PaddlePaddle图像分类神经网络构建正则化笔记
本文主要根据第二次作业进行分析
作业要求：
补全网络代码，并运行手写数字识别项目。以出现最后的图片和预测结果为准。
首先导入必要的包
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
from PIL import Image
import matplotlib.pyplot as plt
import os
import paddle
print("本教程基于Paddle的版本号为："+paddle.__version__)
本教程基于Paddle的版本号为：2.0.1
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
加载完成#让我们一起看看数据集中的图片是什么样子的
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/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead
'a.item() instead', DeprecationWarning, stacklevel=1)
#让我们再来看看数据样子是什么样的吧
print(train_data0)
[[-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. ]
[-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. ]
[-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. ]
[-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. ]
[-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. ]
[-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-0.9764706 -0.85882354 -0.85882354 -0.85882354 -0.01176471 0.06666667
0.37254903 -0.79607844 0.3019608 1. 0.9372549 -0.00392157
-1. -1. -1. -1. ]
[-1. -1. -1. -1. -1. -1.
-1. -1. -0.7647059 -0.7176471 -0.2627451 0.20784314
0.33333334 0.9843137 0.9843137 0.9843137 0.9843137 0.9843137
0.7647059 0.34901962 0.9843137 0.8980392 0.5294118 -0.49803922
-1. -1. -1. -1. ]
[-1. -1. -1. -1. -1. -1.
-1. -0.6156863 0.8666667 0.9843137 0.9843137 0.9843137
0.9843137 0.9843137 0.9843137 0.9843137 0.9843137 0.96862745
-0.27058825 -0.35686275 -0.35686275 -0.56078434 -0.69411767 -1.
-1. -1. -1. -1. ]
[-1. -1. -1. -1. -1. -1.
-1. -0.85882354 0.7176471 0.9843137 0.9843137 0.9843137
0.9843137 0.9843137 0.5529412 0.42745098 0.9372549 0.8901961
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. ]
[-1. -1. -1. -1. -1. -1.
-1. -1. -0.37254903 0.22352941 -0.16078432 0.9843137
0.9843137 0.60784316 -0.9137255 -1. -0.6627451 0.20784314
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. ]
[-1. -1. -1. -1. -1. -1.
-1. -1. -1. -0.8901961 -0.99215686 0.20784314
0.9843137 -0.29411766 -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. ]
[-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. 0.09019608
0.9843137 0.49019608 -0.9843137 -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. ]
[-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -0.9137255
0.49019608 0.9843137 -0.4509804 -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. ]
[-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-0.7254902 0.8901961 0.7647059 0.25490198 -0.15294118 -0.99215686
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. ]
[-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -0.3647059 0.88235295 0.9843137 0.9843137 -0.06666667
-0.8039216 -1. -1. -1. -1. -1.
-1. -1. -1. -1. ]
[-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -0.64705884 0.45882353 0.9843137 0.9843137
0.1764706 -0.7882353 -1. -1. -1. -1.
-1. -1. -1. -1. ]
[-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -0.8745098 -0.27058825 0.9764706
0.9843137 0.46666667 -1. -1. -1. -1.
-1. -1. -1. -1. ]
[-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. 0.9529412
0.9843137 0.9529412 -0.49803922 -1. -1. -1.
-1. -1. -1. -1. ]
[-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -0.6392157 0.01960784 0.43529412 0.9843137
0.9843137 0.62352943 -0.9843137 -1. -1. -1.
-1. -1. -1. -1. ]
[-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-0.69411767 0.16078432 0.79607844 0.9843137 0.9843137 0.9843137
0.9607843 0.42745098 -1. -1. -1. -1.
-1. -1. -1. -1. ]
[-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -0.8117647 -0.10588235
0.73333335 0.9843137 0.9843137 0.9843137 0.9843137 0.5764706
-0.3882353 -1. -1. -1. -1. -1.
-1. -1. -1. -1. ]
[-1. -1. -1. -1. -1. -1.
-1. -1. -0.81960785 -0.48235294 0.67058825 0.9843137
0.9843137 0.9843137 0.9843137 0.5529412 -0.3647059 -0.9843137
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. ]
[-1. -1. -1. -1. -1. -1.
-0.85882354 0.34117648 0.7176471 0.9843137 0.9843137 0.9843137
0.9843137 0.5294118 -0.37254903 -0.92941177 -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. ]
[-1. -1. -1. -1. -0.5686275 0.34901962
0.77254903 0.9843137 0.9843137 0.9843137 0.9843137 0.9137255
0.04313726 -0.9137255 -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. ]
[-1. -1. -1. -1. 0.06666667 0.9843137
0.9843137 0.9843137 0.6627451 0.05882353 0.03529412 -0.8745098
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. ]
[-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. ]
[-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. ]
[-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. ]]Step2.网络配置
以下的代码判断就是定义一个简单的多层感知器，一共有三层，两个大小为100的隐层和一个大小为10的输出层，因为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。最后输出层的激活函数是Softmax，所以最后的输出层相当于一个分类器。加上一个输入层的话，多层感知器的结构是：输入层-->>隐层-->>隐层-->>输出层。
重头戏开始了！！！！
构建的每一个步骤都去查了查官方的API文档说明，大致了解了构建神经网络的过程。
问题最大的就在于“正则化”这个概念：
正则化简单来说就是找一条曲线去贴合坐标系的点，当点逐渐多的时候，曲线的契合度如果过高会导致预测结果没有普遍性（由于点的离散程度不同）
如上图，在拟合的过程中我们要保证贴合效果，需要“舍弃”一些点，那么到底舍弃多少呢？我选择了20%的点
paddle.nn.Dropout（）默认是0.5，也就是舍弃50%的点去进行拟合，从而达到预测效果
这里附上大佬写的正则化详解:【直观详解】什么是正则化 https://blog.csdn.net/gqixf/article/details/85319510
# 定义多层感知器
#动态图定义多层感知器
class multilayer_perceptron(paddle.nn.Layer):
def __init__(self):
super(multilayer_perceptron,self).__init__()
#请在这里补全网络代码
self.flatten = paddle.nn.Flatten()#将一个连续维度的Tensor展平成一维Tensor
self.linear_1 = paddle.nn.Linear(784, 128)##第一层感知机
self.linear_2 = paddle.nn.Linear(128, 10)##第二层感知机
self.relu = paddle.nn.ReLU()##激活层
self.dropout = paddle.nn.Dropout(0.2)##正则化，归零率为0.5
def forward(self, x):
#请在这里补全传播过程的代码
y = self.flatten(x)
y = self.linear_1(y)
y = self.relu(y)
y = self.dropout(y)
y = self.linear_2(y)
return y
##自定义感知网络#请在这里定义卷积网络的代码
#注意：定义完成卷积的代码后，后面的代码是需要修改的！
LeNet = multilayer_perceptron()# 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='multilayer_perceptron',verbose=1)
运行时长: 15秒660毫秒
结束时间: 2021-03-12 09:56:12
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: 0.9485 - acc: 0.5526 - ETA: 7s - 8ms/step
/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 938/938 [==============================] - loss: 0.2986 - acc: 0.8793 - 7ms/step
save checkpoint at /home/aistudio/multilayer_perceptron/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: 0.0635 - acc: 0.9431 - 6ms/step
Eval samples: 10000
Epoch 2/2
step 938/938 [==============================] - loss: 0.1512 - acc: 0.9350 - 8ms/step
save checkpoint at /home/aistudio/multilayer_perceptron/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: 0.0231 - acc: 0.9546 - 6ms/step
Eval samples: 10000
save checkpoint at /home/aistudio/multilayer_perceptron/final# 训练保存并验证模型
model.fit(train_dataset,test_dataset,epochs=2,batch_size=64,save_dir='multilayer_perceptron',verbose=1)
运行时长: 17秒487毫秒
结束时间: 2021-03-12 09:56:30
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 938/938 [==============================] - loss: 0.1907 - acc: 0.9479 - 10ms/step
save checkpoint at /home/aistudio/multilayer_perceptron/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: 0.0292 - acc: 0.9619 - 6ms/step
Eval samples: 10000
Epoch 2/2
step 938/938 [==============================] - loss: 0.0512 - acc: 0.9520 - 7ms/step
save checkpoint at /home/aistudio/multilayer_perceptron/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: 0.0102 - acc: 0.9659 - 6ms/step
Eval samples: 10000
save checkpoint at /home/aistudio/multilayer_perceptron/final#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
运行时长: 12秒828毫秒
结束时间: 2021-03-12 09:56:42
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
写在最后：
一直想学习一些深度学习的东西，感觉这个图像分类课程很值得学习
附上课程连接：
https://aistudio.baidu.com/aistudio/course/introduce/11939?directly=1&shared=1