【Pytorch深度学习开发实践学习】B站刘二大人课程笔记整理lecture09 Softmax多分类

代码：

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

batch_size = 64
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]) #把原始图像转为tensor  这是均值和方差

train_set = datasets.MNIST(root='./data/mnist', train=True, download=True, transform=transform)
train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)

test_set = datasets.MNIST(root='./data/mnist', train=False, download=True, transform=transform)
test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=True)

class LinearClassifier(nn.Module):
    def __init__(self):
        super(LinearClassifier, self).__init__()
        self.l1 = nn.Linear(784, 512)
        self.l2 = nn.Linear(512, 256)
        self.l3 = nn.Linear(256, 128)
        self.l4 = nn.Linear(128, 64)
        self.l5 = 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)

criterion = nn.CrossEntropyLoss()
model = LinearClassifier()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

def train(epoch):
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 300 == 299:    # print every 300 mini-batches
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 300))
            running_loss = 0.0

def test(epoch):
    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            images, labels = data
            outputs = model(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

    print('Accuracy of the network on the 10000 test images: %d %%' % (
        100 * correct / total))

if __name__ == '__main__':
    for epoch in range(100):
        train(epoch)
        if epoch % 10 == 0:
            test(epoch)