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百度飞桨(PaddlePaddle)
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手写数字识别任务 用于对 0 ~ 9 的十类数字进行分类，即输入手写数字的图片，可识别出这个图片中的数字。 使用 pip 工具安装 matplotlib 和 numpypython -m pip install matplotlib numpy -i https://mirror.baidu.com/pypi/simple D:\OpenSource\PaddlePaddle>python -m pip install matplotlib numpy -i https://mirror.baidu.com/pypi/simple Looking in indexes: https://mirror.baidu.com/pypi/simple Collecting matplotlib Downloading https://mirror.baidu.com/pypi/packages/92/01/2c04d328db6955d77f8f60c17068dde8aa66f153b2c599ca03c2cb0d5567/matplotlib-3.7.1-cp38-cp38-win_amd64.whl (7.6 MB) |████████████████████████████████| 7.6 MB ... Requirement already satisfied: numpy in d:\program files\python38\lib\site-packages (1.24.3) Collecting packaging>=20.0 Downloading https://mirror.baidu.com/pypi/packages/ab/c3/57f0601a2d4fe15de7a553c00adbc901425661bf048f2a22dfc500caf121/packaging-23.1-py3-none-any.whl (48 kB) |████████████████████████████████| 48 kB 1.2 MB/s Collecting cycler>=0.10 Downloading https://mirror.baidu.com/pypi/packages/5c/f9/695d6bedebd747e5eb0fe8fad57b72fdf25411273a39791cde838d5a8f51/cycler-0.11.0-py3-none-any.whl (6.4 kB) Requirement already satisfied: pillow>=6.2.0 in d:\program files\python38\lib\site-packages (from matplotlib) (9.5.0) Collecting python-dateutil>=2.7 Downloading https://mirror.baidu.com/pypi/packages/36/7a/87837f39d0296e723bb9b62bbb257d0355c7f6128853c78955f57342a56d/python_dateutil-2.8.2-py2.py3-none-any.whl (247 kB) |████████████████████████████████| 247 kB ... Collecting importlib-resources>=3.2.0 Downloading https://mirror.baidu.com/pypi/packages/38/71/c13ea695a4393639830bf96baea956538ba7a9d06fcce7cef10bfff20f72/importlib_resources-5.12.0-py3-none-any.whl (36 kB) Collecting fonttools>=4.22.0 Downloading https://mirror.baidu.com/pypi/packages/16/07/1c7547e27f559ec078801d522cc4d5127cdd4ef8e831c8ddcd9584668a07/fonttools-4.39.3-py3-none-any.whl (1.0 MB) |████████████████████████████████| 1.0 MB ... Collecting pyparsing>=2.3.1 Downloading https://mirror.baidu.com/pypi/packages/6c/10/a7d0fa5baea8fe7b50f448ab742f26f52b80bfca85ac2be9d35cdd9a3246/pyparsing-3.0.9-py3-none-any.whl (98 kB) |████████████████████████████████| 98 kB 862 kB/s Collecting contourpy>=1.0.1 Downloading https://mirror.baidu.com/pypi/packages/08/ce/9bfe9f028cb5a8ee97898da52f4905e0e2d9ca8203ffdcdbe80e1769b549/contourpy-1.0.7-cp38-cp38-win_amd64.whl (162 kB) |████████████████████████████████| 162 kB ... Collecting kiwisolver>=1.0.1 Downloading https://mirror.baidu.com/pypi/packages/4f/05/59b34e788bf2b45c7157c3d898d567d28bc42986c1b6772fb1af329eea0d/kiwisolver-1.4.4-cp38-cp38-win_amd64.whl (55 kB) |████████████████████████████████| 55 kB 784 kB/s Collecting zipp>=3.1.0 Downloading https://mirror.baidu.com/pypi/packages/5b/fa/c9e82bbe1af6266adf08afb563905eb87cab83fde00a0a08963510621047/zipp-3.15.0-py3-none-any.whl (6.8 kB) Requirement already satisfied: six>=1.5 in d:\program files\python38\lib\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0) Installing collected packages: zipp, python-dateutil, pyparsing, packaging, kiwisolver, importlib-resources, fonttools, cycler, contourpy, matplotlib Successfully installed contourpy-1.0.7 cycler-0.11.0 fonttools-4.39.3 importlib-resources-5.12.0 kiwisolver-1.4.4 matplotlib-3.7.1 packaging-23.1 pyparsing-3.0.9 python-dateutil-2.8.2 zipp-3.15.0 WARNING: You are using pip version 21.1.1; however, version 23.1.2 is available. You should consider upgrading via the 'D:\Program Files\Python38\python.exe -m pip install --upgrade pip' command. D:\OpenSource\PaddlePaddle>创建 DigitalRecognition.py官网代码少了 plt.show() # 要加上这句，才会显示图片 import paddle import numpy as np from paddle.vision.transforms import Normalize transform = Normalize(mean=[127.5], std=[127.5], data_format='CHW') # 下载数据集并初始化 DataSet ''' 飞桨在 paddle.vision.datasets 下内置了计算机视觉（Computer Vision，CV）领域常见的数据集， 如 MNIST、Cifar10、Cifar100、FashionMNIST 和 VOC2012 等。在本任务中， 先后加载了 MNIST 训练集（mode='train'）和测试集（mode='test'），训练集用于训练模型，测试集用于评估模型效果。 ''' train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform) test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=transform) # 打印数据集里图片数量 60000 images in train_dataset, 10000 images in test_dataset # print('{} images in train_dataset, {} images in test_dataset'.format(len(train_dataset), len(test_dataset))) # 模型组网并初始化网络 lenet = paddle.vision.models.LeNet(num_classes=10) model = paddle.Model(lenet) # 模型训练的配置准备，准备损失函数，优化器和评价指标 model.prepare(paddle.optimizer.Adam(parameters=model.parameters()), paddle.nn.CrossEntropyLoss(), paddle.metric.Accuracy()) # 模型训练 model.fit(train_dataset, epochs=5, batch_size=64, verbose=1) # 模型评估 model.evaluate(test_dataset, batch_size=64, verbose=1) # 保存模型 model.save('./output/mnist') # 加载模型 model.load('output/mnist') # 从测试集中取出一张图片 img, label = test_dataset[0] # 将图片shape从1*28*28变为1*1*28*28，增加一个batch维度，以匹配模型输入格式要求 img_batch = np.expand_dims(img.astype('float32'), axis=0) # 执行推理并打印结果，此处predict_batch返回的是一个list，取出其中数据获得预测结果 out = model.predict_batch(img_batch)[0] pred_label = out.argmax() print('true label: {}, pred label: {}'.format(label[0], pred_label)) # 可视化图片 from matplotlib import pyplot as plt plt.imshow(img[0]) plt.show() # 要加上这句，才会显示图片PyCharm运行(推荐，有错误能显示出来)Python MatplotlibDeprecationWarning Matplotlib 3.6 and will be removed two minor releases later File -> Settings -> Tools -> Python Scientific -> 取消 Show plots in tool window， 取消后，将不会看到红字警告提示
D:\OpenSource\PaddlePaddle>python DigitalRecognition.py
如果碰到下列错误，需要加上 plt.show()Python MatplotlibDeprecationWarning Matplotlib 3.6 and will be removed two minor releases later MatplotlibDeprecationWarning: Support for FigureCanvases without a required_interactive_framework attribute was deprecated in Matplotlib 3.6 and will be removed two minor releases later. plt.imshow(img[0])数据集定义与加载飞桨在 paddle.vision.datasets 下内置了计算机视觉（Computer Vision，CV）领域常见的数据集，如 MNIST、Cifar10、Cifar100、FashionMNIST 和 VOC2012 等。在本任务中，先后加载了 MNIST 训练集（mode='train'）和测试集（mode='test'），训练集用于训练模型，测试集用于评估模型效果。 飞桨除了内置了 CV 领域常见的数据集，还在 paddle.text 下内置了自然语言处理（Natural Language Processing，NLP）领域常见的数据集，并提供了自定义数据集与加载功能的 paddle.io.Dataset 和 paddle.io.DataLoader API，详细使用方法可参考『数据集定义与加载』 章节。 另外在 paddle.vision.transforms 下提供了一些常用的图像变换操作，如对图像的翻转、裁剪、调整亮度等处理，可实现数据增强，以增加训练样本的多样性，提升模型的泛化能力。本任务在初始化 MNIST 数据集时通过 transform 字段传入了 Normalize 变换对图像进行归一化，对图像进行归一化可以加快模型训练的收敛速度。该功能的具体使用方法可参考『数据预处理』 章节。 数据集定义与加载数据预处理模型组网https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/beginner/quick_start_cn.html#moxingzuwang 模型训练与评估https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/beginner/quick_start_cn.html#moxingxunlianyupinggu 模型推理https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/beginner/quick_start_cn.html#moxingtuili
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