如何在TensorFlow中使用K-折交叉验证

import numpy as np
import tensorflow as tf
from sklearn.model_selection import KFold
from sklearn.datasets import make_classification

# 生成模拟数据
X, y = make_classification(n_samples=1000, n_features=20, random_state=42)

# 定义K值
k_folds = 5
kf = KFold(n_splits=k_folds, shuffle=True, random_state=42)

# 初始化性能指标列表
accuracies = []

for train_index, test_index in kf.split(X):
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]
    
    # 构建模型
    model = tf.keras.Sequential([
        tf.keras.layers.Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
        tf.keras.layers.Dense(1, activation='sigmoid')
    ])
    
    # 编译模型
    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
    
    # 训练模型
    model.fit(X_train, y_train, epochs=10, batch_size=32, verbose=0)
    
    # 评估模型
    _, accuracy = model.evaluate(X_test, y_test, verbose=0)
    accuracies.append(accuracy)

# 输出平均准确率
print(f"Average Accuracy across {k_folds} folds: {np.mean(accuracies):.4f}")