目录
补充知识
python类里面的魔法方法
transforms.Resize()
python里面的OS库
BP实战之猫狗分类数据集
猫狗数据集
注意事项
使用类创建自己的猫狗分类数据集
代码
实例化对象尝试
代码
结果
利用DataLoader加载数据集
BP神经网络的搭建以及对象的使用
运行结果的可视化
完整代码
完整代码运行结果以及图像显示
本次实战总结
补充知识
python类里面的魔法方法
本次我们只是提一下我们这次要用到的，也是常用的魔法方法，至于具体详情可以看这篇博客非常全的通俗易懂 Python 魔法方法指南（上） - 知乎 (zhihu.com)
关于这个__getitem__方法需要提一下，看代码：
class A:
def __init__(self,a,b):
self.a=a
self.b=b
def __getitem__(self, item):
return 0
def __len__(self):
return 2
a=A(1,3)
print(a['154545'])
它就相当于让类成为一个类似字典得容器，我们可以用它做很多事情。
至于__len__方法就是用来返回容器的长度或者说返回容器里面元素得个数（当然这个个数是我们自己写好的代码跑出来的）。
__init__方法就是我们很常见的方法，我们可以在实例化对象的同时来调用它。
要注意这个方法没办法有返回值
transforms.Resize()
它可以更改PIL类型的图片数据的大小，本质上其实更改的是像素多少。下面给出代码示例
import d2l.torch as d2l
import math
import torch
from torch.utils.data import DataLoader
import torchvision
from torchvision import transforms
from torch.utils.data import DataLoader
import numpy as np
from PIL import Image
transform=transforms.Compose([transforms.Resize(224)])
path = "D:\浏览器下载\-_(゜-゜)つロ___-_Powered_by_Discuz!\DM_20231006172330_026.jpg"
image = Image.open(path)
b=np.array(image)
# a=np.array(image)
a=transform(image)
a=np.array(a)
print('修剪之前：',b)
print('修建过后:',a)
print('修建之前的大小',b.shape)
print("修建过后得大小",a.shape)
image.show()
a=Image.fromarray(a)
a.show()
D:\Anaconda3\envs\pytorch\python.exe D:\learn_pytorch\prictice.py
修剪之前： [[[159 131 107]
[152 128 104]
[148 126 105]
...
[ 48 34 21]
[ 41 34 26]
[ 50 49 45]]
[[153 125 101]
[147 123 99]
[142 120 99]
...
[ 35 21 8]
[ 23 16 8]
[ 29 28 24]]
[[151 123 99]
[144 120 96]
[139 117 96]
...
[ 36 22 9]
[ 27 20 12]
[ 35 34 30]]
...
[[150 102 54]
[147 99 51]
[144 96 48]
...
[119 76 33]
[117 74 32]
[125 82 40]]
[[153 105 57]
[151 103 55]
[148 100 52]
...
[128 85 43]
[126 83 41]
[131 88 46]]
[[163 112 65]
[162 111 64]
[159 108 61]
...
[142 99 57]
[143 100 58]
[150 107 65]]]
修建过后: [[[151 125 102]
[143 122 101]
[140 115 91]
...
[128 100 77]
[ 68 50 34]
[ 36 30 23]]
[[147 122 98]
[139 118 97]
[136 111 87]
...
[125 97 73]
[ 64 46 30]
[ 30 25 18]]
[[147 122 98]
[139 117 96]
[137 112 88]
...
[127 99 74]
[ 72 54 37]
[ 31 26 19]]
...
[[149 102 53]
[147 99 51]
[147 99 51]
...
[125 80 38]
[126 83 40]
[134 91 49]]
[[149 101 53]
[146 98 50]
[146 98 50]
...
[126 82 40]
[123 80 38]
[125 82 40]]
[[155 106 58]
[152 102 55]
[153 103 56]
...
[136 93 50]
[134 91 49]
[134 91 49]]]
修建之前的大小 (656, 460, 3)
修建过后得大小 (319, 224, 3)
进程已结束,退出代码0
可以看到明显的变化，其实我们眼中看到的在电脑里是“变小了”可实际上它的尺寸并没有变，这个就是涉及到分辨率和像素还有尺寸的关系，感兴趣可以多搜一搜。
python里面的OS库
可能会有些看不清楚，方法就可以了，接下来用代码展示一些 常用的操作
import torch
from torch.utils.data import DataLoader
import torchvision
from torchvision import transforms
from torch.utils.data import DataLoader
import numpy as np
from PIL import Image
import os
path='D:\learn_pytorch\数据集\PetImages'
a=os.listdir(path)
print(a)
for x in a:
print(os.path.join(path,x))
D:\Anaconda3\envs\pytorch\python.exe D:\learn_pytorch\prictice.py
['Cat', 'Dog']
D:\learn_pytorch\数据集\PetImages\Cat
D:\learn_pytorch\数据集\PetImages\Dog
进程已结束,退出代码0
BP实战之猫狗分类数据集
猫狗数据集
下载连接：
官网：Dogs vs. Cats | Kaggle
微软直接下载：从官方Microsoft下载中心下载 Kaggle 猫和狗数据集
数据集介绍：
Kaggle
https://so.csdn.net/so/search?q=Kaggle&spm=1001.2101.3001.7020该数据集是Kaggle在2013年公开的猫狗数据集，该数据集总共25000张图片，猫狗各12500张。
我们下载的只是最基本的图片数据，还需要自行的创建我们的数据集，一般而言我们都是通过创建类的方法来实现。
注意事项
应为这是当时竞赛给的数据，所以在进行数据处理的时候可能会出现图片数据损坏，或者图片数据的形状，维度不太对劲等问题，这个时候就需要我们仔细地进行图片清晰，我采取的做法是利用os库里的库函数直接把错误的数据删除掉。
使用类创建自己的猫狗分类数据集
我们要用到三个方法：
__init__（），__getitem__（），__len__（）
以及os库里面的库函数
这些上面都已经讲述过具体的用法，这里就不多加赘述。
代码
class catanddog(Dataset):
def __init__(self,rootpath,label_dir):
self.rootpath=rootpath
self.label_dir=label_dir
self.path=os.path.join(rootpath,label_dir)
self.imge_all=os.listdir(self.path)
self.transform=transforms.Compose([transforms.Resize((224,224)),transforms.ToTensor()])
def __getitem__(self, item):
imge_name=self.imge_all[item]
imge=Image.open(os.path.join(self.path,imge_name))
imge=self.transform(imge)
if self.label_dir=='Cat':
target=0
else:
target=1
if imge.shape[0]!=3:
print(self.imge_all[item],target)
os.remove(os.path.join(self.path,imge_name))
return imge,target
def __len__(self):
return len(self.imge_all)
rootpath='D:\learn_pytorch\数据集\PetImages'
test_rootpath='D:\learn_pytorch\数据集\PetImages\Test'
cat='Cat'#标签对应0
dog='Dog'#标签对应1
catdatasets=catanddog(rootpath,cat)#猫的数据集
#print(len(catdatasets))#12500
dogdatasets=catanddog(rootpath,dog)#狗的数据集
testcat=catanddog(test_rootpath,cat)
testdog=catanddog(test_rootpath,dog)
traindata=catdatasets+dogdatasets
testdata=testcat+testdog __init__（）方法下面主要是用来通过文件夹路径找到文件夹里面的文件，然后得到一个由文件夹里面文件的名字组成的列表。
__getitem()__（）方法下面主要是通过由文件夹里面问价的名字组成的列表里面的元素，来和之前传入的文件夹的路径拼接得到文件路径，从而得到文件数据，并且通过传入参数来制作文件对应的标签，同时该方法里面内涵迭代器方法，所以这样让该类的实例化对象可以迭代。
__len__(）方法下面就是发挥有多少数据。
实例化对象尝试
代码
import matplotlib.pyplot as plt
from matplotlib import font_manager
print('猫狗分类数据集---------------------')
import torch
from torch.utils.data import DataLoader, Dataset
import torchvision
from torchvision import transforms
from PIL import Image
import os
#使用类创建自己的猫狗分类数据集
class catanddog(Dataset):
def __init__(self,rootpath,label_dir):
self.rootpath=rootpath
self.label_dir=label_dir
self.path=os.path.join(rootpath,label_dir)
self.imge_all=os.listdir(self.path)
self.transform=transforms.Compose([transforms.Resize((224,224)),transforms.ToTensor()])
def __getitem__(self, item):
imge_name=self.imge_all[item]
imge=Image.open(os.path.join(self.path,imge_name))
imge=self.transform(imge)
if self.label_dir=='Cat':
target=0
else:
target=1
if imge.shape[0]!=3:
print(self.imge_all[item],target)
os.remove(os.path.join(self.path,imge_name))
return imge,target
def __len__(self):
return len(self.imge_all)
rootpath='D:\learn_pytorch\数据集\PetImages'
test_rootpath='D:\learn_pytorch\数据集\PetImages\Test'
cat='Cat'#标签对应0
dog='Dog'#标签对应1
catdatasets=catanddog(rootpath,cat)#猫的数据集
#print(len(catdatasets))#12500
dogdatasets=catanddog(rootpath,dog)#狗的数据集
testcat=catanddog(test_rootpath,cat)
testdog=catanddog(test_rootpath,dog)
traindata=catdatasets+dogdatasets
testdata=testcat+testdog
print(traindata[12500])
print(testdata[0])
for x , y in testdata:
print(x,y)
pass
print(len(testdog))
print(len(testcat))结果
D:\Anaconda3\envs\pytorch\python.exe D:\learn_pytorch\prictice.py
猫狗分类数据集---------------------
(tensor([[[0.9843, 0.9843, 0.9843, ..., 0.6314, 0.6588, 0.6196],
[0.9882, 0.9843, 0.9922, ..., 0.6627, 0.6314, 0.6235],
[1.0000, 1.0000, 1.0000, ..., 0.6706, 0.6510, 0.6510],
...,
[0.5804, 0.5098, 0.5098, ..., 0.4980, 0.6314, 0.7451],
[0.5176, 0.5529, 0.5216, ..., 0.4941, 0.6196, 0.6980],
[0.5137, 0.5569, 0.4706, ..., 0.5294, 0.5725, 0.6824]],
[[0.9765, 0.9765, 0.9765, ..., 0.4784, 0.5020, 0.4588],
[0.9529, 0.9490, 0.9608, ..., 0.4667, 0.4627, 0.4667],
[0.9490, 0.9490, 0.9647, ..., 0.4392, 0.4706, 0.4863],
...,
[0.6392, 0.5490, 0.5608, ..., 0.6196, 0.7294, 0.8275],
[0.5804, 0.5961, 0.5686, ..., 0.5922, 0.6941, 0.7608],
[0.5765, 0.6000, 0.5216, ..., 0.6078, 0.6588, 0.7647]],
[[0.8392, 0.8392, 0.8392, ..., 0.3333, 0.4039, 0.3725],
[0.8353, 0.8314, 0.8431, ..., 0.3451, 0.3569, 0.3647],
[0.8471, 0.8471, 0.8627, ..., 0.3412, 0.3647, 0.3765],
...,
[0.3255, 0.2431, 0.2549, ..., 0.2392, 0.3373, 0.4471],
[0.2588, 0.2863, 0.2706, ..., 0.2314, 0.3176, 0.3922],
[0.2471, 0.2863, 0.2235, ..., 0.2627, 0.2863, 0.3922]]]), 1)
(tensor([[[0.2235, 0.2157, 0.2157, ..., 0.1765, 0.1961, 0.2039],
[0.2235, 0.2196, 0.2235, ..., 0.1765, 0.2000, 0.2039],
[0.2314, 0.2314, 0.2314, ..., 0.1725, 0.1922, 0.1961],
...,
[0.6275, 0.4902, 0.4235, ..., 0.6588, 0.6706, 0.5490],
[0.6235, 0.4196, 0.3647, ..., 0.5922, 0.6745, 0.6039],
[0.5843, 0.3373, 0.2745, ..., 0.6000, 0.6784, 0.6000]],
[[0.2118, 0.2039, 0.2039, ..., 0.1020, 0.1098, 0.1098],
[0.2118, 0.2078, 0.2118, ..., 0.1020, 0.1098, 0.1137],
[0.2196, 0.2196, 0.2196, ..., 0.1059, 0.1137, 0.1137],
...,
[0.6039, 0.4667, 0.3961, ..., 0.6510, 0.6667, 0.5451],
[0.6196, 0.4078, 0.3451, ..., 0.5843, 0.6706, 0.6000],
[0.5961, 0.3333, 0.2627, ..., 0.5922, 0.6745, 0.5961]],
[[0.1843, 0.1765, 0.1765, ..., 0.1176, 0.0980, 0.0941],
[0.1843, 0.1804, 0.1843, ..., 0.1098, 0.1020, 0.0980],
[0.1922, 0.1922, 0.1922, ..., 0.1098, 0.1020, 0.0980],
...,
[0.4824, 0.3922, 0.3647, ..., 0.6588, 0.6941, 0.5765],
[0.5098, 0.3490, 0.3255, ..., 0.5882, 0.6941, 0.6314],
[0.5020, 0.2902, 0.2588, ..., 0.5961, 0.6980, 0.6275]]]), 0)
tensor([[[0.2235, 0.2157, 0.2157, ..., 0.1765, 0.1961, 0.2039],
[0.2235, 0.2196, 0.2235, ..., 0.1765, 0.2000, 0.2039],
[0.2314, 0.2314, 0.2314, ..., 0.1725, 0.1922, 0.1961],
...,
[0.6275, 0.4902, 0.4235, ..., 0.6588, 0.6706, 0.5490],
[0.6235, 0.4196, 0.3647, ..., 0.5922, 0.6745, 0.6039],
[0.5843, 0.3373, 0.2745, ..., 0.6000, 0.6784, 0.6000]],
[[0.2118, 0.2039, 0.2039, ..., 0.1020, 0.1098, 0.1098],
[0.2118, 0.2078, 0.2118, ..., 0.1020, 0.1098, 0.1137],
[0.2196, 0.2196, 0.2196, ..., 0.1059, 0.1137, 0.1137],
...,
[0.6039, 0.4667, 0.3961, ..., 0.6510, 0.6667, 0.5451],
[0.6196, 0.4078, 0.3451, ..., 0.5843, 0.6706, 0.6000],
[0.5961, 0.3333, 0.2627, ..., 0.5922, 0.6745, 0.5961]],
[[0.1843, 0.1765, 0.1765, ..., 0.1176, 0.0980, 0.0941],
[0.1843, 0.1804, 0.1843, ..., 0.1098, 0.1020, 0.0980],
[0.1922, 0.1922, 0.1922, ..., 0.1098, 0.1020, 0.0980],
...,
[0.4824, 0.3922, 0.3647, ..., 0.6588, 0.6941, 0.5765],
[0.5098, 0.3490, 0.3255, ..., 0.5882, 0.6941, 0.6314],
[0.5020, 0.2902, 0.2588, ..., 0.5961, 0.6980, 0.6275]]]) 0
tensor([[[0.7608, 0.3373, 0.3569, ..., 0.5451, 0.5412, 0.5294],
[0.7490, 0.2745, 0.3020, ..., 0.3451, 0.3412, 0.3569],
[0.7529, 0.2863, 0.3059, ..., 0.2549, 0.2588, 0.2706],
...,
[0.8980, 0.7176, 0.7216, ..., 0.1059, 0.1020, 0.1059],
[0.8980, 0.7216, 0.7216, ..., 0.1020, 0.1020, 0.0980],
[0.9020, 0.7373, 0.7294, ..., 0.1020, 0.1020, 0.0980]],
[[0.7294, 0.2667, 0.2549, ..., 0.4549, 0.4510, 0.4431],
[0.7020, 0.1843, 0.1804, ..., 0.2902, 0.2863, 0.2980],
[0.7020, 0.1922, 0.1843, ..., 0.2510, 0.2510, 0.2588],
...,
[0.9176, 0.7412, 0.7490, ..., 0.1255, 0.1255, 0.1333],
[0.9176, 0.7451, 0.7490, ..., 0.1216, 0.1294, 0.1255],
[0.9216, 0.7608, 0.7608, ..., 0.1216, 0.1294, 0.1255]],
[[0.6902, 0.2196, 0.1961, ..., 0.3686, 0.3647, 0.3529],
[0.6510, 0.1255, 0.1059, ..., 0.2510, 0.2471, 0.2627],
[0.6471, 0.1294, 0.1176, ..., 0.2824, 0.2863, 0.2941],
...,
[0.8784, 0.6863, 0.6863, ..., 0.1098, 0.1098, 0.1137],
[0.8745, 0.6941, 0.6784, ..., 0.1020, 0.1020, 0.1020],
[0.8784, 0.7059, 0.6863, ..., 0.0980, 0.1020, 0.0980]]]) 0
tensor([[[0.3176, 0.3176, 0.3176, ..., 0.1333, 0.1333, 0.1333],
[0.3176, 0.3176, 0.3176, ..., 0.1333, 0.1333, 0.1333],
[0.3176, 0.3176, 0.3176, ..., 0.1333, 0.1333, 0.1333],
...,
[0.0549, 0.0549, 0.0549, ..., 0.0314, 0.0314, 0.0314],
[0.0549, 0.0549, 0.0549, ..., 0.0314, 0.0314, 0.0314],
[0.0549, 0.0549, 0.0549, ..., 0.0314, 0.0314, 0.0314]],
[[0.1451, 0.1451, 0.1451, ..., 0.1490, 0.1490, 0.1490],
[0.1451, 0.1451, 0.1451, ..., 0.1490, 0.1490, 0.1490],
[0.1451, 0.1451, 0.1451, ..., 0.1490, 0.1490, 0.1490],
...,
[0.0196, 0.0196, 0.0235, ..., 0.0431, 0.0431, 0.0431],
[0.0196, 0.0196, 0.0235, ..., 0.0431, 0.0431, 0.0431],
[0.0196, 0.0196, 0.0235, ..., 0.0431, 0.0431, 0.0431]],
[[0.1098, 0.1098, 0.1098, ..., 0.1529, 0.1529, 0.1529],
[0.1098, 0.1098, 0.1098, ..., 0.1529, 0.1529, 0.1529],
[0.1098, 0.1098, 0.1098, ..., 0.1529, 0.1529, 0.1529],
...,
[0.0314, 0.0314, 0.0353, ..., 0.0784, 0.0784, 0.0784],
[0.0314, 0.0314, 0.0353, ..., 0.0784, 0.0784, 0.0784],
[0.0314, 0.0314, 0.0353, ..., 0.0784, 0.0784, 0.0784]]]) 0
tensor([[[0.9725, 0.9765, 0.9765, ..., 0.5098, 0.4980, 0.4902],
[0.9725, 0.9765, 0.9804, ..., 0.5294, 0.5098, 0.5020],
[0.9765, 0.9765, 0.9804, ..., 0.5490, 0.5255, 0.5137],
...,
[0.4039, 0.4941, 0.3765, ..., 0.5608, 0.6118, 0.6902],
[0.4000, 0.4941, 0.3529, ..., 0.4235, 0.4275, 0.4549],
[0.4118, 0.4471, 0.3961, ..., 0.6196, 0.5373, 0.3882]],
[[0.9725, 0.9765, 0.9765, ..., 0.6275, 0.6235, 0.6196],
[0.9725, 0.9765, 0.9804, ..., 0.6235, 0.6118, 0.6000],
[0.9765, 0.9765, 0.9804, ..., 0.6157, 0.5961, 0.5765],
...,
[0.6392, 0.6431, 0.5647, ..., 0.7255, 0.8471, 0.8824],
[0.6196, 0.6431, 0.5294, ..., 0.6627, 0.7373, 0.7176],
[0.6314, 0.6039, 0.5529, ..., 0.8745, 0.8118, 0.6275]],
[[0.9725, 0.9765, 0.9765, ..., 0.6353, 0.6392, 0.6353],
[0.9725, 0.9765, 0.9804, ..., 0.6392, 0.6275, 0.6196],
[0.9765, 0.9765, 0.9804, ..., 0.6353, 0.6157, 0.6000],
...,
[0.6706, 0.7020, 0.6196, ..., 0.8902, 0.9490, 0.9765],
[0.6549, 0.6941, 0.5843, ..., 0.8196, 0.8745, 0.8667],
[0.6588, 0.6431, 0.6157, ..., 0.9569, 0.9255, 0.7765]]]) 0
tensor([[[0.6549, 0.6863, 0.6784, ..., 0.4941, 0.5020, 0.5098],
[0.6667, 0.6824, 0.6824, ..., 0.4902, 0.4902, 0.4863],
[0.6980, 0.6941, 0.6941, ..., 0.4902, 0.4980, 0.4745],
...,
[0.6588, 0.6314, 0.5725, ..., 0.5373, 0.5020, 0.5059],
[0.6510, 0.6314, 0.5686, ..., 0.5529, 0.5294, 0.5373],
[0.3255, 0.3294, 0.2902, ..., 0.2902, 0.2784, 0.2902]],
[[0.5373, 0.5686, 0.5647, ..., 0.4353, 0.4431, 0.4510],
[0.5490, 0.5686, 0.5686, ..., 0.4314, 0.4353, 0.4235],
[0.5843, 0.5843, 0.5804, ..., 0.4314, 0.4314, 0.4118],
...,
[0.5686, 0.5451, 0.4980, ..., 0.4314, 0.4157, 0.4353],
[0.5647, 0.5490, 0.4980, ..., 0.4471, 0.4431, 0.4627],
[0.2392, 0.2471, 0.2196, ..., 0.1843, 0.1922, 0.2118]],
[[0.4392, 0.4667, 0.4549, ..., 0.3098, 0.3451, 0.3804],
[0.4392, 0.4510, 0.4431, ..., 0.3098, 0.3451, 0.3608],
[0.4549, 0.4431, 0.4353, ..., 0.3216, 0.3569, 0.3529],
...,
[0.4118, 0.4078, 0.3882, ..., 0.3804, 0.3608, 0.3765],
[0.3843, 0.3922, 0.3725, ..., 0.4000, 0.3922, 0.4078],
[0.0471, 0.0706, 0.0863, ..., 0.1412, 0.1451, 0.1647]]]) 0
tensor([[[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.]],
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tensor([[[0.5804, 0.5490, 0.5451, ..., 0.5294, 0.5294, 0.5333],
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tensor([[[0.5216, 0.4706, 0.4000, ..., 0.6196, 0.7373, 0.8157],
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tensor([[[0.3020, 0.3020, 0.3098, ..., 0.0706, 0.0706, 0.0706],
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tensor([[[0.6196, 0.6157, 0.6118, ..., 0.6471, 0.6431, 0.6431],
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tensor([[[0.7569, 0.7647, 0.7608, ..., 0.7725, 0.7529, 0.7294],
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[[0.2667, 0.2824, 0.2941, ..., 0.2275, 0.2392, 0.2353],
[0.2902, 0.2980, 0.3059, ..., 0.2314, 0.2431, 0.2392],
[0.2706, 0.2863, 0.3020, ..., 0.2431, 0.2471, 0.2471],
...,
[0.0745, 0.0745, 0.0745, ..., 0.1490, 0.1686, 0.1804],
[0.0745, 0.0745, 0.0745, ..., 0.1451, 0.1608, 0.1725],
[0.0667, 0.0667, 0.0667, ..., 0.1333, 0.1529, 0.1608]]]) 1
tensor([[[0.2824, 0.2902, 0.2824, ..., 0.4314, 0.4235, 0.3961],
[0.2824, 0.2549, 0.2549, ..., 0.3216, 0.3373, 0.3294],
[0.4118, 0.3490, 0.3529, ..., 0.2627, 0.2706, 0.2627],
...,
[0.1686, 0.1725, 0.1765, ..., 0.0706, 0.0745, 0.0941],
[0.2275, 0.2078, 0.1961, ..., 0.0706, 0.0706, 0.0745],
[0.2275, 0.1922, 0.1725, ..., 0.0824, 0.0706, 0.0667]],
[[0.4235, 0.4314, 0.4275, ..., 0.5686, 0.5529, 0.5255],
[0.4078, 0.3843, 0.3843, ..., 0.4510, 0.4627, 0.4510],
[0.5255, 0.4745, 0.4784, ..., 0.3804, 0.3882, 0.3843],
...,
[0.2275, 0.2392, 0.2549, ..., 0.1569, 0.1412, 0.1569],
[0.2980, 0.2784, 0.2706, ..., 0.1529, 0.1412, 0.1373],
[0.3098, 0.2667, 0.2431, ..., 0.1608, 0.1373, 0.1294]],
[[0.1843, 0.1961, 0.1922, ..., 0.3176, 0.3373, 0.3176],
[0.2196, 0.1961, 0.2000, ..., 0.2471, 0.2706, 0.2667],
[0.3255, 0.2706, 0.2745, ..., 0.2510, 0.2392, 0.2314],
...,
[0.1255, 0.1294, 0.1412, ..., 0.1176, 0.1020, 0.1137],
[0.1765, 0.1608, 0.1608, ..., 0.1216, 0.1020, 0.0941],
[0.1647, 0.1412, 0.1333, ..., 0.1294, 0.1020, 0.0902]]]) 1
tensor([[[0.2745, 0.2392, 0.1529, ..., 0.8157, 0.8196, 0.8118],
[0.2784, 0.2235, 0.1569, ..., 0.8196, 0.8314, 0.8275],
[0.2627, 0.1882, 0.1608, ..., 0.8275, 0.8353, 0.8275],
...,
[0.4706, 0.4784, 0.4902, ..., 0.3373, 0.3137, 0.3098],
[0.4784, 0.4824, 0.4980, ..., 0.3373, 0.3176, 0.3098],
[0.4706, 0.4627, 0.4627, ..., 0.3373, 0.3216, 0.3176]],
[[0.1569, 0.1451, 0.1020, ..., 0.8353, 0.8353, 0.8275],
[0.1686, 0.1373, 0.1059, ..., 0.8392, 0.8471, 0.8431],
[0.1608, 0.1098, 0.1137, ..., 0.8471, 0.8510, 0.8431],
...,
[0.4392, 0.4431, 0.4510, ..., 0.1451, 0.1412, 0.1412],
[0.4431, 0.4471, 0.4588, ..., 0.1412, 0.1333, 0.1333],
[0.4353, 0.4235, 0.4235, ..., 0.1412, 0.1451, 0.1451]],
[[0.0706, 0.0824, 0.0627, ..., 0.8471, 0.8471, 0.8392],
[0.0863, 0.0706, 0.0627, ..., 0.8510, 0.8588, 0.8549],
[0.0824, 0.0471, 0.0745, ..., 0.8588, 0.8627, 0.8549],
...,
[0.3647, 0.3765, 0.4000, ..., 0.0471, 0.0471, 0.0510],
[0.3765, 0.3843, 0.4078, ..., 0.0431, 0.0431, 0.0431],
[0.3686, 0.3686, 0.3765, ..., 0.0431, 0.0510, 0.0510]]]) 1
tensor([[[0.8000, 0.9176, 0.9451, ..., 0.4706, 0.4784, 0.4745],
[0.7686, 0.8275, 0.9176, ..., 0.4588, 0.4627, 0.4667],
[0.5608, 0.7451, 0.8039, ..., 0.4627, 0.4745, 0.4706],
...,
[0.4353, 0.4353, 0.4275, ..., 0.1922, 0.2118, 0.2863],
[0.4196, 0.4196, 0.4157, ..., 0.3333, 0.3490, 0.3176],
[0.4078, 0.4157, 0.4078, ..., 0.4196, 0.3608, 0.2471]],
[[0.8275, 0.9373, 0.9608, ..., 0.6275, 0.6353, 0.6314],
[0.8000, 0.8549, 0.9373, ..., 0.6157, 0.6196, 0.6314],
[0.5922, 0.7765, 0.8275, ..., 0.6196, 0.6314, 0.6431],
...,
[0.1176, 0.1216, 0.1137, ..., 0.3373, 0.3490, 0.4039],
[0.1216, 0.1216, 0.1176, ..., 0.4471, 0.4471, 0.4157],
[0.1137, 0.1216, 0.1137, ..., 0.5216, 0.4510, 0.3412]],
[[0.8314, 0.9490, 0.9882, ..., 0.7255, 0.7333, 0.7294],
[0.8078, 0.8667, 0.9647, ..., 0.7137, 0.7176, 0.7255],
[0.6078, 0.7961, 0.8588, ..., 0.7176, 0.7294, 0.7373],
...,
[0.2039, 0.2078, 0.2196, ..., 0.1098, 0.1765, 0.2118],
[0.2039, 0.2039, 0.2039, ..., 0.2431, 0.2706, 0.2314],
[0.1961, 0.2039, 0.1961, ..., 0.3255, 0.2706, 0.1608]]]) 1
90
90
进程已结束,退出代码0
结果还是蛮对的。
利用DataLoader加载数据集
trainload=DataLoader(dataset=traindata,shuffle=True,batch_size=64)
testLoad=DataLoader(dataset=testdata,shuffle=False,batch_size=16)这个在深度学习之使用BP神经网络识别MNIST数据集-CSDN博客 里面介绍了，这里就不多说了。
BP神经网络的搭建以及对象的使用
同样的这里的搭建原理在深度学习之使用BP神经网络识别MNIST数据集-CSDN博客里面已经阐述过，这里就不多加赘述了。
#搭建bp神经网络
class BPNetwork(torch.nn.Module):
def __init__(self):
super(BPNetwork,self).__init__()
#我们的每张图片都是224*224*3个像素点
#第一个隐藏层
self.linear1=torch.nn.Linear(224*224*3,128)
#激活函数，这里选择Relu
self.relu1=torch.nn.ReLU()
#第二个隐藏层
self.linear2=torch.nn.Linear(128,64)
#激活函数
self.relu2=torch.nn.ReLU()
#第三个隐藏层:
self.linear3=torch.nn.Linear(64,32)
# 激活函数
self.relu3 = torch.nn.ReLU()
#第四个隐藏层
self.linear4=torch.nn.Linear(32,10)
self.relu4 = torch.nn.ReLU()
#输出层
self.linear5=torch.nn.Linear(10,2)
# 激活函数
self.softmax=torch.nn.LogSoftmax()
#前向传播
def forward(self,x):
#修改每一个批次的样本集尺寸，修改为64*3*224*224，因为我们的图片是3*224*224
x=x.reshape(x.shape[0],-1)
#前向传播
x=self.linear1(x)#224*224*3*128
x=self.relu1(x)
x=self.linear2(x)#128*64
x=self.relu2(x)
x=self.linear3(x)#64*32
x=self.relu3(x)
x=self.linear4(x)#输出层32*10
x=self.relu4(x)
x=self.linear5(x)
x=self.softmax(x)#最后输出的数值我们需要利用到独热编码的思想
#上面的这些都可以这几使用x=self.model(x)来代替,为什么能用它，我的理解是，我们继承的class moudle 然后对立面写好的模型框架进行定义，而这个方法就是可以直接调用我们定义好的神经网络
return x
#建立我们的神经网络对象
model=BPNetwork()
# #定义损失函数
critimizer=torch.nn.NLLLoss()
#定义优化器
optimizer=torch.optim.SGD(model.parameters(),lr=0.001,momentum=0.9)
epochs=25
#每轮抽取次数的和
a=0
loss_=[]
a_=[]
font = font_manager.FontProperties(fname="C:\\Users\\ASUS\\Desktop\\Fonts\\STZHONGS.TTF")
for i in range(epochs):
# 损失值参数
sumloss = 0
for imges,labels in trainload:
a+=1
#前向传播
output=model(imges)
#反向传播
loss=critimizer(output,labels)
loss.backward()
#参数更新
optimizer.step()
#梯度清零
optimizer.zero_grad()
#损失累加
sumloss+=loss.item()
loss_.append(sumloss)
a_.append(a)
print(f"第{i+1}轮的损失：{sumloss},抽取次数和:{a}") 唯一要注意的估计就是这里比那篇博客多了一个隐藏层，并且改了改里面线性层的参数，比较数据集不一样。
运行结果的可视化
plt.figure()
plt.plot(a_,loss_)
plt.title('损失值随着抽取总次数得变化情况：',fontproperties=font, fontsize=18)
plt.show()
# #开始预测
example=enumerate(testLoad)#从测试集里面随机抽64份并且记录下来里面的内容和下标
batch_index,(imagess,labelss)=next(example)
bath_index=0
# # imagess=0
# # labelss=0
# # for i,j in example:
# # bath_index=i
# # (imagess, labelss)=j
fig=plt.figure()
for i in range(16):
a=torch.unsqueeze(imagess[i],dim=0) #升维度加1
print(a.shape)
pre=model(a)#预测
#第一张图片对应的pre得格式：
#接下来我们要用到独热编码的思想，我们取最大的数，也就是最高的概率对应得下标，就相当于这个最高概率对应得独热编码里面的1，其他是0
pro = list(pre.detach().numpy()[0])
pre_label=pro.index(max(pro))
print(pre_label)
#图像显示
img=torch.squeeze(a) #去掉维度中的一个‘1’，大小变成3*224*224 需要转换
img_=img.permute(1,-1 , 0) #224*224*3这个我们的图像才可以显示
imge=img_.numpy()
# print(img_.shape)
# print(img_)
plt.subplot(4,4,i+1)
plt.tight_layout()
plt.imshow(imge,cmap='gray',interpolation='none')
plt.title(f"预测值：{pre_label}",fontproperties=font, fontsize=9)
plt.xticks([])
plt.yticks([])
plt.show()
这里就是要注意，图片数据的升维度，降维度还有后面的修改图像的形状 。
完整代码
import matplotlib.pyplot as plt
from matplotlib import font_manager
print('猫狗分类数据集---------------------')
import torch
from torch.utils.data import DataLoader, Dataset
import torchvision
from torchvision import transforms
from PIL import Image
import os
#使用类创建自己的猫狗分类数据集
class catanddog(Dataset):
def __init__(self,rootpath,label_dir):
self.rootpath=rootpath
self.label_dir=label_dir
self.path=os.path.join(rootpath,label_dir)
self.imge_all=os.listdir(self.path)
self.transform=transforms.Compose([transforms.Resize((224,224)),transforms.ToTensor()])
def __getitem__(self, item):
imge_name=self.imge_all[item]
imge=Image.open(os.path.join(self.path,imge_name))
imge=self.transform(imge)
if self.label_dir=='Cat':
target=0
else:
target=1
if imge.shape[0]!=3:
print(self.imge_all[item],target)
os.remove(os.path.join(self.path,imge_name))
return imge,target
def __len__(self):
return len(self.imge_all)
rootpath='D:\learn_pytorch\数据集\PetImages'
test_rootpath='D:\learn_pytorch\数据集\PetImages\Test'
cat='Cat'#标签对应0
dog='Dog'#标签对应1
catdatasets=catanddog(rootpath,cat)#猫的数据集
#print(len(catdatasets))#12500
dogdatasets=catanddog(rootpath,dog)#狗的数据集
testcat=catanddog(test_rootpath,cat)
testdog=catanddog(test_rootpath,dog)
traindata=catdatasets+dogdatasets
testdata=testcat+testdog
# print(traindata[12500])
# print(testdata[0])
# for x , y in testdata:
# print(x,y)
# pass
# 利用DataLoader加载数据集
trainload=DataLoader(dataset=traindata,shuffle=True,batch_size=64)
testLoad=DataLoader(dataset=testdata,shuffle=False,batch_size=16)
#搭建bp神经网络
class BPNetwork(torch.nn.Module):
def __init__(self):
super(BPNetwork,self).__init__()
#我们的每张图片都是224*224*3个像素点
#第一个隐藏层
self.linear1=torch.nn.Linear(224*224*3,128)
#激活函数，这里选择Relu
self.relu1=torch.nn.ReLU()
#第二个隐藏层
self.linear2=torch.nn.Linear(128,64)
#激活函数
self.relu2=torch.nn.ReLU()
#第三个隐藏层:
self.linear3=torch.nn.Linear(64,32)
# 激活函数
self.relu3 = torch.nn.ReLU()
#第四个隐藏层
self.linear4=torch.nn.Linear(32,10)
self.relu4 = torch.nn.ReLU()
#输出层
self.linear5=torch.nn.Linear(10,2)
# 激活函数
self.softmax=torch.nn.LogSoftmax()
#前向传播
def forward(self,x):
#修改每一个批次的样本集尺寸，修改为64*3*224*224，因为我们的图片是3*224*224
x=x.reshape(x.shape[0],-1)
#前向传播
x=self.linear1(x)#224*224*3*128
x=self.relu1(x)
x=self.linear2(x)#128*64
x=self.relu2(x)
x=self.linear3(x)#64*32
x=self.relu3(x)
x=self.linear4(x)#输出层32*10
x=self.relu4(x)
x=self.linear5(x)
x=self.softmax(x)#最后输出的数值我们需要利用到独热编码的思想
#上面的这些都可以这几使用x=self.model(x)来代替,为什么能用它，我的理解是，我们继承的class moudle 然后对立面写好的模型框架进行定义，而这个方法就是可以直接调用我们定义好的神经网络
return x
#建立我们的神经网络对象
model=BPNetwork()
# #定义损失函数
critimizer=torch.nn.NLLLoss()
#定义优化器
optimizer=torch.optim.SGD(model.parameters(),lr=0.001,momentum=0.9)
epochs=25
#每轮抽取次数的和
a=0
loss_=[]
a_=[]
font = font_manager.FontProperties(fname="C:\\Users\\ASUS\\Desktop\\Fonts\\STZHONGS.TTF")
for i in range(epochs):
# 损失值参数
sumloss = 0
for imges,labels in trainload:
a+=1
#前向传播
output=model(imges)
#反向传播
loss=critimizer(output,labels)
loss.backward()
#参数更新
optimizer.step()
#梯度清零
optimizer.zero_grad()
#损失累加
sumloss+=loss.item()
loss_.append(sumloss)
a_.append(a)
print(f"第{i+1}轮的损失：{sumloss},抽取次数和:{a}")
plt.figure()
plt.plot(a_,loss_)
plt.title('损失值随着抽取总次数得变化情况：',fontproperties=font, fontsize=18)
plt.show()
# #开始预测
example=enumerate(testLoad)#从测试集里面随机抽64份并且记录下来里面的内容和下标
batch_index,(imagess,labelss)=next(example)
bath_index=0
# # imagess=0
# # labelss=0
# # for i,j in example:
# # bath_index=i
# # (imagess, labelss)=j
fig=plt.figure()
for i in range(16):
a=torch.unsqueeze(imagess[i],dim=0) #升维度加1
print(a.shape)
pre=model(a)#预测
#第一张图片对应的pre得格式：
#接下来我们要用到独热编码的思想，我们取最大的数，也就是最高的概率对应得下标，就相当于这个最高概率对应得独热编码里面的1，其他是0
pro = list(pre.detach().numpy()[0])
pre_label=pro.index(max(pro))
print(pre_label)
#图像显示
img=torch.squeeze(a) #去掉维度中的一个‘1’，大小变成3*224*224 需要转换
img_=img.permute(1,-1 , 0) #224*224*3这个我们的图像才可以显示
imge=img_.numpy()
# print(img_.shape)
# print(img_)
plt.subplot(4,4,i+1)
plt.tight_layout()
plt.imshow(imge,cmap='gray',interpolation='none')
plt.title(f"预测值：{pre_label}",fontproperties=font, fontsize=9)
plt.xticks([])
plt.yticks([])
plt.show()
完整代码运行结果以及图像显示
D:\Anaconda3\envs\pytorch\python.exe D:\learn_pytorch\学习过程\第四周的代码\代码二：BP实战猫狗分类数据集.py
猫狗分类数据集---------------------
D:\learn_pytorch\学习过程\第四周的代码\代码二：BP实战猫狗分类数据集.py:91: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.
x=self.softmax(x)#最后输出的数值我们需要利用到独热编码的思想
D:\Anaconda3\envs\pytorch\lib\site-packages\PIL\TiffImagePlugin.py:858: UserWarning: Truncated File Read
warnings.warn(str(msg))
第1轮的损失：270.51873564720154,抽取次数和:390
第2轮的损失：263.72216778993607,抽取次数和:780
第3轮的损失：257.4639049768448,抽取次数和:1170
第4轮的损失：253.7732720375061,抽取次数和:1560
第5轮的损失：250.9577106833458,抽取次数和:1950
第6轮的损失：248.24800008535385,抽取次数和:2340
第7轮的损失：246.03753757476807,抽取次数和:2730
第8轮的损失：243.95308458805084,抽取次数和:3120
第9轮的损失：241.23376768827438,抽取次数和:3510
第10轮的损失：239.46903285384178,抽取次数和:3900
第11轮的损失：236.259095788002,抽取次数和:4290
第12轮的损失：235.41773441433907,抽取次数和:4680
第13轮的损失：236.35172924399376,抽取次数和:5070
第14轮的损失：231.80556109547615,抽取次数和:5460
第15轮的损失：231.69989922642708,抽取次数和:5850
第16轮的损失：228.5429847240448,抽取次数和:6240
第17轮的损失：230.10409697890282,抽取次数和:6630
第18轮的损失：227.88593095541,抽取次数和:7020
第19轮的损失：224.83855456113815,抽取次数和:7410
第20轮的损失：223.20675492286682,抽取次数和:7800
第21轮的损失：222.7491433620453,抽取次数和:8190
第22轮的损失：218.916668176651,抽取次数和:8580
第23轮的损失：219.3764647245407,抽取次数和:8970
第24轮的损失：216.6209041774273,抽取次数和:9360
第25轮的损失：215.46695718169212,抽取次数和:9750
torch.Size([1, 3, 224, 224])
0
torch.Size([1, 3, 224, 224])
0
torch.Size([1, 3, 224, 224])
0
torch.Size([1, 3, 224, 224])
0
torch.Size([1, 3, 224, 224])
1
torch.Size([1, 3, 224, 224])
0
torch.Size([1, 3, 224, 224])
0
torch.Size([1, 3, 224, 224])
0
torch.Size([1, 3, 224, 224])
0
torch.Size([1, 3, 224, 224])
0
torch.Size([1, 3, 224, 224])
0
torch.Size([1, 3, 224, 224])
0
torch.Size([1, 3, 224, 224])
0
torch.Size([1, 3, 224, 224])
0
torch.Size([1, 3, 224, 224])
0
torch.Size([1, 3, 224, 224])
0
进程已结束,退出代码0
从这张图可以看到，随着我们训练批次的增加，我们的损失值也在不断的减少。
可以看出来经过25轮的训练，我们的预测的结果以及基本上算是可以的了。
本次实战总结
1.学习率是一个很关键的超参数不能太大也不能太小，经过多次调试，我选择了0.001
2.我们的matplotlib画图，接受的numpy数组是H、W、C，分别对应numpy数组的三个维度和图片的高、宽、通道数。如果不是这样的形式还需要把形状给转一下，本次使用的是permute()函数。
3.因为我们使用的是BP神经网络实现的，所以隐藏层使用多少层也是个值得思考的问题，层数多了参数开销会比较大，训练时间也会比较长一些，我这次使用的是四个隐藏层。
4.在我们进行测试数据集测试的时候，我们的每张图片是彩色的，经过处理后传入的是（3，224，224），与我们的这一步有些小问题。
所以我们需要把图片数据进行升维度，利用torch.unsqueeze(,dim=0)进行升维度。所以，图片数据的维度也是我们应该考虑的问题。
5.损失函数的选择以及激活函数的选择也是个考虑的问题。
