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答疑贴-PaddleCamp第四期14个实战任务
Ta的回复 :老师我有一个问题比如花的五分类问题,如何增加一个other分类呢,比如我上传不属于这五种花的其他图片,预测为其他,应该怎么设置训练
24
答疑贴-PaddleCamp第四期14个实战任务
Ta的回复 :问题解决了,貌似是我加载的预处理模型不对导致的
24
答疑贴-PaddleCamp第四期14个实战任务
Ta的回复 :我把原5种花分类的代码移植到102种花分类,参数就改了数据集路径和class_dim,发现这句报错 optimizer.minimize(avg_cost), 项目地址木有找到原因具体日志如下: 2019-10-08 18:42:42,412 - [line:554] - INFO: train config: {'image_enhance_strategy': {'contrast_delta': 0.5, 'brightness_delta': 0.125, 'need_crop': True, 'saturation_delta': 0.5, 'hue_prob': 0.5, 'hue_delta': 18, 'need_distort': True, 'brightness_prob': 0.5, 'saturation_prob': 0.5, 'contrast_prob': 0.5, 'need_rotate': True, 'need_flip': True}, 'save_persistable_dir': './persistable-params', 'label_dict': {'32': 28, '74': 74, '15': 9, '52': 50, '85': 86, '93': 95, '68': 67, '45': 42, '66': 65, '3': 25, '89': 90, '27': 22, '18': 12, '56': 54, '42': 39, '75': 75, '64': 63, '35': 31, '96': 98, '41': 38, '79': 79, '12': 6, '1': 0, '54': 52, '36': 32, '86': 87, '62': 61, '61': 60, '2': 14, '63': 62, '51': 49, '47': 44, '99': 101, '40': 37, '28': 23, '73': 73, '95': 97, '60': 59, '83': 84, '8': 80, '94': 96, '34': 30, '82': 83, '22': 17, '10': 1, '7': 69, '5': 47, '71': 71, '37': 33, '50': 48, '13': 7, '38': 34, '65': 64, '101': 3, '14': 8, '31': 27, '19': 13, '76': 76, '23': 18, '69': 68, '84': 85, '33': 29, '100': 2, '55': 53, '90': 92, '21': 16, '53': 51, '30': 26, '46': 43, '43': 40, '98': 100, '97': 99, '11': 5, '9': 91, '24': 19, '17': 11, '4': 36, '25': 20, '91': 93, '102': 4, '16': 10, '49': 46, '20': 15, '92': 94, '77': 77, '88': 89, '80': 81, '44': 41, '81': 82, '59': 57, '29': 24, '70': 70, '67': 66, '26': 21, '58': 56, '48': 45, '87': 88, '57': 55, '78': 78, '6': 58, '39': 35, '72': 72}, 'image_count': 5194, 'label_file': 'label_list.txt', 'early_stop': {'good_acc1': 0.92, 'sample_frequency': 50, 'successive_limit': 3}, 'class_dim': 102, 'input_size': [3, 224, 224], 'train_file_list': 'train.txt', 'rsm_strategy': {'lr_epochs': [20, 40, 60, 80, 100], 'learning_rate': 0.002, 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'save_freeze_dir': './freeze-model', 'adam_strategy': {'learning_rate': 0.002}, 'train_batch_size': 24, 'momentum_strategy': {'lr_epochs': [20, 40, 60, 80, 100], 'learning_rate': 0.002, 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'continue_train': True, 'pretrained': True, 'mode': 'train', 'pretrained_dir': 'ResNet50_pretrained', 'data_dir': 'data/data12479/hackathon-blossom-flower-classification/flower_data/train', 'sgd_strategy': {'lr_epochs': [20, 40, 60, 80, 100], 'learning_rate': 0.002, 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'mean_rgb': [127.5, 127.5, 127.5], 'num_epochs': 120, 'use_gpu': True} 2019-10-08 18:42:42,412 - [line:554] - INFO: train config: {'image_enhance_strategy': {'contrast_delta': 0.5, 'brightness_delta': 0.125, 'need_crop': True, 'saturation_delta': 0.5, 'hue_prob': 0.5, 'hue_delta': 18, 'need_distort': True, 'brightness_prob': 0.5, 'saturation_prob': 0.5, 'contrast_prob': 0.5, 'need_rotate': True, 'need_flip': True}, 'save_persistable_dir': './persistable-params', 'label_dict': {'32': 28, '74': 74, '15': 9, '52': 50, '85': 86, '93': 95, '68': 67, '45': 42, '66': 65, '3': 25, '89': 90, '27': 22, '18': 12, '56': 54, '42': 39, '75': 75, '64': 63, '35': 31, '96': 98, '41': 38, '79': 79, '12': 6, '1': 0, '54': 52, '36': 32, '86': 87, '62': 61, '61': 60, '2': 14, '63': 62, '51': 49, '47': 44, '99': 101, '40': 37, '28': 23, '73': 73, '95': 97, '60': 59, '83': 84, '8': 80, '94': 96, '34': 30, '82': 83, '22': 17, '10': 1, '7': 69, '5': 47, '71': 71, '37': 33, '50': 48, '13': 7, '38': 34, '65': 64, '101': 3, '14': 8, '31': 27, '19': 13, '76': 76, '23': 18, '69': 68, '84': 85, '33': 29, '100': 2, '55': 53, '90': 92, '21': 16, '53': 51, '30': 26, '46': 43, '43': 40, '98': 100, '97': 99, '11': 5, '9': 91, '24': 19, '17': 11, '4': 36, '25': 20, '91': 93, '102': 4, '16': 10, '49': 46, '20': 15, '92': 94, '77': 77, '88': 89, '80': 81, '44': 41, '81': 82, '59': 57, '29': 24, '70': 70, '67': 66, '26': 21, '58': 56, '48': 45, '87': 88, '57': 55, '78': 78, '6': 58, '39': 35, '72': 72}, 'image_count': 5194, 'label_file': 'label_list.txt', 'early_stop': {'good_acc1': 0.92, 'sample_frequency': 50, 'successive_limit': 3}, 'class_dim': 102, 'input_size': [3, 224, 224], 'train_file_list': 'train.txt', 'rsm_strategy': {'lr_epochs': [20, 40, 60, 80, 100], 'learning_rate': 0.002, 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'save_freeze_dir': './freeze-model', 'adam_strategy': {'learning_rate': 0.002}, 'train_batch_size': 24, 'momentum_strategy': {'lr_epochs': [20, 40, 60, 80, 100], 'learning_rate': 0.002, 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'continue_train': True, 'pretrained': True, 'mode': 'train', 'pretrained_dir': 'ResNet50_pretrained', 'data_dir': 'data/data12479/hackathon-blossom-flower-classification/flower_data/train', 'sgd_strategy': {'lr_epochs': [20, 40, 60, 80, 100], 'learning_rate': 0.002, 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'mean_rgb': [127.5, 127.5, 127.5], 'num_epochs': 120, 'use_gpu': True} 2019-10-08 18:42:42,412 - [line:554] - INFO: train config: {'image_enhance_strategy': {'contrast_delta': 0.5, 'brightness_delta': 0.125, 'need_crop': True, 'saturation_delta': 0.5, 'hue_prob': 0.5, 'hue_delta': 18, 'need_distort': True, 'brightness_prob': 0.5, 'saturation_prob': 0.5, 'contrast_prob': 0.5, 'need_rotate': True, 'need_flip': True}, 'save_persistable_dir': './persistable-params', 'label_dict': {'32': 28, '74': 74, '15': 9, '52': 50, '85': 86, '93': 95, '68': 67, '45': 42, '66': 65, '3': 25, '89': 90, '27': 22, '18': 12, '56': 54, '42': 39, '75': 75, '64': 63, '35': 31, '96': 98, '41': 38, '79': 79, '12': 6, '1': 0, '54': 52, '36': 32, '86': 87, '62': 61, '61': 60, '2': 14, '63': 62, '51': 49, '47': 44, '99': 101, '40': 37, '28': 23, '73': 73, '95': 97, '60': 59, '83': 84, '8': 80, '94': 96, '34': 30, '82': 83, '22': 17, '10': 1, '7': 69, '5': 47, '71': 71, '37': 33, '50': 48, '13': 7, '38': 34, '65': 64, '101': 3, '14': 8, '31': 27, '19': 13, '76': 76, '23': 18, '69': 68, '84': 85, '33': 29, '100': 2, '55': 53, '90': 92, '21': 16, '53': 51, '30': 26, '46': 43, '43': 40, '98': 100, '97': 99, '11': 5, '9': 91, '24': 19, '17': 11, '4': 36, '25': 20, '91': 93, '102': 4, '16': 10, '49': 46, '20': 15, '92': 94, '77': 77, '88': 89, '80': 81, '44': 41, '81': 82, '59': 57, '29': 24, '70': 70, '67': 66, '26': 21, '58': 56, '48': 45, '87': 88, '57': 55, '78': 78, '6': 58, '39': 35, '72': 72}, 'image_count': 5194, 'label_file': 'label_list.txt', 'early_stop': {'good_acc1': 0.92, 'sample_frequency': 50, 'successive_limit': 3}, 'class_dim': 102, 'input_size': [3, 224, 224], 'train_file_list': 'train.txt', 'rsm_strategy': {'lr_epochs': [20, 40, 60, 80, 100], 'learning_rate': 0.002, 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'save_freeze_dir': './freeze-model', 'adam_strategy': {'learning_rate': 0.002}, 'train_batch_size': 24, 'momentum_strategy': {'lr_epochs': [20, 40, 60, 80, 100], 'learning_rate': 0.002, 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'continue_train': True, 'pretrained': True, 'mode': 'train', 'pretrained_dir': 'ResNet50_pretrained', 'data_dir': 'data/data12479/hackathon-blossom-flower-classification/flower_data/train', 'sgd_strategy': {'lr_epochs': [20, 40, 60, 80, 100], 'learning_rate': 0.002, 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'mean_rgb': [127.5, 127.5, 127.5], 'num_epochs': 120, 'use_gpu': True} 2019-10-08 18:42:42,412 - [line:554] - INFO: train config: {'image_enhance_strategy': {'contrast_delta': 0.5, 'brightness_delta': 0.125, 'need_crop': True, 'saturation_delta': 0.5, 'hue_prob': 0.5, 'hue_delta': 18, 'need_distort': True, 'brightness_prob': 0.5, 'saturation_prob': 0.5, 'contrast_prob': 0.5, 'need_rotate': True, 'need_flip': True}, 'save_persistable_dir': './persistable-params', 'label_dict': {'32': 28, '74': 74, '15': 9, '52': 50, '85': 86, '93': 95, '68': 67, '45': 42, '66': 65, '3': 25, '89': 90, '27': 22, '18': 12, '56': 54, '42': 39, '75': 75, '64': 63, '35': 31, '96': 98, '41': 38, '79': 79, '12': 6, '1': 0, '54': 52, '36': 32, '86': 87, '62': 61, '61': 60, '2': 14, '63': 62, '51': 49, '47': 44, '99': 101, '40': 37, '28': 23, '73': 73, '95': 97, '60': 59, '83': 84, '8': 80, '94': 96, '34': 30, '82': 83, '22': 17, '10': 1, '7': 69, '5': 47, '71': 71, '37': 33, '50': 48, '13': 7, '38': 34, '65': 64, '101': 3, '14': 8, '31': 27, '19': 13, '76': 76, '23': 18, '69': 68, '84': 85, '33': 29, '100': 2, '55': 53, '90': 92, '21': 16, '53': 51, '30': 26, '46': 43, '43': 40, '98': 100, '97': 99, '11': 5, '9': 91, '24': 19, '17': 11, '4': 36, '25': 20, '91': 93, '102': 4, '16': 10, '49': 46, '20': 15, '92': 94, '77': 77, '88': 89, '80': 81, '44': 41, '81': 82, '59': 57, '29': 24, '70': 70, '67': 66, '26': 21, '58': 56, '48': 45, '87': 88, '57': 55, '78': 78, '6': 58, '39': 35, '72': 72}, 'image_count': 5194, 'label_file': 'label_list.txt', 'early_stop': {'good_acc1': 0.92, 'sample_frequency': 50, 'successive_limit': 3}, 'class_dim': 102, 'input_size': [3, 224, 224], 'train_file_list': 'train.txt', 'rsm_strategy': {'lr_epochs': [20, 40, 60, 80, 100], 'learning_rate': 0.002, 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'save_freeze_dir': './freeze-model', 'adam_strategy': {'learning_rate': 0.002}, 'train_batch_size': 24, 'momentum_strategy': {'lr_epochs': [20, 40, 60, 80, 100], 'learning_rate': 0.002, 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'continue_train': True, 'pretrained': True, 'mode': 'train', 'pretrained_dir': 'ResNet50_pretrained', 'data_dir': 'data/data12479/hackathon-blossom-flower-classification/flower_data/train', 'sgd_strategy': {'lr_epochs': [20, 40, 60, 80, 100], 'learning_rate': 0.002, 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'mean_rgb': [127.5, 127.5, 127.5], 'num_epochs': 120, 'use_gpu': True} 2019-10-08 18:42:42,412 - [line:554] - INFO: train config: {'image_enhance_strategy': {'contrast_delta': 0.5, 'brightness_delta': 0.125, 'need_crop': True, 'saturation_delta': 0.5, 'hue_prob': 0.5, 'hue_delta': 18, 'need_distort': True, 'brightness_prob': 0.5, 'saturation_prob': 0.5, 'contrast_prob': 0.5, 'need_rotate': True, 'need_flip': True}, 'save_persistable_dir': './persistable-params', 'label_dict': {'32': 28, '74': 74, '15': 9, '52': 50, '85': 86, '93': 95, '68': 67, '45': 42, '66': 65, '3': 25, '89': 90, '27': 22, '18': 12, '56': 54, '42': 39, '75': 75, '64': 63, '35': 31, '96': 98, '41': 38, '79': 79, '12': 6, '1': 0, '54': 52, '36': 32, '86': 87, '62': 61, '61': 60, '2': 14, '63': 62, '51': 49, '47': 44, '99': 101, '40': 37, '28': 23, '73': 73, '95': 97, '60': 59, '83': 84, '8': 80, '94': 96, '34': 30, '82': 83, '22': 17, '10': 1, '7': 69, '5': 47, '71': 71, '37': 33, '50': 48, '13': 7, '38': 34, '65': 64, '101': 3, '14': 8, '31': 27, '19': 13, '76': 76, '23': 18, '69': 68, '84': 85, '33': 29, '100': 2, '55': 53, '90': 92, '21': 16, '53': 51, '30': 26, '46': 43, '43': 40, '98': 100, '97': 99, '11': 5, '9': 91, '24': 19, '17': 11, '4': 36, '25': 20, '91': 93, '102': 4, '16': 10, '49': 46, '20': 15, '92': 94, '77': 77, '88': 89, '80': 81, '44': 41, '81': 82, '59': 57, '29': 24, '70': 70, '67': 66, '26': 21, '58': 56, '48': 45, '87': 88, '57': 55, '78': 78, '6': 58, '39': 35, '72': 72}, 'image_count': 5194, 'label_file': 'label_list.txt', 'early_stop': {'good_acc1': 0.92, 'sample_frequency': 50, 'successive_limit': 3}, 'class_dim': 102, 'input_size': [3, 224, 224], 'train_file_list': 'train.txt', 'rsm_strategy': {'lr_epochs': [20, 40, 60, 80, 100], 'learning_rate': 0.002, 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'save_freeze_dir': './freeze-model', 'adam_strategy': {'learning_rate': 0.002}, 'train_batch_size': 24, 'momentum_strategy': {'lr_epochs': [20, 40, 60, 80, 100], 'learning_rate': 0.002, 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'continue_train': True, 'pretrained': True, 'mode': 'train', 'pretrained_dir': 'ResNet50_pretrained', 'data_dir': 'data/data12479/hackathon-blossom-flower-classification/flower_data/train', 'sgd_strategy': {'lr_epochs': [20, 40, 60, 80, 100], 'learning_rate': 0.002, 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'mean_rgb': [127.5, 127.5, 127.5], 'num_epochs': 120, 'use_gpu': True} 2019-10-08 18:42:42,412 - [line:554] - INFO: train config: {'image_enhance_strategy': {'contrast_delta': 0.5, 'brightness_delta': 0.125, 'need_crop': True, 'saturation_delta': 0.5, 'hue_prob': 0.5, 'hue_delta': 18, 'need_distort': True, 'brightness_prob': 0.5, 'saturation_prob': 0.5, 'contrast_prob': 0.5, 'need_rotate': True, 'need_flip': True}, 'save_persistable_dir': './persistable-params', 'label_dict': {'32': 28, '74': 74, '15': 9, '52': 50, '85': 86, '93': 95, '68': 67, '45': 42, '66': 65, '3': 25, '89': 90, '27': 22, '18': 12, '56': 54, '42': 39, '75': 75, '64': 63, '35': 31, '96': 98, '41': 38, '79': 79, '12': 6, '1': 0, '54': 52, '36': 32, '86': 87, '62': 61, '61': 60, '2': 14, '63': 62, '51': 49, '47': 44, '99': 101, '40': 37, '28': 23, '73': 73, '95': 97, '60': 59, '83': 84, '8': 80, '94': 96, '34': 30, '82': 83, '22': 17, '10': 1, '7': 69, '5': 47, '71': 71, '37': 33, '50': 48, '13': 7, '38': 34, '65': 64, '101': 3, '14': 8, '31': 27, '19': 13, '76': 76, '23': 18, '69': 68, '84': 85, '33': 29, '100': 2, '55': 53, '90': 92, '21': 16, '53': 51, '30': 26, '46': 43, '43': 40, '98': 100, '97': 99, '11': 5, '9': 91, '24': 19, '17': 11, '4': 36, '25': 20, '91': 93, '102': 4, '16': 10, '49': 46, '20': 15, '92': 94, '77': 77, '88': 89, '80': 81, '44': 41, '81': 82, '59': 57, '29': 24, '70': 70, '67': 66, '26': 21, '58': 56, '48': 45, '87': 88, '57': 55, '78': 78, '6': 58, '39': 35, '72': 72}, 'image_count': 5194, 'label_file': 'label_list.txt', 'early_stop': {'good_acc1': 0.92, 'sample_frequency': 50, 'successive_limit': 3}, 'class_dim': 102, 'input_size': [3, 224, 224], 'train_file_list': 'train.txt', 'rsm_strategy': {'lr_epochs': [20, 40, 60, 80, 100], 'learning_rate': 0.002, 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'save_freeze_dir': './freeze-model', 'adam_strategy': {'learning_rate': 0.002}, 'train_batch_size': 24, 'momentum_strategy': {'lr_epochs': [20, 40, 60, 80, 100], 'learning_rate': 0.002, 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'continue_train': True, 'pretrained': True, 'mode': 'train', 'pretrained_dir': 'ResNet50_pretrained', 'data_dir': 'data/data12479/hackathon-blossom-flower-classification/flower_data/train', 'sgd_strategy': {'lr_epochs': [20, 40, 60, 80, 100], 'learning_rate': 0.002, 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'mean_rgb': [127.5, 127.5, 127.5], 'num_epochs': 120, 'use_gpu': True} 2019-10-08 18:42:42,412 - [line:554] - INFO: train config: {'image_enhance_strategy': {'contrast_delta': 0.5, 'brightness_delta': 0.125, 'need_crop': True, 'saturation_delta': 0.5, 'hue_prob': 0.5, 'hue_delta': 18, 'need_distort': True, 'brightness_prob': 0.5, 'saturation_prob': 0.5, 'contrast_prob': 0.5, 'need_rotate': True, 'need_flip': True}, 'save_persistable_dir': './persistable-params', 'label_dict': {'32': 28, '74': 74, '15': 9, '52': 50, '85': 86, '93': 95, '68': 67, '45': 42, '66': 65, '3': 25, '89': 90, '27': 22, '18': 12, '56': 54, '42': 39, '75': 75, '64': 63, '35': 31, '96': 98, '41': 38, '79': 79, '12': 6, '1': 0, '54': 52, '36': 32, '86': 87, '62': 61, '61': 60, '2': 14, '63': 62, '51': 49, '47': 44, '99': 101, '40': 37, '28': 23, '73': 73, '95': 97, '60': 59, '83': 84, '8': 80, '94': 96, '34': 30, '82': 83, '22': 17, '10': 1, '7': 69, '5': 47, '71': 71, '37': 33, '50': 48, '13': 7, '38': 34, '65': 64, '101': 3, '14': 8, '31': 27, '19': 13, '76': 76, '23': 18, '69': 68, '84': 85, '33': 29, '100': 2, '55': 53, '90': 92, '21': 16, '53': 51, '30': 26, '46': 43, '43': 40, '98': 100, '97': 99, '11': 5, '9': 91, '24': 19, '17': 11, '4': 36, '25': 20, '91': 93, '102': 4, '16': 10, '49': 46, '20': 15, '92': 94, '77': 77, '88': 89, '80': 81, '44': 41, '81': 82, '59': 57, '29': 24, '70': 70, '67': 66, '26': 21, '58': 56, '48': 45, '87': 88, '57': 55, '78': 78, '6': 58, '39': 35, '72': 72}, 'image_count': 5194, 'label_file': 'label_list.txt', 'early_stop': {'good_acc1': 0.92, 'sample_frequency': 50, 'successive_limit': 3}, 'class_dim': 102, 'input_size': [3, 224, 224], 'train_file_list': 'train.txt', 'rsm_strategy': {'lr_epochs': [20, 40, 60, 80, 100], 'learning_rate': 0.002, 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'save_freeze_dir': './freeze-model', 'adam_strategy': {'learning_rate': 0.002}, 'train_batch_size': 24, 'momentum_strategy': {'lr_epochs': [20, 40, 60, 80, 100], 'learning_rate': 0.002, 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'continue_train': True, 'pretrained': True, 'mode': 'train', 'pretrained_dir': 'ResNet50_pretrained', 'data_dir': 'data/data12479/hackathon-blossom-flower-classification/flower_data/train', 'sgd_strategy': {'lr_epochs': [20, 40, 60, 80, 100], 'learning_rate': 0.002, 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'mean_rgb': [127.5, 127.5, 127.5], 'num_epochs': 120, 'use_gpu': True} 2019-10-08 18:42:42,412 - [line:554] - INFO: train config: {'image_enhance_strategy': {'contrast_delta': 0.5, 'brightness_delta': 0.125, 'need_crop': True, 'saturation_delta': 0.5, 'hue_prob': 0.5, 'hue_delta': 18, 'need_distort': True, 'brightness_prob': 0.5, 'saturation_prob': 0.5, 'contrast_prob': 0.5, 'need_rotate': True, 'need_flip': True}, 'save_persistable_dir': './persistable-params', 'label_dict': {'32': 28, '74': 74, '15': 9, '52': 50, '85': 86, '93': 95, '68': 67, '45': 42, '66': 65, '3': 25, '89': 90, '27': 22, '18': 12, '56': 54, '42': 39, '75': 75, '64': 63, '35': 31, '96': 98, '41': 38, '79': 79, '12': 6, '1': 0, '54': 52, '36': 32, '86': 87, '62': 61, '61': 60, '2': 14, '63': 62, '51': 49, '47': 44, '99': 101, '40': 37, '28': 23, '73': 73, '95': 97, '60': 59, '83': 84, '8': 80, '94': 96, '34': 30, '82': 83, '22': 17, '10': 1, '7': 69, '5': 47, '71': 71, '37': 33, '50': 48, '13': 7, '38': 34, '65': 64, '101': 3, '14': 8, '31': 27, '19': 13, '76': 76, '23': 18, '69': 68, '84': 85, '33': 29, '100': 2, '55': 53, '90': 92, '21': 16, '53': 51, '30': 26, '46': 43, '43': 40, '98': 100, '97': 99, '11': 5, '9': 91, '24': 19, '17': 11, '4': 36, '25': 20, '91': 93, '102': 4, '16': 10, '49': 46, '20': 15, '92': 94, '77': 77, '88': 89, '80': 81, '44': 41, '81': 82, '59': 57, '29': 24, '70': 70, '67': 66, '26': 21, '58': 56, '48': 45, '87': 88, '57': 55, '78': 78, '6': 58, '39': 35, '72': 72}, 'image_count': 5194, 'label_file': 'label_list.txt', 'early_stop': {'good_acc1': 0.92, 'sample_frequency': 50, 'successive_limit': 3}, 'class_dim': 102, 'input_size': [3, 224, 224], 'train_file_list': 'train.txt', 'rsm_strategy': {'lr_epochs': [20, 40, 60, 80, 100], 'learning_rate': 0.002, 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'save_freeze_dir': './freeze-model', 'adam_strategy': {'learning_rate': 0.002}, 'train_batch_size': 24, 'momentum_strategy': {'lr_epochs': [20, 40, 60, 80, 100], 'learning_rate': 0.002, 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'continue_train': True, 'pretrained': True, 'mode': 'train', 'pretrained_dir': 'ResNet50_pretrained', 'data_dir': 'data/data12479/hackathon-blossom-flower-classification/flower_data/train', 'sgd_strategy': {'lr_epochs': [20, 40, 60, 80, 100], 'learning_rate': 0.002, 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'mean_rgb': [127.5, 127.5, 127.5], 'num_epochs': 120, 'use_gpu': True} 2019-10-08 18:42:42,412 - [line:554] - INFO: train config: {'image_enhance_strategy': {'contrast_delta': 0.5, 'brightness_delta': 0.125, 'need_crop': True, 'saturation_delta': 0.5, 'hue_prob': 0.5, 'hue_delta': 18, 'need_distort': True, 'brightness_prob': 0.5, 'saturation_prob': 0.5, 'contrast_prob': 0.5, 'need_rotate': True, 'need_flip': True}, 'save_persistable_dir': './persistable-params', 'label_dict': {'32': 28, '74': 74, '15': 9, '52': 50, '85': 86, '93': 95, '68': 67, '45': 42, '66': 65, '3': 25, '89': 90, '27': 22, '18': 12, '56': 54, '42': 39, '75': 75, '64': 63, '35': 31, '96': 98, '41': 38, '79': 79, '12': 6, '1': 0, '54': 52, '36': 32, '86': 87, '62': 61, '61': 60, '2': 14, '63': 62, '51': 49, '47': 44, '99': 101, '40': 37, '28': 23, '73': 73, '95': 97, '60': 59, '83': 84, '8': 80, '94': 96, '34': 30, '82': 83, '22': 17, '10': 1, '7': 69, '5': 47, '71': 71, '37': 33, '50': 48, '13': 7, '38': 34, '65': 64, '101': 3, '14': 8, '31': 27, '19': 13, '76': 76, '23': 18, '69': 68, '84': 85, '33': 29, '100': 2, '55': 53, '90': 92, '21': 16, '53': 51, '30': 26, '46': 43, '43': 40, '98': 100, '97': 99, '11': 5, '9': 91, '24': 19, '17': 11, '4': 36, '25': 20, '91': 93, '102': 4, '16': 10, '49': 46, '20': 15, '92': 94, '77': 77, '88': 89, '80': 81, '44': 41, '81': 82, '59': 57, '29': 24, '70': 70, '67': 66, '26': 21, '58': 56, '48': 45, '87': 88, '57': 55, '78': 78, '6': 58, '39': 35, '72': 72}, 'image_count': 5194, 'label_file': 'label_list.txt', 'early_stop': {'good_acc1': 0.92, 'sample_frequency': 50, 'successive_limit': 3}, 'class_dim': 102, 'input_size': [3, 224, 224], 'train_file_list': 'train.txt', 'rsm_strategy': {'lr_epochs': [20, 40, 60, 80, 100], 'learning_rate': 0.002, 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'save_freeze_dir': './freeze-model', 'adam_strategy': {'learning_rate': 0.002}, 'train_batch_size': 24, 'momentum_strategy': {'lr_epochs': [20, 40, 60, 80, 100], 'learning_rate': 0.002, 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'continue_train': True, 'pretrained': True, 'mode': 'train', 'pretrained_dir': 'ResNet50_pretrained', 'data_dir': 'data/data12479/hackathon-blossom-flower-classification/flower_data/train', 'sgd_strategy': {'lr_epochs': [20, 40, 60, 80, 100], 'learning_rate': 0.002, 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'mean_rgb': [127.5, 127.5, 127.5], 'num_epochs': 120, 'use_gpu': True} 2019-10-08 18:42:42,412 - [line:554] - INFO: train config: {'image_enhance_strategy': {'contrast_delta': 0.5, 'brightness_delta': 0.125, 'need_crop': True, 'saturation_delta': 0.5, 'hue_prob': 0.5, 'hue_delta': 18, 'need_distort': True, 'brightness_prob': 0.5, 'saturation_prob': 0.5, 'contrast_prob': 0.5, 'need_rotate': True, 'need_flip': True}, 'save_persistable_dir': './persistable-params', 'label_dict': {'32': 28, '74': 74, '15': 9, '52': 50, '85': 86, '93': 95, '68': 67, '45': 42, '66': 65, '3': 25, '89': 90, '27': 22, '18': 12, '56': 54, '42': 39, '75': 75, '64': 63, '35': 31, '96': 98, '41': 38, '79': 79, '12': 6, '1': 0, '54': 52, '36': 32, '86': 87, '62': 61, '61': 60, '2': 14, '63': 62, '51': 49, '47': 44, '99': 101, '40': 37, '28': 23, '73': 73, '95': 97, '60': 59, '83': 84, '8': 80, '94': 96, '34': 30, '82': 83, '22': 17, '10': 1, '7': 69, '5': 47, '71': 71, '37': 33, '50': 48, '13': 7, '38': 34, '65': 64, '101': 3, '14': 8, '31': 27, '19': 13, '76': 76, '23': 18, '69': 68, '84': 85, '33': 29, '100': 2, '55': 53, '90': 92, '21': 16, '53': 51, '30': 26, '46': 43, '43': 40, '98': 100, '97': 99, '11': 5, '9': 91, '24': 19, '17': 11, '4': 36, '25': 20, '91': 93, '102': 4, '16': 10, '49': 46, '20': 15, '92': 94, '77': 77, '88': 89, '80': 81, '44': 41, '81': 82, '59': 57, '29': 24, '70': 70, '67': 66, '26': 21, '58': 56, '48': 45, '87': 88, '57': 55, '78': 78, '6': 58, '39': 35, '72': 72}, 'image_count': 5194, 'label_file': 'label_list.txt', 'early_stop': {'good_acc1': 0.92, 'sample_frequency': 50, 'successive_limit': 3}, 'class_dim': 102, 'input_size': [3, 224, 224], 'train_file_list': 'train.txt', 'rsm_strategy': {'lr_epochs': [20, 40, 60, 80, 100], 'learning_rate': 0.002, 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'save_freeze_dir': './freeze-model', 'adam_strategy': {'learning_rate': 0.002}, 'train_batch_size': 24, 'momentum_strategy': {'lr_epochs': [20, 40, 60, 80, 100], 'learning_rate': 0.002, 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'continue_train': True, 'pretrained': True, 'mode': 'train', 'pretrained_dir': 'ResNet50_pretrained', 'data_dir': 'data/data12479/hackathon-blossom-flower-classification/flower_data/train', 'sgd_strategy': {'lr_epochs': [20, 40, 60, 80, 100], 'learning_rate': 0.002, 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'mean_rgb': [127.5, 127.5, 127.5], 'num_epochs': 120, 'use_gpu': True} 2019-10-08 18:42:42,412 - [line:554] - INFO: train config: {'image_enhance_strategy': {'contrast_delta': 0.5, 'brightness_delta': 0.125, 'need_crop': True, 'saturation_delta': 0.5, 'hue_prob': 0.5, 'hue_delta': 18, 'need_distort': True, 'brightness_prob': 0.5, 'saturation_prob': 0.5, 'contrast_prob': 0.5, 'need_rotate': True, 'need_flip': True}, 'save_persistable_dir': './persistable-params', 'label_dict': {'32': 28, '74': 74, '15': 9, '52': 50, '85': 86, '93': 95, '68': 67, '45': 42, '66': 65, '3': 25, '89': 90, '27': 22, '18': 12, '56': 54, '42': 39, '75': 75, '64': 63, '35': 31, '96': 98, '41': 38, '79': 79, '12': 6, '1': 0, '54': 52, '36': 32, '86': 87, '62': 61, '61': 60, '2': 14, '63': 62, '51': 49, '47': 44, '99': 101, '40': 37, '28': 23, '73': 73, '95': 97, '60': 59, '83': 84, '8': 80, '94': 96, '34': 30, '82': 83, '22': 17, '10': 1, '7': 69, '5': 47, '71': 71, '37': 33, '50': 48, '13': 7, '38': 34, '65': 64, '101': 3, '14': 8, '31': 27, '19': 13, '76': 76, '23': 18, '69': 68, '84': 85, '33': 29, '100': 2, '55': 53, '90': 92, '21': 16, '53': 51, '30': 26, '46': 43, '43': 40, '98': 100, '97': 99, '11': 5, '9': 91, '24': 19, '17': 11, '4': 36, '25': 20, '91': 93, '102': 4, '16': 10, '49': 46, '20': 15, '92': 94, '77': 77, '88': 89, '80': 81, '44': 41, '81': 82, '59': 57, '29': 24, '70': 70, '67': 66, '26': 21, '58': 56, '48': 45, '87': 88, '57': 55, '78': 78, '6': 58, '39': 35, '72': 72}, 'image_count': 5194, 'label_file': 'label_list.txt', 'early_stop': {'good_acc1': 0.92, 'sample_frequency': 50, 'successive_limit': 3}, 'class_dim': 102, 'input_size': [3, 224, 224], 'train_file_list': 'train.txt', 'rsm_strategy': {'lr_epochs': [20, 40, 60, 80, 100], 'learning_rate': 0.002, 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'save_freeze_dir': './freeze-model', 'adam_strategy': {'learning_rate': 0.002}, 'train_batch_size': 24, 'momentum_strategy': {'lr_epochs': [20, 40, 60, 80, 100], 'learning_rate': 0.002, 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'continue_train': True, 'pretrained': True, 'mode': 'train', 'pretrained_dir': 'ResNet50_pretrained', 'data_dir': 'data/data12479/hackathon-blossom-flower-classification/flower_data/train', 'sgd_strategy': {'lr_epochs': [20, 40, 60, 80, 100], 'learning_rate': 0.002, 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'mean_rgb': [127.5, 127.5, 127.5], 'num_epochs': 120, 'use_gpu': True} 2019-10-08 18:42:42,412 - [line:554] - INFO: train config: {'image_enhance_strategy': {'contrast_delta': 0.5, 'brightness_delta': 0.125, 'need_crop': True, 'saturation_delta': 0.5, 'hue_prob': 0.5, 'hue_delta': 18, 'need_distort': True, 'brightness_prob': 0.5, 'saturation_prob': 0.5, 'contrast_prob': 0.5, 'need_rotate': True, 'need_flip': True}, 'save_persistable_dir': './persistable-params', 'label_dict': {'32': 28, '74': 74, '15': 9, '52': 50, '85': 86, '93': 95, '68': 67, '45': 42, '66': 65, '3': 25, '89': 90, '27': 22, '18': 12, '56': 54, '42': 39, '75': 75, '64': 63, '35': 31, '96': 98, '41': 38, '79': 79, '12': 6, '1': 0, '54': 52, '36': 32, '86': 87, '62': 61, '61': 60, '2': 14, '63': 62, '51': 49, '47': 44, '99': 101, '40': 37, '28': 23, '73': 73, '95': 97, '60': 59, '83': 84, '8': 80, '94': 96, '34': 30, '82': 83, '22': 17, '10': 1, '7': 69, '5': 47, '71': 71, '37': 33, '50': 48, '13': 7, '38': 34, '65': 64, '101': 3, '14': 8, '31': 27, '19': 13, '76': 76, '23': 18, '69': 68, '84': 85, '33': 29, '100': 2, '55': 53, '90': 92, '21': 16, '53': 51, '30': 26, '46': 43, '43': 40, '98': 100, '97': 99, '11': 5, '9': 91, '24': 19, '17': 11, '4': 36, '25': 20, '91': 93, '102': 4, '16': 10, '49': 46, '20': 15, '92': 94, '77': 77, '88': 89, '80': 81, '44': 41, '81': 82, '59': 57, '29': 24, '70': 70, '67': 66, '26': 21, '58': 56, '48': 45, '87': 88, '57': 55, '78': 78, '6': 58, '39': 35, '72': 72}, 'image_count': 5194, 'label_file': 'label_list.txt', 'early_stop': {'good_acc1': 0.92, 'sample_frequency': 50, 'successive_limit': 3}, 'class_dim': 102, 'input_size': [3, 224, 224], 'train_file_list': 'train.txt', 'rsm_strategy': {'lr_epochs': [20, 40, 60, 80, 100], 'learning_rate': 0.002, 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'save_freeze_dir': './freeze-model', 'adam_strategy': {'learning_rate': 0.002}, 'train_batch_size': 24, 'momentum_strategy': {'lr_epochs': [20, 40, 60, 80, 100], 'learning_rate': 0.002, 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'continue_train': True, 'pretrained': True, 'mode': 'train', 'pretrained_dir': 'ResNet50_pretrained', 'data_dir': 'data/data12479/hackathon-blossom-flower-classification/flower_data/train', 'sgd_strategy': {'lr_epochs': [20, 40, 60, 80, 100], 'learning_rate': 0.002, 'lr_decay': [1, 0.5, 0.25, 0.1, 0.01, 0.002]}, 'mean_rgb': [127.5, 127.5, 127.5], 'num_epochs': 120, 'use_gpu': True} 2019-10-08 18:42:42,420 - [line:555] - INFO: build input custom reader and data feeder 2019-10-08 18:42:42,420 - [line:555] - INFO: build input custom reader and data feeder 2019-10-08 18:42:42,420 - [line:555] - INFO: build input custom reader and data feeder 2019-10-08 18:42:42,420 - [line:555] - INFO: build input custom reader and data feeder 2019-10-08 18:42:42,420 - [line:555] - INFO: build input custom reader and data feeder 2019-10-08 18:42:42,420 - [line:555] - INFO: build input custom reader and data feeder 2019-10-08 18:42:42,420 - [line:555] - INFO: build input custom reader and data feeder 2019-10-08 18:42:42,420 - [line:555] - INFO: build input custom reader and data feeder 2019-10-08 18:42:42,420 - [line:555] - INFO: build input custom reader and data feeder 2019-10-08 18:42:42,420 - [line:555] - INFO: build input custom reader and data feeder 2019-10-08 18:42:42,420 - [line:555] - INFO: build input custom reader and data feeder 2019-10-08 18:42:42,420 - [line:555] - INFO: build input custom reader and data feeder 2019-10-08 18:42:42,429 - [line:568] - INFO: build newwork 2019-10-08 18:42:42,429 - [line:568] - INFO: build newwork 2019-10-08 18:42:42,429 - [line:568] - INFO: build newwork 2019-10-08 18:42:42,429 - [line:568] - INFO: build newwork 2019-10-08 18:42:42,429 - [line:568] - INFO: build newwork 2019-10-08 18:42:42,429 - [line:568] - INFO: build newwork 2019-10-08 18:42:42,429 - [line:568] - INFO: build newwork 2019-10-08 18:42:42,429 - [line:568] - INFO: build newwork 2019-10-08 18:42:42,429 - [line:568] - INFO: build newwork 2019-10-08 18:42:42,429 - [line:568] - INFO: build newwork 2019-10-08 18:42:42,429 - [line:568] - INFO: build newwork 2019-10-08 18:42:42,429 - [line:568] - INFO: build newwork ------------ name: "cross_entropy2_9.tmp_0" type { type: LOD_TENSOR lod_tensor { tensor { data_type: FP32 dims: -1 dims: 1 } lod_level: 0 } } persistable: false ------------ ---------------------------------------------------------------------------EnforceNotMet Traceback (most recent call last) in 654 init_log_config() 655 init_train_parameters() --> 656 train() in train() 580 print(cost) 581 print("------------") --> 582 optimizer.minimize(avg_cost) 583 exe = fluid.Executor(place) 584 in minimize(self, loss, startup_program, parameter_list, no_grad_set, grad_clip) /opt/conda/envs/python35-paddle120-env/lib/python3.5/site-packages/paddle/fluid/wrapped_decorator.py in __impl__(func, *args, **kwargs) 23 def __impl__(func, *args, **kwargs): 24 wrapped_func = decorator_func(func) ---> 25 return wrapped_func(*args, **kwargs) 26 27 return __impl__ /opt/conda/envs/python35-paddle120-env/lib/python3.5/site-packages/paddle/fluid/dygraph/base.py in __impl__(*args, **kwargs) 86 def __impl__(*args, **kwargs): 87 with _switch_tracer_mode_guard_(is_train=False): ---> 88 return func(*args, **kwargs) 89 90 return __impl__ /opt/conda/envs/python35-paddle120-env/lib/python3.5/site-packages/paddle/fluid/optimizer.py in minimize(self, loss, startup_program, parameter_list, no_grad_set, grad_clip) 591 startup_program=startup_program, 592 parameter_list=parameter_list, --> 593 no_grad_set=no_grad_set) 594 595 if grad_clip is not None and framework.in_dygraph_mode(): /opt/conda/envs/python35-paddle120-env/lib/python3.5/site-packages/paddle/fluid/optimizer.py in backward(self, loss, startup_program, parameter_list, no_grad_set, callbacks) 491 with program_guard(program, startup_program): 492 params_grads = append_backward(loss, parameter_list, --> 493 no_grad_set, callbacks) 494 # Note: since we can't use all_reduce_op now, 495 # dgc_op should be the last op of one grad. /opt/conda/envs/python35-paddle120-env/lib/python3.5/site-packages/paddle/fluid/backward.py in append_backward(loss, parameter_list, no_grad_set, callbacks) 568 grad_to_var, 569 callbacks, --> 570 input_grad_names_set=input_grad_names_set) 571 572 # Because calc_gradient may be called multiple times, /opt/conda/envs/python35-paddle120-env/lib/python3.5/site-packages/paddle/fluid/backward.py in _append_backward_ops_(block, ops, target_block, no_grad_dict, grad_to_var, callbacks, input_grad_names_set) 308 # Getting op's corresponding grad_op 309 grad_op_desc, op_grad_to_var = core.get_grad_op_desc( --> 310 op.desc, cpt.to_text(no_grad_dict[block.idx]), grad_sub_block_list) 311 312 # If input_grad_names_set is not None, extend grad_op_descs only when EnforceNotMet: grad_op_maker_ should not be null Operator GradOpMaker has not been registered. at [/paddle/paddle/fluid/framework/op_info.h:69] PaddlePaddle Call Stacks: 0 0x7f4188b04808p void paddle::platform::EnforceNotMet::Init(std::string, char const*, int) + 360 1 0x7f4188b04b57p paddle::platform::EnforceNotMet::EnforceNotMet(std::string const&, char const*, int) + 87 2 0x7f4188b05b1cp paddle::framework::OpInfo::GradOpMaker() const + 108 3 0x7f4188afd21ep 4 0x7f4188b36ca6p 5 0x7f420cb59199p PyCFunction_Call + 233 6 0x7f420cbf3dbep PyEval_EvalFrameEx + 31950 7 0x7f420cbf64b6p 8 0x7f420cbf35b5p PyEval_EvalFrameEx + 29893 9 0x7f420cbf64b6p 10 0x7f420cbf35b5p PyEval_EvalFrameEx + 29893 11 0x7f420cbf64b6p 12 0x7f420cbf35b5p PyEval_EvalFrameEx + 29893 13 0x7f420cbf64b6p 14 0x7f420cbf65a8p PyEval_EvalCodeEx + 72 15 0x7f420cb35c33p 16 0x7f420cb0433ap PyObject_Call + 106 17 0x7f420cbee6eep PyEval_EvalFrameEx + 9726 18 0x7f420cbf64b6p 19 0x7f420cbf65a8p PyEval_EvalCodeEx + 72 20 0x7f420cb35c33p 21 0x7f420cb0433ap PyObject_Call + 106 22 0x7f420cbee6eep PyEval_EvalFrameEx + 9726 23 0x7f420cbf64b6p 24 0x7f420cbf35b5p PyEval_EvalFrameEx + 29893 25 0x7f420cbf64b6p 26 0x7f420cbf35b5p PyEval_EvalFrameEx + 29893 27 0x7f420cbf41d0p PyEval_EvalFrameEx + 32992 28 0x7f420cbf64b6p 29 0x7f420cbf65a8p PyEval_EvalCodeEx + 72 30 0x7f420cbf65ebp PyEval_EvalCode + 59 31 0x7f420cbe9c5dp 32 0x7f420cb59179p PyCFunction_Call + 201 33 0x7f420cbf3dbep PyEval_EvalFrameEx + 31950 34 0x7f420cb2d410p _PyGen_Send + 128 35 0x7f420cbf2953p PyEval_EvalFrameEx + 26723 36 0x7f420cb2d410p _PyGen_Send + 128 37 0x7f420cbf2953p PyEval_EvalFrameEx + 26723 38 0x7f420cb2d410p _PyGen_Send + 128 39 0x7f420cbf3d60p PyEval_EvalFrameEx + 31856 40 0x7f420cbf41d0p PyEval_EvalFrameEx + 32992 41 0x7f420cbf41d0p PyEval_EvalFrameEx + 32992 42 0x7f420cbf64b6p 43 0x7f420cbf65a8p PyEval_EvalCodeEx + 72 44 0x7f420cb35c33p 45 0x7f420cb0433ap PyObject_Call + 106 46 0x7f420cbee6eep PyEval_EvalFrameEx + 9726 47 0x7f420cbf64b6p 48 0x7f420cbf35b5p PyEval_EvalFrameEx + 29893 49 0x7f420cb2c6bap 50 0x7f420cbe7af6p 51 0x7f420cb59179p PyCFunction_Call + 201 52 0x7f420cbf3dbep PyEval_EvalFrameEx + 31950 53 0x7f420cbf64b6p 54 0x7f420cbf35b5p PyEval_EvalFrameEx + 29893 55 0x7f420cb2c6bap 56 0x7f420cbe7af6p 57 0x7f420cb59179p PyCFunction_Call + 201 58 0x7f420cbf3dbep PyEval_EvalFrameEx + 31950 59 0x7f420cbf64b6p 60 0x7f420cbf35b5p PyEval_EvalFrameEx + 29893 61 0x7f420cb2c6bap 62 0x7f420cbe7af6p 63 0x7f420cb59179p PyCFunction_Call + 201 64 0x7f420cbf3dbep PyEval_EvalFrameEx + 31950 65 0x7f420cbf64b6p 66 0x7f420cbf65a8p PyEval_EvalCodeEx + 72 67 0x7f420cb35b56p 68 0x7f420cb0433ap PyObject_Call + 106 69 0x7f420cbee6eep PyEval_EvalFrameEx + 9726 70 0x7f420cb2d410p _PyGen_Send + 128 71 0x7f420cbf3d60p PyEval_EvalFrameEx + 31856 72 0x7f420cbf41d0p PyEval_EvalFrameEx + 32992 73 0x7f420cbf64b6p 74 0x7f420cbf65a8p PyEval_EvalCodeEx + 72 75 0x7f420cb35c33p 76 0x7f420cb0433ap PyObject_Call + 106 77 0x7f420cbee6eep PyEval_EvalFrameEx + 9726 78 0x7f420cbf64b6p 79 0x7f420cbf65a8p PyEval_EvalCodeEx + 72 80 0x7f420cb35b56p 81 0x7f420cb0433ap PyObject_Call + 106 82 0x7f420cc69ccap 83 0x7f420cb0433ap PyObject_Call + 106 84 0x7f420cbf04c5p PyEval_EvalFrameEx + 17365 85 0x7f420cbf64b6p 86 0x7f420cbf65a8p PyEval_EvalCodeEx + 72 87 0x7f420cb35b56p 88 0x7f420cb0433ap PyObject_Call + 106 89 0x7f420cbee6eep PyEval_EvalFrameEx + 9726 90 0x7f420cbf41d0p PyEval_EvalFrameEx + 32992 91 0x7f420cbf41d0p PyEval_EvalFrameEx + 32992 92 0x7f420cbf41d0p PyEval_EvalFrameEx + 32992 93 0x7f420cbf41d0p PyEval_EvalFrameEx + 32992 94 0x7f420cbf41d0p PyEval_EvalFrameEx + 32992 95 0x7f420cbf64b6p 96 0x7f420cbf35b5p PyEval_EvalFrameEx + 29893 97 0x7f420cbf64b6p 98 0x7f420cbf65a8p PyEval_EvalCodeEx + 72 99 0x7f420cbf65ebp PyEval_EvalCode + 59
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【干货任意门】深度学习第一课
Ta的回复 :好贴啊,感谢整理
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