1. VGG网络¶






2. 总结¶

1. VGG网络(使用自定义)¶
In [1]:
import torch
from torch import nn
from d2l import torch as d2l
def vgg_block(num_convs,in_channels,out_channels): # 卷积层个数、输入通道数、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) # *layers表示把列表里面的元素按顺序作为参数输入函数
conv_arch = ((1,64),(1,128),(2,256),(2,512),(2,512)) # 第一个参数为几层卷积,第二个参数为输出通道数
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
return 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))
net = vgg(conv_arch)
# 观察每个层输出的形状
X = torch.randn(size=(1,1,224,224))
for blk in net:
X = blk(X)
print(blk.__class__.__name__,'output shape:\t', X.shape) # VGG使得高宽减半,通道数加倍
Sequential output shape: torch.Size([1, 64, 112, 112]) Sequential output shape: torch.Size([1, 128, 56, 56]) Sequential output shape: torch.Size([1, 256, 28, 28]) Sequential output shape: torch.Size([1, 512, 14, 14]) Sequential output shape: torch.Size([1, 512, 7, 7]) Flatten output shape: torch.Size([1, 25088]) Linear output shape: torch.Size([1, 4096]) ReLU output shape: torch.Size([1, 4096]) Dropout output shape: torch.Size([1, 4096]) Linear output shape: torch.Size([1, 4096]) ReLU output shape: torch.Size([1, 4096]) Dropout output shape: torch.Size([1, 4096]) Linear output shape: torch.Size([1, 10])
In [2]:
# 由于VGG-11比AlexNet计算量更大,因此构建了一个通道数较少的网络
ratio = 4
small_conv_arch = [(pair[0], pair[1]//ratio) for pair in conv_arch] # 所有输出通道除以4
net = vgg(small_conv_arch)
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())
loss 0.179, train acc 0.934, test acc 0.915 593.8 examples/sec on cuda:0