目录

1.残差网络基础

1.1基本概念

非常非常深的神经网络是很难训练的，因为存在梯度消失和梯度爆炸问题。ResNets是由残差块（Residual block）构建的，首先解释一下什么是残差块。
残差块结构：

1.2VGG19、ResNet34结构图

1.3 梯度弥散和网络退化

1.4 残差块变体

自AlexNet以来，state-of-the-art的CNN结构都在不断地变深。VGG和GoogLeNet分别有19个和22个卷积层，而AlexNet只有5个。

ResNet基本思想：引入跳过一层或多层的shortcut connection。

1.5 ResNet模型变体

（1）ResNeXt


（2）DenseNet

1.6 Residual Network补充

1.7 1*1卷积核（补充）

*1的卷积核相当于对一个切片上的nc个单元，都应用了一个全连接的神经网络

2.残差网络介绍（何凯明）

3.ResNet-50(Ng)

3.1 非常深的神经网络问题

3.2 建立一个残差网络

使用 short connect的残差块，能很容易的学习标识功能。在其他ResNet块上进行堆栈，不会损害训练集的性能。
（1）identity block （标准块）

（2）convolutional block
当输入和输出的尺寸不匹配时，使用

3.3first ResNet model（50layers）

3.4 代码实现

def identity_block(X, f, filters, stage, block):"""X       -- 输入tensor, shape=(m, n_H_prev, n_W_prev, n_C_prev)f       -- identity block主路径，中间卷积层的滤波器大小filters -- [f1,f2,f3]对应主路径中三个卷积层的滤波器个数stage   -- 用于命名层，阶段（数字表示:1/2/3/4/5...）block   -- 用于命名层，每个阶段中的残差块（字符表示：a/b/c/d/e/f...）Return   X  -- identity block 输出tensor  shape=(m, n_H, n_W, n_C)    """"define name basis"conv_name_base = 'res' + str(stage) + block + '_branch'bn_name_base   = 'bn'  + str(stage) + block + '_branch'"get filter number"F1, F2, F3 = filters"save the input value"X_shortcut = X"主路径的第一层：conv-bn-relu"X = Conv2D(filters=F1, kernel_size=(1,1), strides=(1,1), padding='valid',name=conv_name_base+'2a', kernel_initializer=glorot_uniform(seed=0))(X)X = BatchNormalization(axis=3, name=bn_name_base+'2a')(X)X = Activation('relu')(X)"主路径的第二层：conv-bn-relu"X = Conv2D(filters=F2, kernel_size=(f,f), strides=(1,1), padding='same',name=conv_name_base+'2b', kernel_initializer=glorot_uniform(seed=0))(X)X = BatchNormalization(axis=3, name=bn_name_base+'2b')(X)X = Activation('relu')(X)"主路径的第三层：conv-bn"X = Conv2D(filters=F3, kernel_size=(1,1), strides=(1,1), padding='valid',name=conv_name_base+'2c', kernel_initializer=glorot_uniform(seed=0))(X)X = BatchNormalization(axis=3, name=bn_name_base+'2c')(X)"add shortcut and relu"X = Add()([X, X_shortcut])X = Activation('relu')(X)return X"""s -- convolutional block中，主路径第一个卷积层和shortcut卷积层，滤波器滑动步长，s=2，用来降低图片大小"""
def convolutional_block(X, f, filters, stage, block, s=2):conv_name_base = 'res' + str(stage) + block + '_branch'bn_name_base   = 'bn'  + str(stage) + block + '_branch'F1, F2, F3 = filtersX_shortcut = X"1st layer"X = Conv2D(filters=F1, kernel_size=(1,1), strides=(s,s), padding='valid',name=conv_name_base+'2a', kernel_initializer=glorot_uniform(seed=0))(X)X = BatchNormalization(axis=3, name=bn_name_base+'2a')(X)X = Activation('relu')(X)"2nd layer"X = Conv2D(filters=F2, kernel_size=(f,f), strides=(1,1), padding='same',name=conv_name_base+'2b', kernel_initializer=glorot_uniform(seed=0))(X)X = BatchNormalization(axis=3, name=bn_name_base+'2b')(X)X = Activation('relu')(X)"3rd layer"X = Conv2D(filters=F3, kernel_size=(1,1), strides=(1,1), padding='valid',name=conv_name_base+'2c', kernel_initializer=glorot_uniform(seed=0))(X)X = BatchNormalization(axis=3, name=bn_name_base+'2c')(X)"shortcut path"X_shortcut = Conv2D(filters=F3, kernel_size=(1,1), strides=(s,s), padding='valid',name=conv_name_base+'1', kernel_initializer=glorot_uniform(seed=0))(X_shortcut)X_shortcut = BatchNormalization(axis=3, name=bn_name_base+'1')(X_shortcut)X = Add()([X, X_shortcut])X = Activation('relu')(X)return X

def ResNet50(input_shape=(64,64,3), classes=6):"""input - zero padding stage1：conv - BatchNorm - relu - maxpoolstage2: conv block - identity block *2stage3: conv block - identity block *3stage4: conv block - identity block *5stage5: conv block - identity block *2avgpool - flatten - fully connectinput_shape  -- 输入图像的shapeclasses      -- 类别数Return： a model() instance in Keras""""用input_shape定义一个输入tensor"X_input = Input(input_shape)"zero padding"X = ZeroPadding2D((3,3))(X_input)"stage 1"X = Conv2D(filters=64, kernel_size=(7,7), strides=(2,2), padding='valid',name='conv1', kernel_initializer=glorot_uniform(seed=0))(X)X = BatchNormalization(axis=3, name='bn_conv1')(X)X = MaxPooling2D((3,3), strides=(2,2))(X)"stage 2"X = convolutional_block(X, f=3, filters=[64,64,256], stage=2, block='a', s=1)X = identity_block(X,f=3, filters=[64,64,256], stage=2, block='b')X = identity_block(X,f=3, filters=[64,64,256], stage=2, block='c')"stage 3"X = convolutional_block(X, f=3, filters=[128,128,512], stage=3, block='a', s=2)X = identity_block(X, f=3, filters=[128,128,512], stage=3, block='b')X = identity_block(X, f=3, filters=[128,128,512], stage=3, block='c')X = identity_block(X, f=3, filters=[128,128,512], stage=3, block='d')"stage 4"X = convolutional_block(X, f=3, filters=[256,256,1024], stage=4, block='a', s=2)X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='b')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='c')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='d')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='e')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='f')"stage 5"X = convolutional_block(X, f=3, filters=[512,512,2048], stage=5, block='a', s=2)X = identity_block(X, f=3, filters=[512,512,2048], stage=5, block='b')X = identity_block(X, f=3, filters=[512,512,2048], stage=5, block='c')"fully connect"X = AveragePooling2D((2,2), name='avg_pool')(X)X = Flatten()(X)X = Dense(classes, activation='softmax', name='fc'+str(classes),kernel_initializer=glorot_uniform(seed=0))(X)"a model() instance in Keras"model = Model(inputs=X_input, outputs=X, name='ResNet50')return model

4.ResNet 100-1000层

4.1 152层

代码

def ResNet101(input_shape=(64,64,3), classes=6):"""input - zero padding stage1：conv - BatchNorm - relu - maxpoolstage2: conv block - identity block *2stage3: conv block - identity block *3stage4: conv block - identity block *22stage5: conv block - identity block *2avgpool - flatten - fully connectinput_shape  -- 输入图像的shapeclasses      -- 类别数Return： a model() instance in Keras""""用input_shape定义一个输入tensor"X_input = Input(input_shape)"zero padding"X = ZeroPadding2D((3,3))(X_input)"stage 1"X = Conv2D(filters=64, kernel_size=(7,7), strides=(2,2), padding='valid',name='conv1', kernel_initializer=glorot_uniform(seed=0))(X)X = BatchNormalization(axis=3, name='bn_conv1')(X)X = MaxPooling2D((3,3), strides=(2,2))(X)"stage 2"X = convolutional_block(X, f=3, filters=[4,4,16], stage=2, block='a', s=1)X = identity_block(X,f=3, filters=[4,4,16], stage=2, block='b')X = identity_block(X,f=3, filters=[4,4,16], stage=2, block='c')"stage 3"X = convolutional_block(X, f=3, filters=[8,8,32], stage=3, block='a', s=2)X = identity_block(X, f=3, filters=[8,8,32], stage=3, block='b')X = identity_block(X, f=3, filters=[8,8,32], stage=3, block='c')X = identity_block(X, f=3, filters=[8,8,32], stage=3, block='d')"stage 4""identity block from 5(50 layers) to 22(101 layers)"X = convolutional_block(X, f=3, filters=[16,16,64], stage=4, block='a', s=2)X = identity_block(X, f=3, filters=[16,16,64], stage=4, block='b')X = identity_block(X, f=3, filters=[16,16,64], stage=4, block='c')X = identity_block(X, f=3, filters=[16,16,64], stage=4, block='d')X = identity_block(X, f=3, filters=[16,16,64], stage=4, block='e')X = identity_block(X, f=3, filters=[16,16,64], stage=4, block='f')X = identity_block(X, f=3, filters=[16,16,64], stage=4, block='g')X = identity_block(X, f=3, filters=[16,16,64], stage=4, block='h')X = identity_block(X, f=3, filters=[16,16,64], stage=4, block='i')X = identity_block(X, f=3, filters=[16,16,64], stage=4, block='j')X = identity_block(X, f=3, filters=[16,16,64], stage=4, block='k')X = identity_block(X, f=3, filters=[16,16,64], stage=4, block='l')X = identity_block(X, f=3, filters=[16,16,64], stage=4, block='m')X = identity_block(X, f=3, filters=[16,16,64], stage=4, block='n')X = identity_block(X, f=3, filters=[16,16,64], stage=4, block='o')X = identity_block(X, f=3, filters=[16,16,64], stage=4, block='p')X = identity_block(X, f=3, filters=[16,16,64], stage=4, block='q')X = identity_block(X, f=3, filters=[16,16,64], stage=4, block='r')X = identity_block(X, f=3, filters=[16,16,64], stage=4, block='s')X = identity_block(X, f=3, filters=[16,16,64], stage=4, block='t')X = identity_block(X, f=3, filters=[16,16,64], stage=4, block='u')X = identity_block(X, f=3, filters=[16,16,64], stage=4, block='v')X = identity_block(X, f=3, filters=[16,16,64], stage=4, block='w')"stage 5"X = convolutional_block(X, f=3, filters=[32,32,128], stage=5, block='a', s=2)X = identity_block(X, f=3, filters=[32,32,128], stage=5, block='b')X = identity_block(X, f=3, filters=[32,32,128], stage=5, block='c')"fully connect"X = AveragePooling2D((2,2), name='avg_pool')(X)X = Flatten()(X)X = Dense(classes, activation='softmax', name='fc'+str(classes),kernel_initializer=glorot_uniform(seed=0))(X)"a model() instance in Keras"model = Model(inputs=X_input, outputs=X, name='ResNet101')return model

def ResNet152(input_shape=(64,64,3), classes=6):"""input - zero padding stage1：conv - BatchNorm - relu - maxpoolstage2: conv block - identity block *2stage3: conv block - identity block *7stage4: conv block - identity block *35stage5: conv block - identity block *2avgpool - flatten - fully connectinput_shape  -- 输入图像的shapeclasses      -- 类别数Return： a model() instance in Keras""""用input_shape定义一个输入tensor"X_input = Input(input_shape)"zero padding"X = ZeroPadding2D((3,3))(X_input)"stage 1"X = Conv2D(filters=64, kernel_size=(7,7), strides=(2,2), padding='valid',name='conv1', kernel_initializer=glorot_uniform(seed=0))(X)X = BatchNormalization(axis=3, name='bn_conv1')(X)X = MaxPooling2D((3,3), strides=(2,2))(X)"stage 2"X = convolutional_block(X, f=3, filters=[64,64,256], stage=2, block='a', s=1)X = identity_block(X,f=3, filters=[64,64,256], stage=2, block='b')X = identity_block(X,f=3, filters=[64,64,256], stage=2, block='c')"stage 3""identity block from 3(101 layers) to 7(152 layers)"X = convolutional_block(X, f=3, filters=[128,128,512], stage=3, block='a', s=2)X = identity_block(X, f=3, filters=[128,128,512], stage=3, block='b')X = identity_block(X, f=3, filters=[128,128,512], stage=3, block='c')X = identity_block(X, f=3, filters=[128,128,512], stage=3, block='d')X = identity_block(X, f=3, filters=[128,128,512], stage=3, block='e')X = identity_block(X, f=3, filters=[128,128,512], stage=3, block='f')X = identity_block(X, f=3, filters=[128,128,512], stage=3, block='g')X = identity_block(X, f=3, filters=[128,128,512], stage=3, block='h')"stage 4""identity block from 22(101 layers) to 35(152 layers)"X = convolutional_block(X, f=3, filters=[256,256,1024], stage=4, block='a', s=2)X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='b')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='c')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='d')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='e')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='f')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='g')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='h')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='i')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='j')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='k')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='l')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='m')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='n')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='o')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='p')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='q')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='r')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='s')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='t')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='u')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='v')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='w')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='x')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='y')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='z')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='z1')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='z2')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='z3')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='z4')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='z5')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='z6')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='z7')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='z8')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='z9')X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block='z10')"stage 5"X = convolutional_block(X, f=3, filters=[512,512,2048], stage=5, block='a', s=2)X = identity_block(X, f=3, filters=[512,512,2048], stage=5, block='b')X = identity_block(X, f=3, filters=[512,512,2048], stage=5, block='c')"fully connect"X = AveragePooling2D((2,2), name='avg_pool')(X)X = Flatten()(X)X = Dense(classes, activation='softmax', name='fc'+str(classes),kernel_initializer=glorot_uniform(seed=0))(X)"a model() instance in Keras"model = Model(inputs=X_input, outputs=X, name='ResNet152')return model