需求

在4*4的图片中，比较外围黑色像素点和内圈黑色像素点个数的大小将图片分类

如上图图片外围黑色像素点5个大于内圈黑色像素点1个分为0类反之1类

想法

• 通过numpy、PIL构造4*4的图像数据集
• 构造自己的数据集类
• 读取数据集对数据集选取减少偏斜
• cnn设计因为特征少，直接1*1卷积层
• 或者在4*4外围添加padding成6*6,设计2*2的卷积核得出3*3再接上全连接层

代码

import torchimport torchvisionimport torchvision.transforms as transformsimport numpy as npfrom PIL import Image

构造数据集

import csvimport collectionsimport osimport shutildef buildDataset(root,dataType,dataSize): """构造数据集 构造的图片存到root/{dataType}Data 图片地址和标签的csv文件存到 root/{dataType}DataInfo.csv Args: root:str 项目目录 dataType:str 'train'或者‘test' dataNum:int 数据大小 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): # 创建0，1 数组 imageArray=np.random.randint(0,2,(4,4)) # 计算0，1数量得到标签 allBlackNum = collections.Counter(imageArray.flatten())[0] innerBlackNum = collections.Counter(imageArray[1:3,1:3].flatten())[0] label = 0 if (allBlackNum-innerBlackNum)>innerBlackNum else 1 # 将图片保存 path = f'{dataPath}/{i}.jpg' dataInfo.append([path,label]) im = Image.fromarray(np.uint8(imageArray*255)) im = im.convert('1') im.save(path) # 将图片地址和标签存入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'

构造训练数据集

buildDataset(root,'train',20000)

构造测试数据集

buildDataset(root,'test',10000)

读取数据集

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 = [] # 读取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())

处理数据集使得数据集不偏斜

import itertoolsdef chooseData(dataset,scale): # 将类别为1的排序到前面 dataset.imgs.sort(key=lambda x:x[1],reverse=True) # 获取类别1的数目 ，取scale倍的数组，得数据不那么偏斜 trueNum =collections.Counter(itertools.chain.from_iterable(dataset.imgs))[1] end = min(trueNum*scale,len(dataset)) dataset.imgs=dataset.imgs[:end]scale = 4chooseData(trainData,scale)chooseData(testData,scale)len(trainData),len(testData)(2250, 1122)import torch.utils.data as Data# 超参数batchSize = 50lr = 0.1numEpochs = 20trainIter = Data.DataLoader(dataset=trainData, batch_size=batchSize, shuffle=True)testIter = Data.DataLoader(dataset=testData, batch_size=batchSize)

定义模型

from torch import nnfrom torch.autograd import Variablefrom torch.nn import Module,Linear,Sequential,Conv2d,ReLU,ConstantPad2dimport torch.nn.functional as Fclass Net(Module): def __init__(self): super(Net, self).__init__() self.cnnLayers = Sequential( # padding添加1层常数1,设定卷积核为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 xclass 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

定义损失函数

# 交叉熵损失函数loss = nn.CrossEntropyLoss()loss2 = nn.CrossEntropyLoss()

定义优化算法

net = Net()optimizer = torch.optim.SGD(net.parameters(),lr = lr)net2 = Net2()optimizer2 = torch.optim.SGD(net2.parameters(),lr = lr)

训练模型

# 计算准确率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 / ndef 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() # 计算训练准确度和loss trainLossSum += l.item() trainAccSum += (yHat.argmax(dim=1) == y).sum().item() n += y.shape[0] # 评估测试准确度 testAcc = evaluateAccuracy(testIter, net) print('epoch {:d}, loss {:.4f}, train acc {:.3f}, test acc {:.3f}'.format(epoch + 1, trainLossSum / n, trainAccSum / n, testAcc))

Net模型训练

train(net, trainIter, testIter, loss, numEpochs, batchSize,optimizer)epoch 1, loss 0.0128, train acc 0.667, test acc 0.667epoch 2, loss 0.0118, train acc 0.683, test acc 0.760epoch 3, loss 0.0104, train acc 0.742, test acc 0.807epoch 4, loss 0.0093, train acc 0.769, test acc 0.772epoch 5, loss 0.0085, train acc 0.797, test acc 0.745epoch 6, loss 0.0084, train acc 0.798, test acc 0.807epoch 7, loss 0.0082, train acc 0.804, test acc 0.816epoch 8, loss 0.0078, train acc 0.816, test acc 0.812epoch 9, loss 0.0077, train acc 0.818, test acc 0.817epoch 10, loss 0.0074, train acc 0.824, test acc 0.826epoch 11, loss 0.0072, train acc 0.836, test acc 0.819epoch 12, loss 0.0075, train acc 0.823, test acc 0.829epoch 13, loss 0.0071, train acc 0.839, test acc 0.797epoch 14, loss 0.0067, train acc 0.849, test acc 0.824epoch 15, loss 0.0069, train acc 0.848, test acc 0.843epoch 16, loss 0.0064, train acc 0.864, test acc 0.851epoch 17, loss 0.0062, train acc 0.867, test acc 0.780epoch 18, loss 0.0060, train acc 0.871, test acc 0.864epoch 19, loss 0.0057, train acc 0.881, test acc 0.890epoch 20, loss 0.0055, train acc 0.885, test acc 0.897

Net2模型训练

# batchSize = 50 # lr = 0.1# numEpochs = 15 下得出的结果train(net2, trainIter, testIter, loss2, numEpochs, batchSize,optimizer2)epoch 1, loss 0.0119, train acc 0.638, test acc 0.676epoch 2, loss 0.0079, train acc 0.823, test acc 0.986epoch 3, loss 0.0046, train acc 0.987, test acc 0.977epoch 4, loss 0.0030, train acc 0.983, test acc 0.973epoch 5, loss 0.0023, train acc 0.981, test acc 0.976epoch 6, loss 0.0019, train acc 0.980, test acc 0.988epoch 7, loss 0.0016, train acc 0.984, test acc 0.984epoch 8, loss 0.0014, train acc 0.985, test acc 0.986epoch 9, loss 0.0013, train acc 0.987, test acc 0.992epoch 10, loss 0.0011, train acc 0.989, test acc 0.993epoch 11, loss 0.0010, train acc 0.989, test acc 0.996epoch 12, loss 0.0010, train acc 0.992, test acc 0.994epoch 13, loss 0.0009, train acc 0.993, test acc 0.994epoch 14, loss 0.0008, train acc 0.995, test acc 0.996epoch 15, loss 0.0008, train acc 0.994, test acc 0.998

测试

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])testtensor([[[[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)# 比较结果print(f'Net测试结果{predictions.eq(target)}')print(f'Net2测试结果{predictions2.eq(target)}')Net测试结果tensor([ True, True, False, True, True, True, True])Net2测试结果tensor([False, True, False, True, True, False, True])

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