Deep Learning 33：读论文“Densely Connected Convolutional Networks”-------DenseNet 简单理解
一.读前说明
1.论文"Densely Connected Convolutional Networks"是现在为止效果最好的CNN架构,比Resnet还好,有必要学习一下它为什么效果这么好.
2.代码地址：https://github.com/liuzhuang13/DenseNet
3.这篇论文主要参考了Highway Networks，Residual Networks (ResNets)和GoogLeNet,所以在读本篇论文之前,有必要读一下这几篇论文,另外还可以看一下Very Deep Learning with Highway Networks
4.参考文献 :ResNet && DenseNet(原理篇), DenseNet模型
二.阅读笔记
Abstract
最近的一些论文表明,如果卷积神经网络的各层到输入层和输出层的连接更短,那么该网络就大体上可以设计得更深、更准确、训练得更有效。本文基于此提出了“稠密卷积网络（DensNet），该网络每一层均以前馈的形式与其他任一层连接。因此，传统卷积网络有L层就只有L个连接，而DenseNet的任一层不仅与相邻层有连接，而且与它的随后的所有层都有直接连接，所以该网络有L(L+1)/2个直接连接。任意一层的输入都是其前面所有层的特征图，而该层自己的特征图是其随后所有层的输入。DenseNet有以下几个令人激动的优点：1.减轻了梯度消失问题；2.强化了特征传播；3.大幅度减少了参数数量。该网络结构在4个高竞争性的目标识别基准数据集上进行了评估，包括：CIFAR-10,CIFAR-100,SVHN,ImageNet。DenseNet在这些数据集上大部分都获得了巨大的提高，达到目前为止最高的识别准确率。
1.Introduction
在视觉识别中，CNN是一种强大的机器学习方法。尽管CNN在20年以前就被提出来，但是只是在最近几年，计算机硬件和网络结构的提高才使得真正的深层CNN的训练变成可能。最开始的LeNet5包含5层，VGG包含19层，只有去年的Highway Networks和ResNets才超过了100层这个关卡。
三.阅读感想：
翻译了一半，居然感觉完全不用翻译，真接看英文原文也能看懂，嗯对，这篇文章写得通俗易懂，根本不用像看那些什么hiton、begio、yanlecun之类大牛写的文章一样，直接一遍看过去，看得似懂非懂的。看这篇论文看完之后，感觉像吃了蜂蜜一样，看了还想看，连连最后实验结果分析和discuss也写得非常好，特别是discuss中那个图，该文创意非常棒，并且简单，最主要的是该文创意来源就是我最喜欢的那种，就是总结以前很多文章中效果好的原因，找出它们的共性，然后强化这个共性，从而得到更好的结果。
四.DenseNet结构：
.在CIFAR-10上用训练时的结构DenseNet-BC：
如果depth=40, growth_rate=12, bottleneck=True, reduction=0.5=1-compression，则每个denseblock里面的层数n_layers=((40-4)/3)//2=6.其中//2表示除以2后向下取整。
注：conv表示正常的2D卷积，CONV表示BN-ReLU-conv
结构如下：
input：(32,32,3)
conv(24,3,3), % 其中conv(24,3,3)=conv(filters=2*growth_rate=24,kernel_size=3,3) #第1个dense block
CONV(48,1,1)-CONV(12,3,3)-merge(36)- % 其中CONV(48,1,1)=CONV(filters=inter_channel = nb_filter*4=48,1,1),merge后nb_filter=24+12=36
CONV(48,1,1)-CONV(12,3,3)-merge(48)- % 同上，merge后nb_filter=36+12=48
CONV(48,1,1)-CONV(12,3,3)-merge(60)-
CONV(48,1,1)-CONV(12,3,3)-merge(72)-
CONV(48,1,1)-CONV(12,3,3)-merge(84)-
CONV(48,1,1)-CONV(12,3,3)-merge(96)- % 此时nb_filter每多一层就增加growth_rate=12个，这里1个dense block里有6层，故增加72个，所以nb_falter=24+72=96 #第1个Transition Layer
CONV(48,1,1) % nb_filter=nb_filter*compression=96*0.5=48
AveragePool(2,2,(2,2)) % pool_size=2,2 strides=(2,2) #第2个dense block
CONV(48,1,1)-CONV(12,3,3)-merge(108)- % 其中CONV(48,1,1)=CONV(filters=inter_channel = nb_filter*4=48,1,1),merge后nb_filter=96+12=108
CONV(48,1,1)-CONV(12,3,3)-merge(120)-
CONV(48,1,1)-CONV(12,3,3)-merge(132)-
CONV(48,1,1)-CONV(12,3,3)-merge(144)-
CONV(48,1,1)-CONV(12,3,3)-merge(156)-
CONV(48,1,1)-CONV(12,3,3)-merge(168)- % 此时nb_filter每多一层就增加growth_rate=12个，这里1个dense block里有6层，故增加72个，所以nb_falter=96+72=168
#第2个Transition Layer
CONV(60,1,1) % nb_filter=nb_filter*compression=120*0.5=60
AveragePool(2,2,(2,2)) % pool_size=2,2 strides=(2,2)
#第3个dense block
CONV(48,1,1)-CONV(12,3,3)-merge(180)- % 其中CONV(48,1,1)=CONV(filters=inter_channel = nb_filter*4=48,1,1)
CONV(48,1,1)-CONV(12,3,3)-merge(192)-
CONV(48,1,1)-CONV(12,3,3)-merge(204)-
CONV(48,1,1)-CONV(12,3,3)-merge(216)-
CONV(48,1,1)-CONV(12,3,3)-merge(228)-
CONV(48,1,1)-CONV(12,3,3)-merge(240)- % 此时nb_filter每多一层就增加growth_rate=12个，这里1个dense block里有6层，故增加72个，所以nb_falter=168+72=240 Relu-GlobalAveragePool-softmax
为验证以上的分析，用keras==1.2.0版本验证结果如下：
Model created
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
input_1 (InputLayer) (None, , , )
____________________________________________________________________________________________________
initial_conv2D (Convolution2D) (None, , , ) input_1[][]
____________________________________________________________________________________________________
batchnormalization_1 (BatchNorma (None, , , ) initial_conv2D[][]
____________________________________________________________________________________________________
activation_1 (Activation) (None, , , ) batchnormalization_1[][]
____________________________________________________________________________________________________
convolution2d_1 (Convolution2D) (None, , , ) activation_1[][]
____________________________________________________________________________________________________
batchnormalization_2 (BatchNorma (None, , , ) convolution2d_1[][]
____________________________________________________________________________________________________
activation_2 (Activation) (None, , , ) batchnormalization_2[][]
____________________________________________________________________________________________________
convolution2d_2 (Convolution2D) (None, , , ) activation_2[][]
____________________________________________________________________________________________________
merge_1 (Merge) (None, , , ) initial_conv2D[][]
convolution2d_2[][]
____________________________________________________________________________________________________
batchnormalization_3 (BatchNorma (None, , , ) merge_1[][]
____________________________________________________________________________________________________
activation_3 (Activation) (None, , , ) batchnormalization_3[][]
____________________________________________________________________________________________________
convolution2d_3 (Convolution2D) (None, , , ) activation_3[][]
____________________________________________________________________________________________________
batchnormalization_4 (BatchNorma (None, , , ) convolution2d_3[][]
____________________________________________________________________________________________________
activation_4 (Activation) (None, , , ) batchnormalization_4[][]
____________________________________________________________________________________________________
convolution2d_4 (Convolution2D) (None, , , ) activation_4[][]
____________________________________________________________________________________________________
merge_2 (Merge) (None, , , ) initial_conv2D[][]
convolution2d_2[][]
convolution2d_4[][]
____________________________________________________________________________________________________
batchnormalization_5 (BatchNorma (None, , , ) merge_2[][]
____________________________________________________________________________________________________
activation_5 (Activation) (None, , , ) batchnormalization_5[][]
____________________________________________________________________________________________________
convolution2d_5 (Convolution2D) (None, , , ) activation_5[][]
____________________________________________________________________________________________________
batchnormalization_6 (BatchNorma (None, , , ) convolution2d_5[][]
____________________________________________________________________________________________________
activation_6 (Activation) (None, , , ) batchnormalization_6[][]
____________________________________________________________________________________________________
convolution2d_6 (Convolution2D) (None, , , ) activation_6[][]
____________________________________________________________________________________________________
merge_3 (Merge) (None, , , ) initial_conv2D[][]
convolution2d_2[][]
convolution2d_4[][]
convolution2d_6[][]
____________________________________________________________________________________________________
batchnormalization_7 (BatchNorma (None, , , ) merge_3[][]
____________________________________________________________________________________________________
activation_7 (Activation) (None, , , ) batchnormalization_7[][]
____________________________________________________________________________________________________
convolution2d_7 (Convolution2D) (None, , , ) activation_7[][]
____________________________________________________________________________________________________
batchnormalization_8 (BatchNorma (None, , , ) convolution2d_7[][]
____________________________________________________________________________________________________
activation_8 (Activation) (None, , , ) batchnormalization_8[][]
____________________________________________________________________________________________________
convolution2d_8 (Convolution2D) (None, , , ) activation_8[][]
____________________________________________________________________________________________________
merge_4 (Merge) (None, , , ) initial_conv2D[][]
convolution2d_2[][]
convolution2d_4[][]
convolution2d_6[][]
convolution2d_8[][]
____________________________________________________________________________________________________
batchnormalization_9 (BatchNorma (None, , , ) merge_4[][]
____________________________________________________________________________________________________
activation_9 (Activation) (None, , , ) batchnormalization_9[][]
____________________________________________________________________________________________________
convolution2d_9 (Convolution2D) (None, , , ) activation_9[][]
____________________________________________________________________________________________________
batchnormalization_10 (BatchNorm (None, , , ) convolution2d_9[][]
____________________________________________________________________________________________________
activation_10 (Activation) (None, , , ) batchnormalization_10[][]
____________________________________________________________________________________________________
convolution2d_10 (Convolution2D) (None, , , ) activation_10[][]
____________________________________________________________________________________________________
merge_5 (Merge) (None, , , ) initial_conv2D[][]
convolution2d_2[][]
convolution2d_4[][]
convolution2d_6[][]
convolution2d_8[][]
convolution2d_10[][]
____________________________________________________________________________________________________
batchnormalization_11 (BatchNorm (None, , , ) merge_5[][]
____________________________________________________________________________________________________
activation_11 (Activation) (None, , , ) batchnormalization_11[][]
____________________________________________________________________________________________________
convolution2d_11 (Convolution2D) (None, , , ) activation_11[][]
____________________________________________________________________________________________________
batchnormalization_12 (BatchNorm (None, , , ) convolution2d_11[][]
____________________________________________________________________________________________________
activation_12 (Activation) (None, , , ) batchnormalization_12[][]
____________________________________________________________________________________________________
convolution2d_12 (Convolution2D) (None, , , ) activation_12[][]
____________________________________________________________________________________________________
merge_6 (Merge) (None, , , ) initial_conv2D[][]
convolution2d_2[][]
convolution2d_4[][]
convolution2d_6[][]
convolution2d_8[][]
convolution2d_10[][]
convolution2d_12[][]
____________________________________________________________________________________________________
batchnormalization_13 (BatchNorm (None, , , ) merge_6[][]
____________________________________________________________________________________________________
activation_13 (Activation) (None, , , ) batchnormalization_13[][]
____________________________________________________________________________________________________
convolution2d_13 (Convolution2D) (None, , , ) activation_13[][]
____________________________________________________________________________________________________
averagepooling2d_1 (AveragePooli (None, , , ) convolution2d_13[][]
____________________________________________________________________________________________________
batchnormalization_14 (BatchNorm (None, , , ) averagepooling2d_1[][]
____________________________________________________________________________________________________
activation_14 (Activation) (None, , , ) batchnormalization_14[][]
____________________________________________________________________________________________________
convolution2d_14 (Convolution2D) (None, , , ) activation_14[][]
____________________________________________________________________________________________________
batchnormalization_15 (BatchNorm (None, , , ) convolution2d_14[][]
____________________________________________________________________________________________________
activation_15 (Activation) (None, , , ) batchnormalization_15[][]
____________________________________________________________________________________________________
convolution2d_15 (Convolution2D) (None, , , ) activation_15[][]
____________________________________________________________________________________________________
merge_7 (Merge) (None, , , ) averagepooling2d_1[][]
convolution2d_15[][]
____________________________________________________________________________________________________
batchnormalization_16 (BatchNorm (None, , , ) merge_7[][]
____________________________________________________________________________________________________
activation_16 (Activation) (None, , , ) batchnormalization_16[][]
____________________________________________________________________________________________________
convolution2d_16 (Convolution2D) (None, , , ) activation_16[][]
____________________________________________________________________________________________________
batchnormalization_17 (BatchNorm (None, , , ) convolution2d_16[][]
____________________________________________________________________________________________________
activation_17 (Activation) (None, , , ) batchnormalization_17[][]
____________________________________________________________________________________________________
convolution2d_17 (Convolution2D) (None, , , ) activation_17[][]
____________________________________________________________________________________________________
merge_8 (Merge) (None, , , ) averagepooling2d_1[][]
convolution2d_15[][]
convolution2d_17[][]
____________________________________________________________________________________________________
batchnormalization_18 (BatchNorm (None, , , ) merge_8[][]
____________________________________________________________________________________________________
activation_18 (Activation) (None, , , ) batchnormalization_18[][]
____________________________________________________________________________________________________
convolution2d_18 (Convolution2D) (None, , , ) activation_18[][]
____________________________________________________________________________________________________
batchnormalization_19 (BatchNorm (None, , , ) convolution2d_18[][]
____________________________________________________________________________________________________
activation_19 (Activation) (None, , , ) batchnormalization_19[][]
____________________________________________________________________________________________________
convolution2d_19 (Convolution2D) (None, , , ) activation_19[][]
____________________________________________________________________________________________________
merge_9 (Merge) (None, , , ) averagepooling2d_1[][]
convolution2d_15[][]
convolution2d_17[][]
convolution2d_19[][]
____________________________________________________________________________________________________
batchnormalization_20 (BatchNorm (None, , , ) merge_9[][]
____________________________________________________________________________________________________
activation_20 (Activation) (None, , , ) batchnormalization_20[][]
____________________________________________________________________________________________________
convolution2d_20 (Convolution2D) (None, , , ) activation_20[][]
____________________________________________________________________________________________________
batchnormalization_21 (BatchNorm (None, , , ) convolution2d_20[][]
____________________________________________________________________________________________________
activation_21 (Activation) (None, , , ) batchnormalization_21[][]
____________________________________________________________________________________________________
convolution2d_21 (Convolution2D) (None, , , ) activation_21[][]
____________________________________________________________________________________________________
merge_10 (Merge) (None, , , ) averagepooling2d_1[][]
convolution2d_15[][]
convolution2d_17[][]
convolution2d_19[][]
convolution2d_21[][]
____________________________________________________________________________________________________
batchnormalization_22 (BatchNorm (None, , , ) merge_10[][]
____________________________________________________________________________________________________
activation_22 (Activation) (None, , , ) batchnormalization_22[][]
____________________________________________________________________________________________________
convolution2d_22 (Convolution2D) (None, , , ) activation_22[][]
____________________________________________________________________________________________________
batchnormalization_23 (BatchNorm (None, , , ) convolution2d_22[][]
____________________________________________________________________________________________________
activation_23 (Activation) (None, , , ) batchnormalization_23[][]
____________________________________________________________________________________________________
convolution2d_23 (Convolution2D) (None, , , ) activation_23[][]
____________________________________________________________________________________________________
merge_11 (Merge) (None, , , ) averagepooling2d_1[][]
convolution2d_15[][]
convolution2d_17[][]
convolution2d_19[][]
convolution2d_21[][]
convolution2d_23[][]
____________________________________________________________________________________________________
batchnormalization_24 (BatchNorm (None, , , ) merge_11[][]
____________________________________________________________________________________________________
activation_24 (Activation) (None, , , ) batchnormalization_24[][]
____________________________________________________________________________________________________
convolution2d_24 (Convolution2D) (None, , , ) activation_24[][]
____________________________________________________________________________________________________
batchnormalization_25 (BatchNorm (None, , , ) convolution2d_24[][]
____________________________________________________________________________________________________
activation_25 (Activation) (None, , , ) batchnormalization_25[][]
____________________________________________________________________________________________________
convolution2d_25 (Convolution2D) (None, , , ) activation_25[][]
____________________________________________________________________________________________________
merge_12 (Merge) (None, , , ) averagepooling2d_1[][]
convolution2d_15[][]
convolution2d_17[][]
convolution2d_19[][]
convolution2d_21[][]
convolution2d_23[][]
convolution2d_25[][]
____________________________________________________________________________________________________
batchnormalization_26 (BatchNorm (None, , , ) merge_12[][]
____________________________________________________________________________________________________
activation_26 (Activation) (None, , , ) batchnormalization_26[][]
____________________________________________________________________________________________________
convolution2d_26 (Convolution2D) (None, , , ) activation_26[][]
____________________________________________________________________________________________________
averagepooling2d_2 (AveragePooli (None, , , ) convolution2d_26[][]
____________________________________________________________________________________________________
batchnormalization_27 (BatchNorm (None, , , ) averagepooling2d_2[][]
____________________________________________________________________________________________________
activation_27 (Activation) (None, , , ) batchnormalization_27[][]
____________________________________________________________________________________________________
convolution2d_27 (Convolution2D) (None, , , ) activation_27[][]
____________________________________________________________________________________________________
batchnormalization_28 (BatchNorm (None, , , ) convolution2d_27[][]
____________________________________________________________________________________________________
activation_28 (Activation) (None, , , ) batchnormalization_28[][]
____________________________________________________________________________________________________
convolution2d_28 (Convolution2D) (None, , , ) activation_28[][]
____________________________________________________________________________________________________
merge_13 (Merge) (None, , , ) averagepooling2d_2[][]
convolution2d_28[][]
____________________________________________________________________________________________________
batchnormalization_29 (BatchNorm (None, , , ) merge_13[][]
____________________________________________________________________________________________________
activation_29 (Activation) (None, , , ) batchnormalization_29[][]
____________________________________________________________________________________________________
convolution2d_29 (Convolution2D) (None, , , ) activation_29[][]
____________________________________________________________________________________________________
batchnormalization_30 (BatchNorm (None, , , ) convolution2d_29[][]
____________________________________________________________________________________________________
activation_30 (Activation) (None, , , ) batchnormalization_30[][]
____________________________________________________________________________________________________
convolution2d_30 (Convolution2D) (None, , , ) activation_30[][]
____________________________________________________________________________________________________
merge_14 (Merge) (None, , , ) averagepooling2d_2[][]
convolution2d_28[][]
convolution2d_30[][]
____________________________________________________________________________________________________
batchnormalization_31 (BatchNorm (None, , , ) merge_14[][]
____________________________________________________________________________________________________
activation_31 (Activation) (None, , , ) batchnormalization_31[][]
____________________________________________________________________________________________________
convolution2d_31 (Convolution2D) (None, , , ) activation_31[][]
____________________________________________________________________________________________________
batchnormalization_32 (BatchNorm (None, , , ) convolution2d_31[][]
____________________________________________________________________________________________________
activation_32 (Activation) (None, , , ) batchnormalization_32[][]
____________________________________________________________________________________________________
convolution2d_32 (Convolution2D) (None, , , ) activation_32[][]
____________________________________________________________________________________________________
merge_15 (Merge) (None, , , ) averagepooling2d_2[][]
convolution2d_28[][]
convolution2d_30[][]
convolution2d_32[][]
____________________________________________________________________________________________________
batchnormalization_33 (BatchNorm (None, , , ) merge_15[][]
____________________________________________________________________________________________________
activation_33 (Activation) (None, , , ) batchnormalization_33[][]
____________________________________________________________________________________________________
convolution2d_33 (Convolution2D) (None, , , ) activation_33[][]
____________________________________________________________________________________________________
batchnormalization_34 (BatchNorm (None, , , ) convolution2d_33[][]
____________________________________________________________________________________________________
activation_34 (Activation) (None, , , ) batchnormalization_34[][]
____________________________________________________________________________________________________
convolution2d_34 (Convolution2D) (None, , , ) activation_34[][]
____________________________________________________________________________________________________
merge_16 (Merge) (None, , , ) averagepooling2d_2[][]
convolution2d_28[][]
convolution2d_30[][]
convolution2d_32[][]
convolution2d_34[][]
____________________________________________________________________________________________________
batchnormalization_35 (BatchNorm (None, , , ) merge_16[][]
____________________________________________________________________________________________________
activation_35 (Activation) (None, , , ) batchnormalization_35[][]
____________________________________________________________________________________________________
convolution2d_35 (Convolution2D) (None, , , ) activation_35[][]
____________________________________________________________________________________________________
batchnormalization_36 (BatchNorm (None, , , ) convolution2d_35[][]
____________________________________________________________________________________________________
activation_36 (Activation) (None, , , ) batchnormalization_36[][]
____________________________________________________________________________________________________
convolution2d_36 (Convolution2D) (None, , , ) activation_36[][]
____________________________________________________________________________________________________
merge_17 (Merge) (None, , , ) averagepooling2d_2[][]
convolution2d_28[][]
convolution2d_30[][]
convolution2d_32[][]
convolution2d_34[][]
convolution2d_36[][]
____________________________________________________________________________________________________
batchnormalization_37 (BatchNorm (None, , , ) merge_17[][]
____________________________________________________________________________________________________
activation_37 (Activation) (None, , , ) batchnormalization_37[][]
____________________________________________________________________________________________________
convolution2d_37 (Convolution2D) (None, , , ) activation_37[][]
____________________________________________________________________________________________________
batchnormalization_38 (BatchNorm (None, , , ) convolution2d_37[][]
____________________________________________________________________________________________________
activation_38 (Activation) (None, , , ) batchnormalization_38[][]
____________________________________________________________________________________________________
convolution2d_38 (Convolution2D) (None, , , ) activation_38[][]
____________________________________________________________________________________________________
merge_18 (Merge) (None, , , ) averagepooling2d_2[][]
convolution2d_28[][]
convolution2d_30[][]
convolution2d_32[][]
convolution2d_34[][]
convolution2d_36[][]
convolution2d_38[][]
____________________________________________________________________________________________________
batchnormalization_39 (BatchNorm (None, , , ) merge_18[][]
____________________________________________________________________________________________________
activation_39 (Activation) (None, , , ) batchnormalization_39[][]
____________________________________________________________________________________________________
globalaveragepooling2d_1 (Global (None, ) activation_39[][]
____________________________________________________________________________________________________
dense_1 (Dense) (None, ) globalaveragepooling2d_1[][]
====================================================================================================
Total params: ,
Trainable params: ,
Non-trainable params: ,
____________________________________________________________________________________________________
Finished compiling
Building model...
五.疑问：
1.运行完keras实验之后发现，居然在每个CONV(48,1,1)-CONV(12,3,3)- 后面都有一个Merge，可是在代码中我并没有发现呀，哪里来的？肯定是我看漏了，可是它是从哪来的呢？
答：原来在dense_block的定义中有这样一句话看掉了：
for i in range(nb_layers):
x = conv_block(x, growth_rate, bottleneck, dropout_rate, weight_decay)
feature_list.append(x)
x = merge(feature_list, mode='concat', concat_axis=concat_axis)
nb_filter += growth_rate
意思就是在每个这样一个模块后，都要进行Merge，即：就是把每一层的输出都串联在一起，从而组成一个新的tensor。
2.为什么每个denseblock里面的层数n_layers=((40-4)/3)//2=6.其中//2表示除以2后向下取整？即为什么是减4？
答：因为该结构中层，除了dense block 中有很多层外，还1个初始的卷积层、2个过渡层、以及1个最后分类输出层。注意：在该论文中，讲的结构深度depth为L，它并不包括输入层在内。
所以对本论文中的深度depth或L的定义如下：
a.初始的卷积conv，算作1层；
b.每个过渡层，算作1层；
c.每个dense block中的CONV(48,1,1)-CONV(12,3,3)模块，算作2层，即：1个CONV就算作1层；
d.最后的输出模块Relu-GlobalAveragePool-softmax，算作1层。
也可这么说：深度就是卷积层的层数加上1个softmax层。
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