请帮我使用2.2.0的TensorFlow来搭建一个ResNet-50的网络框架

好的，以下是使用TensorFlow 2.2.0搭建ResNet-50的网络框架的代码示例：

import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, BatchNormalization, ReLU, Add, GlobalAveragePooling2D, Dense
from tensorflow.keras.models import Model

def identity_block(X, filters, stage, block):
    """
    实现恒等块
    
    参数：
    X - 输入的tensor类型的数据，维度为(批量大小, 图像高度, 图像宽度, 图像通道数)
    filters - 整数，指定主路径中间的卷积层的过滤器数目
    stage - 整数，用于命名层，取决于其在网络中的位置
    block - 用于命名层，取决于其在网络中的位置
    
    返回：
    X - 恒等块的输出，tensor类型，维度为(批量大小, 图像高度, 图像宽度, 图像通道数)
    """
    
    # 定义命名规则
    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'
    
    # 获取过滤器
    F1, F2 = filters
    
    # 保存输入数据，用于后续添加
    X_shortcut = X
    
    # 主路径的第一部分
    X = Conv2D(filters=F1, kernel_size=(1, 1), strides=(1, 1), padding='valid', name=conv_name_base+'2a', kernel_initializer=tf.keras.initializers.glorot_uniform(seed=0))(X)
    X = BatchNormalization(axis=3, name=bn_name_base+'2a')(X)
    X = ReLU()(X)
    
    # 主路径的第二部分
    X = Conv2D(filters=F2, kernel_size=(3, 3), strides=(1, 1), padding='same', name=conv_name_base+'2b', kernel_initializer=tf.keras.initializers.glorot_uniform(seed=0))(X)
    X = BatchNormalization(axis=3, name=bn_name_base+'2b')(X)
    X = ReLU()(X)
    
    # 主路径的第三部分，添加恒等块
    X = Add()([X, X_shortcut])
    X = ReLU()(X)
    
    return X

def convolutional_block(X, filters, stage, block, strides=(2, 2)):
    """
    实现卷积块
    
    参数：
    X - 输入的tensor类型的数据，维度为(批量大小, 图像高度, 图像宽度, 图像通道数)
    filters - 整数，指定主路径中间的卷积层的过滤器数目
    stage - 整数，用于命名层，取决于其在网络中的位置
    block - 用于命名层，取决于其在网络中的位置
    strides - 步幅
    
    返回：
    X - 卷积块的输出，tensor类型，维度为(批量大小, 图像高度, 图像宽度, 图像通道数)
    """
    
    # 定义命名规则
    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'
    
    # 获取过滤器
    F1, F2 = filters
    
    # 保存输入数据，用于后续添加
    X_shortcut = X
    
    # 主路径的第一部分
    X = Conv2D(filters=F1, kernel_size=(1, 1), strides=strides, padding='valid', name=conv_name_base+'2a', kernel_initializer=tf.keras.initializers.glorot_uniform(seed=0))(X)
    X = BatchNormalization(axis=3, name=bn_name_base+'2a')(X)
    X = ReLU()(X)
    
    # 主路径的第二部分
    X = Conv2D(filters=F2, kernel_size=(3, 3), strides=(1, 1), padding='same', name=conv_name_base+'2b', kernel_initializer=tf.keras.initializers.glorot_uniform(seed=0))(X)
    X = BatchNormalization(axis=3, name=bn_name_base+'2b')(X)
    X = ReLU()(X)
    
    # 附加路径
    X_shortcut = Conv2D(filters=F2, kernel_size=(1, 1), strides=strides, padding='valid', name=conv_name_base+'1', kernel_initializer=tf.keras.initializers.glorot_uniform(seed=0))(X_shortcut)
    X_shortcut = BatchNormalization(axis=3, name=bn_name_base+'1')(X_shortcut)
    
    # 主路径的第三部分，添加卷积块
    X = Add()([X, X_shortcut])
    X = ReLU()(X)
    
    return X

def ResNet50(input_shape=(224, 224, 3), classes=1000):
    """
    实现ResNet50
    
    参数：
    input_shape - 输入图像的维度
    classes - 分类数目
    
    返回：
    model - Keras框架的模型
    """
    
    # 定义模型输入
    X_input = Input(input_shape)
    
    # 0填充
    X = tf.keras.layers.ZeroPadding2D((3, 3))(X_input)
    
    # Stage 1
    X = Conv2D(filters=64, kernel_size=(7, 7), strides=(2, 2), name='conv1', kernel_initializer=tf.keras.initializers.glorot_uniform(seed=0))(X)
    X = BatchNormalization(axis=3, name='bn_conv1')(X)
    X = ReLU()(X)
    X = MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(X)
    
    # Stage 2
    X = convolutional_block(X, filters=[64, 256], stage=2, block='a', strides=(1, 1))
    X = identity_block(X, filters=[64, 256], stage=2, block='b')
    X = identity_block(X, filters=[64, 256], stage=2, block='c')
    
    # Stage 3
    X = convolutional_block(X, filters=[128, 512], stage=3, block='a', strides=(2, 2))
    X = identity_block(X, filters=[128, 512], stage=3, block='b')
    X = identity_block(X, filters=[128, 512], stage=3, block='c')
    X = identity_block(X, filters=[128, 512], stage=3, block='d')
    
    # Stage 4
    X = convolutional_block(X, filters=[256, 1024], stage=4, block='a', strides=(2, 2))
    X = identity_block(X, filters=[256, 1024], stage=4, block='b')
    X = identity_block(X, filters=[256, 1024], stage=4, block='c')
    X = identity_block(X, filters=[256, 1024], stage=4, block='d')
    X = identity_block(X, filters=[256, 1024], stage=4, block='e')
    X = identity_block(X, filters=[256, 1024], stage=4, block='f')
    
    # Stage 5
    X = convolutional_block(X, filters=[512, 2048], stage=5, block='a', strides=(2, 2))
    X = identity_block(X, filters=[512, 2048], stage=5, block='b')
    X = identity_block(X, filters=[512, 2048], stage=5, block='c')
    
    # 平均池化层
    X = GlobalAveragePooling2D()(X)
    
    # 全连接层
    X = Dense(units=classes, activation='softmax', name='fc'+str(classes), kernel_initializer=tf.keras.initializers.glorot_uniform(seed=0))(X)
    
    # 创建模型
    model = Model(inputs=X_input, outputs=X, name='ResNet50')
    
    return model

您可以按照以下方式使用该模型：

model = ResNet50(input_shape=(224, 224, 3), classes=1000)
model.summary()

这将打印出模型结构和参数数量的概述。