Pytorch写数字识别LeNet模型

from PIL import Image import cv2 import matplotlib.pyplot as plt import torchvision from torchvision import transforms import torch from torch.utils.data import DataLoader import torch.nn as nn import numpy as np import tqdm as tqdm class 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 = 15 batch = 32 lr=0.9 loss = 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}')

images_np = cv2.imread("/content/R-C.png-600",cv2.IMREAD_GRAYSCALE) h,w = images_np.shape images_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')))

correct = 0 i = 0 cnt = 1 for 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%16==0):     i+=1   cnt+=1 acc_g = correct/len(test_images) print(f'acc_g:{acc_g}')

acc_g:0.50625