import torch
from torch import nn
from d2l import torch as d2l
def vgg_block(num_convs,in_channels,out_channels):
layers = []
for _ in range(num_convs):
layers.append(nn.Conv2d(
in_channels,out_channels,kernel_size=3, padding=1
))
layers.append(nn.ReLU())
# 每个输出保证都是一样的
in_channels = out_channels
layers.append(nn.MaxPool2d(
kernel_size=2,stride=2
))
return nn.Sequential(*layers)
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
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)
x = torch.randn(size=(1,1,224,224))
for blk in net:
x = blk(x)
print(blk.__class__.__name__,'output shape:\t',x.shape)
ratio = 4
small_conv_arch=[(pair[0],pair[1]//ratio) for pair in conv_arch]
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())