使用块的网络VGG

VGG
能不能更深更大获得更好的精度
更多的全连接层
更多的卷积层
将卷积层组合成块

深但窄效果更好
多个VGG块后接全连接层
不同次数的重复块得到不同的架构
VGG-16，VGG-19
更大更深的AlexNet
非常的占内存

import torch
from torch import nn
from d2l import torch as d2l

# VGG块的实现
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)

conv_arch = ((1, 64), (1, 128), (2, 256), (2, 512), (2, 512))

    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)