何凯明大神在2015年提出了Resnet结构，于2016年对于该结构进行了优化，提出了Resnet-bottlenet结构，本文代码基于Resnet-bottlenet结构进行实现
# -*- coding:utf-8 -*-
__author__ = 'xuy'
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import Conv2D, AveragePooling2D, MaxPooling2D, ZeroPadding2D
from keras.layers.core import Activation, Flatten, Dense, Dropout
from keras.layers import Input, add
from keras.models import Model
from keras.regularizers import l2#这里加入l2正则化目的是为了防止过拟合
from keras.utils.vis_utils import plot_model
import keras.backend as K
#resnet做加和操作，因此用add函数，
# googlenet以及densenet做filter的拼接，因此用concatenate
#add和concatenate的区别参考链接：https://blog.csdn.net/u012193416/article/details/79479935
class ResNet:
@staticmethod
def residual_module(x, K, stride, chanDim, reduce=False, reg=1e-4, bnEps=2e-5, bnMom=0.9):#结构参考Figure 12.3右图,引入了shortcut概念，是主网络的侧网络
"""
The residual module of the ResNet architecture.
Parameters:
x: The input to the residual module.
K: The number of the filters that will be learned by the final CONV in the bottlenecks.最终卷积层的输出
stride: Controls the stride of the convolution, help reduce the spatial dimensions of the volume *without*
resorting to max-pooling.
chanDim: Define the axis which will perform batch normalization.
reduce: Cause not all residual module will be responsible for reducing the dimensions of spatial volums -- the
red boolean will control whether reducing spatial dimensions (True) or not (False).是否降维，
reg: Controls the regularization strength to all CONV layers in the residual module.
bnEps: Controls the ε responsible for avoiding 'division by zero' errors when normalizing inputs.防止BN层出现除以0的异常
bnMom: Controls the momentum for the moving average.
Return:
x: Return the output of the residual module.
"""
# The shortcut branch of the ResNet module should be initialize as the input(identity) data.
shortcut = x
# The first block of the ResNet module -- 1x1 CONVs.
bn1 = BatchNormalization(axis=chanDim, epsilon=bnEps, momentum=bnMom)(x)
act1 = Activation("relu")(bn1)
# Because the biases are in the BN layers that immediately follow the convolutions, so there is no need to introduce
#a *second* bias term since we had changed the typical CONV block order, instead of using the *pre-activation* method.
conv1 = Conv2D(int(K * 0.25), (1, 1), use_bias=False, kernel_regularizer=l2(reg))(act1)#filter=K*0.25,kernel_size=(1,1),stride=(1,1)
# The second block of the ResNet module -- 3x3 CONVs.
bn2 = BatchNormalization(axis=chanDim, epsilon=bnEps, momentum=bnMom)(conv1)
act2 = Activation("relu")(bn2)
conv2 = Conv2D(int(K * 0.25), (3, 3), strides=stride, padding="same", use_bias=False, kernel_regularizer=l2(reg))(act2)
# The third block of the ResNet module -- 1x1 CONVs.
bn3 = BatchNormalization(axis=chanDim, epsilon=bnEps, momentum=bnMom)(conv2)
act3 = Activation("relu")(bn3)
conv3 = Conv2D(K, (1, 1), use_bias=False, kernel_regularizer=l2(reg))(act3)
# If we would like to reduce the spatial size, apply a CONV layer to the shortcut.
if reduce:#是否降维，如果降维的话，需要将stride设置为大于1
shortcut = Conv2D(K, (1, 1), strides=stride, use_bias=False, kernel_regularizer=l2(reg))(act1)
# Add together the shortcut (shortcut branch) and the final CONV (main branch).
x = add([conv3, shortcut])
# Return the addition as the output of the Residual module.
return x
@staticmethod
def build(width, height, depth, classes, stages, filters, reg=1e-4, bnEps=2e-5, bnMom=0.9, dataset="cifar"):
# Initialize the input shape to be "channels last" and the channels dimension itself.
inputShape = (height, width, depth)
chanDim = -1
# If channels order is "channels first", modify the input shape and channels dimension.
if K.image_data_format() == "channels_first":
inputShape = (depth, height, width)
chanDim = 1
# Set the input and apply BN layer.
input = Input(shape=inputShape)
# Use BN layer as the first layer, acts as an added level of normalization.在这里第一层使用BN层而不是使用conv，这样可以替代取平均值的操作
x = BatchNormalization(axis=chanDim, epsilon=bnEps, momentum=bnMom)(input)
# Check if trained on the CIFAR dataset.
if dataset == "cifar":
# Apply the first and single CONV layer.
x = Conv2D(filters[0], (3, 3), use_bias=False, padding="same", kernel_regularizer=l2(reg))(x)
# Loop over the number of stages (block names).
for i in range(0, len(stages)):#每阶段的遍历
# Initialize the stride, then apply a residual module used to reduce the spatial size of the input volume.
# If this is the first entry in the stage, we’ll set the stride to (1, 1), indicating that no downsampling
#should be performed. However, for every subsequent stage we’ll apply a residual module with a stride of (2, 2),
#which will allow us to decrease the volume size.
stride = (1, 1) if i == 0 else (2, 2)
# Once we have stacked stages[i] residual modules on top of each other, our for loop brings us back up to here
#where we decrease the spatial dimensions of the volume and repeat the process.
x = ResNet.residual_module(x, filters[i + 1], stride=stride, chanDim=chanDim, reduce=True, bnEps=bnEps, bnMom=bnMom)#进行降维
# Loop over the number of layers in the stage.
for j in range(0, stages[i] - 1):#每层的遍历
# Apply a residual module.
x = ResNet.residual_module(x, filters[i + 1], stride=(1, 1), chanDim=chanDim, bnEps=bnEps, bnMom=bnMom)#不进行降维
# After stacked all the residual modules on top of each other, we would move to the classifier stage.
# Apply BN=>ACT=>POOL, in order to avoid using dense/FC layers we would instead apply Global Averager POOL to reduce
#the volume size to 1x1xclasses.
x = BatchNormalization(axis=chanDim, epsilon=bnEps, momentum=bnMom)(x)
x = Activation("relu")(x)
x = AveragePooling2D((8, 8))(x)
# Softmax classifier.
x = Flatten()(x)
x = Dense(classes, kernel_regularizer=l2(reg))(x)
x = Activation("softmax")(x)
# Construct the model.
model = Model(input, x, name="ResNet")
model.summary()#输出网络结构信息
# plot_model(model, to_file='./output/resnet_visualization.png', show_shapes=True, show_layer_names=True)
# Return the build network architecture.
return model对于网络结构的说明： 我们可以通过这段代码生成resnet.png文件,stages和filters的实现 首先我们在Input之后并不是+conv，而是+BN,这样可以省去对于图像的均值处理 然后由于我们输入是cifar10，因此需要加上卷积层 然后开始进入residual_module函数 1）调用参数reduce=True的residual_model用来降维 2）循环stages[i]-1次,调用residual_model，其参数reduce=False 因此最终residual_module的调用次数是： [1+(3-1)]+[1+(4-1)]+[1+(6-1)]=13
可视化resnet:
import Resnet
from keras.utils import plot_model
model = ResNet.build(32, 32, 3, 10,stages=[3,4,6],filters=[64,128,256,512])#因为googleNet默认输入32*32的图片
plot_model(model, to_file="output/resnet.png", show_shapes=True)最终的网络结构的图片显示：
