python实现resnet

以下是Python实现ResNet的代码示例：

import tensorflow as tf

def identity_block(X, f, filters, stage, block):
    """
    实现恒等块
    
    参数：
        X - 输入的tensor类型的数据，维度为( m, n_H_prev, n_W_prev, n_H_prev )
        f - 整数，指定主路径中间的CONV窗口的维度
        filters - python整数列表，定义了主路径每层的卷积层的过滤器数量
        stage - 整数，用于命名层，取决于他们在网络中的位置
        block - 字符串/字符，用于命名层，取决于他们在网络中的位置
        
    返回：
        X - 恒等块的输出，tensor类型，维度为(n_H, n_W, n_C)
    """
    
    # 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, F3 = filters
    
    # 保存输入数据，将会用于为主路径添加捷径
    X_shortcut = X
    
    # 主路径的第一部分
    ## 卷积层
    X = tf.keras.layers.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)
    ## 归一化
    X = tf.keras.layers.BatchNormalization(axis=3, name=bn_name_base+"2a")(X)
    ## 使用ReLU激活函数
    X = tf.keras.layers.Activation("relu")(X)
    
    # 主路径的第二部分
    ## 卷积层
    X = tf.keras.layers.Conv2D(filters=F2, kernel_size=(f, f), strides=(1, 1), padding="same", name=conv_name_base+"2b", kernel_initializer=tf.keras.initializers.glorot_uniform(seed=0))(X)
    ## 归一化
    X = tf.keras.layers.BatchNormalization(axis=3, name=bn_name_base+"2b")(X)
    ## 使用ReLU激活函数
    X = tf.keras.layers.Activation("relu")(X)
    
    # 主路径的第三部分
    ## 卷积层
    X = tf.keras.layers.Conv2D(filters=F3, kernel_size=(1, 1), strides=(1, 1), padding="valid", name=conv_name_base+"2c", kernel_initializer=tf.keras.initializers.glorot_uniform(seed=0))(X)
    ## 归一化
    X = tf.keras.layers.BatchNormalization(axis=3, name=bn_name_base+"2c")(X)
    
    # 主路径的最后部分，添加捷径并使用ReLU激活函数
    X = tf.keras.layers.Add()([X, X_shortcut])
    X = tf.keras.layers.Activation("relu")(X)
    
    return X

def convolutional_block(X, f, filters, stage, block, s=2):
    """
    实现卷积块
    
    参数：
        X - 输入的tensor类型的数据，维度为( m, n_H_prev, n_W_prev, n_H_prev )
        f - 整数，指定主路径中间的CONV窗口的维度
        filters - python整数列表，定义了主路径每层的卷积层的过滤器数量
        stage - 整数，用于命名层，取决于他们在网络中的位置
        block - 字符串/字符，用于命名层，取决于他们在网络中的位置
        s - 整数，指定要使用的步幅
    
    返回：
        X - 卷积块的输出，tensor类型，维度为(n_H, n_W, n_C)
    """
    
    # 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, F3 = filters
    
    # 保存输入数据，将会用于为主路径添加捷径
    X_shortcut = X
    
    # 主路径的第一部分
    ## 卷积层
    X = tf.keras.layers.Conv2D(filters=F1, kernel_size=(1, 1), strides=(s, s), padding="valid", name=conv_name_base+"2a", kernel_initializer=tf.keras.initializers.glorot_uniform(seed=0))(X)
    ## 归一化
    X = tf.keras.layers.BatchNormalization(axis=3, name=bn_name_base+"2a")(X)
    ## 使用ReLU激活函数
    X = tf.keras.layers.Activation("relu")(X)
    
    # 主路径的第二部分
    ## 卷积层
    X = tf.keras.layers.Conv2D(filters=F2, kernel_size=(f, f), strides=(1, 1), padding="same", name=conv_name_base+"2b", kernel_initializer=tf.keras.initializers.glorot_uniform(seed=0))(X)
    ## 归一化
    X = tf.keras.layers.BatchNormalization(axis=3, name=bn_name_base+"2b")(X)
    ## 使用ReLU激活函数
    X = tf.keras.layers.Activation("relu")(X)
    
    # 主路径的第三部分
    ## 卷积层
    X = tf.keras.layers.Conv2D(filters=F3, kernel_size=(1, 1), strides=(1, 1), padding="valid", name=conv_name_base+"2c", kernel_initializer=tf.keras.initializers.glorot_uniform(seed=0))(X)
    ## 归一化
    X = tf.keras.layers.BatchNormalization(axis=3, name=bn_name_base+"2c")(X)
    
    # 捷径的第二部分
    X_shortcut = tf.keras.layers.Conv2D(filters=F3, kernel_size=(1, 1), strides=(s, s), padding="valid", name=conv_name_base+"1", kernel_initializer=tf.keras.initializers.glorot_uniform(seed=0))(X_shortcut)
    X_shortcut = tf.keras.layers.BatchNormalization(axis=3, name=bn_name_base+"1")(X_shortcut)
    
    # 主路径的最后部分，添加捷径并使用ReLU激活函数
    X = tf.keras.layers.Add()([X, X_shortcut])
    X = tf.keras.layers.Activation("relu")(X)
    
    return X

def ResNet50(input_shape=(64, 64, 3), classes=6):
    """
    实现ResNet50
    
    参数：
        input_shape - 图像数据集的维度
        classes - 整数，分类数
    
    返回：
        model - Keras框架的模型
    
    """
    
    # 定义tensor类型的输入数据
    X_input = tf.keras.layers.Input(input_shape)
    
    # 0填充
    X = tf.keras.layers.ZeroPadding2D((3, 3))(X_input)
    
    # stage1
    X = tf.keras.layers.Conv2D(filters=64, kernel_size=(7, 7), strides=(2, 2), name="conv1", kernel_initializer=tf.keras.initializers.glorot_uniform(seed=0))(X)
    X = tf.keras.layers.BatchNormalization(axis=3, name="bn_conv1")(X)
    X = tf.keras.layers.Activation("relu")(X)
    X = tf.keras.layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(X)
    
    # stage2
    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")
    
    # stage3
    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")
    
    # stage4
    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")
    
    # stage5
    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")
    
    # 平均池化层
    X = tf.keras.layers.AveragePooling2D(pool_size=(2, 2), padding="same")(X)
    
    # 输出层
    X = tf.keras.layers.Flatten()(X)
    X = tf.keras.layers.Dense(classes, activation="softmax", name="fc"+str(classes), kernel_initializer=tf.keras.initializers.glorot_uniform(seed=0))(X)
    
    # 创建模型
    model = tf.keras.models.Model(inputs=X_input, outputs=X, name="ResNet50")
    
    return model