百度飞桨(PaddlePaddle)-数字识别

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)
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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)
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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)
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  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)
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Collecting pyparsing>=2.3.1
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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)
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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>

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

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])