帮我写一个resnet50的训练代码，cifar-10数据集

好的，以下是一个使用ResNet50模型在CIFAR-10数据集上进行训练的示例代码，你可以在此基础上进行修改和调整：

import tensorflow as tf
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, GlobalAveragePooling2D, Add, Dense, Activation, BatchNormalization
from tensorflow.keras.models import Model
from tensorflow.keras.regularizers import l2

# 加载数据集
(x_train, y_train), (x_test, y_test) = cifar10.load_data()

# 数据预处理
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255
y_train = tf.keras.utils.to_categorical(y_train, 10)
y_test = tf.keras.utils.to_categorical(y_test, 10)

# 数据增强
datagen_train = ImageDataGenerator(
    width_shift_range=0.1,
    height_shift_range=0.1,
    horizontal_flip=True
)
datagen_train.fit(x_train)

# 定义ResNet50模型
def resnet_block(inputs, filters, strides=1):
    x = Conv2D(filters, kernel_size=3, strides=strides, padding='same', kernel_regularizer=l2(1e-4))(inputs)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = Conv2D(filters, kernel_size=3, strides=1, padding='same', kernel_regularizer=l2(1e-4))(x)
    x = BatchNormalization()(x)
    if strides != 1 or inputs.shape[3] != filters:
        inputs = Conv2D(filters, kernel_size=1, strides=strides, padding='same', kernel_regularizer=l2(1e-4))(inputs)
        inputs = BatchNormalization()(inputs)
    x = Add()([inputs, x])
    x = Activation('relu')(x)
    return x

inputs = Input(shape=(32, 32, 3))
x = Conv2D(64, kernel_size=3, strides=1, padding='same', kernel_regularizer=l2(1e-4))(inputs)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = resnet_block(x, 64)
x = resnet_block(x, 64)
x = resnet_block(x, 128, strides=2)
x = resnet_block(x, 128)
x = resnet_block(x, 256, strides=2)
x = resnet_block(x, 256)
x = GlobalAveragePooling2D()(x)
outputs = Dense(10, activation='softmax')(x)
model = Model(inputs=inputs, outputs=outputs)

# 编译模型
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# 训练模型
model.fit(datagen_train.flow(x_train, y_train, batch_size=128), 
          steps_per_epoch=x_train.shape[0] // 128, 
          epochs=50, 
          validation_data=(x_test, y_test))

在上面的代码中，我们使用了ResNet50模型，并在模型的基础上构建了一个CIFAR-10分类器。我们还使用了数据增强来增加模型的鲁棒性，并使用了L2正则化来避免过拟合。最后，我们使用Adam优化器来训练模型，并在50个epoch后进行评估。