请给上一段代码中加入正则化和交叉验证过程，给出完整代码，并指名批次大小是多少，训练轮数是多少

好的，以下是加入正则化和交叉验证过程的完整代码：

import tensorflow as tf
from sklearn.model_selection import KFold

# 加载数据
data = tf.keras.datasets.mnist.load_data()
train_data, test_data = data[0], data[1]

# 数据预处理
x_train, y_train = train_data[0], train_data[1]
x_test, y_test = test_data[0], test_data[1]
x_train, x_test = x_train / 255.0, x_test / 255.0

# 定义模型
model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(128, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.01)),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(10, activation='softmax')
])

# 定义交叉验证划分器
kf = KFold(n_splits=5, shuffle=True, random_state=42)

# 定义优化器、损失函数和评价指标
optimizer = tf.keras.optimizers.Adam()
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy()
metrics = ['accuracy']

# 训练模型
batch_size = 32
epochs = 5
for train_index, val_index in kf.split(x_train):
    x_train_fold, x_val_fold = x_train[train_index], x_train[val_index]
    y_train_fold, y_val_fold = y_train[train_index], y_train[val_index]
    
    model.compile(optimizer=optimizer, loss=loss_fn, metrics=metrics)
    
    model.fit(x_train_fold, y_train_fold, batch_size=batch_size, epochs=epochs, validation_data=(x_val_fold, y_val_fold))
    
# 评估模型
model.evaluate(x_test, y_test)

批次大小为32，训练轮数为5。在5折交叉验证过程中，每个fold内部的训练轮数为5。同时，我们加入了L2正则化，正则化参数为0.01。