lecture 9：Residual Network （ResNet）
目录
1、残差网络基础
1.1 VGG19、ResNet34结构图
1.2 ResNet残差块
考虑计算成本，对残差块做了计算优化，即将两个3x3的卷积层替换为1x1 + 3x3 + 1x1。
新结构中的中间3x3的卷积层首先在一个降维1x1卷积层下减少了计算，然后在另一个1x1的卷积层下做了还原，既保持了精度又减少了计算量。
ResNet在ImageNet2015夺得冠军
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）short connect/skip connect
（2）残差网络表现好的原因
1.7 1*1卷积核
1*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、ResNet（何凯明PPT）
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。
- 残差块：主路径 + short connect
- 残差块：indentity block & convolutional block，取决于输入/输出的尺寸是否相同
（1）identity block （标准块）
卷积层滤波器:
Filter1 : shape=[1,1], strides=[1,1], padding=’valid’, name=’2a’
Filter2: shape=[f,f], strides=[1,1], padding=’same’, name=’2b’
Filter3: shape=[1,1], strides=[1,1], padding=’valid’, name=’2c’
BatchNorm 归一化 channels axis
（2）convolutional block
当输入和输出的尺寸不匹配时，使用
Short connect 中的卷积层用于调整X的尺寸，使得匹配后面的加法。
Filter1 : shape=[1,1], strides=[s,s], padding=’valid’, name=’2a’
Filter2: shape=[f,f], strides=[1,1], padding=’same’, name=’2b’
Filter3: shape=[1,1], strides=[1,1], padding=’valid’, name=’2c’
Short connect Filter: shape=[1,1], strides=[s,s], ‘valid’, name=’1’
3.3 first ResNet model（50layers）
ResNet-50 每一层输出shape
卷积/池化前后图像大小：(n+2p-f)/s+1
3.4 代码
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 = filters
X_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 - maxpool stage2: conv block - identity block *2 stage3: conv block - identity block *3 stage4: conv block - identity block *5 stage5: conv block - identity block *2 avgpool - flatten - fully connect input_shape -- 输入图像的shape classes -- 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
4.2 1000层
4.3 何凯明paper
4.4代码
def ResNet101(input_shape=(64,64,3), classes=6):
""" input - zero padding stage1：conv - BatchNorm - relu - maxpool stage2: conv block - identity block *2 stage3: conv block - identity block *3 stage4: conv block - identity block *22 stage5: conv block - identity block *2 avgpool - flatten - fully connect input_shape -- 输入图像的shape classes -- 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 - maxpool stage2: conv block - identity block *2 stage3: conv block - identity block *7 stage4: conv block - identity block *35 stage5: conv block - identity block *2 avgpool - flatten - fully connect input_shape -- 输入图像的shape classes -- 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