Pytorch写数字识别LeNet模型 Pytorch写数字识别LeNet模型

LeNet网络
训练结果
泛化能力测试

LeNet网络

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LeNet网络过卷积层时候保持分辨率不变，过池化层时候分辨率变小。实现如下

from PIL import Imageimport cv2import matplotlib.pyplot as pltimport torchvisionfrom torchvision import transformsimport torchfrom torch.utils.data import DataLoaderimport torch.nn as nnimport numpy as npimport tqdm as tqdmclass LeNet(nn.Module):def __init__(self) -> None:super().__init__()self.sequential = nn.Sequential(nn.Conv2d(1,6,kernel_size=5,padding=2),nn.Sigmoid(),nn.AvgPool2d(kernel_size=2,stride=2),nn.Conv2d(6,16,kernel_size=5),nn.Sigmoid(),nn.AvgPool2d(kernel_size=2,stride=2),nn.Flatten(),nn.Linear(16*25,120),nn.Sigmoid(),nn.Linear(120,84),nn.Sigmoid(),nn.Linear(84,10))def forward(self,x):return self.sequential(x)class MLP(nn.Module):def __init__(self) -> None:super().__init__()self.sequential = nn.Sequential(nn.Flatten(),nn.Linear(28*28,120),nn.Sigmoid(),nn.Linear(120,84),nn.Sigmoid(),nn.Linear(84,10))def forward(self,x):return self.sequential(x)epochs = 15batch = 32lr=0.9loss = nn.CrossEntropyLoss()model = LeNet()optimizer = torch.optim.SGD(model.parameters(),lr)device = torch.device('cuda')root = r"./"trans_compose= transforms.Compose([transforms.ToTensor(),])train_data = https://www.it610.com/article/torchvision.datasets.MNIST(root,train=True,transform=trans_compose,download=True)test_data = torchvision.datasets.MNIST(root,train=False,transform=trans_compose,download=True)train_loader = DataLoader(train_data,batch_size=batch,shuffle=True)test_loader = DataLoader(test_data,batch_size=batch,shuffle=False)model.to(device)loss.to(device)# model.apply(init_weights)for epoch in range(epochs):train_loss = 0test_loss = 0correct_train = 0correct_test = 0for index,(x,y) in enumerate(train_loader):x = x.to(device)y = y.to(device)predict = model(x)L = loss(predict,y)optimizer.zero_grad()L.backward()optimizer.step()train_loss = train_loss + Lcorrect_train += (predict.argmax(dim=1)==y).sum()acc_train = correct_train/(batch*len(train_loader))with torch.no_grad():for index,(x,y) in enumerate(test_loader):[x,y] = [x.to(device),y.to(device)]predict = model(x)L1 = loss(predict,y)test_loss = test_loss + L1correct_test += (predict.argmax(dim=1)==y).sum()acc_test = correct_test/(batch*len(test_loader))print(f'epoch:{epoch},train_loss:{train_loss/batch},test_loss:{test_loss/batch},acc_train:{acc_train},acc_test:{acc_test}')

训练结果

epoch:12,train_loss:2.235553741455078,test_loss:0.3947642743587494,acc_train:0.9879833459854126,acc_test:0.9851238131523132
epoch:13,train_loss:2.028963804244995,test_loss:0.3220392167568207,acc_train:0.9891499876976013,acc_test:0.9875199794769287
epoch:14,train_loss:1.8020273447036743,test_loss:0.34837451577186584,acc_train:0.9901833534240723,acc_test:0.98702073097229

泛化能力测试找了一张图片，将其分割成只含一个数字的图片进行测试

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images_np = cv2.imread("/content/R-C.png",cv2.IMREAD_GRAYSCALE)h,w = images_np.shapeimages_np = np.array(255*torch.ones(h,w))-images_np#图片反色images = Image.fromarray(images_np)plt.figure(1)plt.imshow(images)test_images = []for i in range(10):for j in range(16):test_images.append(images_np[h//10*i:h//10+h//10*i,w//16*j:w//16*j+w//16])sample = test_images[77]sample_tensor = torch.tensor(sample).unsqueeze(0).unsqueeze(0).type(torch.FloatTensor).to(device)sample_tensor = torch.nn.functional.interpolate(sample_tensor,(28,28))predict = model(sample_tensor)output = predict.argmax()print(output)plt.figure(2)plt.imshow(np.array(sample_tensor.squeeze().to('cpu')))

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此时预测结果为4，预测正确。从这段代码中可以看到有一个反色的步骤，若不反色，结果会受到影响，如下图所示，预测为0，错误。
模型用于输入的图片是单通道的黑白图片，这里由于可视化出现了黄色，但实际上是黑白色，反色操作说明了数据的预处理十分的重要，很多数据如果是不清理过是无法直接用于推理的。

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将所有用来泛化性测试的图片进行准确率测试：

correct = 0i = 0cnt = 1for sample in test_images:sample_tensor = torch.tensor(sample).unsqueeze(0).unsqueeze(0).type(torch.FloatTensor).to(device)sample_tensor = torch.nn.functional.interpolate(sample_tensor,(28,28))predict = model(sample_tensor)output = predict.argmax()if(output==i):correct+=1if(cnt%16==0):i+=1cnt+=1acc_g = correct/len(test_images)print(f'acc_g:{acc_g}')

【Pytorch写数字识别LeNet模型】如果不反色，acc_g=0.15