python
复制代码
import torch
import torchvision.datasets
from torch import nn
from torch.utils.data import DataLoader
dataset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True,
transform=torchvision.transforms.ToTensor())
dataloader = DataLoader(dataset, batch_size=64, shuffle=True)
class Tudui(nn.Module):
def __init__(self):
super(Tudui, self).__init__()
self.model1 = nn.Sequential(
nn.Conv2d(in_channels=3, out_channels=32, kernel_size=5, stride=1, padding=2),
nn.MaxPool2d(kernel_size=2),
nn.Conv2d(in_channels=32, out_channels=32, kernel_size=5, stride=1, padding=2),
nn.MaxPool2d(2),
nn.Conv2d(32, 64, 5, padding=2),
nn.MaxPool2d(2),
nn.Flatten(),
nn.Linear(1024, 64),
nn.Linear(64, 10)
)
def forward(self, x):
x = self.model1(x)
return x
tudui = Tudui()
loss = nn.CrossEntropyLoss()
optim = torch.optim.SGD(tudui.parameters(), lr=0.01)
for epoch in range(20):
running_loss = 0.0
for data in dataloader:
imgs, targets = data
outputs = tudui(imgs)
result_loss = loss(outputs, targets)
optim.zero_grad()
result_loss.backward()
optim.step()
running_loss += result_loss
print(running_loss)
