VGG网络模型

结构图

代码实现

1
2
3
4
5
6
7
import torch
from torch import nn
from d2l import torch as d2l
from torch.utils import data
from torchvision import transforms
import torchvision
%matplotlib inline
1
2
3
4
5
6
7
8
9
10
11
12
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())
in_channels = out_channels
layers.append(nn.MaxPool2d(kernel_size=2, stride=2))
return nn.Sequential(*layers)
# 等效之前的Sequential装网络
# 这是一个单独的块

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
conv_arch = ((1, 64), (1, 128), (2, 256), (2, 512), (2, 512))
# 分成5块,高宽减半,通道数翻倍


def vgg(conv_arch):
conv_blks = [] # 建立一个列表
in_channels = 1
for(num_convs, out_channels) in conv_arch:
conv_blks.append(vgg_block(num_convs=num_convs,
in_channels=in_channels, out_channels=out_channels))
in_channels = out_channels
# 建立多个块
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=conv_arch)

观察一下每一层的形状

1
2
3
4
5
X = torch.randn(size=(1, 1, 224, 224))
for blk in net:
X = blk(X)
print(blk.__class__.__name__, 'output shape:\t', X.shape)

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])

开始训练

1
2
3
ratio = 4
small_conv_arch = [(pair[0], pair[1]//ratio)for pair in conv_arch]
net = vgg(small_conv_arch)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
def get_dataloader_workers():
return 4


def load_data_fashion_mnist(batch_size, resize=None):
"""下载Fashion-MNIST数据集,然后将其加载到内存中"""
trans = [transforms.ToTensor()]
if resize:
trans.insert(0, transforms.Resize(resize))
trans = transforms.Compose(trans)
mnist_train = torchvision.datasets.FashionMNIST(
root="./data", train=True, transform=trans, download=True)
mnist_test = torchvision.datasets.FashionMNIST(
root="./data", train=False, transform=trans, download=True)
return (data.DataLoader(mnist_train, batch_size, shuffle=True,
num_workers=get_dataloader_workers()),
data.DataLoader(mnist_test, batch_size, shuffle=False,
num_workers=get_dataloader_workers()))

1
2
3
4
lr, num_epochs, batch_size = 0.05, 10, 128
train_iter, test_iter = load_data_fashion_mnist(batch_size, resize=224)
d2l.train_ch6(net, train_iter, test_iter, num_epochs, lr, d2l.try_gpu())