# 深度学习第19讲：CNN经典论文研读之残差网络ResNet及其keras实现

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Identity Block 的图示如下：



def identity_block(X, f, filters, stage, block):
 
 """
 
 Implementation of the identity block as defined in Figure 3
 Arguments: 
 X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev)
 
 f -- integer, specifying the shape of the middle CONV's window for the main path
 
 filters -- python list of integers, defining the number of filters in the CONV layers of the main path
 stage -- integer, used to name the layers, depending on their position in the network 
 Returns:
 block -- string/character, used to name the layers, depending on their position in the network X -- output of the identity block, tensor of shape (n_H, n_W, n_C) 
 """ # defining name basis
 
 conv_name_base = 'res' + str(stage) + block + '_branch'
 
 bn_name_base = 'bn' + str(stage) + block + '_branch' # Retrieve Filters
 
 F1, F2, F3 = filters
 
 # Save the input value. You'll need this later to add back to the main path.
 
 X_shortcut = X
 
 # First component of main path
 
 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)
 
 # Second component of main path
 
 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)
 
 # Third component of main path
 
 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)
 
 # Final step: Add shortcut value to main path, and pass it through a RELU activation
 
 X = Add()([X, X_shortcut])
 
 X = Activation('relu')(X)
 

 
 return X
 

Convolutional Block 的图示如下：



def convolutional_block(X, f, filters, stage, block, s = 2):
 
 """
 
 Implementation of the convolutional block as defined in Figure 4
 Arguments: 
 f -- integer, specifying the shape of the middle CONV's window for the main path
 X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev) 
 filters -- python list of integers, defining the number of filters in the CONV layers of the main path
 stage -- integer, used to name the layers, depending on their position in the network 
 X -- output of the convolutional block, tensor of shape (n_H, n_W, n_C)
 block -- string/character, used to name the layers, depending on their position in the network s -- Integer, specifying the stride to be used Returns: 
 """ # defining name basis
 
 conv_name_base = 'res' + str(stage) + block + '_branch'
 
 bn_name_base = 'bn' + str(stage) + block + '_branch' # Retrieve Filters
 
 F1, F2, F3 = filters
 
 # Save the input value
 
 X_shortcut = X
 

 
 ##### MAIN PATH ##### # First component of main path
 
 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)
 
 # Second component of main path
 
 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)
 
 # Third component of main path
 
 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)
 
 # Final step: Add shortcut value to main path, and pass it through a RELU activation
 
 X = Add()([X, X_shortcut])
 
 X = Activation('relu')(X)
 
 return X
 

CONV2D -> BATCHNORM -> RELU -> MAXPOOL -> CONVBLOCK -> IDBLOCK2 -> CONVBLOCK -> IDBLOCK3 -> CONVBLOCK -> IDBLOCK5 -> CONVBLOCK -> IDBLOCK2 -> AVGPOOL -> TOPLAYER



def ResNet50(input_shape = (64, 64, 3), classes = 6):
 

 
 # Define the input as a tensor with shape input_shape
 
 X_input = Input(input_shape)
 
 # Zero-Padding
 
 X = ZeroPadding2D((3, 3))(X_input)
 
 # Stage 1
 
 X = Conv2D(64, (7, 7), strides = (2, 2), name = 'conv1', kernel_initializer = glorot_uniform(seed=0))(X)
 
 X = BatchNormalization(axis = 3, name = 'bn_conv1')(X)
 
 X = Activation('relu')(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, 3, [64, 64, 256], stage=2, block='b')
 
 X = identity_block(X, 3, [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, 3, [128, 128, 512], stage=3, block='b')
 
 X = identity_block(X, 3, [128, 128, 512], stage=3, block='c')
 
 X = identity_block(X, 3, [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, 3, [256, 256, 1024], stage=4, block='b')
 
 X = identity_block(X, 3, [256, 256, 1024], stage=4, block='c')
 
 X = identity_block(X, 3, [256, 256, 1024], stage=4, block='d')
 
 X = identity_block(X, 3, [256, 256, 1024], stage=4, block='e')
 
 X = identity_block(X, 3, [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, 3, [512, 512, 2048], stage=5, block='b')
 
 X = identity_block(X, 3, [512, 512, 2048], stage=5, block='c')
 
 # AVGPOOL (≈1 line). Use "X = AveragePooling2D(...)(X)"
 
 X = AveragePooling2D((2, 2), strides=(2, 2))(X)
 
 # output layer
 
 X = Flatten()(X)
 
 X = Dense(classes, activation='softmax', name='fc' + str(classes), kernel_initializer = glorot_uniform(seed=0))(X)
 
 # Create model
 
 model = Model(inputs = X_input, outputs = X, name='ResNet50')
 

 
 return model
 

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