AlexNet网络模型
相比于LeNet,AlexNet网络规模大大增加,其中3*3的卷积核也是深度CNN发展的一个重要标志。AlexNet作为一个比较典型的深度卷积神经网络,除了增大了网络的规模以为还用到了很多当时的新技术可以借鉴与联系,这里总结一下:
- 应用ReLU:现在的CNN激活函数一般还是用ReLU及其衍生
- Dropout:Dropout主要在FC中使用,还是很常用的技术
- 使用最大池化:AlexNet里用的是覆盖的池化操作,即pool size大于stride。
- 数据增强:数据增强仍然比较广泛使用,是很有效的技术
- GPU加速计算
AlexNet模型代码
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
__all__ = ['AlexNet', 'alexnet']
model_urls = {
'alexnet': 'https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth',
}
class AlexNet(nn.Module):
def __init__(self, num_classes=1000):
super(AlexNet, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(64, 192, kernel_size=5, padding=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(192, 384, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(384, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
)
self.classifier = nn.Sequential(
nn.Dropout(),
nn.Linear(256 * 6 * 6, 4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(4096, 4096),
nn.ReLU(inplace=True),
nn.Linear(4096, num_classes),
)
def forward(self, x):
x = self.features(x)
x = x.view(x.size(0), 256 * 6 * 6)
x = self.classifier(x)
return x
def alexnet(pretrained=False, model_root=None, **kwargs):
model = AlexNet(**kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['alexnet'], model_root))
return model