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，下面是使用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. 导入必要的库和数据集

import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# 导入数据集
boston = load_boston()
X, y = boston.data, boston.target

2. 数据预处理

# 将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, y, 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)

# 将标签转换为独热编码
y_train = tf.keras.utils.to_categorical(y_train)
y_test = tf.keras.utils.to_categorical(y_test)

3. 建立模型

model = tf.keras.models.Sequential([
    tf.keras.layers.Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
    tf.keras.layers.Dense(32, activation='relu'),
    tf.keras.layers.Dense(2, activation='softmax')
])

4. 编译模型

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

5. 训练模型

history = model.fit(X_train, y_train,
                    epochs=100,
                    batch_size=32,
                    validation_data=(X_test, y_test))

6. 模型评估

score = model.evaluate(X_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

以上就是使用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。