使用经典的全连接神经网络实现手写数字图片识别
库函数介绍
- numpy ：python第三方库，用于进行科学计算
- PIL ：Python Image Library,python第三方图像处理库
- matplotlib：python的绘图库 pyplot:matplotlib的绘图框架
- os ：提供了丰富的方法来处理文件和目录
#导入需要的包 import numpy as np import paddle import paddle.nn as nn from PIL import Image import matplotlib.pyplot as plt import os print("本教程基于Paddle的版本号为："+paddle.__version__)
Step1：准备数据。
(1)数据集介绍
MNIST数据集包含60000个训练集和10000测试数据集。分为图片和标签，图片是28*28的像素矩阵，标签为0~9共10个数字。
(2)transform函数是定义了一个归一化标准化的标准
(3)train_dataset和test_dataset
paddle.vision.datasets.MNIST()中的mode='train'和mode='test'分别用于获取mnist训练集和测试集
transform=transform参数则为归一化标准
#导入数据集Compose的作用是将用于数据集预处理的接口以列表的方式进行组合。 #导入数据集Normalize的作用是图像归一化处理，支持两种方式： 1. 用统一的均值和标准差值对图像的每个通道进行归一化处理； 2. 对每个通道指定不同的均值和标准差值进行归一化处理。 from paddle.vision.transforms import Compose, Normalize transform = Compose([Normalize(mean=[127.5],std=[127.5],data_format='CHW')]) # 使用transform对数据集做归一化 print('下载并加载训练数据') train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform) test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=transform) print('加载完成')
下载并加载训练数据 Cache file /home/aistudio/.cache/paddle/dataset/mnist/train-images-idx3-ubyte.gz not found, downloading https://dataset.bj.bcebos.com/mnist/train-images-idx3-ubyte.gz Begin to download Download finished Cache file /home/aistudio/.cache/paddle/dataset/mnist/train-labels-idx1-ubyte.gz not found, downloading https://dataset.bj.bcebos.com/mnist/train-labels-idx1-ubyte.gz Begin to download ........ Download finished Cache file /home/aistudio/.cache/paddle/dataset/mnist/t10k-images-idx3-ubyte.gz not found, downloading https://dataset.bj.bcebos.com/mnist/t10k-images-idx3-ubyte.gz Begin to download Download finished Cache file /home/aistudio/.cache/paddle/dataset/mnist/t10k-labels-idx1-ubyte.gz not found, downloading https://dataset.bj.bcebos.com/mnist/t10k-labels-idx1-ubyte.gz Begin to download .. Download finished 加载完成
# 输出图片 train_data0, train_label_0 = train_dataset[0][0],train_dataset[0][1] train_data0 = train_data0.reshape([28,28]) plt.figure(figsize=(2,2)) print(plt.imshow(train_data0, cmap=plt.cm.binary)) print('train_data0 的标签为: ' + str(train_label_0))
AxesImage(18,18;111.6x108.72) train_data0 的标签为: [5] /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2349: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working if isinstance(obj, collections.Iterator): /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2366: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working return list(data) if isinstance(data, collections.MappingView) else data /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/image.py:425: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead a_min = np.asscalar(a_min.astype(scaled_dtype)) /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/image.py:426: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead a_max = np.asscalar(a_max.astype(scaled_dtype))
# print(train_data0)
Step2.网络配置
以下的代码判断就是定义一个简单的全连接神经网络，本示例一共有三层，两个大小为128的隐层和一个大小为10的输出层。
MNIST数据集是手写0到9的灰度图像，类别有10个，所以最后的输出大小是10。
本实践的网络结构为：输入层-->>隐层-->>隐层-->>输出层。
# 定义全连接神经网络 class Mnist(nn.Layer): def __init__(self): super(Mnist,self).__init__() self.fc1 = nn.Linear(in_features = 28*28, out_features = 256) self.fc2 = nn.Linear(in_features = 256, out_features =128) self.fc3 = nn.Linear(in_features = 128, out_features = 10) def forward(self, input): bsz = input.shape[0] x = paddle.reshape(input,[bsz,-1]) x = nn.functional.relu(self.fc1(x)) x = nn.functional.relu(self.fc2(x)) y = self.fc3(x) return y
from paddle.metric import Accuracy # 用Model封装模型 model = paddle.Model(Mnist()) # 定义损失函数 optim = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters()) # 配置模型 model.prepare(optim,paddle.nn.CrossEntropyLoss(),Accuracy()) # 训练保存并验证模型 model.fit(train_dataset,test_dataset,epochs=2,batch_size=64,save_dir='model.pd',verbose=1)
W1204 13:02:37.934275 128 device_context.cc:362] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1 W1204 13:02:37.939237 128 device_context.cc:372] device: 0, cuDNN Version: 7.6. The loss value printed in the log is the current step, and the metric is the average value of previous step. Epoch 1/2 step 30/938 [..............................] - loss: 0.4677 - acc: 0.6531 - ETA: 17s - 20ms/st /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dataloader/dataloader_iter.py:89: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations if isinstance(slot[0], (np.ndarray, np.bool, numbers.Number)): /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:77: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working return (isinstance(seq, collections.Sequence) and step 50/938 [>.............................] - loss: 0.6765 - acc: 0.7234 - ETA: 12s - 14ms/stepstep 938/938 [==============================] - loss: 0.2223 - acc: 0.9111 - 7ms/step save checkpoint at /home/aistudio/model.pd/0 Eval begin... The loss value printed in the log is the current batch, and the metric is the average value of previous step. step 157/157 [==============================] - loss: 0.0849 - acc: 0.9543 - 6ms/step Eval samples: 10000 Epoch 2/2 step 938/938 [==============================] - loss: 0.0974 - acc: 0.9580 - 6ms/step save checkpoint at /home/aistudio/model.pd/1 Eval begin... The loss value printed in the log is the current batch, and the metric is the average value of previous step. step 157/157 [==============================] - loss: 0.0055 - acc: 0.9571 - 6ms/step Eval samples: 10000 save checkpoint at /home/aistudio/model.pd/final
Step3.模型评估
res = model.evaluate(test_dataset,batch_size=64, verbose=1) print("测试集损失值：{}".format(res["loss"])) print("测试集准确率：{}".format(res["acc"]))
Eval begin... The loss value printed in the log is the current batch, and the metric is the average value of previous step. step 157/157 [==============================] - loss: 0.0055 - acc: 0.9571 - 6ms/step Eval samples: 10000 测试集损失值：[0.0054676016] 测试集准确率：0.9571
Step4.模型预测
#获取测试集的第一个图片 test_data0, test_label_0 = test_dataset[0][0],test_dataset[0][1] test_data0 = test_data0.reshape([28,28]) plt.figure(figsize=(2,2)) #展示测试集中的第一个图片 print(plt.imshow(test_data0, cmap=plt.cm.binary)) print('test_data0 的标签为: ' + str(test_label_0)) #模型预测 result = model.predict(test_dataset, batch_size=1) #打印模型预测的结果 print('test_data0 预测的数值为：%d' % np.argsort(result[0][0])[0][-1])
AxesImage(18,18;111.6x108.72) test_data0 的标签为: [7] Predict begin... step 10000/10000 [==============================] - 1ms/step Predict samples: 10000 test_data0 预测的数值为：7
