VGG:更大更深、VGG块
VGG

VGG块的组成规律是:连续重复使用数个相同的

  • 填充为1、窗口形状为3×3的卷积层后(33卷积比55卷积好)
  • 接上一个步幅为2、窗口形状为2×2的最大池化层。
    卷积层保持输入的高和宽不变,而池化层则对其减半

VGG架构:


import time
import torch
from torch import nn, optim



def vgg_block(num_convs,in_channels,out_channels):
"""定义vgg块

Args:
num_convs (int): 需要卷积层数量
in_channels (int): 输入通道数
out_channels (int): 输出通道数
"""
layers = []
for _ in range(num_convs):
layers.append(
nn.Conv2d(in_channels,out_channels,kernel_size=3,padding=1)
)
layers.append(nn.ReLU())
in_channels = out_channels
layers.append(nn.MaxPool2d(kernel_size=2,stride=2))
return nn.Sequential(*layers)



# 实现vgg架构,vgg含有5个vgg块
conv_arch = (
(1,64),
(1,128),
(2,256),
(2,512),
(2,512),
)



def vgg(conv_arch):
conv_blks = []
in_channels = 1
for (num_convs,out_channels) in conv_arch:
conv_blks.append(
vgg_block(num_convs,in_channels,out_channels)
)
in_channels = out_channels
net = nn.Sequential(
*conv_blks,
nn.Flatten(),
nn.Linear(out_channels*7*7,4096),nn.ReLU(),nn.Dropout(0.5),
nn.Linear(4096,4096),nn.ReLU(),nn.Dropout(0.5),
nn.Linear(4096,10)
)
return net


net = vgg(conv_arch)


# 经典设计模式,每次图片大小缩小一半 通道数增加一杯
X = torch.randn(size=(1, 1, 224, 224))
for blk in net:
X = blk(X)
print(blk.__class__.__name__,'output shape:\t',X.shape)



# 这里是作为演示,因为VGG-11比AlexNet计算量更大,因此我们构建一个通道数较少的神经网络
ratio = 4
small_conv_arch = [(pair[0], pair[1] // ratio) for pair in conv_arch]
print(small_conv_arch)
net = vgg(small_conv_arch)



# X = torch.randn(size=(1, 1, 224, 224))
# for blk in net:
# X = blk(X)
# print(blk.__class__.__name__,'output shape:\t',X.shape)


lr, num_epochs, batch_size = 0.05, 10, 128
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, resize=224)
d2l.train_ch6(net, train_iter, test_iter, num_epochs, lr, d2l.try_gpu())

总结:

  • VGG使用可重复使用的卷积块来构建深度卷积神经网络
  • 不同的卷积块个数个超参数可以得到不同复杂度的变种

我电脑已经跑不动了,菜的人只能看着理论……