08-Tensorflow2.x Wide&Deep模型

Wide & Deep model简要介绍

模型的主要程序，在model建立中介绍如下：

1.函数式API ，功能API

# model建立部分
# 函数式API 功能API
input = keras.layers.Input(shape=x_train.shape[1:])
hidden1 = keras.layers.Dense(30,activation='relu')(input)
hidden2 = keras.layers.Dense(30,activation='relu')(hidden1)
# 复合函数：f(x) = h(g(x))
concat = keras.layers.concatenate([input,hidden2])
output = keras.layers.Dense(1)(concat)
model = keras.models.Model(inputs=[input],
                           outputs=[output])

2.子类API

# model建立部分
# 子类API
class WideDeepModel(keras.models.Model):
    def __init__(self):
        super(WideDeepModel,self).__init__()
        """定义模型的层次"""
        self.hidden1_layer = keras.layers.Dense(30,activation='relu')
        self.hidden2_layer = keras.layers.Dense(30,activation='relu')
        self.output_layer = keras.layers.Dense(1)

    def call(self,input):
        '''完成模型的正向计算'''
        hidden1 = self.hidden1_layer(input)
        hidden2 = self.hidden2_layer(hidden1)
        concat = keras.layers.concatenate([input,hidden2])
        output = self.output_layer(concat)
        return output

model = keras.models.Sequential([
    WideDeepModel(),
])
model.build(input_shape=(None,8))

3.完成的程序

#-*- coding:utf-8 _*-
import os
import sys
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn
import tensorflow as tf
from tensorflow import keras

# 输出库的名字和版本
print(sys.version_info)
for module in tf, mpl, np, pd, sklearn, tf, keras:
    print(module.__name__, module.__version__)
# 指定GPU
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = '1'
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.compat.v1.Session(config=config)

'''1.数据引入及数据集分类''' 
from sklearn.datasets import fetch_california_housing

housing = fetch_california_housing()
# print(housing.DESCR)
# print(housing.data.shape)
# print(housing.target.shape)

# import pprint
# pprint.pprint(housing.data[0:5])
# pprint.pprint(housing.target[0:5])

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)
print(x_valid.shape, y_valid.shape)
print(x_test.shape, y_test.shape)

'''2.数据归一化''' 
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)

'''3.model建立''' 

# # 函数式API 功能API
# input = keras.layers.Input(shape=x_train.shape[1:])
# hidden1 = keras.layers.Dense(30,activation='relu')(input)
# hidden2 = keras.layers.Dense(30,activation='relu')(hidden1)
# # 复合函数：f(x) = h(g(x))
# concat = keras.layers.concatenate([input,hidden2])
# output = keras.layers.Dense(1)(concat)
# model = keras.models.Model(inputs=[input],
#                            outputs=[output])

# 子类API
class WideDeepModel(keras.models.Model):
    def __init__(self):
        super(WideDeepModel,self).__init__()
        """定义模型的层次"""
        self.hidden1_layer = keras.layers.Dense(30,activation='relu')
        self.hidden2_layer = keras.layers.Dense(30,activation='relu')
        self.output_layer = keras.layers.Dense(1)

    def call(self,input):
        '''完成模型的正向计算'''
        hidden1 = self.hidden1_layer(input)
        hidden2 = self.hidden2_layer(hidden1)
        concat = keras.layers.concatenate([input,hidden2])
        output = self.output_layer(concat)
        return output

model = keras.models.Sequential([
    WideDeepModel(),
])
model.build(input_shape=(None,8))

'''4.模型编译'''
model.summary()
model.compile(loss="mean_squared_error", optimizer="sgd")
callbacks = [keras.callbacks.EarlyStopping(patience=5, min_delta=1e-2)]

'''5.模型训练'''
history = model.fit(x_train_scaled, y_train,
                    validation_data=(x_valid_scaled, y_valid),
                    epochs=100,
                    callbacks=callbacks)

'''6.绘制曲线图'''
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)

'''7.模型测试'''
model.evaluate(x_test_scaled, y_test)