pytorch手寫數字識別，MNIST數據集

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
 #training set 設置每個包中的圖片數據個數爲64個
batch_size = 64

#MNIST Dataset
#下載訓練集以及測試集

train_dataset = datasets.MNIST(root='./num/', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = datasets.MNIST(root='/num/', train=False, transform=transforms.ToTensor(), download=True)

# root:下載後的數據存放的路徑
# train：true代表訓練集，false代表測試集
# transform:表示下載好數據之後要對數據做何種變換
# dowload:自動下載的數據集

#數據的裝載和預覽
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)


class Net(nn.Module):
    def __init__(self):
        super(Net , self).__init__()
        #輸入1通道，輸出10通道 ，kernel 5*5
        self.conv1 = nn.Conv2d(1,10,kernel_size=5)
        self.conv2 = nn.Conv2d(10,20,kernel_size=5)
        self.mp = nn.MaxPool2d(2)
        self.fc = nn.Linear(320,10)

    def forward(self,x):
        in_size = x.size(0) #one batch(64)
        #x:64*10*12*12
        x = F.relu(self.mp(self.conv1(x)))
        #x=64*20*4*4
        x = F.relu(self.mp(self.conv2(x)))
        # x = 64*320
        x = x.view(in_size,-1) #flatten the tensor對參數進行扁平化
        # x = 64*10
        x = self.fc(x)
        return F.log_softmax(x,dim=1)


model = Net()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)


def train(epoch):
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = Variable(data), Variable(target)
        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if batch_size % 200 == 0:
            print('train Epoch:{} [{}/{} ({:.0f}%)]\tLoss:.6f'.format(epoch, batch_idx*len(data), len(train_loader.dataset), 100.*batch_idx/len(train_loader), loss.data[0]))


def test():
    test_loss = 0
    correct = 0
    for data, target in test_loader:
        data, target = Variable(data), Variable(target)
        output = model(data)
        test_loss += F.nll_loss(output, target, reduction='sum').item()

        pred = output.data.max(1, keepdim=True)[1]
        correct += pred.eq(target.data.view_as(pred)).cpu().sum()

        test_loss /= len(test_loader.dataset)

        print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
            test_loss, correct, len(test_loader.dataset),
            100. * correct / len(test_loader.dataset)))
for epoch in range(1, 10):
    train(epoch)
    test()

在執行下載數據集時候如果中斷了程序，在次執行時會報錯，此時應刪除下載的數據文件然後重新執行；若數據集下載完畢後中斷了程序，再次執行時可將參數download設置成False。

程序執行的結果如下：