Pytorch入门——MNIST手写数字识别代码

MNIST手写数字识别教程

本文仅仅放出该教程的代码
具体教程请看 Pytorch入门——手把手教你MNIST手写数字识别

import torch
import torchvision 
from tqdm import tqdm
import matplotlib

#By: Elwin https://editor.csdn.net/md?not_checkout=1&articleId=112980305

class Net(torch.nn.Module):
    def __init__(self):
        super(Net,self).__init__()
        self.model = torch.nn.Sequential(
            #The size of the picture is 28x28
            torch.nn.Conv2d(in_channels = 1,out_channels = 16,kernel_size = 3,stride = 1,padding = 1),
            torch.nn.ReLU(),
            torch.nn.MaxPool2d(kernel_size = 2,stride = 2),
            
            #The size of the picture is 14x14
            torch.nn.Conv2d(in_channels = 16,out_channels = 32,kernel_size = 3,stride = 1,padding = 1),
            torch.nn.ReLU(),
            torch.nn.MaxPool2d(kernel_size = 2,stride = 2),
            
            #The size of the picture is 7x7
            torch.nn.Conv2d(in_channels = 32,out_channels = 64,kernel_size = 3,stride = 1,padding = 1),
            torch.nn.ReLU(),
            
            torch.nn.Flatten(),
            torch.nn.Linear(in_features = 7 * 7 * 64,out_features = 128),
            torch.nn.ReLU(),
            torch.nn.Linear(in_features = 128,out_features = 10),
            torch.nn.Softmax(dim=1)
        )
        
    def forward(self,input):
        output = self.model(input)
        return output
        
        
device = "cuda:0" if torch.cuda.is_available() else "cpu"
transform = torchvision.transforms.Compose([torchvision.transforms.ToTensor(),
                                torchvision.transforms.Normalize(mean = [0.5],std = [0.5])])
      
BATCH_SIZE = 256
EPOCHS = 10
trainData = torchvision.datasets.MNIST('./data/',train = True,transform = transform,download = True)
testData = torchvision.datasets.MNIST('./data/',train = False,transform = transform)


trainDataLoader = torch.utils.data.DataLoader(dataset = trainData,batch_size = BATCH_SIZE,shuffle = True)
testDataLoader = torch.utils.data.DataLoader(dataset = testData,batch_size = BATCH_SIZE)
net = Net()
print(net.to(device))



history = {
    
    'Test Loss':[],'Test Accuracy':[]}
for epoch in range(1,EPOCHS + 1):
    processBar = tqdm(trainDataLoader,unit = 'step')
    net.train(True)
    for step,(trainImgs,labels) in enumerate(processBar):
        trainImgs = trainImgs.to(device)
        labels = labels.to(device)

        net.zero_grad()
        outputs = net(trainImgs)
        loss = lossF(outputs,labels)
        predictions = torch.argmax(outputs, dim = 1)
        accuracy = torch.sum(predictions == labels)/labels.shape[0]
        loss.backward()

        optimizer.step()
        processBar.set_description("[%d/%d] Loss: %.4f, Acc: %.4f" % 
                                   (epoch,EPOCHS,loss.item(),accuracy.item()))
        
        if step == len(processBar)-1:
            correct,totalLoss = 0,0
            net.train(False)
            for testImgs,labels in testDataLoader:
                testImgs = testImgs.to(device)
                labels = labels.to(device)
                outputs = net(testImgs)
                loss = lossF(outputs,labels)
                predictions = torch.argmax(outputs,dim = 1)
                
                totalLoss += loss
                correct += torch.sum(predictions == labels)
                
            testAccuracy = correct/(BATCH_SIZE * len(testDataLoader))
            testLoss = totalLoss/len(testDataLoader)
            history['Test Loss'].append(testLoss.item())
            history['Test Accuracy'].append(testAccuracy.item())
            
            processBar.set_description("[%d/%d] Loss: %.4f, Acc: %.4f, Test Loss: %.4f, Test Acc: %.4f" % 
                                   (epoch,EPOCHS,loss.item(),accuracy.item(),testLoss.item(),testAccuracy.item()))
    processBar.close()
    

matplotlib.pyplot.plot(history['Test Loss'],label = 'Test Loss')
matplotlib.pyplot.legend(loc='best')
matplotlib.pyplot.grid(True)
matplotlib.pyplot.xlabel('Epoch')
matplotlib.pyplot.ylabel('Loss')
matplotlib.pyplot.show()

matplotlib.pyplot.plot(history['Test Accuracy'],color = 'red',label = 'Test Accuracy')
matplotlib.pyplot.legend(loc='best')
matplotlib.pyplot.grid(True)
matplotlib.pyplot.xlabel('Epoch')
matplotlib.pyplot.ylabel('Accuracy')
matplotlib.pyplot.show()

torch.save(net,'./model.pth')