tensorflow2.0实现实现波士顿预测房价交叉验证

好的，下面是使用 TensorFlow 2.0 实现波士顿预测房价交叉验证的步骤：

1. 导入所需的库和数据集

import tensorflow as tf
from sklearn.datasets import load_boston
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler

# 加载波士顿房价数据集
boston = load_boston()

# 数据集特征归一化处理
scaler = StandardScaler()
X = scaler.fit_transform(boston.data)

# 数据集划分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, boston.target, test_size=0.2, random_state=42)

2. 构建模型

def build_model():
    model = tf.keras.Sequential([
        tf.keras.layers.Dense(64, activation='relu', input_dim=X_train.shape[1]),
        tf.keras.layers.Dense(64, activation='relu'),
        tf.keras.layers.Dense(1)
    ])
    model.compile(loss='mean_squared_error', optimizer=tf.keras.optimizers.Adam())
    return model

3. 定义交叉验证函数

def cross_val_score(X, y, k=5):
    scores = []
    kfold = KFold(n_splits=k, shuffle=True, random_state=42)
    for train_idx, val_idx in kfold.split(X):
        X_train, y_train = X[train_idx], y[train_idx]
        X_val, y_val = X[val_idx], y[val_idx]
        model = build_model()
        model.fit(X_train, y_train, epochs=100, batch_size=16, verbose=0)
        score = model.evaluate(X_val, y_val, verbose=0)
        scores.append(score)
    return scores

4. 调用交叉验证函数并输出得分

scores = cross_val_score(X_train, y_train, k=5)
print(f'Cross-Validation Scores: {scores}')
print(f'Mean Score: {np.mean(scores)}')

以上就是使用 TensorFlow 2.0 实现波士顿预测房价交叉验证的完整代码。