[tensorflow2.0]08.wide_and_deep

本文通过Keras的函数式API和子类API实现加州房价预测模型,详细介绍了数据预处理、模型构建、训练及评估过程。使用了California Housing数据集,并通过标准化、模型训练和验证曲线绘制展示了模型的学习效果。

摘要生成于 C知道 ,由 DeepSeek-R1 满血版支持, 前往体验 >

import matplotlib as mpl
import matplotlib.pyplot as plt

import numpy as np
import pandas as pd
import sklearn
import os
import sys
import time
import tensorflow as tf
import pprint

from tensorflow import keras

print('Tensorflows Version:{}'.format(tf.__version__))
# print('Is gpu available:{}'.format(tf.test.is_gpu_available()))
print(sys.version_info)
for module in mpl, np, pd, sklearn, tf, keras:
    print(module.__name__, module.__version__)


from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

housing = fetch_california_housing()
x_train_all, x_test, y_train_all, y_test = train_test_split(
    housing.data, housing.target, random_state=7, test_size=0.25)
x_train, x_vaild, y_train, y_vaild = train_test_split(
    x_train_all, y_train_all, random_state=7, test_size=0.25)

scaler = StandardScaler()
x_train_scaler = scaler.fit_transform(x_train)
x_vaild_scaler = scaler.transform(x_vaild)
x_test_scaler = scaler.transform(x_test)

'''
# 函数API
input = keras.layers.Input(shape=x_train_scaler.shape[1:])
hidden1 = keras.layers.Dense(30, activation='relu')(input)
hidden2 = keras.layers.Dense(30, activation='relu')(hidden1)
concat  = keras.layers.concatenate([input, hidden2])
output  = keras.layers.Dense(1)(concat)

#固化模型
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, inputs, training=None, mask=None):
        '完成模型的正向计算'
        hidden1 = self.hidden1_layer(inputs)
        hidden2 = self.hidden2_layer(hidden1)
        concat  = keras.layers.concatenate([inputs, hidden2])
        return self.output_layer(concat)

model = WideDeepModel()
model.build(input_shape=(None, 8))

model.compile(optimizer='adam',
              loss=keras.losses.mean_absolute_error)

callbacks = [keras.callbacks.EarlyStopping(patience=5, min_delta=1e-3)]

history = model.fit(x_train_scaler, y_train,
                    epochs=100,
                    validation_data=(x_vaild_scaler, y_vaild),
                    callbacks=callbacks)

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)

print('model.evaluate==================')
model.evaluate(x_test_scaler, y_test)

评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值