代码：
python
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)