Pytorch 使用CNN圖像分類的實(shí)現(xiàn)
更新時間：2020年06月16日 09:56:21 作者：NULL
這篇文章主要介紹了Pytorch 使用CNN圖像分類的實(shí)現(xiàn)，文中通過示例代碼介紹的非常詳細(xì)，對大家的學(xué)習(xí)或者工作具有一定的參考學(xué)習(xí)價值，需要的朋友們下面隨著小編來一起學(xué)習(xí)學(xué)習(xí)吧
需求
在4*4的圖片中，比較外圍黑色像素點(diǎn)和內(nèi)圈黑色像素點(diǎn)個數(shù)的大小將圖片分類
如上圖圖片外圍黑色像素點(diǎn)5個大于內(nèi)圈黑色像素點(diǎn)1個分為0類反之1類
想法
- 通過numpy、PIL構(gòu)造4*4的圖像數(shù)據(jù)集
- 構(gòu)造自己的數(shù)據(jù)集類
- 讀取數(shù)據(jù)集對數(shù)據(jù)集選取減少偏斜
- cnn設(shè)計因為特征少，直接1*1卷積層
- 或者在4*4外圍添加padding成6*6,設(shè)計2*2的卷積核得出3*3再接上全連接層
代碼
import torch import torchvision import torchvision.transforms as transforms import numpy as np from PIL import Image
構(gòu)造數(shù)據(jù)集
import csv
import collections
import os
import shutil
def buildDataset(root,dataType,dataSize):
"""構(gòu)造數(shù)據(jù)集
構(gòu)造的圖片存到root/{dataType}Data
圖片地址和標(biāo)簽的csv文件存到 root/{dataType}DataInfo.csv
Args:
root:str
項目目錄
dataType:str
'train'或者‘test'
dataNum:int
數(shù)據(jù)大小
Returns:
"""
dataInfo = []
dataPath = f'{root}/{dataType}Data'
if not os.path.exists(dataPath):
os.makedirs(dataPath)
else:
shutil.rmtree(dataPath)
os.mkdir(dataPath)
for i in range(dataSize):
# 創(chuàng)建0，1 數(shù)組
imageArray=np.random.randint(0,2,(4,4))
# 計算0，1數(shù)量得到標(biāo)簽
allBlackNum = collections.Counter(imageArray.flatten())[0]
innerBlackNum = collections.Counter(imageArray[1:3,1:3].flatten())[0]
label = 0 if (allBlackNum-innerBlackNum)>innerBlackNum else 1
# 將圖片保存
path = f'{dataPath}/{i}.jpg'
dataInfo.append([path,label])
im = Image.fromarray(np.uint8(imageArray*255))
im = im.convert('1')
im.save(path)
# 將圖片地址和標(biāo)簽存入csv文件
filePath = f'{root}/{dataType}DataInfo.csv'
with open(filePath, 'w') as f:
writer = csv.writer(f)
writer.writerows(dataInfo)
root=r'/Users/null/Documents/PythonProject/Classifier'
構(gòu)造訓(xùn)練數(shù)據(jù)集
buildDataset(root,'train',20000)
構(gòu)造測試數(shù)據(jù)集
buildDataset(root,'test',10000)
讀取數(shù)據(jù)集
class MyDataset(torch.utils.data.Dataset):
def __init__(self, root, datacsv, transform=None):
super(MyDataset, self).__init__()
with open(f'{root}/{datacsv}', 'r') as f:
imgs = []
# 讀取csv信息到imgs列表
for path,label in map(lambda line:line.rstrip().split(','),f):
imgs.append((path, int(label)))
self.imgs = imgs
self.transform = transform if transform is not None else lambda x:x
def __getitem__(self, index):
path, label = self.imgs[index]
img = self.transform(Image.open(path).convert('1'))
return img, label
def __len__(self):
return len(self.imgs)
trainData=MyDataset(root = root,datacsv='trainDataInfo.csv', transform=transforms.ToTensor()) testData=MyDataset(root = root,datacsv='testDataInfo.csv', transform=transforms.ToTensor())
處理數(shù)據(jù)集使得數(shù)據(jù)集不偏斜
import itertools def chooseData(dataset,scale): # 將類別為1的排序到前面 dataset.imgs.sort(key=lambda x:x[1],reverse=True) # 獲取類別1的數(shù)目 ，取scale倍的數(shù)組，得數(shù)據(jù)不那么偏斜 trueNum =collections.Counter(itertools.chain.from_iterable(dataset.imgs))[1] end = min(trueNum*scale,len(dataset)) dataset.imgs=dataset.imgs[:end] scale = 4 chooseData(trainData,scale) chooseData(testData,scale) len(trainData),len(testData) (2250, 1122)
import torch.utils.data as Data # 超參數(shù) batchSize = 50 lr = 0.1 numEpochs = 20 trainIter = Data.DataLoader(dataset=trainData, batch_size=batchSize, shuffle=True) testIter = Data.DataLoader(dataset=testData, batch_size=batchSize)
定義模型
from torch import nn
from torch.autograd import Variable
from torch.nn import Module,Linear,Sequential,Conv2d,ReLU,ConstantPad2d
import torch.nn.functional as F
class Net(Module):
def __init__(self):
super(Net, self).__init__()
self.cnnLayers = Sequential(
# padding添加1層常數(shù)1,設(shè)定卷積核為2*2
ConstantPad2d(1, 1),
Conv2d(1, 1, kernel_size=2, stride=2,bias=True)
)
self.linearLayers = Sequential(
Linear(9, 2)
)
def forward(self, x):
x = self.cnnLayers(x)
x = x.view(x.shape[0], -1)
x = self.linearLayers(x)
return x
class Net2(Module):
def __init__(self):
super(Net2, self).__init__()
self.cnnLayers = Sequential(
Conv2d(1, 1, kernel_size=1, stride=1,bias=True)
)
self.linearLayers = Sequential(
ReLU(),
Linear(16, 2)
)
def forward(self, x):
x = self.cnnLayers(x)
x = x.view(x.shape[0], -1)
x = self.linearLayers(x)
return x
定義損失函數(shù)
# 交叉熵?fù)p失函數(shù) loss = nn.CrossEntropyLoss() loss2 = nn.CrossEntropyLoss()
定義優(yōu)化算法
net = Net() optimizer = torch.optim.SGD(net.parameters(),lr = lr)
net2 = Net2() optimizer2 = torch.optim.SGD(net2.parameters(),lr = lr)
訓(xùn)練模型
# 計算準(zhǔn)確率
def evaluateAccuracy(dataIter, net):
accSum, n = 0.0, 0
with torch.no_grad():
for X, y in dataIter:
accSum += (net(X).argmax(dim=1) == y).float().sum().item()
n += y.shape[0]
return accSum / n
def train(net, trainIter, testIter, loss, numEpochs, batchSize,
optimizer):
for epoch in range(numEpochs):
trainLossSum, trainAccSum, n = 0.0, 0.0, 0
for X,y in trainIter:
yHat = net(X)
l = loss(yHat,y).sum()
optimizer.zero_grad()
l.backward()
optimizer.step()
# 計算訓(xùn)練準(zhǔn)確度和loss
trainLossSum += l.item()
trainAccSum += (yHat.argmax(dim=1) == y).sum().item()
n += y.shape[0]
# 評估測試準(zhǔn)確度
testAcc = evaluateAccuracy(testIter, net)
print('epoch {:d}, loss {:.4f}, train acc {:.3f}, test acc {:.3f}'.format(epoch + 1, trainLossSum / n, trainAccSum / n, testAcc))
Net模型訓(xùn)練
train(net, trainIter, testIter, loss, numEpochs, batchSize,optimizer) epoch 1, loss 0.0128, train acc 0.667, test acc 0.667 epoch 2, loss 0.0118, train acc 0.683, test acc 0.760 epoch 3, loss 0.0104, train acc 0.742, test acc 0.807 epoch 4, loss 0.0093, train acc 0.769, test acc 0.772 epoch 5, loss 0.0085, train acc 0.797, test acc 0.745 epoch 6, loss 0.0084, train acc 0.798, test acc 0.807 epoch 7, loss 0.0082, train acc 0.804, test acc 0.816 epoch 8, loss 0.0078, train acc 0.816, test acc 0.812 epoch 9, loss 0.0077, train acc 0.818, test acc 0.817 epoch 10, loss 0.0074, train acc 0.824, test acc 0.826 epoch 11, loss 0.0072, train acc 0.836, test acc 0.819 epoch 12, loss 0.0075, train acc 0.823, test acc 0.829 epoch 13, loss 0.0071, train acc 0.839, test acc 0.797 epoch 14, loss 0.0067, train acc 0.849, test acc 0.824 epoch 15, loss 0.0069, train acc 0.848, test acc 0.843 epoch 16, loss 0.0064, train acc 0.864, test acc 0.851 epoch 17, loss 0.0062, train acc 0.867, test acc 0.780 epoch 18, loss 0.0060, train acc 0.871, test acc 0.864 epoch 19, loss 0.0057, train acc 0.881, test acc 0.890 epoch 20, loss 0.0055, train acc 0.885, test acc 0.897
Net2模型訓(xùn)練
# batchSize = 50 # lr = 0.1 # numEpochs = 15 下得出的結(jié)果 train(net2, trainIter, testIter, loss2, numEpochs, batchSize,optimizer2) epoch 1, loss 0.0119, train acc 0.638, test acc 0.676 epoch 2, loss 0.0079, train acc 0.823, test acc 0.986 epoch 3, loss 0.0046, train acc 0.987, test acc 0.977 epoch 4, loss 0.0030, train acc 0.983, test acc 0.973 epoch 5, loss 0.0023, train acc 0.981, test acc 0.976 epoch 6, loss 0.0019, train acc 0.980, test acc 0.988 epoch 7, loss 0.0016, train acc 0.984, test acc 0.984 epoch 8, loss 0.0014, train acc 0.985, test acc 0.986 epoch 9, loss 0.0013, train acc 0.987, test acc 0.992 epoch 10, loss 0.0011, train acc 0.989, test acc 0.993 epoch 11, loss 0.0010, train acc 0.989, test acc 0.996 epoch 12, loss 0.0010, train acc 0.992, test acc 0.994 epoch 13, loss 0.0009, train acc 0.993, test acc 0.994 epoch 14, loss 0.0008, train acc 0.995, test acc 0.996 epoch 15, loss 0.0008, train acc 0.994, test acc 0.998
測試
test = torch.Tensor([[[[0,0,0,0],[0,1,1,0],[0,1,1,0],[0,0,0,0]]],
[[[1,1,1,1],[1,0,0,1],[1,0,0,1],[1,1,1,1]]],
[[[0,1,0,1],[1,0,0,1],[1,0,0,1],[0,0,0,1]]],
[[[0,1,1,1],[1,0,0,1],[1,0,0,1],[0,0,0,1]]],
[[[0,0,1,1],[1,0,0,1],[1,0,0,1],[1,0,1,0]]],
[[[0,0,1,0],[0,1,0,1],[0,0,1,1],[1,0,1,0]]],
[[[1,1,1,0],[1,0,0,1],[1,0,1,1],[1,0,1,1]]]
])
target=torch.Tensor([0,1,0,1,1,0,1])
test
tensor([[[[0., 0., 0., 0.],
[0., 1., 1., 0.],
[0., 1., 1., 0.],
[0., 0., 0., 0.]]],
[[[1., 1., 1., 1.],
[1., 0., 0., 1.],
[1., 0., 0., 1.],
[1., 1., 1., 1.]]],
[[[0., 1., 0., 1.],
[1., 0., 0., 1.],
[1., 0., 0., 1.],
[0., 0., 0., 1.]]],
[[[0., 1., 1., 1.],
[1., 0., 0., 1.],
[1., 0., 0., 1.],
[0., 0., 0., 1.]]],
[[[0., 0., 1., 1.],
[1., 0., 0., 1.],
[1., 0., 0., 1.],
[1., 0., 1., 0.]]],
[[[0., 0., 1., 0.],
[0., 1., 0., 1.],
[0., 0., 1., 1.],
[1., 0., 1., 0.]]],
[[[1., 1., 1., 0.],
[1., 0., 0., 1.],
[1., 0., 1., 1.],
[1., 0., 1., 1.]]]])
with torch.no_grad():
output = net(test)
output2 = net2(test)
predictions =output.argmax(dim=1)
predictions2 =output2.argmax(dim=1)
# 比較結(jié)果
print(f'Net測試結(jié)果{predictions.eq(target)}')
print(f'Net2測試結(jié)果{predictions2.eq(target)}')
Net測試結(jié)果tensor([ True, True, False, True, True, True, True])
Net2測試結(jié)果tensor([False, True, False, True, True, False, True])
到此這篇關(guān)于Pytorch 使用CNN圖像分類的實(shí)現(xiàn)的文章就介紹到這了,更多相關(guān)Pytorch CNN圖像分類內(nèi)容請搜索腳本之家以前的文章或繼續(xù)瀏覽下面的相關(guān)文章希望大家以后多多支持腳本之家！
相關(guān)文章
Python列表刪除元素del、pop()和remove()的區(qū)別小結(jié)
這篇文章主要給大家介紹了關(guān)于Python列表刪除元素del、pop()和remove()的區(qū)別，文中通過示例代碼介紹的非常詳細(xì)，對大家學(xué)習(xí)或者使用Python具有一定的參考學(xué)習(xí)價值，需要的朋友們下面來一起學(xué)習(xí)學(xué)習(xí)吧2019-09-09
日志模塊主要用于輸出運(yùn)行日志,可以設(shè)置輸出日志的等級、日志保存路徑、日志文件回滾等,這篇文章主要介紹了python日志模塊logging,需要的朋友可以參考下2024-01-01
這篇文章主要介紹了Python多線程入門學(xué)習(xí)，小編覺得挺不錯的，現(xiàn)在分享給大家，也給大家做個參考。一起跟隨小編過來看看吧2021-12-12
python3實(shí)現(xiàn)的zip格式壓縮文件夾操作示例
這篇文章主要介紹了python3實(shí)現(xiàn)的zip格式壓縮文件夾操作,結(jié)合實(shí)例形式分析了Python3基于zipfile模塊實(shí)現(xiàn)zip格式文件壓縮的相關(guān)操作技巧,需要的朋友可以參考下2019-08-08
windows環(huán)境下tensorflow安裝過程詳解
這篇文章主要為大家詳細(xì)介紹了windows環(huán)境下tensorflow安裝過程，具有一定的參考價值，感興趣的小伙伴們可以參考一下2018-03-03
python3 循環(huán)讀取excel文件并寫入json操作
這篇文章主要介紹了python3 循環(huán)讀取excel文件并寫入json操作，具有很好的參考價值，希望對大家有所幫助。一起跟隨小編過來看看吧2020-07-07
用python畫個奧運(yùn)五環(huán)(附完整代碼)
大家好，本篇文章主要講的是用python畫個奧運(yùn)五環(huán)（附完整代碼），感興趣的同學(xué)趕快來看一看吧，對你有幫助的話記得收藏一下2022-01-01
Python不要再使用while死循環(huán),定時器代替效果更佳
在python開發(fā)的過程中，經(jīng)常見到小伙伴直接使用while True的死循環(huán)+sleep的方式來保存程序的一直運(yùn)行。這種方式雖然能達(dá)到效果，但是說不定什么時候就直接崩潰了，其實(shí)使用定時器效果也不錯哦2023-03-03
Python開發(fā)必知必會標(biāo)識符UUID全面使用指南
在Python編程中,UUID（通用唯一標(biāo)識符）是一個非常有用的工具,用于生成唯一的標(biāo)識符,本文將深入探討Python中UUID的用法、不同版本的UUID、以及如何在實(shí)際應(yīng)用中充分利用UUID的優(yōu)勢2023-12-12
這篇文章主要介紹了基于python3實(shí)現(xiàn)倒敘字符串,文中通過示例代碼介紹的非常詳細(xì)，對大家的學(xué)習(xí)或者工作具有一定的參考學(xué)習(xí)價值,需要的朋友可以參考下2020-02-02