基于Paddle2.0的猫12分类
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本项目用Paddle2.0rc解决12类猫的分类问题，旨在掌握其图像分类使用方法。数据集含2160张训练图、240张测试图，先解压并加载库，配置参数后划分训练和验证集，定义数据集类，初始化mobilenet_v1模型，配置优化器等，训练10轮，最后评估、测试并保存模型与结果。
项目描述
本项目采用Paddle2.0rc版本解决猫的分类问题。
主要目的是跑通流程，掌握Paddle2.0rc版本在图像分类问题上的使用方法。
数据集介绍
训练集共有2160张图片，测试集共有240张图片。共有12个猫的分类。
解压缩原始数据集
In [ ]!unzip -q data/data63045/cat_12_train.zip -d work/!unzip -q data/data63045/cat_12_test.zip -d work/!cp data/data63045/train_list.txt work/登录后复制
加载需要的库
In [ ]import osimport shutilimport numpy as npimport paddlefrom paddle.io import Datasetfrom paddle.vision.datasets import DatasetFolder, ImageFolderfrom paddle.vision.transforms import Compose, Resize, Transpose登录后复制
全局参数配置
In [ ]'''参数配置'''train_parameters = { "class_dim": 12, #分类数 "target_path":"/home/aistudio/work/", 'train_image_dir': '/home/aistudio/work/trainImages', 'eval_image_dir': '/home/aistudio/work/evalImages', 'test_image_dir': '/home/aistudio/work/cat_12_test', 'train_list_path': '/home/aistudio/work/train_list.txt',}登录后复制划分训练集和验证集
In [ ]def create_train_eval(): ''' 划分训练集和验证集 ''' train_dir = train_parameters['train_image_dir'] eval_dir = train_parameters['eval_image_dir'] train_list_path = train_parameters['train_list_path'] target_path = train_parameters['target_path'] print('creating training and eval images') if not os.path.exists(train_dir): os.mkdir(train_dir) if not os.path.exists(eval_dir): os.mkdir(eval_dir) with open(train_list_path, 'r') as f: data = f.readlines() for i in range(len(data)): img_path = data[i].split('\t')[0] class_label = data[i].split('\t')[1][:-1] if i % 8 == 0: # 每8张图片取一个做验证数据 eval_target_dir = os.path.join(eval_dir, str(class_label)) eval_img_path = os.path.join(target_path, img_path) if not os.path.exists(eval_target_dir): os.mkdir(eval_target_dir) shutil.copy(eval_img_path, eval_target_dir) else: train_target_dir = os.path.join(train_dir, str(class_label)) train_img_path = os.path.join(target_path, img_path) if not os.path.exists(train_target_dir): os.mkdir(train_target_dir) shutil.copy(train_img_path, train_target_dir) print ('划分训练集和验证集完成！')登录后复制In [ ]create_train_eval()登录后复制
creating training and eval images划分训练集和验证集完成！登录后复制
定义数据集
In [ ]class CatDataset(Dataset): """ 步骤一：继承paddle.io.Dataset类 """ def __init__(self, mode='train'): """ 步骤二：实现构造函数，定义数据读取方式，划分训练和测试数据集 """ super(CatDataset, self).__init__() train_image_dir = train_parameters['train_image_dir'] eval_image_dir = train_parameters['eval_image_dir'] test_image_dir = train_parameters['test_image_dir'] transform_train = Compose([Resize(size=(112,112)), Transpose()]) transform_eval = Compose([Resize(size=(112,112)), Transpose()]) train_data_folder = DatasetFolder(train_image_dir, transform=transform_train) eval_data_folder = DatasetFolder(eval_image_dir, transform=transform_eval) test_data_folder = ImageFolder(test_image_dir, transform=transform_eval) self.mode = mode if self.mode == 'train': self.data = train_data_folder elif self.mode == 'eval': self.data = eval_data_folder elif self.mode == 'test': self.data = test_data_folder def __getitem__(self, index): """ 步骤三：实现__getitem__方法，定义指定index时如何获取数据，并返回单条数据（训练数据，对应的标签） """ data = np.array(self.data[index][0]).astype('float32') if self.mode == 'test': return data else: label = np.array([self.data[index][1]]).astype('int64') return data, label def __len__(self): """ 步骤四：实现__len__方法，返回数据集总数目 """ return len(self.data)登录后复制In [ ]train_dataset = CatDataset(mode='train')val_dataset = CatDataset(mode='eval')test_dataset = CatDataset(mode='test')登录后复制
初始化模型
In [ ]# 使用内置的模型,这边可以选择多种不同网络，这里选了mobilenet_v1网络model = paddle.vision.models.mobilenet_v1(pretrained=True, num_classes=train_parameters["class_dim"])model = paddle.Model(model)登录后复制
100%|██████████| 25072/25072 [00:00<00:00, 28070.41it/s]/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py:1175: UserWarning: Skip loading for fc.weight. fc.weight receives a shape [1024, 1000], but the expected shape is [1024, 12]. warnings.warn(("Skip loading for {}. ".format(key) + str(err)))/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py:1175: UserWarning: Skip loading for fc.bias. fc.bias receives a shape [1000], but the expected shape is [12]. warnings.warn(("Skip loading for {}. ".format(key) + str(err)))登录后复制In [ ]## 查看模型结构model.summary((-1, 3, 224, 224))登录后复制
--------------------------------------------------------------------------------- Layer (type) Input Shape Output Shape Param # ================================================================================= Conv2D-160 [[1, 3, 224, 224]] [1, 32, 112, 112] 864 BatchNorm2D-107 [[1, 32, 112, 112]] [1, 32, 112, 112] 128 ReLU-88 [[1, 32, 112, 112]] [1, 32, 112, 112] 0 ConvBNLayer-1 [[1, 3, 224, 224]] [1, 32, 112, 112] 0 Conv2D-161 [[1, 32, 112, 112]] [1, 32, 112, 112] 288 BatchNorm2D-108 [[1, 32, 112, 112]] [1, 32, 112, 112] 128 ReLU-89 [[1, 32, 112, 112]] [1, 32, 112, 112] 0 ConvBNLayer-2 [[1, 32, 112, 112]] [1, 32, 112, 112] 0 Conv2D-162 [[1, 32, 112, 112]] [1, 64, 112, 112] 2,048 BatchNorm2D-109 [[1, 64, 112, 112]] [1, 64, 112, 112] 256 ReLU-90 [[1, 64, 112, 112]] [1, 64, 112, 112] 0 ConvBNLayer-3 [[1, 32, 112, 112]] [1, 64, 112, 112] 0 DepthwiseSeparable-1 [[1, 32, 112, 112]] [1, 64, 112, 112] 0 Conv2D-163 [[1, 64, 112, 112]] [1, 64, 56, 56] 576 BatchNorm2D-110 [[1, 64, 56, 56]] [1, 64, 56, 56] 256 ReLU-91 [[1, 64, 56, 56]] [1, 64, 56, 56] 0 ConvBNLayer-4 [[1, 64, 112, 112]] [1, 64, 56, 56] 0 Conv2D-164 [[1, 64, 56, 56]] [1, 128, 56, 56] 8,192 BatchNorm2D-111 [[1, 128, 56, 56]] [1, 128, 56, 56] 512 ReLU-92 [[1, 128, 56, 56]] [1, 128, 56, 56] 0 ConvBNLayer-5 [[1, 64, 56, 56]] [1, 128, 56, 56] 0 DepthwiseSeparable-2 [[1, 64, 112, 112]] [1, 128, 56, 56] 0 Conv2D-165 [[1, 128, 56, 56]] [1, 128, 56, 56] 1,152 BatchNorm2D-112 [[1, 128, 56, 56]] [1, 128, 56, 56] 512 ReLU-93 [[1, 128, 56, 56]] [1, 128, 56, 56] 0 ConvBNLayer-6 [[1, 128, 56, 56]] [1, 128, 56, 56] 0 Conv2D-166 [[1, 128, 56, 56]] [1, 128, 56, 56] 16,384 BatchNorm2D-113 [[1, 128, 56, 56]] [1, 128, 56, 56] 512 ReLU-94 [[1, 128, 56, 56]] [1, 128, 56, 56] 0 ConvBNLayer-7 [[1, 128, 56, 56]] [1, 128, 56, 56] 0 DepthwiseSeparable-3 [[1, 128, 56, 56]] [1, 128, 56, 56] 0 Conv2D-167 [[1, 128, 56, 56]] [1, 128, 28, 28] 1,152 BatchNorm2D-114 [[1, 128, 28, 28]] [1, 128, 28, 28] 512 ReLU-95 [[1, 128, 28, 28]] [1, 128, 28, 28] 0 ConvBNLayer-8 [[1, 128, 56, 56]] [1, 128, 28, 28] 0 Conv2D-168 [[1, 128, 28, 28]] [1, 256, 28, 28] 32,768 BatchNorm2D-115 [[1, 256, 28, 28]] [1, 256, 28, 28] 1,024 ReLU-96 [[1, 256, 28, 28]] [1, 256, 28, 28] 0 ConvBNLayer-9 [[1, 128, 28, 28]] [1, 256, 28, 28] 0 DepthwiseSeparable-4 [[1, 128, 56, 56]] [1, 256, 28, 28] 0 Conv2D-169 [[1, 256, 28, 28]] [1, 256, 28, 28] 2,304 BatchNorm2D-116 [[1, 256, 28, 28]] [1, 256, 28, 28] 1,024 ReLU-97 [[1, 256, 28, 28]] [1, 256, 28, 28] 0 ConvBNLayer-10 [[1, 256, 28, 28]] [1, 256, 28, 28] 0 Conv2D-170 [[1, 256, 28, 28]] [1, 256, 28, 28] 65,536 BatchNorm2D-117 [[1, 256, 28, 28]] [1, 256, 28, 28] 1,024 ReLU-98 [[1, 256, 28, 28]] [1, 256, 28, 28] 0 ConvBNLayer-11 [[1, 256, 28, 28]] [1, 256, 28, 28] 0 DepthwiseSeparable-5 [[1, 256, 28, 28]] [1, 256, 28, 28] 0 Conv2D-171 [[1, 256, 28, 28]] [1, 256, 14, 14] 2,304 BatchNorm2D-118 [[1, 256, 14, 14]] [1, 256, 14, 14] 1,024 ReLU-99 [[1, 256, 14, 14]] [1, 256, 14, 14] 0 ConvBNLayer-12 [[1, 256, 28, 28]] [1, 256, 14, 14] 0 Conv2D-172 [[1, 256, 14, 14]] [1, 512, 14, 14] 131,072 BatchNorm2D-119 [[1, 512, 14, 14]] [1, 512, 14, 14] 2,048 ReLU-100 [[1, 512, 14, 14]] [1, 512, 14, 14] 0 ConvBNLayer-13 [[1, 256, 14, 14]] [1, 512, 14, 14] 0 DepthwiseSeparable-6 [[1, 256, 28, 28]] [1, 512, 14, 14] 0 Conv2D-173 [[1, 512, 14, 14]] [1, 512, 14, 14] 4,608 BatchNorm2D-120 [[1, 512, 14, 14]] [1, 512, 14, 14] 2,048 ReLU-101 [[1, 512, 14, 14]] [1, 512, 14, 14] 0 ConvBNLayer-14 [[1, 512, 14, 14]] [1, 512, 14, 14] 0 Conv2D-174 [[1, 512, 14, 14]] [1, 512, 14, 14] 262,144 BatchNorm2D-121 [[1, 512, 14, 14]] [1, 512, 14, 14] 2,048 ReLU-102 [[1, 512, 14, 14]] [1, 512, 14, 14] 0 ConvBNLayer-15 [[1, 512, 14, 14]] [1, 512, 14, 14] 0 DepthwiseSeparable-7 [[1, 512, 14, 14]] [1, 512, 14, 14] 0 Conv2D-175 [[1, 512, 14, 14]] [1, 512, 14, 14] 4,608 BatchNorm2D-122 [[1, 512, 14, 14]] [1, 512, 14, 14] 2,048 ReLU-103 [[1, 512, 14, 14]] [1, 512, 14, 14] 0 ConvBNLayer-16 [[1, 512, 14, 14]] [1, 512, 14, 14] 0 Conv2D-176 [[1, 512, 14, 14]] [1, 512, 14, 14] 262,144 BatchNorm2D-123 [[1, 512, 14, 14]] [1, 512, 14, 14] 2,048 ReLU-104 [[1, 512, 14, 14]] [1, 512, 14, 14] 0 ConvBNLayer-17 [[1, 512, 14, 14]] [1, 512, 14, 14] 0 DepthwiseSeparable-8 [[1, 512, 14, 14]] [1, 512, 14, 14] 0 Conv2D-177 [[1, 512, 14, 14]] [1, 512, 14, 14] 4,608 BatchNorm2D-124 [[1, 512, 14, 14]] [1, 512, 14, 14] 2,048 ReLU-105 [[1, 512, 14, 14]] [1, 512, 14, 14] 0 ConvBNLayer-18 [[1, 512, 14, 14]] [1, 512, 14, 14] 0 Conv2D-178 [[1, 512, 14, 14]] [1, 512, 14, 14] 262,144 BatchNorm2D-125 [[1, 512, 14, 14]] [1, 512, 14, 14] 2,048 ReLU-106 [[1, 512, 14, 14]] [1, 512, 14, 14] 0 ConvBNLayer-19 [[1, 512, 14, 14]] [1, 512, 14, 14] 0 DepthwiseSeparable-9 [[1, 512, 14, 14]] [1, 512, 14, 14] 0 Conv2D-179 [[1, 512, 14, 14]] [1, 512, 14, 14] 4,608 BatchNorm2D-126 [[1, 512, 14, 14]] [1, 512, 14, 14] 2,048 ReLU-107 [[1, 512, 14, 14]] [1, 512, 14, 14] 0 ConvBNLayer-20 [[1, 512, 14, 14]] [1, 512, 14, 14] 0 Conv2D-180 [[1, 512, 14, 14]] [1, 512, 14, 14] 262,144 BatchNorm2D-127 [[1, 512, 14, 14]] [1, 512, 14, 14] 2,048 ReLU-108 [[1, 512, 14, 14]] [1, 512, 14, 14] 0 ConvBNLayer-21 [[1, 512, 14, 14]] [1, 512, 14, 14] 0 DepthwiseSeparable-10 [[1, 512, 14, 14]] [1, 512, 14, 14] 0 Conv2D-181 [[1, 512, 14, 14]] [1, 512, 14, 14] 4,608 BatchNorm2D-128 [[1, 512, 14, 14]] [1, 512, 14, 14] 2,048 ReLU-109 [[1, 512, 14, 14]] [1, 512, 14, 14] 0 ConvBNLayer-22 [[1, 512, 14, 14]] [1, 512, 14, 14] 0 Conv2D-182 [[1, 512, 14, 14]] [1, 512, 14, 14] 262,144 BatchNorm2D-129 [[1, 512, 14, 14]] [1, 512, 14, 14] 2,048 ReLU-110 [[1, 512, 14, 14]] [1, 512, 14, 14] 0 ConvBNLayer-23 [[1, 512, 14, 14]] [1, 512, 14, 14] 0 DepthwiseSeparable-11 [[1, 512, 14, 14]] [1, 512, 14, 14] 0 Conv2D-183 [[1, 512, 14, 14]] [1, 512, 7, 7] 4,608 BatchNorm2D-130 [[1, 512, 7, 7]] [1, 512, 7, 7] 2,048 ReLU-111 [[1, 512, 7, 7]] [1, 512, 7, 7] 0 ConvBNLayer-24 [[1, 512, 14, 14]] [1, 512, 7, 7] 0 Conv2D-184 [[1, 512, 7, 7]] [1, 1024, 7, 7] 524,288 BatchNorm2D-131 [[1, 1024, 7, 7]] [1, 1024, 7, 7] 4,096 ReLU-112 [[1, 1024, 7, 7]] [1, 1024, 7, 7] 0 ConvBNLayer-25 [[1, 512, 7, 7]] [1, 1024, 7, 7] 0 DepthwiseSeparable-12 [[1, 512, 14, 14]] [1, 1024, 7, 7] 0 Conv2D-185 [[1, 1024, 7, 7]] [1, 1024, 7, 7] 9,216 BatchNorm2D-132 [[1, 1024, 7, 7]] [1, 1024, 7, 7] 4,096 ReLU-113 [[1, 1024, 7, 7]] [1, 1024, 7, 7] 0 ConvBNLayer-26 [[1, 1024, 7, 7]] [1, 1024, 7, 7] 0 Conv2D-186 [[1, 1024, 7, 7]] [1, 1024, 7, 7] 1,048,576 BatchNorm2D-133 [[1, 1024, 7, 7]] [1, 1024, 7, 7] 4,096 ReLU-114 [[1, 1024, 7, 7]] [1, 1024, 7, 7] 0 ConvBNLayer-27 [[1, 1024, 7, 7]] [1, 1024, 7, 7] 0 DepthwiseSeparable-13 [[1, 1024, 7, 7]] [1, 1024, 7, 7] 0 AdaptiveAvgPool2D-3 [[1, 1024, 7, 7]] [1, 1024, 1, 1] 0 Linear-3 [[1, 1024]] [1, 12] 12,300 =================================================================================Total params: 3,241,164Trainable params: 3,197,388Non-trainable params: 43,776---------------------------------------------------------------------------------Input size (MB): 0.57Forward/backward pass size (MB): 174.57Params size (MB): 12.36Estimated Total Size (MB): 187.51---------------------------------------------------------------------------------登录后复制
{'total_params': 3241164, 'trainable_params': 3197388}登录后复制模型配置：为模型训练做准备，设置优化器，损失函数和精度计算方式
In [ ]# 调用飞桨框架的VisualDL模块，保存信息到目录中。callback = paddle.callbacks.VisualDL(log_dir='visualdl_log_dir')登录后复制In [ ]
model.prepare(optimizer=paddle.optimizer.Adam( learning_rate=0.001, parameters=model.parameters()), loss=paddle.nn.CrossEntropyLoss(), metrics=paddle.metric.Accuracy())登录后复制
模型训练
In [ ]model.fit(train_dataset, val_dataset, epochs=10, batch_size=32, callbacks=callback, verbose=1)登录后复制
Epoch 1/10登录后复制
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/nn/layer/norm.py:637: UserWarning: When training, we now always track global mean and variance. "When training, we now always track global mean and variance.")登录后复制
step 60/60 [==============================] - loss: 2.2463 - acc: 0.6090 - 294ms/step Eval begin...step 9/9 [==============================] - loss: 0.4193 - acc: 0.6704 - 285ms/stepEval samples: 270Epoch 2/10step 60/60 [==============================] - loss: 1.6789 - acc: 0.8571 - 292ms/step Eval begin...step 9/9 [==============================] - loss: 1.3652 - acc: 0.7630 - 280ms/stepEval samples: 270Epoch 3/10step 60/60 [==============================] - loss: 2.7546 - acc: 0.9069 - 301ms/step Eval begin...step 9/9 [==============================] - loss: 0.5788 - acc: 0.7704 - 277ms/stepEval samples: 270Epoch 4/10step 60/60 [==============================] - loss: 2.8266 - acc: 0.9212 - 294ms/step Eval begin...step 9/9 [==============================] - loss: 1.0253 - acc: 0.7630 - 289ms/stepEval samples: 270Epoch 5/10step 60/60 [==============================] - loss: 2.9948 - acc: 0.9111 - 298ms/step Eval begin...step 9/9 [==============================] - loss: 1.0942 - acc: 0.6963 - 279ms/stepEval samples: 270Epoch 6/10step 60/60 [==============================] - loss: 0.8608 - acc: 0.9101 - 293ms/step Eval begin...step 9/9 [==============================] - loss: 1.5086 - acc: 0.7444 - 282ms/stepEval samples: 270Epoch 7/10step 60/60 [==============================] - loss: 2.5132 - acc: 0.9392 - 296ms/step Eval begin...step 9/9 [==============================] - loss: 1.0570 - acc: 0.7593 - 276ms/stepEval samples: 270Epoch 8/10step 60/60 [==============================] - loss: 0.8880 - acc: 0.9508 - 298ms/step Eval begin...step 9/9 [==============================] - loss: 1.3956 - acc: 0.7111 - 285ms/stepEval samples: 270Epoch 9/10step 60/60 [==============================] - loss: 0.0301 - acc: 0.9370 - 291ms/step Eval begin...step 9/9 [==============================] - loss: 1.6584 - acc: 0.7333 - 280ms/stepEval samples: 270Epoch 10/10step 60/60 [==============================] - loss: 8.0418 - acc: 0.9757 - 294ms/step Eval begin...step 9/9 [==============================] - loss: 0.2750 - acc: 0.7630 - 279ms/stepEval samples: 270登录后复制
可视化结果
启动模型评估，指定数据集，设置日志格式
In [ ]model.evaluate(val_dataset, verbose=1)登录后复制
Eval begin...step 270/270 [==============================] - loss: 1.6444 - acc: 0.7630 - 16ms/step Eval samples: 270登录后复制
{'loss': [1.6443943], 'acc': 0.762962962962963}登录后复制启动模型测试，指定测试集
In [ ]results = model.predict(test_dataset)登录后复制
Predict begin...step 240/240 [==============================] - 14ms/step Predict samples: 240登录后复制
预测结果写入csv文件
In [ ]labels = []for result in results[0]: lab = np.argmax(result) labels.append(lab)登录后复制In [ ]
test_paths = os.listdir('work/cat_12_test')登录后复制In [ ]final_result=[]for i in range(len(labels)): final_result.append(test_paths[i] + ',' + str(labels[i]) + '\n')登录后复制In [ ]
with open("work/result.csv","w") as f: f.writelines(final_result)登录后复制保存模型
In [ ]model.save('work/model') # save for training登录后复制
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PAAL代币揭秘：深度解析Web3社区治理的核心钥匙 在去中心化自治组织的浪潮中，谁真正掌握了项目的话语权？PAAL代币提供了一套系统化的答案。它不仅是生态内流转的价值媒介，更是开启链上治理大门的核心凭证。通过持有并质押PAAL代币，用户能够对协议升级、资金分配乃至战略方向等关键事务投出决定性的一票