残差网络resnet
残差网络
论文链接：https://arxiv.org/pdf/1512.03385.pdf
产生背景：随着网络深度的增加,导致了训练误差的增大；究其原因是，网络没有被很好的优化。
残差网络可以很深的原因：在一定程度上解决了梯度消失。
残差单元如下图：
当 维 度 x = F 当维度x=F 当维度x=F
y = F ( x , { W i } ) + x F = W 2 σ ( W 1 x ) \begin{aligned} y &= F(x,\{W_i\})+x \\ F &=W_2\sigma(W_1x) \end{aligned} yF=F(x,{Wi})+x=W2σ(W1x)
当 维 度 x ≠ F 当维度x\ne F 当维度x̸=F
y = F ( x , { W i } ) + W s x F = W 2 σ ( W 1 x ) \begin{aligned} y &= F(x,\{W_i\})+W_sx \\ F &=W_2\sigma(W_1x) \end{aligned} yF=F(x,{Wi})+Wsx=W2σ(W1x)
主要的网络结构
resnet网络的设计原则：
1.使用3x3卷积
2.stride=2时，ls通道数变2倍
4.几乎没有pooling
5.没有全连接层
6.没有使用dropout
caffe中的残差网络
protobuf链接:https://github.com/KaimingHe/deep-residual-networks
对于每个卷积层都是：conv->bn->scale->relu
layer {
bottom: "data"
top: "conv1"
name: "conv1"
type: "Convolution"
convolution_param {
num_output: 64
kernel_size: 7
pad: 3
stride: 2
bias_term: false
}
}
layer {
bottom: "conv1"
top: "conv1"
name: "bn_conv1"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "conv1"
top: "conv1"
name: "scale_conv1"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
top: "conv1"
bottom: "conv1"
name: "conv1_relu"
type: "ReLU"
}
其可视化效果为(使用netscope可视化)：
为了减小计算，从conv2_x到conv3_x,都增加一个1x1的卷积核，即为
layer {
bottom: "res2a_branch2a"
top: "res2a_branch2a"
name: "bn2a_branch2a"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "res2a_branch2a"
top: "res2a_branch2a"
name: "scale2a_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
top: "res2a_branch2a"
bottom: "res2a_branch2a"
name: "res2a_branch2a_relu"
type: "ReLU"
}
layer {
bottom: "res2a_branch2a"
top: "res2a_branch2b"
name: "res2a_branch2b"
type: "Convolution"
convolution_param {
num_output: 64
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
bottom: "res2a_branch2b"
top: "res2a_branch2b"
name: "bn2a_branch2b"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "res2a_branch2b"
top: "res2a_branch2b"
name: "scale2a_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
top: "res2a_branch2b"
bottom: "res2a_branch2b"
name: "res2a_branch2b_relu"
type: "ReLU"
}
layer {
bottom: "res2a_branch2b"
top: "res2a_branch2c"
name: "res2a_branch2c"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
}
layer {
bottom: "res2a_branch2c"
top: "res2a_branch2c"
name: "bn2a_branch2c"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "res2a_branch2c"
top: "res2a_branch2c"
name: "scale2a_branch2c"
type: "Scale"
scale_param {
bias_term: true
}
}
其图为
从conv1到conv2过渡时，由于通读数不同，需要对x进行project.
layer {
bottom: "pool1"
top: "res2a_branch1"
name: "res2a_branch1"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
}
layer {
bottom: "res2a_branch1"
top: "res2a_branch1"
name: "bn2a_branch1"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "res2a_branch1"
top: "res2a_branch1"
name: "scale2a_branch1"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "pool1"
top: "res2a_branch2a"
name: "res2a_branch2a"
type: "Convolution"
convolution_param {
num_output: 64
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
}
layer {
bottom: "res2a_branch2a"
top: "res2a_branch2a"
name: "bn2a_branch2a"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "res2a_branch2a"
top: "res2a_branch2a"
name: "scale2a_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
top: "res2a_branch2a"
bottom: "res2a_branch2a"
name: "res2a_branch2a_relu"
type: "ReLU"
}
layer {
bottom: "res2a_branch2a"
top: "res2a_branch2b"
name: "res2a_branch2b"
type: "Convolution"
convolution_param {
num_output: 64
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
bottom: "res2a_branch2b"
top: "res2a_branch2b"
name: "bn2a_branch2b"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "res2a_branch2b"
top: "res2a_branch2b"
name: "scale2a_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
top: "res2a_branch2b"
bottom: "res2a_branch2b"
name: "res2a_branch2b_relu"
type: "ReLU"
}
layer {
bottom: "res2a_branch2b"
top: "res2a_branch2c"
name: "res2a_branch2c"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
}
layer {
bottom: "res2a_branch2c"
top: "res2a_branch2c"
name: "bn2a_branch2c"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "res2a_branch2c"
top: "res2a_branch2c"
name: "scale2a_branch2c"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "res2a_branch1"
bottom: "res2a_branch2c"
top: "res2a"
name: "res2a"
type: "Eltwise"
}
layer {
bottom: "res2a"
top: "res2a"
name: "res2a_relu"
type: "ReLU"
}
当x和F维度相同时，就不需要projection了。
layer {
bottom: "res2a_branch1"
bottom: "res2a_branch2c"
top: "res2a"
name: "res2a"
type: "Eltwise"
}
layer {
bottom: "res2a"
top: "res2a"
name: "res2a_relu"
type: "ReLU"
}
layer {
bottom: "res2a"
top: "res2b_branch2a"
name: "res2b_branch2a"
type: "Convolution"
convolution_param {
num_output: 64
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
}
layer {
bottom: "res2b_branch2a"
top: "res2b_branch2a"
name: "bn2b_branch2a"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "res2b_branch2a"
top: "res2b_branch2a"
name: "scale2b_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
top: "res2b_branch2a"
bottom: "res2b_branch2a"
name: "res2b_branch2a_relu"
type: "ReLU"
}
layer {
bottom: "res2b_branch2a"
top: "res2b_branch2b"
name: "res2b_branch2b"
type: "Convolution"
convolution_param {
num_output: 64
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
bottom: "res2b_branch2b"
top: "res2b_branch2b"
name: "bn2b_branch2b"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "res2b_branch2b"
top: "res2b_branch2b"
name: "scale2b_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
top: "res2b_branch2b"
bottom: "res2b_branch2b"
name: "res2b_branch2b_relu"
type: "ReLU"
}
layer {
bottom: "res2b_branch2b"
top: "res2b_branch2c"
name: "res2b_branch2c"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
}
layer {
bottom: "res2b_branch2c"
top: "res2b_branch2c"
name: "bn2b_branch2c"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "res2b_branch2c"
top: "res2b_branch2c"
name: "scale2b_branch2c"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "res2a"
bottom: "res2b_branch2c"
top: "res2b"
name: "res2b"
type: "Eltwise"
}
layer {
bottom: "res2b"
top: "res2b"
name: "res2b_relu"
type: "ReLU"
}
