tensorflow采用Keras序列模型,实现波士顿房价预测问题的建模与训练

好的，以下是用TensorFlow的Keras序列模型实现波士顿房价预测问题的建模与训练的步骤：

1. 导入所需库

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

2. 准备数据

# 加载波士顿房价数据集
boston = load_boston()

# 划分数据集
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target, test_size=0.2, random_state=42)

# 数据标准化
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

3. 构建模型

model = keras.Sequential([
    keras.layers.Dense(64, activation='relu', input_shape=[X_train.shape[1]]),
    keras.layers.Dense(64, activation='relu'),
    keras.layers.Dense(1)
])

4. 编译模型

model.compile(loss='mse', optimizer='adam', metrics=['mae'])

5. 训练模型

history = model.fit(X_train, y_train, epochs=100, validation_split=0.2)

6. 评估模型

model.evaluate(X_test, y_test)

7. 进行预测

predictions = model.predict(X_test)

以上就是用TensorFlow的Keras序列模型实现波士顿房价预测问题的建模与训练的步骤。