09多分类问题

import torch
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.optim as optim

# prepare dataset

# 把原始图像转换为图像张量，C*W*H  然后对数据进行归一化，类比正态分布转化为标准形式
batch_size = 64
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,),(0.3081,))])

train_dataset = datasets.MNIST(root='../dataset/mnist/', train=True, download=True,transform=transform)
train_loader = DataLoader(train_dataset, shuffle=True,batch_size=batch_size)
test_dataset = datasets.MNIST(root='../dataset/mnist/', train=False, download=True,transform=transform)
test_loader = DataLoader(test_dataset, shuffle=False, batch_size=batch_size)

# design model using class
class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.l1 = torch.nn.Linear(784, 512)
        self.l2 = torch.nn.Linear(512, 256)
        self.l3 = torch.nn.Linear(256, 128)
        self.l4 = torch.nn.Linear(128, 64)
        self.l5 = torch.nn.Linear(64, 10)


    def forward(self, x):
        x = x.view(-1, 784)         # 把整个图片变成一个行向量，-1是自动获取mini_batch
        x = F.relu(self.l1(x))
        x = F.relu(self.l2(x))
        x = F.relu(self.l3(x))
        x = F.relu(self.l4(x))
        return self.l5(x)            #最后一层不用非线性变换


model = Net()

criterion = torch.nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(),lr=0.01, momentum=0.5)

def train(epoch):
    running_loss = 0.0
    for batch_idx, data in enumerate(train_loader, 0):
        inputs, target = data
        outputs = model(inputs)
        loss = criterion(outputs, target)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if batch_idx % 300 == 299:
            print('[%d,%5d]loss:%.3f' % (epoch+1, batch_idx+1, running_loss/300))


def Test():
    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            images,labels =data
            outputs = model(images)
            _, predicted = torch.max(outputs.data, dim=1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    print('accuracy on test set: %d %%' % (100*correct/total))


if __name__ == '__main__':
    for epoch in range(10):
        train(epoch)
        Test()