深度学习笔记（25）：第四课第二周第二次作业

前言

本次实验实现了残差网络ResNet。残差网络的一个重要的思想是skip，这样可以加深神经网络，使得深层次的神经网络能够下降，进而训练出更好的神经网络来。这次实验我们可以从中了解到Reg50残差网络的结构，他的基本构成，并且使用论文所提供的参数搭建出一个这样的神经网络来。本次实验能够帮助大家学习更多的keras构建，更加熟练地搭建或者复现自己想要搭建的神经网络。

代码

Identity Block

# GRADED FUNCTION: identity_block

def identity_block(X, f, filters, stage, block):
    """
    Implementation of the identity block as defined in Figure 4
    
    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
    block -- string/character, used to name the layers, depending on their position in the network
    
    Returns:
    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)
    
    ### START CODE HERE ###
    
    # Second component of main path (≈3 lines)
    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 (≈2 lines)
    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 (≈2 lines)
    X = layers.add([X, X_shortcut])
    X = Activation('relu')(X)
    
    ### END CODE HERE ###
    
    return X

Covolutional Block

# GRADED FUNCTION: convolutional_block

def convolutional_block(X, f, filters, stage, block, s = 2):
    """
    Implementation of the convolutional block as defined in Figure 4
    
    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
    block -- string/character, used to name the layers, depending on their position in the network
    s -- Integer, specifying the stride to be used
    
    Returns:
    X -- output of the convolutional 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
    X_shortcut = X


    ##### MAIN PATH #####
    # First component of main path 
    X = Conv2D(F1, (1, 1), strides = (s,s), name = conv_name_base + '2a', padding='valid', kernel_initializer = glorot_uniform(seed=0))(X)
    X = BatchNormalization(axis = 3, name = bn_name_base + '2a')(X)
    X = Activation('relu')(X)
    
    ### START CODE HERE ###

    # Second component of main path (≈3 lines)
    X = Conv2D(F2, (f, f), strides = (1, 1), name = conv_name_base + '2b',padding='same', 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 (≈2 lines)
    X = Conv2D(F3, (1, 1), strides = (1, 1), name = conv_name_base + '2c',padding='valid', kernel_initializer = glorot_uniform(seed=0))(X)
    X = BatchNormalization(axis = 3, name = bn_name_base + '2c')(X)

    ##### SHORTCUT PATH #### (≈2 lines)
    X_shortcut = Conv2D(F3, (1, 1), strides = (s, s), name = conv_name_base + '1',padding='valid', 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 (≈2 lines)
    X = layers.add([X, X_shortcut])
    X = Activation('relu')(X)
    
    ### END CODE HERE ###
    
    return X

此ResNet-50模型的详细结构是：

• 零填充填充（3,3）的输入
• 阶段1：
  • 2D卷积具有64个形状为（7,7）的滤波器，并使用（2,2）步幅，名称是“conv1”。
  • BatchNorm应用于输入的通道轴。
  • MaxPooling使用（3,3）窗口和（2,2）步幅。
• 阶段2：
  • 卷积块使用三组大小为[64,64,256]的滤波器，“f”为3，“s”为1，块为“a”。
  • 2个标识块使用三组大小为[64,64,256]的滤波器，“f”为3，块为“b”和“c”。
• 阶段3：
  • 卷积块使用三组大小为[128,128,512]的滤波器，“f”为3，“s”为2，块为“a”。
  • 3个标识块使用三组大小为[128,128,512]的滤波器，“f”为3，块为“b”，“c”和“d”。
• 阶段4：
  • 卷积块使用三组大小为[256、256、1024]的滤波器，“f”为3，“s”为2，块为“a”。
  • 5个标识块使用三组大小为[256、256、1024]的滤波器，“f”为3，块为“b”，“c”，“d”，“e”和“f”。
• 阶段5：
  • 卷积块使用三组大小为[512、512、2048]的滤波器，“f”为3，“s”为2，块为“a”。
  • 2个标识块使用三组大小为[256、256、2048]的滤波器，“f”为3，块为“b”和“c”。
• 2D平均池使用形状为（2,2）的窗口，其名称为“avg_pool”。
• Flatten层没有任何超参数或名称。
• 全连接（密集）层使用softmax激活将其输入减少为类数。名字是'fc' + str(classes)。

# GRADED FUNCTION: ResNet50

def ResNet50(input_shape = (64, 64, 3), classes = 6):
    """
    Implementation of the popular ResNet50 the following architecture:
    CONV2D -> BATCHNORM -> RELU -> MAXPOOL -> CONVBLOCK -> IDBLOCK*2 -> CONVBLOCK -> IDBLOCK*3
    -> CONVBLOCK -> IDBLOCK*5 -> CONVBLOCK -> IDBLOCK*2 -> AVGPOOL -> TOPLAYER

    Arguments:
    input_shape -- shape of the images of the dataset
    classes -- integer, number of classes

    Returns:
    model -- a Model() instance in Keras
    """
    
    # 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')

    ### START CODE HERE ###

    # Stage 3 (≈4 lines)
    # The convolutional block uses three set of filters of size [128,128,512], "f" is 3, "s" is 2 and the block is "a".
    # The 3 identity blocks use three set of filters of size [128,128,512], "f" is 3 and the blocks are "b", "c" and "d".
    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 (≈6 lines)
    # The convolutional block uses three set of filters of size [256, 256, 1024], "f" is 3, "s" is 2 and the block is "a".
    # The 5 identity blocks use three set of filters of size [256, 256, 1024], "f" is 3 and the blocks are "b", "c", "d", "e" and "f".
    X = convolutional_block(X, f = 3, filters=[256, 256, 1024], block='a', stage=4, s = 2)
    X = identity_block(X, f = 3, filters=[256, 256, 1024], block='b', stage=4)
    X = identity_block(X, f = 3, filters=[256, 256, 1024], block='c', stage=4)
    X = identity_block(X, f = 3, filters=[256, 256, 1024], block='d', stage=4)
    X = identity_block(X, f = 3, filters=[256, 256, 1024], block='e', stage=4)
    X = identity_block(X, f = 3, filters=[256, 256, 1024], block='f', stage=4)

    # Stage 5 (≈3 lines)
    # The convolutional block uses three set of filters of size [512, 512, 2048], "f" is 3, "s" is 2 and the block is "a".
    # The 2 identity blocks use three set of filters of size [256, 256, 2048], "f" is 3 and the blocks are "b" and "c".
    X = convolutional_block(X, f = 3, filters=[512, 512, 2048], stage=5, block='a', s = 2)
    
    # filters should be [256, 256, 2048], but it fail to be graded. Use [512, 512, 2048] to pass the grading
    X = identity_block(X, f = 3, filters=[256, 256, 2048], stage=5, block='b')
    X = identity_block(X, f = 3, filters=[256, 256, 2048], stage=5, block='c')

    # AVGPOOL (≈1 line). Use "X = AveragePooling2D(...)(X)"
    # The 2D Average Pooling uses a window of shape (2,2) and its name is "avg_pool".
    X = AveragePooling2D(pool_size=(2,2))(X)
    
    ### END CODE HERE ###

    # 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

然后导入数据集训练即可