用python帮我写一个基于transformer的图像分类
时间: 2023-02-24 22:52:45 浏览: 82
好的,我可以帮你写一个基于Transformer的图像分类的代码。
首先,你需要准备好一个图像数据集,然后对图像数据进行预处理。接下来,你可以使用PyTorch框架来搭建Transformer模型,并训练模型。
具体代码如下:
```
import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.nn.functional as F
# 加载图像数据集
transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4, shuffle=False, num_workers=2)
# 定义Transformer模型
class Transformer(nn.Module):
def __init__(self):
super(Transformer, self).__init__()
self.fc1 = nn.Linear(224 * 224 * 3, 500)
self.fc2 = nn.Linear(500, 256)
self.fc3 = nn.Linear(256, 10)
def forward(self, x):
x = x.view(-1, 224 * 224 * 3)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
# 实例化模型
net = Transformer()
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
# 训练模型
for epoch in range(2):
running_loss = 0.0
for i, data in enumerate(trainloader,