tensorflow2.0，神经网络多输出模型

数据集为sklearn提供的加利福利亚房价预测数据集

import matplotlib.pyplot as plt
from tensorflow import keras
import tensorflow as tf
import matplotlib as mpl
import pandas as pd
import numpy as np
import sklearn
import time
import sys
import os

# 加载数据
from sklearn.datasets import fetch_california_housing
housing = fetch_california_housing()
print(housing.data.shape)   # (20640, 8)
print(housing.target.shape) # (20640, )

# 切分数据集
from sklearn.model_selection import train_test_split
x_train_all, x_test, y_train_all, y_test = train_test_split(housing.data, housing.target, random_state = 7)
x_train, x_valid, y_train, y_valid = train_test_split(x_train_all, y_train_all, random_state = 11)
print(x_train.shape, y_train.shape) # (11610, 8) (11610,)
print(x_valid.shape, y_valid.shape) # (3870, 8) (3870,)
print(x_test.shape, y_test.shape)   # (5160, 8) (5160,)

# 数据归一化 x = (x - u) / d
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
x_train_scaled = scaler.fit_transform(x_train)
x_valid_scaled = scaler.transform(x_valid)
x_test_scaled = scaler.transform(x_test)

# 建模 多输出
input_wide = keras.layers.Input(shape=[5])
input_deep = keras.layers.Input(shape=[6])
hidden1 = keras.layers.Dense(30, activation='relu')(input_deep)
hidden2 = keras.layers.Dense(30, activation='relu')(hidden1)
concat = keras.layers.concatenate([input_wide, hidden2])
output = keras.layers.Dense(1)(concat)
output2 = keras.layers.Dense(1)(hidden2)
model = keras.models.Model(inputs=[input_wide, input_deep],
                           outputs=[output, output2])

model.summary()
model.compile(loss="mean_squared_error",
              optimizer="sgd"
              )
# 连续5个epoch loss的变化小于1e-2 earlystop
callbacks = [keras.callbacks.EarlyStopping(patience=5, min_delta=1e-2)]

# 未设置频率 默认每个epoch验证一次
x_train_scaled_wide = x_train_scaled[:, :5]
x_train_scaled_deep = x_train_scaled[:, 2:]
x_valid_scaled_wide = x_valid_scaled[:, :5]
x_valid_scaled_deep = x_valid_scaled[:, 2:]
x_test_scaled_wide = x_test_scaled[:, :5]
x_test_scaled_deep = x_test_scaled[:, 2:]
history = model.fit([x_train_scaled_wide, x_train_scaled_deep],
                    [y_train, y_train],
                    validation_data = ([x_valid_scaled_wide, x_valid_scaled_deep],
                                       [y_valid, y_valid]),
                    epochs = 100,
                    callbacks = callbacks)


# 画学习曲线
def plot_learning_curves(history):
    pd.DataFrame(history.history).plot(figsize=(8, 5))
    plt.grid(True)
    plt.gca().set_ylim(0, 1)
    plt.show()
plot_learning_curves(history)

# 测试
model.evaluate([x_test_scaled_wide, x_test_scaled_deep],[y_test, y_test])