Epoch 13/50 60/60 [==============================] - 5s 79ms/step - loss: 37.4175 - accuracy: 0.9000 - val_loss: 37.7885 - val_accuracy: 0.7152 16/16 - 0s - loss: 34.0621 - accuracy: 0.2765 - 307ms/epoch - 19ms/step Test accuracy: 0.27650728821754456

Based on the log you provided, it seems like you have trained a machine learning model for classification. The model achieved an accuracy of 0.78 on the training set and 0.28 on the validation set ...

2021-03-26 20:54:33,596 - Model - INFO - Epoch 1 (1/200): 2021-03-26 20:57:40,380 - Model - INFO - Train Instance Accuracy: 0.571037 2021-03-26 20:58:16,623 - Model - INFO - Test Instance Accuracy: 0.718528, Class Accuracy: 0.627357 2021-03-26 20:58:16,623 - Model - INFO - Best Instance Accuracy: 0.718528, Class Accuracy: 0.627357 2021-03-26 20:58:16,623 - Model - INFO - Save model... 2021-03-26 20:58:16,623 - Model - INFO - Saving at log/classification/pointnet2_msg_normals/checkpoints/best_model.pth 2021-03-26 20:58:16,698 - Model - INFO - Epoch 2 (2/200): 2021-03-26 21:01:26,685 - Model - INFO - Train Instance Accuracy: 0.727947 2021-03-26 21:02:03,642 - Model - INFO - Test Instance Accuracy: 0.790858, Class Accuracy: 0.702316 2021-03-26 21:02:03,642 - Model - INFO - Best Instance Accuracy: 0.790858, Class Accuracy: 0.702316 2021-03-26 21:02:03,642 - Model - INFO - Save model... 2021-03-26 21:02:03,643 - Model - INFO - Saving at log/classification/pointnet2_msg_normals/checkpoints/best_model.pth 2021-03-26 21:02:03,746 - Model - INFO - Epoch 3 (3/200): 2021-03-26 21:05:15,349 - Model - INFO - Train Instance Accuracy: 0.781606 2021-03-26 21:05:51,538 - Model - INFO - Test Instance Accuracy: 0.803641, Class Accuracy: 0.738575 2021-03-26 21:05:51,538 - Model - INFO - Best Instance Accuracy: 0.803641, Class Accuracy: 0.738575 2021-03-26 21:05:51,539 - Model - INFO - Save model... 2021-03-26 21:05:51,539 - Model - INFO - Saving at log/classification/pointnet2_msg_normals/checkpoints/best_model.pth 我有类似于这样的一段txt文件，请你帮我写一段代码来可视化这些训练结果

train_accuracies.append(float(match.group(1))) test_accuracies.append(float(match.group(2))) class_accuracies.append(float(match.group(3))) # 绘制训练和测试精度曲线 plt.plot(train_accuracies, ...

Training Begin e:/B19060708share/ENDING/spectrum_anomaly_detection-master/LSTM.py:177: UserWarning: model.fit_generator is deprecated and will be removed in a future version. Please use Model.fit, which supports generators. steps_per_epoch=t_len, callbacks=callbacks, validation_data=valid_generator, validation_steps=v_len) 2023-05-16 00:50:29.234124: W tensorflow/c/c_api.cc:291] Operation '{name:'batch_normalization_1/moving_variance/Assign' id:578 op device:{requested: '', assigned: ''} def:{{{node batch_normalization_1/moving_variance/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](batch_normalization_1/moving_variance, batch_normalization_1/moving_variance/Initializer/ones)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session. 我在训练LSTM模型，但是运行文件后终端一直卡在前面这句话，这是运行失败了吗？怎么知道我的模型有没有在训练？

这个警告不是训练失败的原因，只是TensorFlow给出的一个提示。如果你想看到训练过程中的输出，你可以在代码中添加打印语句或者使用TensorBoard来可视化训练过程。在训练过程中，你应该能够看到每个epoch的损失值和...

2025-07-27 06:34:49.050571: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered WARNING: All log messages before absl::InitializeLog() is called are written to STDERR E0000 00:00:1753598089.235406 36 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered E0000 00:00:1753598089.287919 36 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz 17464789/17464789 ━━━━━━━━━━━━━━━━━━━━ 0s 0us/step 数据集形状: 训练集: (20000, 128) (20000,) 验证集: (5000, 128) (5000,) 测试集: (25000, 128) (25000,) I0000 00:00:1753598105.743405 36 gpu_device.cc:2022] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15513 MB memory: -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0 Model: "functional_1" ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ │ input_layer (InputLayer) │ (None, 128) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ embedding (Embedding) │ (None, 128, 64) │ 640,000 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ lite_transformer │ (None, 128, 64) │ 33,472 │ │ (LiteTransformer) │ │ │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ global_average_pooling1d │ (None, 64) │ 0 │ │ (GlobalAveragePooling1D) │ │ │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_2 (Dense) │ (None, 1) │ 65 │ └─────────────────────────────────┴────────────────────────┴───────────────┘ Total params: 673,537 (2.57 MB) Trainable params: 673,537 (2.57 MB) Non-trainable params: 0 (0.00 B) Epoch 1/15 WARNING: All log messages before absl::InitializeLog() is called are written to STDERR I0000 00:00:1753598112.016146 94 service.cc:148] XLA service 0x7ef2ec012540 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: I0000 00:00:1753598112.016592 94 service.cc:156] StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0 I0000 00:00:1753598112.533355 94 cuda_dnn.cc:529] Loaded cuDNN version 90300 36/313 ━━━━━━━━━━━━━━━━━━━━ 1s 5ms/step - accuracy: 0.5287 - loss: 0.6993 I0000 00:00:1753598116.120238 94 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process. 313/313 ━━━━━━━━━━━━━━━━━━━━ 15s 21ms/step - accuracy: 0.6466 - loss: 0.6406 - val_accuracy: 0.8130 - val_loss: 0.4329 - learning_rate: 1.0000e-04 Epoch 2/15 313/313 ━━━━━━━━━━━━━━━━━━━━ 2s 5ms/step - accuracy: 0.8495 - loss: 0.3622 - val_accuracy: 0.8554 - val_loss: 0.3257 - learning_rate: 1.0000e-04 Epoch 3/15 313/313 ━━━━━━━━━━━━━━━━━━━━ 2s 5ms/step - accuracy: 0.9026 - loss: 0.2480 - val_accuracy: 0.8598 - val_loss: 0.3216 - learning_rate: 1.0000e-04 Epoch 4/15 313/313 ━━━━━━━━━━━━━━━━━━━━ 2s 5ms/step - accuracy: 0.9239 - loss: 0.2061 - val_accuracy: 0.8616 - val_loss: 0.3358 - learning_rate: 1.0000e-04 Epoch 5/15 313/313 ━━━━━━━━━━━━━━━━━━━━ 2s 5ms/step - accuracy: 0.9342 - loss: 0.1736 - val_accuracy: 0.8578 - val_loss: 0.3611 - learning_rate: 1.0000e-04 Epoch 6/15 313/313 ━━━━━━━━━━━━━━━━━━━━ 2s 5ms/step - accuracy: 0.9529 - loss: 0.1365 - val_accuracy: 0.8586 - val_loss: 0.3935 - learning_rate: 5.0000e-05 测试集准确率: 0.8540, 测试集损失: 0.3377 /tmp/ipykernel_36/130590330.py:135: UserWarning: Glyph 35757 (\N{CJK UNIFIED IDEOGRAPH-8BAD}) missing from current font. plt.tight_layout() /tmp/ipykernel_36/130590330.py:135: UserWarning: Glyph 32451 (\N{CJK UNIFIED IDEOGRAPH-7EC3}) missing from current font. plt.tight_layout() /tmp/ipykernel_36/130590330.py:135: UserWarning: Glyph 36718 (\N{CJK UNIFIED IDEOGRAPH-8F6E}) missing from current font. plt.tight_layout() /tmp/ipykernel_36/130590330.py:135: UserWarning: Glyph 27425 (\N{CJK UNIFIED IDEOGRAPH-6B21}) missing from current font. plt.tight_layout() /tmp/ipykernel_36/130590330.py:135: UserWarning: Glyph 25439 (\N{CJK UNIFIED IDEOGRAPH-635F}) missing from current font. plt.tight_layout() /tmp/ipykernel_36/130590330.py:135: UserWarning: Glyph 22833 (\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font. plt.tight_layout() /tmp/ipykernel_36/130590330.py:135: UserWarning: Glyph 26354 (\N{CJK UNIFIED IDEOGRAPH-66F2}) missing from current font. 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/usr/local/lib/python3.11/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 36127 (\N{CJK UNIFIED IDEOGRAPH-8D1F}) missing from current font. fig.canvas.print_figure(bytes_io, **kw) /usr/local/lib/python3.11/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 38754 (\N{CJK UNIFIED IDEOGRAPH-9762}) missing from current font. fig.canvas.print_figure(bytes_io, **kw) /usr/local/lib/python3.11/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 35780 (\N{CJK UNIFIED IDEOGRAPH-8BC4}) missing from current font. fig.canvas.print_figure(bytes_io, **kw) /usr/local/lib/python3.11/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 35770 (\N{CJK UNIFIED IDEOGRAPH-8BBA}) missing from current font. fig.canvas.print_figure(bytes_io, **kw) /usr/local/lib/python3.11/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 27491 (\N{CJK UNIFIED IDEOGRAPH-6B63}) missing from current font. fig.canvas.print_figure(bytes_io, **kw) /usr/local/lib/python3.11/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 28151 (\N{CJK UNIFIED IDEOGRAPH-6DF7}) missing from current font. fig.canvas.print_figure(bytes_io, **kw) /usr/local/lib/python3.11/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 28102 (\N{CJK UNIFIED IDEOGRAPH-6DC6}) missing from current font. fig.canvas.print_figure(bytes_io, **kw) /usr/local/lib/python3.11/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 30697 (\N{CJK UNIFIED IDEOGRAPH-77E9}) missing from current font. fig.canvas.print_figure(bytes_io, **kw) /usr/local/lib/python3.11/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 38453 (\N{CJK UNIFIED IDEOGRAPH-9635}) missing from current font. fig.canvas.print_figure(bytes_io, **kw) --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) /tmp/ipykernel_36/130590330.py in <cell line: 0>() 205 206 # 可视化一个样本的注意力 --> 207 visualize_attention(model, sample_idx=42) /tmp/ipykernel_36/130590330.py in visualize_attention(model, sample_idx) 154 def visualize_attention(model, sample_idx): 155 # 创建返回注意力权重的模型 --> 156 attention_output = model.layers[2].attn.output[1] # 获取注意力权重 157 attention_model = keras.Model( 158 inputs=model.input, /usr/local/lib/python3.11/dist-packages/keras/src/ops/operation.py in output(self) 264 Output tensor or list of output tensors. 265 """ --> 266 return self._get_node_attribute_at_index(0, "output_tensors", "output") 267 268 def _get_node_attribute_at_index(self, node_index, attr, attr_name): /usr/local/lib/python3.11/dist-packages/keras/src/ops/operation.py in _get_node_attribute_at_index(self, node_index, attr, attr_name) 283 """ 284 if not self._inbound_nodes: --> 285 raise AttributeError( 286 f"The layer {self.name} has never been called " 287 f"and thus has no defined {attr_name}." AttributeError: The layer multi_head_attention has never been called and thus has no defined output.

1. 在导入TensorFlow或运行模型时出现的关于cuFFT、cuDNN和cuBLAS的插件注册警告（或错误） 2. 在可视化注意力机制时出现的AttributeError: The layer multi_head_attention has never been called during model ...

def main(): t0 = time.time() ​ # 选择模型 model = build_lstm_model() ​ # 编译模型 model.compile(optimizer=tf.keras.optimizers.Adam(0.001), loss=tf.keras.losses.BinaryCrossentropy(), metrics=['accuracy']) ​ # 训练模型 checkpoint = ModelCheckpoint('model_checkpoint.h5', save_weights_only=True, verbose=1, save_freq='epoch') model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, callbacks=[checkpoint]) ​ # 评估模型 loss, accuracy = model.evaluate(x_test, y_test) print(f"Test Loss: {loss}, Test Accuracy: {accuracy}") ​ t1 = time.time() print(f"模型运行的时间为：{t1 - t0:.2f} 秒") ​ if __name__ == '__main__': main() 10秒 WARNING:tensorflow:From /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/initializers.py:118: calling RandomUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version. Instructions for updating: Call initializer instance with the dtype argument instead of passing it to the constructor WARNING:tensorflow:From /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1623: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version. Instructions for updating: If using Keras pass *_constraint arguments to layers. NotImplementedError: Cannot convert a symbolic Tensor (lstm/strided_slice:0) to a numpy array. --------------------------------------------------------------------------- NotImplementedError Traceback (most recent call last) Cell In[21], line 24 21 print(f"模型运行的时间为：{t1 - t0:.2f} 秒") 23 if __name__ == '__main__': ---> 24 main() Cell In[21], line 5, in main() 2 t0 = time.time() 4 # 选择模型 ----> 5 model = build_lstm_model() 7 # 编译模型 8 model.compile(optimizer=tf.keras.optimizers.Adam(0.001), 9 loss=tf.keras.losses.BinaryCrossentropy(), 10 metrics=['accuracy']) Cell In[15], line 4, in build_lstm_model() 3 def build_lstm_model(): ----> 4 model = keras.Sequential([ 5 layers.Embedding(total_words, embedding_len, input_length=max_review_len), 6 layers.LSTM(64, return_sequences=False), 7 layers.Dense(1, activation='sigmoid') 8 ]) 9 return model File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/training/tracking/base.py:457, in no_automatic_dependency_tracking.<locals>._method_wrapper(self, *args, **kwargs) 455 self._self_setattr_tracking = False # pylint: disable=protected-access 456 try: --> 457 result = method(self, *args, **kwargs) 458 finally: 459 self._self_setattr_tracking = previous_value # pylint: disable=protected-access File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/engine/sequential.py:113, in Sequential.__init__(self, layers, name) 111 tf_utils.assert_no_legacy_layers(layers) 112 for layer in layers: --> 113 self.add(layer) File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/training/tracking/base.py:457, in no_automatic_dependency_tracking.<locals>._method_wrapper(self, *args, **kwargs) 455 self._self_setattr_tracking = False # pylint: disable=protected-access 456 try: --> 457 result = method(self, *args, **kwargs) 458 finally: 459 self._self_setattr_tracking = previous_value # pylint: disable=protected-access File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/engine/sequential.py:195, in Sequential.add(self, layer) 190 self.inputs = layer_utils.get_source_inputs(self.outputs[0]) 192 elif self.outputs: 193 # If the model is being built continuously on top of an input layer: 194 # refresh its output. --> 195 output_tensor = layer(self.outputs[0]) 196 if len(nest.flatten(output_tensor)) != 1: 197 raise TypeError('All layers in a Sequential model ' 198 'should have a single output tensor. ' 199 'For multi-output layers, ' 200 'use the functional API.') File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/layers/recurrent.py:623, in RNN.__call__(self, inputs, initial_state, constants, **kwargs) 617 inputs, initial_state, constants = _standardize_args(inputs, 618 initial_state, 619 constants, 620 self._num_constants) 622 if initial_state is None and constants is None: --> 623 return super(RNN, self).__call__(inputs, **kwargs) 625 # If any of initial_state or constants are specified and are Keras 626 # tensors, then add them to the inputs and temporarily modify the 627 # input_spec to include them. 629 additional_inputs = [] File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/engine/base_layer.py:854, in Layer.__call__(self, inputs, *args, **kwargs) 852 outputs = base_layer_utils.mark_as_return(outputs, acd) 853 else: --> 854 outputs = call_fn(cast_inputs, *args, **kwargs) 856 except errors.OperatorNotAllowedInGraphError as e: 857 raise TypeError('You are attempting to use Python control ' 858 'flow in a layer that was not declared to be ' 859 'dynamic. Pass dynamic=True to the class ' 860 'constructor.\nEncountered error:\n"""\n' + 861 str(e) + '\n"""') File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/layers/recurrent.py:2548, in LSTM.call(self, inputs, mask, training, initial_state) 2546 self.cell.reset_dropout_mask() 2547 self.cell.reset_recurrent_dropout_mask() -> 2548 return super(LSTM, self).call( 2549 inputs, mask=mask, training=training, initial_state=initial_state) File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/layers/recurrent.py:681, in RNN.call(self, inputs, mask, training, initial_state, constants) 675 def call(self, 676 inputs, 677 mask=None, 678 training=None, 679 initial_state=None, 680 constants=None): --> 681 inputs, initial_state, constants = self._process_inputs( 682 inputs, initial_state, constants) 684 if mask is not None: 685 # Time step masks must be the same for each input. 686 # TODO(scottzhu): Should we accept multiple different masks? 687 mask = nest.flatten(mask)[0] File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/layers/recurrent.py:798, in RNN._process_inputs(self, inputs, initial_state, constants) 796 initial_state = self.states 797 else: --> 798 initial_state = self.get_initial_state(inputs) 800 if len(initial_state) != len(self.states): 801 raise ValueError('Layer has ' + str(len(self.states)) + 802 ' states but was passed ' + str(len(initial_state)) + 803 ' initial states.') File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/layers/recurrent.py:605, in RNN.get_initial_state(self, inputs) 603 dtype = inputs.dtype 604 if get_initial_state_fn: --> 605 init_state = get_initial_state_fn( 606 inputs=None, batch_size=batch_size, dtype=dtype) 607 else: 608 init_state = _generate_zero_filled_state(batch_size, self.cell.state_size, 609 dtype) File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/layers/recurrent.py:2313, in LSTMCell.get_initial_state(self, inputs, batch_size, dtype) 2312 def get_initial_state(self, inputs=None, batch_size=None, dtype=None): -> 2313 return list(_generate_zero_filled_state_for_cell( 2314 self, inputs, batch_size, dtype)) File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/layers/recurrent.py:2752, in _generate_zero_filled_state_for_cell(cell, inputs, batch_size, dtype) 2750 batch_size = array_ops.shape(inputs)[0] 2751 dtype = inputs.dtype -> 2752 return _generate_zero_filled_state(batch_size, cell.state_size, dtype) File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/layers/recurrent.py:2768, in _generate_zero_filled_state(batch_size_tensor, state_size, dtype) 2765 return array_ops.zeros(init_state_size, dtype=dtype) 2767 if nest.is_sequence(state_size): -> 2768 return nest.map_structure(create_zeros, state_size) 2769 else: 2770 return create_zeros(state_size) File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/util/nest.py:536, in map_structure(func, *structure, **kwargs) 532 flat_structure = [flatten(s, expand_composites) for s in structure] 533 entries = zip(*flat_structure) 535 return pack_sequence_as( --> 536 structure[0], [func(*x) for x in entries], 537 expand_composites=expand_composites) File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/util/nest.py:536, in (.0) 532 flat_structure = [flatten(s, expand_composites) for s in structure] 533 entries = zip(*flat_structure) 535 return pack_sequence_as( --> 536 structure[0], [func(*x) for x in entries], 537 expand_composites=expand_composites) File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/layers/recurrent.py:2765, in _generate_zero_filled_state.<locals>.create_zeros(unnested_state_size) 2763 flat_dims = tensor_shape.as_shape(unnested_state_size).as_list() 2764 init_state_size = [batch_size_tensor] + flat_dims -> 2765 return array_ops.zeros(init_state_size, dtype=dtype) File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/ops/array_ops.py:2338, in zeros(shape, dtype, name) 2334 if not isinstance(shape, ops.Tensor): 2335 try: 2336 # Create a constant if it won't be very big. 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