简要介绍ALEXNET / VGGNET / RESNET / DENSENET

马春杰杰

4年 ago

AlexNet

　　AlexNet虽然如今在实践中并不常用，但是很具有历史意义，它是第一个在ImageNet比赛中获奖的CNN结构。

AlexNet 结构

在ImageNet中获奖

VGG

　　VGGNet实际上和AlexNet一样都是对CNN进行堆叠，只不过层数更多一些。常用的有Vgga(11层)，Vgg16和Vgg19。结构如下图所示：

Vgg的结构

　　PyTorch表示的结构如下:

# VGG16
Sequential(
  (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (1): ReLU(inplace)
  (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (3): ReLU(inplace)
  (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (6): ReLU(inplace)
  (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (8): ReLU(inplace)
  (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (11): ReLU(inplace)
  (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (13): ReLU(inplace)
  (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (15): ReLU(inplace)
  (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (18): ReLU(inplace)
  (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (20): ReLU(inplace)
  (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (22): ReLU(inplace)
  (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (25): ReLU(inplace)
  (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (27): ReLU(inplace)
  (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (29): ReLU(inplace)
  (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
 
# Convs
0.conv1_1 [3, 64, 3, 3]
2.conv1_2 [64, 64, 3, 3]
5.conv2_1 [64, 128, 3, 3]
7.conv2_2 [128, 128, 3, 3]
10.conv3_1 [128, 256, 3, 3]
12.conv3_2 [256, 256, 3, 3]
14.conv3_3 [256, 256, 3, 3]
17.conv4_1 [256, 512, 3, 3]
19.conv4_2 [512, 512, 3, 3]
21.conv4_3 [512, 512, 3, 3]
24.conv5_1 [512, 512, 3, 3]
26.conv5_2 [512, 512, 3, 3]
28.conv5_3 [512, 512, 3, 3]
 
# VGG19
Sequential(
  (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (1): ReLU(inplace)
  (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (3): ReLU(inplace)
  (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (6): ReLU(inplace)
  (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (8): ReLU(inplace)
  (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (11): ReLU(inplace)
  (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (13): ReLU(inplace)
  (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (15): ReLU(inplace)
  (16): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (17): ReLU(inplace)
  (18): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (19): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (20): ReLU(inplace)
  (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (22): ReLU(inplace)
  (23): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (24): ReLU(inplace)
  (25): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (26): ReLU(inplace)
  (27): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (29): ReLU(inplace)
  (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (31): ReLU(inplace)
  (32): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (33): ReLU(inplace)
  (34): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (35): ReLU(inplace)
  (36): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
 
# Convs
0.conv1_1 [3, 64, 3, 3]
2.conv1_2 [64, 64, 3, 3]
5.conv2_1 [64, 128, 3, 3]
7.conv2_2 [128, 128, 3, 3]
10.conv3_1 [128, 256, 3, 3]
12.conv3_2 [256, 256, 3, 3]
14.conv3_3 [256, 256, 3, 3]
16.conv3_4 [256, 256, 3, 3]
19.conv4_1 [256, 512, 3, 3]
21.conv4_2 [512, 512, 3, 3]
23.conv4_3 [512, 512, 3, 3]
25.conv4_4 [512, 512, 3, 3]
28.conv5_1 [512, 512, 3, 3]
30.conv5_2 [512, 512, 3, 3]
32.conv5_3 [512, 512, 3, 3]
34.conv5_4 [512, 512, 3, 3]
Total number of parameters: 20024384

ResNet

Arxiv https://arxiv.org/abs/1512.03385
　　VGG将卷积层的层数加深了，那么是不是卷积层数越多，网络的效果一定越好呢？
　　随着网络的加深，在反向传播的过程中会出现梯度消失和梯度爆炸的问题。使用正态分布进行参数初始化和Batch Normalization可以略微缓解这样的问题，但是准确率却无法再提高了。ResNet解决的问题是如何在加深网络的同时还能继续提高准确率(至少保证准确率不降低)。
　　为此作者提出了残差映射的概念：令H(x) = F(x)+x 。如下图所示，其中F(x)表示权重层的简单堆叠，在文中使用两层或三层的堆叠(只使用1层没有明显的效果)，F(x)与x相加实现了“快捷连接”。

　　理想情况下，较深的网络不应该比较浅的网络效果更差(实际上却会出现这样的情况，如下图所示)，ResNet将网络的处理与输入相加，如果网络的效果已经达到了饱和，那么再增加层数时，优化中会使F(x)——>0，这样更深的层数就不会降低网络的表现了。事实上，ResNet在深度大幅增加后能够很容易地获得准确率的提升。

　　ResNet的层数大大加深，常用的有Res50(50层)和Res101(101层)。ResNet的参数个数比VGG更少，但是能够取得更好的效果。

DenseNet

　　DenseNet借鉴了ResNet的思想，每一层都和之前的所有层有连接，既增加了特征的利用率，又减少了深度的冗余性。

DenseNet示意图

DenseNet结构　　以DenseNet121为例，第一个DenseBlock有6层，每层分别有一个1×1的卷积和3×3的卷积，先增大再减少channel的个数。每层都将之前的所有层作为输入，通道个数为(64+32×i)->32。PyTorch表示的结构如下:

Dense(
  (conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
  (dense_block1): _DenseBlock(  # DenseBlock1有6层
    (denselayer1): _DenseLayer(
      (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    ) # 64进32出
    (denselayer2): _DenseLayer(
      (conv1): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    ) # (64+32)进32出
    (denselayer3): _DenseLayer(
      (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    ) # (64+32×2)进32出
    (denselayer4): _DenseLayer(
      (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    ) # (64+32×3)进32出
    (denselayer5): _DenseLayer(
      (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    ) # (64+32×4)进32出
    (denselayer6): _DenseLayer(
      (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    ) # (64+32×5)进32出
  )
  (dense_block2):...

参数比较

Model:     n_params     receptive field*
  Alexnet    61,100,840         51
  Vgg16     138,357,544         27
  Vgg19     143,667,240         43
  Res50      25,557,032         983
  Res101     44,549,160        2,071
  Res152     60,192,808        2,967
  Dense121    7,978,856         239

本文最后更新于2021年5月13日，已超过 1 年没有更新，如果文章内容或图片资源失效，请留言反馈，我们会及时处理，谢谢！

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