麻烦生成resnet50代码

好的，以下是生成 resnet50 代码的示例：

import tensorflow as tf
from tensorflow.keras import layers

def resnet_block(inputs, filters, kernel_size, strides):
    x = layers.Conv2D(filters, kernel_size, strides=strides, padding='same')(inputs)
    x = layers.BatchNormalization()(x)
    x = layers.Activation('relu')(x)
    x = layers.Conv2D(filters, kernel_size, padding='same')(x)
    x = layers.BatchNormalization()(x)
    x = layers.add([x, inputs])
    x = layers.Activation('relu')(x)
    return x

def resnet50(input_shape, num_classes):
    inputs = layers.Input(shape=input_shape)
    x = layers.Conv2D(64, 7, strides=2, padding='same')(inputs)
    x = layers.BatchNormalization()(x)
    x = layers.Activation('relu')(x)
    x = layers.MaxPooling2D(pool_size=3, strides=2, padding='same')(x)

    x = resnet_block(x, filters=64, kernel_size=3, strides=1)
    x = resnet_block(x, filters=64, kernel_size=3, strides=1)
    x = resnet_block(x, filters=64, kernel_size=3, strides=1)

    x = resnet_block(x, filters=128, kernel_size=3, strides=2)
    x = resnet_block(x, filters=128, kernel_size=3, strides=1)
    x = resnet_block(x, filters=128, kernel_size=3, strides=1)
    x = resnet_block(x, filters=128, kernel_size=3, strides=1)

    x = resnet_block(x, filters=256, kernel_size=3, strides=2)
    x = resnet_block(x, filters=256, kernel_size=3, strides=1)
    x = resnet_block(x, filters=256, kernel_size=3, strides=1)
    x = resnet_block(x, filters=256, kernel_size=3, strides=1)
    x = resnet_block(x, filters=256, kernel_size=3, strides=1)
    x = resnet_block(x, filters=256, kernel_size=3, strides=1)

    x = resnet_block(x, filters=512, kernel_size=3, strides=2)
    x = resnet_block(x, filters=512, kernel_size=3, strides=1)
    x = resnet_block(x, filters=512, kernel_size=3, strides=1)

    x = layers.GlobalAveragePooling2D()(x)
    x = layers.Dense(num_classes, activation='softmax')(x)

    model = tf.keras.Model(inputs, x)
    return model

model = resnet50(input_shape=(224, 224, 3), num_classes=100)
model.summary()

希望这个代码能够帮到你！