处理minist多分类

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

batch_size = 64

transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.1307, ), (0.3081, ))
])#把[]中的操作整成一个pipline,均值和标准差

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)

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)
        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)

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
        optimizer.zero_grad()
        # forward + backward + update
        outputs = model(inputs)
        loss = criterion(outputs, target)
        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))
            running_loss = 0.0

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()

D:\ANACONDA\envs\pytorch_gpu\python.exe E:/Python面试准备/python基础/练习/xd.py
[1,   300] loss: 2.173
[1,   600] loss: 0.805
[1,   900] loss: 0.419
Accuracy on test set: 88 %
[2,   300] loss: 0.325
[2,   600] loss: 0.270
[2,   900] loss: 0.221
Accuracy on test set: 93 %
[3,   300] loss: 0.188
[3,   600] loss: 0.173
[3,   900] loss: 0.155
Accuracy on test set: 95 %
[4,   300] loss: 0.135
[4,   600] loss: 0.127
[4,   900] loss: 0.115
Accuracy on test set: 96 %
[5,   300] loss: 0.101
[5,   600] loss: 0.100
[5,   900] loss: 0.091
Accuracy on test set: 96 %
[6,   300] loss: 0.080
[6,   600] loss: 0.077
[6,   900] loss: 0.077
Accuracy on test set: 96 %
[7,   300] loss: 0.060
[7,   600] loss: 0.066
[7,   900] loss: 0.066
Accuracy on test set: 97 %
[8,   300] loss: 0.047
[8,   600] loss: 0.055
[8,   900] loss: 0.053
Accuracy on test set: 97 %
[9,   300] loss: 0.040
[9,   600] loss: 0.042
[9,   900] loss: 0.043
Accuracy on test set: 97 %
[10,   300] loss: 0.032
[10,   600] loss: 0.033
[10,   900] loss: 0.036
Accuracy on test set: 97 %

Process finished with exit code 0