7.2 使用块的网络（VGG）

AlexNet 比 LeNet 好用的原因是更大，更深的网络来实现：

• 更多的全连接层（太贵，训练成本太高）
• 更多卷积层（可以用）
  使用块的想法首先出现在牛津大学的视觉几何组（visual geometry group）的_VGG 网络_中。通过将几个卷积层组合成块，构成 VGG

1. VGG 块

3 x 3 的卷积，padding = 1 不会改变输入图片大小，块内的卷积层通道大小一致（除了第一个输入层）

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())
        in_channels = out_channels # 块内的卷积层通道大小一致
    layers.append(nn.MaxPool2d(kernel_size=2,stride=2))
    return nn.Sequential(*layers)

net = vgg_block(3,256,384)

X = torch.randn(1, 256, 224, 224)
for layer in net:
    X=layer(X)
    print(layer.__class__.__name__,'output shape:\t',X.shape)

Conv2d output shape:	 torch.Size([1, 384, 224, 224])
ReLU output shape:	     torch.Size([1, 384, 224, 224])
Conv2d output shape:	 torch.Size([1, 384, 224, 224])
ReLU output shape:	     torch.Size([1, 384, 224, 224])
Conv2d output shape:	 torch.Size([1, 384, 224, 224])
ReLU output shape:	     torch.Size([1, 384, 224, 224])
MaxPool2d output shape:	 torch.Size([1, 384, 112, 112])

2. VGG 架构

多 VGG 块合并，最后用全连接层

conv_arch = ((1, 64), (1, 128), (2, 256), (2, 512), (2, 512)) # 1表示一个 VGG 块中 3x3 卷积层的个数，64 表示输出通道数

def vgg(conv_arch):
    conv_blks = []
    in_channels = 1 
    # 卷积层部分
    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(),
        # 全连接层部分
        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)

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])

训练 VGG 网络

ratio = 4
small_conv_arch = [(pair[0], pair[1] // ratio) for pair in conv_arch]
net = vgg(small_conv_arch)

# 超参数设定
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 0.178, train acc 0.935, test acc 0.920
2463.7 examples/sec on cuda:0

（注意这是预测的效率，不是训练，训练还要慢一倍）VGG 在性能和准确性不占优势，而且效率比较低（横坐标），只是作为经典网络学习^[1]

上图中，圆圈里下方的竖线表示论文的精度，加上一些 trick、调参之后可以提升精度

参考文献