Pytorch写数字怎么识别LeNet模型

这篇文章主要为大家分析了Pytorch写数字怎么识别LeNet模型的相关知识点，内容详细易懂，操作细节合理，具有一定参考价值。如果感兴趣的话，不妨跟着跟随小编一起来看看，下面跟着小编一起深入学习"Pytorch写数字怎么识别LeNet模型"的知识吧。

LeNet网络

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 = 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 = 0  test_loss = 0  correct_train = 0  correct_test = 0  for 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 + L    correct_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 + L1      correct_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

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')))

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。

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+=1  if(cnt==0):    i+=1  cnt+=1acc_g = correct/len(test_images)print(f'acc_g:{acc_g}')

如果不反色，acc_g=0.15

acc_g:0.50625

pytorch的优点

1.PyTorch是相当简洁且高效快速的框架；2.设计追求最少的封装；3.设计符合人类思维，它让用户尽可能地专注于实现自己的想法；4.与google的Tensorflow类似，FAIR的支持足以确保PyTorch获得持续的开发更新；5.PyTorch作者亲自维护的论坛 供用户交流和求教问题6.入门简单

关于"Pytorch写数字怎么识别LeNet模型"就介绍到这了,更多相关内容可以搜索以前的文章，希望能够帮助大家答疑解惑，请多多支持网站！