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神经网络天气预测

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神经网络天气预测

2023-06-09 07:24| 来源: 网络整理| 查看: 265

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本文采用pytorch进行天气预报预测，掌握神经网络模型训练的基本步骤

文章目录 前言预测思路分析总结

前言

提示：本文通过采集部分天气预报数据，采用神经网络对相关数据进行预测分析，通过模型训练，掌握采用pytorh框架进行网络训练

提示：完整源代码可以私信我

预测思路分析

步骤： （1）功能函数导入

import numpy as np import pandas as pd import matplotlib.pyplot as plt import torch import torch.optim as optim import datetime

（2）数据预处理 采集部分地区某段实际的天气情况，进行整理汇总，保存为.csv格式，然后导入数据，数据形式如下：

data=pd.read_csv('temp.csv') print(data.head())#默认的显示前5行 year month day week temp_2 temp_1 average actual random 0 2021 1 1 Fri 45 45 45.6 45 29 1 2021 1 2 Sat 44 45 45.7 44 61 2 2021 1 3 Sun 45 44 45.8 41 56 3 2021 1 4 Mon 44 41 45.9 40 53 4 2021 1 5 Tues 41 40 46.0 44 41 数据表中 year,moth,day,week分别表示的具体的时间 temp_2：前天的最高温度值 temp_1：昨天的最高温度值 average：在历史中，每年这一天的平均最高温度值 actual：这就是我们的标签值了，当天的真实最高温度 random：随机数据。

在这里插入图片描述 将数据进行整理，分别得到年、月、日等常规形式：

datas=[str(int(year))+'-'+str(int(month))+'-'+str(int(day)) for year,month ,day in zip(yaers,months,days)] print(datas) datas=[datetime.datetime.strptime(date,'%Y-%m-%d') for date in datas] print(datas)

显示结果：

['2021-1-1', '2021-1-2', '2021-1-3', '2021-1-4', '2021-1-5', '2021-1-6', '2021-1-7', '2021-1-8', '2021-1-9', '2021-1-10', '2021-1-11', '2021-1-12', '2021-1-13', '2021-1-14', '2021-1-15', '2021-1-16', '2021-1-17', '2021-1-18', '2021-1-19', '2021-1-20', '2021-1-21', '2021-1-22', '2021-1-23', '2021-1-24', '2021-1-25', '2021-1-26', '2021-1-27', '2021-1-28', '2021-1-29', '2021-1-30', '2021-1-31', '2021-2-1', '2021-2-2', '2021-2-3', '2021-2-4', '2021-2-5', '2021-2-6', '2021-2-7', '2021-2-8', '2021-2-9', '2021-2-10', '2021-2-11', '2021-2-12', '2021-2-15', '2021-2-16', '2021-2-17', '2021-2-18', '2021-2-19', '2021-2-20', '2021-2-21', '2021-2-22', '2021-2-23', '2021-2-24', '2021-2-25', '2021-2-26', '2021-2-27', '2021-2-28', '2021-3-1', '2021-3-2', '2021-3-3', '2021-3-4', '2021-3-5', '2021-3-6', '2021-3-7', '2021-3-8', '2021-3-9', '2021-3-10', '2021-3-11', '2021-3-12', '2021-3-13', '2021-3-14', '2021-3-15', '2021-3-16', '2021-3-17', '2021-3-18', '2021-3-19', '2021-3-20', '2021-3-21', '2021-3-22', '2021-3-23', '2021-3-24', '2021-3-25', '2021-3-26', '2021-3-27', '2021-3-28', '2021-3-29', '2021-3-30', '2021-3-31', '2021-4-1', '2021-4-2', '2021-4-3', '2021-4-4', '2021-4-5', '2021-4-6', '2021-4-7', '2021-4-8', '2021-4-9', '2021-4-10', '2021-4-11', '2021-4-12', '2021-4-13', '2021-4-14', '2021-4-15', '2021-4-16', '2021-4-17', '2021-4-18', '2021-4-19', '2021-4-20', '2021-4-21', '2021-4-22', '2021-4-23', '2021-4-24', '2021-4-25', '2021-4-26', '2021-4-27', '2021-4-28', '2021-4-29', '2021-4-30', '2021-5-1', '2021-5-2', '2021-5-3', '2021-5-4', '2021-5-5', '2021-5-6', '2021-5-7', '2021-5-8', '2021-5-9', '2021-5-10', '2021-5-11', '2021-5-12', '2021-5-13', '2021-5-14', '2021-5-15', '2021-5-16', '2021-5-17', '2021-5-18', '2021-5-19', '2021-5-20', '2021-5-21', '2021-5-22', '2021-5-23', '2021-5-24', '2021-5-25', '2021-5-26', '2021-5-27', '2021-5-28', '2021-5-29', '2021-5-30', '2021-5-31', '2021-6-1', '2021-6-2', '2021-6-3', '2021-6-4', '2021-6-5', '2021-6-6', '2021-6-7', '2021-6-8', '2021-6-9', '2021-6-10', '2021-6-11', '2021-6-12', '2021-6-13', '2021-6-14', '2021-6-15', '2021-6-16', '2021-6-17', '2021-6-18', '2021-6-19', '2021-6-20', '2021-6-21', '2021-6-22', '2021-6-23', '2021-6-24', '2021-6-25', '2021-6-26', '2021-6-27', '2021-6-28', '2021-6-29', '2021-6-30', '2021-7-1', '2021-7-2', '2021-7-3', '2021-7-4', '2021-7-5', '2021-7-6', '2021-7-7', '2021-7-8', '2021-7-9', '2021-7-10', '2021-7-11', '2021-7-12', '2021-7-13', '2021-7-14', '2021-7-15', '2021-7-16', '2021-7-17', '2021-7-18', '2021-7-19', '2021-7-20', '2021-7-21', '2021-7-22', '2021-7-23', '2021-7-24', '2021-7-25', '2021-7-26', '2021-7-27', '2021-7-28', '2021-7-29', '2021-7-30', '2021-7-31', '2021-8-1', '2021-8-2', '2021-8-3', '2021-8-4', '2021-8-5', '2021-8-6', '2021-8-7', '2021-8-8', '2021-8-9', '2021-8-10', '2021-8-11', '2021-8-12', '2021-8-13', '2021-8-14', '2021-8-15', '2021-8-16', '2021-8-23', '2021-8-28', '2021-8-30', '2021-9-3', '2021-9-4', '2021-9-5', '2021-9-6', '2021-9-7', '2021-9-8', '2021-9-9', '2021-9-10', '2021-9-11', '2021-9-12', '2021-9-13', '2021-9-14', '2021-9-15', '2021-9-16', '2021-9-17', '2021-9-18', '2021-9-19', '2021-9-20', '2021-9-21', '2021-9-22', '2021-9-23', '2021-9-24', '2021-9-25', '2021-9-26', '2021-9-27', '2021-9-28', '2021-9-29', '2021-9-30', '2021-10-1', '2021-10-2', '2021-10-3', '2021-10-4', '2021-10-5', '2021-10-6', '2021-10-7', '2021-10-8', '2021-10-9', '2021-10-10', '2021-10-11', '2021-10-12', '2021-10-13', '2021-10-14', '2021-10-15', '2021-10-16', '2021-10-17', '2021-10-18', '2021-10-19', '2021-10-20', '2021-10-21', '2021-10-22', '2021-10-23', '2021-10-24', '2021-10-25', '2021-10-26', '2021-10-27', '2021-10-28', '2021-10-29', '2021-10-31', '2021-11-1', '2021-11-2', '2021-11-3', '2021-11-4', '2021-11-5', '2021-11-6', '2021-11-7', '2021-11-8', '2021-11-9', '2021-11-10', '2021-11-11', '2021-11-12', '2021-11-13', '2021-11-14', '2021-11-15', '2021-11-16', '2021-11-17', '2021-11-18', '2021-11-19', '2021-11-20', '2021-11-21', '2021-11-22', '2021-11-23', '2021-11-24', '2021-11-25', '2021-11-26', '2021-11-27', '2021-11-28', '2021-11-29', '2021-11-30', '2021-12-1', '2021-12-2', '2021-12-3', '2021-12-4', '2021-12-5', '2021-12-6', '2021-12-7', '2021-12-8', '2021-12-9', '2021-12-10', '2021-12-11', '2021-12-12', '2021-12-13', '2021-12-14', '2021-12-15', '2021-12-16', '2021-12-17', '2021-12-18', '2021-12-19', '2021-12-20', '2021-12-21', '2021-12-22', '2021-12-23', '2021-12-24', '2021-12-25', '2021-12-26', '2021-12-27', '2021-12-28', '2021-12-29', '2021-12-30', '2021-12-31', '2021-12-31', '2021-12-31'] [datetime.datetime(2021, 1, 1, 0, 0), datetime.datetime(2021, 1, 2, 0, 0), datetime.datetime(2021, 1, 3, 0, 0), datetime.datetime(2021, 1, 4, 0, 0), datetime.datetime(2021, 1, 5, 0, 0), datetime.datetime(2021, 1, 6, 0, 0), datetime.datetime(2021, 1, 7, 0, 0), datetime.datetime(2021, 1, 8, 0, 0), datetime.datetime(2021, 1, 9, 0, 0), datetime.datetime(2021, 1, 10, 0, 0), datetime.datetime(2021, 1, 11, 0, 0), datetime.datetime(2021, 1, 12, 0, 0), datetime.datetime(2021, 1, 13, 0, 0), datetime.datetime(2021, 1, 14, 0, 0), datetime.datetime(2021, 1, 15, 0, 0), datetime.datetime(2021, 1, 16, 0, 0), datetime.datetime(2021, 1, 17, 0, 0), datetime.datetime(2021, 1, 18, 0, 0), datetime.datetime(2021, 1, 19, 0, 0), datetime.datetime(2021, 1, 20, 0, 0), datetime.datetime(2021, 1, 21, 0, 0), datetime.datetime(2021, 1, 22, 0, 0), datetime.datetime(2021, 1, 23, 0, 0), datetime.datetime(2021, 1, 24, 0, 0), datetime.datetime(2021, 1, 25, 0, 0), datetime.datetime(2021, 1, 26, 0, 0), datetime.datetime(2021, 1, 27, 0, 0), datetime.datetime(2021, 1, 28, 0, 0), datetime.datetime(2021, 1, 29, 0, 0), datetime.datetime(2021, 1, 30, 0, 0), datetime.datetime(2021, 1, 31, 0, 0), datetime.datetime(2021, 2, 1, 0, 0), datetime.datetime(2021, 2, 2, 0, 0), datetime.datetime(2021, 2, 3, 0, 0), datetime.datetime(2021, 2, 4, 0, 0), datetime.datetime(2021, 2, 5, 0, 0), datetime.datetime(2021, 2, 6, 0, 0), datetime.datetime(2021, 2, 7, 0, 0), datetime.datetime(2021, 2, 8, 0, 0), datetime.datetime(2021, 2, 9, 0, 0), datetime.datetime(2021, 2, 10, 0, 0), datetime.datetime(2021, 2, 11, 0, 0), datetime.datetime(2021, 2, 12, 0, 0), datetime.datetime(2021, 2, 15, 0, 0), datetime.datetime(2021, 2, 16, 0, 0), datetime.datetime(2021, 2, 17, 0, 0), datetime.datetime(2021, 2, 18, 0, 0), datetime.datetime(2021, 2, 19, 0, 0), datetime.datetime(2021, 2, 20, 0, 0), datetime.datetime(2021, 2, 21, 0, 0), datetime.datetime(2021, 2, 22, 0, 0), datetime.datetime(2021, 2, 23, 0, 0), datetime.datetime(2021, 2, 24, 0, 0), datetime.datetime(2021, 2, 25, 0, 0), datetime.datetime(2021, 2, 26, 0, 0), datetime.datetime(2021, 2, 27, 0, 0), datetime.datetime(2021, 2, 28, 0, 0), datetime.datetime(2021, 3, 1, 0, 0), datetime.datetime(2021, 3, 2, 0, 0), datetime.datetime(2021, 3, 3, 0, 0), datetime.datetime(2021, 3, 4, 0, 0), datetime.datetime(2021, 3, 5, 0, 0), datetime.datetime(2021, 3, 6, 0, 0), datetime.datetime(2021, 3, 7, 0, 0), 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datetime.datetime(2021, 4, 3, 0, 0), datetime.datetime(2021, 4, 4, 0, 0), datetime.datetime(2021, 4, 5, 0, 0), datetime.datetime(2021, 4, 6, 0, 0), datetime.datetime(2021, 4, 7, 0, 0), datetime.datetime(2021, 4, 8, 0, 0), datetime.datetime(2021, 4, 9, 0, 0), datetime.datetime(2021, 4, 10, 0, 0), datetime.datetime(2021, 4, 11, 0, 0), datetime.datetime(2021, 4, 12, 0, 0), datetime.datetime(2021, 4, 13, 0, 0), datetime.datetime(2021, 4, 14, 0, 0), datetime.datetime(2021, 4, 15, 0, 0), datetime.datetime(2021, 4, 16, 0, 0), datetime.datetime(2021, 4, 17, 0, 0), datetime.datetime(2021, 4, 18, 0, 0), datetime.datetime(2021, 4, 19, 0, 0), datetime.datetime(2021, 4, 20, 0, 0), datetime.datetime(2021, 4, 21, 0, 0), datetime.datetime(2021, 4, 22, 0, 0), datetime.datetime(2021, 4, 23, 0, 0), datetime.datetime(2021, 4, 24, 0, 0), datetime.datetime(2021, 4, 25, 0, 0), datetime.datetime(2021, 4, 26, 0, 0), datetime.datetime(2021, 4, 27, 0, 0), datetime.datetime(2021, 4, 28, 0, 0), datetime.datetime(2021, 4, 29, 0, 0), datetime.datetime(2021, 4, 30, 0, 0), datetime.datetime(2021, 5, 1, 0, 0), datetime.datetime(2021, 5, 2, 0, 0), datetime.datetime(2021, 5, 3, 0, 0), datetime.datetime(2021, 5, 4, 0, 0), datetime.datetime(2021, 5, 5, 0, 0), datetime.datetime(2021, 5, 6, 0, 0), datetime.datetime(2021, 5, 7, 0, 0), datetime.datetime(2021, 5, 8, 0, 0), datetime.datetime(2021, 5, 9, 0, 0), datetime.datetime(2021, 5, 10, 0, 0), datetime.datetime(2021, 5, 11, 0, 0), datetime.datetime(2021, 5, 12, 0, 0), datetime.datetime(2021, 5, 13, 0, 0), datetime.datetime(2021, 5, 14, 0, 0), datetime.datetime(2021, 5, 15, 0, 0), datetime.datetime(2021, 5, 16, 0, 0), datetime.datetime(2021, 5, 17, 0, 0), datetime.datetime(2021, 5, 18, 0, 0), datetime.datetime(2021, 5, 19, 0, 0), datetime.datetime(2021, 5, 20, 0, 0), datetime.datetime(2021, 5, 21, 0, 0), datetime.datetime(2021, 5, 22, 0, 0), datetime.datetime(2021, 5, 23, 0, 0), datetime.datetime(2021, 5, 24, 0, 0), datetime.datetime(2021, 5, 25, 0, 0), datetime.datetime(2021, 5, 26, 0, 0), datetime.datetime(2021, 5, 27, 0, 0), datetime.datetime(2021, 5, 28, 0, 0), datetime.datetime(2021, 5, 29, 0, 0), datetime.datetime(2021, 5, 30, 0, 0), datetime.datetime(2021, 5, 31, 0, 0), datetime.datetime(2021, 6, 1, 0, 0), datetime.datetime(2021, 6, 2, 0, 0), datetime.datetime(2021, 6, 3, 0, 0), datetime.datetime(2021, 6, 4, 0, 0), datetime.datetime(2021, 6, 5, 0, 0), datetime.datetime(2021, 6, 6, 0, 0), datetime.datetime(2021, 6, 7, 0, 0), datetime.datetime(2021, 6, 8, 0, 0), datetime.datetime(2021, 6, 9, 0, 0), datetime.datetime(2021, 6, 10, 0, 0), datetime.datetime(2021, 6, 11, 0, 0), datetime.datetime(2021, 6, 12, 0, 0), datetime.datetime(2021, 6, 13, 0, 0), datetime.datetime(2021, 6, 14, 0, 0), datetime.datetime(2021, 6, 15, 0, 0), datetime.datetime(2021, 6, 16, 0, 0), datetime.datetime(2021, 6, 17, 0, 0), datetime.datetime(2021, 6, 18, 0, 0), datetime.datetime(2021, 6, 19, 0, 0), datetime.datetime(2021, 6, 20, 0, 0), datetime.datetime(2021, 6, 21, 0, 0), datetime.datetime(2021, 6, 22, 0, 0), datetime.datetime(2021, 6, 23, 0, 0), datetime.datetime(2021, 6, 24, 0, 0), datetime.datetime(2021, 6, 25, 0, 0), datetime.datetime(2021, 6, 26, 0, 0), datetime.datetime(2021, 6, 27, 0, 0), datetime.datetime(2021, 6, 28, 0, 0), datetime.datetime(2021, 6, 29, 0, 0), datetime.datetime(2021, 6, 30, 0, 0), datetime.datetime(2021, 7, 1, 0, 0), datetime.datetime(2021, 7, 2, 0, 0), datetime.datetime(2021, 7, 3, 0, 0), datetime.datetime(2021, 7, 4, 0, 0), datetime.datetime(2021, 7, 5, 0, 0), datetime.datetime(2021, 7, 6, 0, 0), datetime.datetime(2021, 7, 7, 0, 0), datetime.datetime(2021, 7, 8, 0, 0), datetime.datetime(2021, 7, 9, 0, 0), datetime.datetime(2021, 7, 10, 0, 0), datetime.datetime(2021, 7, 11, 0, 0), datetime.datetime(2021, 7, 12, 0, 0), datetime.datetime(2021, 7, 13, 0, 0), datetime.datetime(2021, 7, 14, 0, 0), datetime.datetime(2021, 7, 15, 0, 0), datetime.datetime(2021, 7, 16, 0, 0), datetime.datetime(2021, 7, 17, 0, 0), datetime.datetime(2021, 7, 18, 0, 0), datetime.datetime(2021, 7, 19, 0, 0), datetime.datetime(2021, 7, 20, 0, 0), datetime.datetime(2021, 7, 21, 0, 0), datetime.datetime(2021, 7, 22, 0, 0), datetime.datetime(2021, 7, 23, 0, 0), datetime.datetime(2021, 7, 24, 0, 0), datetime.datetime(2021, 7, 25, 0, 0), datetime.datetime(2021, 7, 26, 0, 0), datetime.datetime(2021, 7, 27, 0, 0), datetime.datetime(2021, 7, 28, 0, 0), datetime.datetime(2021, 7, 29, 0, 0), datetime.datetime(2021, 7, 30, 0, 0), datetime.datetime(2021, 7, 31, 0, 0), datetime.datetime(2021, 8, 1, 0, 0), datetime.datetime(2021, 8, 2, 0, 0), datetime.datetime(2021, 8, 3, 0, 0), datetime.datetime(2021, 8, 4, 0, 0), datetime.datetime(2021, 8, 5, 0, 0), datetime.datetime(2021, 8, 6, 0, 0), datetime.datetime(2021, 8, 7, 0, 0), datetime.datetime(2021, 8, 8, 0, 0), datetime.datetime(2021, 8, 9, 0, 0), datetime.datetime(2021, 8, 10, 0, 0), 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datetime.datetime(2021, 9, 20, 0, 0), datetime.datetime(2021, 9, 21, 0, 0), datetime.datetime(2021, 9, 22, 0, 0), datetime.datetime(2021, 9, 23, 0, 0), datetime.datetime(2021, 9, 24, 0, 0), datetime.datetime(2021, 9, 25, 0, 0), datetime.datetime(2021, 9, 26, 0, 0), datetime.datetime(2021, 9, 27, 0, 0), datetime.datetime(2021, 9, 28, 0, 0), datetime.datetime(2021, 9, 29, 0, 0), datetime.datetime(2021, 9, 30, 0, 0), datetime.datetime(2021, 10, 1, 0, 0), datetime.datetime(2021, 10, 2, 0, 0), datetime.datetime(2021, 10, 3, 0, 0), datetime.datetime(2021, 10, 4, 0, 0), datetime.datetime(2021, 10, 5, 0, 0), datetime.datetime(2021, 10, 6, 0, 0), datetime.datetime(2021, 10, 7, 0, 0), datetime.datetime(2021, 10, 8, 0, 0), datetime.datetime(2021, 10, 9, 0, 0), datetime.datetime(2021, 10, 10, 0, 0), datetime.datetime(2021, 10, 11, 0, 0), datetime.datetime(2021, 10, 12, 0, 0), datetime.datetime(2021, 10, 13, 0, 0), datetime.datetime(2021, 10, 14, 0, 0), datetime.datetime(2021, 10, 15, 0, 0), datetime.datetime(2021, 10, 16, 0, 0), datetime.datetime(2021, 10, 17, 0, 0), datetime.datetime(2021, 10, 18, 0, 0), datetime.datetime(2021, 10, 19, 0, 0), datetime.datetime(2021, 10, 20, 0, 0), datetime.datetime(2021, 10, 21, 0, 0), datetime.datetime(2021, 10, 22, 0, 0), datetime.datetime(2021, 10, 23, 0, 0), datetime.datetime(2021, 10, 24, 0, 0), datetime.datetime(2021, 10, 25, 0, 0), datetime.datetime(2021, 10, 26, 0, 0), datetime.datetime(2021, 10, 27, 0, 0), datetime.datetime(2021, 10, 28, 0, 0), datetime.datetime(2021, 10, 29, 0, 0), datetime.datetime(2021, 10, 31, 0, 0), datetime.datetime(2021, 11, 1, 0, 0), datetime.datetime(2021, 11, 2, 0, 0), datetime.datetime(2021, 11, 3, 0, 0), datetime.datetime(2021, 11, 4, 0, 0), datetime.datetime(2021, 11, 5, 0, 0), datetime.datetime(2021, 11, 6, 0, 0), datetime.datetime(2021, 11, 7, 0, 0), datetime.datetime(2021, 11, 8, 0, 0), datetime.datetime(2021, 11, 9, 0, 0), datetime.datetime(2021, 11, 10, 0, 0), datetime.datetime(2021, 11, 11, 0, 0), datetime.datetime(2021, 11, 12, 0, 0), datetime.datetime(2021, 11, 13, 0, 0), datetime.datetime(2021, 11, 14, 0, 0), datetime.datetime(2021, 11, 15, 0, 0), datetime.datetime(2021, 11, 16, 0, 0), datetime.datetime(2021, 11, 17, 0, 0), datetime.datetime(2021, 11, 18, 0, 0), datetime.datetime(2021, 11, 19, 0, 0), datetime.datetime(2021, 11, 20, 0, 0), datetime.datetime(2021, 11, 21, 0, 0), datetime.datetime(2021, 11, 22, 0, 0), datetime.datetime(2021, 11, 23, 0, 0), datetime.datetime(2021, 11, 24, 0, 0), datetime.datetime(2021, 11, 25, 0, 0), datetime.datetime(2021, 11, 26, 0, 0), datetime.datetime(2021, 11, 27, 0, 0), datetime.datetime(2021, 11, 28, 0, 0), datetime.datetime(2021, 11, 29, 0, 0), datetime.datetime(2021, 11, 30, 0, 0), datetime.datetime(2021, 12, 1, 0, 0), datetime.datetime(2021, 12, 2, 0, 0), datetime.datetime(2021, 12, 3, 0, 0), datetime.datetime(2021, 12, 4, 0, 0), datetime.datetime(2021, 12, 5, 0, 0), datetime.datetime(2021, 12, 6, 0, 0), datetime.datetime(2021, 12, 7, 0, 0), datetime.datetime(2021, 12, 8, 0, 0), datetime.datetime(2021, 12, 9, 0, 0), datetime.datetime(2021, 12, 10, 0, 0), datetime.datetime(2021, 12, 11, 0, 0), datetime.datetime(2021, 12, 12, 0, 0), datetime.datetime(2021, 12, 13, 0, 0), datetime.datetime(2021, 12, 14, 0, 0), datetime.datetime(2021, 12, 15, 0, 0), datetime.datetime(2021, 12, 16, 0, 0), datetime.datetime(2021, 12, 17, 0, 0), datetime.datetime(2021, 12, 18, 0, 0), datetime.datetime(2021, 12, 19, 0, 0), datetime.datetime(2021, 12, 20, 0, 0), datetime.datetime(2021, 12, 21, 0, 0), datetime.datetime(2021, 12, 22, 0, 0), datetime.datetime(2021, 12, 23, 0, 0), datetime.datetime(2021, 12, 24, 0, 0), datetime.datetime(2021, 12, 25, 0, 0), datetime.datetime(2021, 12, 26, 0, 0), datetime.datetime(2021, 12, 27, 0, 0), datetime.datetime(2021, 12, 28, 0, 0), datetime.datetime(2021, 12, 29, 0, 0), datetime.datetime(2021, 12, 30, 0, 0), datetime.datetime(2021, 12, 31, 0, 0), datetime.datetime(2021, 12, 31, 0, 0), datetime.datetime(2021, 12, 31, 0, 0)]

将上述数据以表格图形式呈现，方便观测对比，顺便复习matplotlib相关知识。

#采用pyplot将数据用表格形式呈现，便于观测 #设置布局为2行，2列，每个框大小为10*10 fig,((x1,x2),(x3,x4))=plt.subplots(nrows=2,ncols=2,figsize=(10,10)) #表格标签设置 plt.style.use('fivethirtyeight') #显示整个400多个真实值actual x1.plot(datas,data['actual']) x1.set_xlabel('') x1.set_ylabel('Temp') x1.set_title('Max Tem') #显示整个400多个真实值temp_1 x2.plot(datas,data['temp_1']) x2.set_xlabel('') x2.set_ylabel('Temp') x2.set_title('temp1 Max Tem') #显示整个400多个真实值temp_2 x3.plot(datas,data['temp_2']) x3.set_xlabel('') x3.set_ylabel('Temp') x3.set_title('temp2 Max Tem') #显示整个400多个真实值random x4.plot(datas,data['random']) x4.set_xlabel('') x4.set_ylabel('Temp') x4.set_title('random Max Tem') plt.show()

在这里插入图片描述

#取出标签，作为标签。 labels=np.array(data['actual']) print(labels) print('ooooooooo') #去掉标签 data=data.drop('actual',axis=1) print(data) print('xxxxxxxx') data_list=list(data.columns) print(data_list) data=np.array(data) print(data)

显示结果：

[45 44 41 40 44 51 45 48 50 52 45 49 55 49 48 54 50 54 48 52 52 57 48 51 54 56 57 56 52 48 47 46 51 49 49 53 49 51 57 62 56 55 58 55 56 57 53 51 53 51 51 60 59 61 60 57 53 58 55 59 57 64 60 53 54 55 56 55 52 54 49 51 53 58 63 61 55 56 57 53 54 57 59 51 56 64 68 73 71 63 69 60 57 68 77 76 66 59 58 60 59 59 60 68 77 89 81 81 73 64 65 55 59 60 61 64 61 68 77 87 74 60 68 77 82 63 67 75 81 77 82 65 57 60 71 64 63 66 59 66 65 66 66 65 64 64 64 71 79 75 71 80 81 92 86 85 67 65 67 65 70 66 60 67 71 67 65 70 76 73 75 68 69 71 78 85 79 74 73 76 76 71 68 69 76 68 74 71 74 74 77 75 77 76 72 80 73 78 82 81 71 75 80 85 79 83 85 88 76 73 77 73 75 80 79 72 72 73 72 76 80 87 90 83 84 81 79 75 70 67 68 68 68 67 72 74 77 70 74 75 79 71 75 68 69 71 67 68 67 64 67 76 77 69 68 66 67 63 65 61 63 66 63 64 68 57 60 62 66 60 60 62 60 60 61 58 62 59 62 62 61 65 58 60 65 68 59 57 57 65 65 58 61 63 71 65 64 63 59 55 57 55 50 52 55 57 55 54 54 49 52 52 53 48 52 52 52 46 50 49 46 40 42 40 41 36 44 44 43 40 39 39 35 35 39 46 51 49 45 40 41 42 42 47 48 48 57 40 40 40] ooooooooo year month day week temp_2 temp_1 average random 0 2021 1 1 Fri 45 45 45.6 29 1 2021 1 2 Sat 44 45 45.7 61 2 2021 1 3 Sun 45 44 45.8 56 3 2021 1 4 Mon 44 41 45.9 53 4 2021 1 5 Tues 41 40 46.0 41 .. ... ... ... ... ... ... ... ... 345 2021 12 29 Thurs 47 48 45.3 65 346 2021 12 30 Fri 48 48 45.4 42 347 2021 12 31 Sat 48 57 45.5 57 348 2021 12 31 Sat 48 57 45.5 57 349 2021 12 31 Sat 48 57 45.5 57 [350 rows x 8 columns] xxxxxxxx ['year', 'month', 'day', 'week', 'temp_2', 'temp_1', 'average', 'random'] [[2021 1 1 ... 45 45.6 29] [2021 1 2 ... 45 45.7 61] [2021 1 3 ... 44 45.8 56] ... [2021 12 31 ... 57 45.5 57] [2021 12 31 ... 57 45.5 57] [2021 12 31 ... 57 45.5 57]]

（3）模型建立、损失函数、迭代训练及显示 3.1 常规模型建立

#独热编码 print('xxxxxxx') data=pd.get_dummies(data) print(data.head()) print('xxxxxxxx') #取出标签，作为标签。 labels=np.array(data['actual']) #去掉标签 data=data.drop('actual',axis=1) print(data) data_list=list(data.columns) data=np.array(data) print(data.shape) #模型建立 from sklearn import preprocessing input_data=preprocessing.StandardScaler().fit_transform(data) x=torch.tensor(input_data,dtype=float) y=torch.tensor(labels,dtype=float) #权重参数初始化 weigh=torch.randn((14,128),dtype=float,requires_grad=True) bias=torch.randn(128,dtype=float,requires_grad=True) #初始化权重参数 weighs=torch.randn((128,1),dtype=float,requires_grad=True) biass=torch.randn(1,dtype=float,requires_grad=True) learning_rate=0.001 losses=[] for i in range(100): #隐藏层 hidden=x.mm(weigh)+bias #加入激活函数 hidden=torch.relu(hidden) #预测结果 predictions=hidden.mm(weighs)+biass #计算损失 loss=torch.mean((predictions-y)**2) losses.append(loss.data.numpy()) #显示结果 if i%10==0: print('loss:',loss) #反向传播 loss.backward() #跟新参数 weigh.data.add_(-learning_rate*weigh.grad.data) bias.data.add_(-learning_rate*bias.grad.data) weighs.data.add_(-learning_rate*weighs.grad.data) biass.data.add_(-learning_rate*biass.grad.data) #每次迭代记得清零 weigh.grad.data.zero_() bias.grad.data.zero_() weighs.grad.data.zero_() biass.grad.data.zero_() print(predictions.shape) xxxxxxx year month day temp_2 ... week_Sun week_Thurs week_Tues week_Wed 0 2021 1 1 45 ... 0 0 0 0 1 2021 1 2 44 ... 0 0 0 0 2 2021 1 3 45 ... 1 0 0 0 3 2021 1 4 44 ... 0 0 0 0 4 2021 1 5 41 ... 0 0 1 0 [5 rows x 15 columns] xxxxxxxx year month day temp_2 ... week_Sun week_Thurs week_Tues week_Wed 0 2021 1 1 45 ... 0 0 0 0 1 2021 1 2 44 ... 0 0 0 0 2 2021 1 3 45 ... 1 0 0 0 3 2021 1 4 44 ... 0 0 0 0 4 2021 1 5 41 ... 0 0 1 0 .. ... ... ... ... ... ... ... ... ... 345 2021 12 29 47 ... 0 1 0 0 346 2021 12 30 48 ... 0 0 0 0 347 2021 12 31 48 ... 0 0 0 0 348 2021 12 31 48 ... 0 0 0 0 349 2021 12 31 48 ... 0 0 0 0 [350 rows x 14 columns] (350, 14) loss: tensor(2109.5258, dtype=torch.float64, grad_fn=) loss: tensor(692.9397, dtype=torch.float64, grad_fn=) loss: tensor(469.2138, dtype=torch.float64, grad_fn=) loss: tensor(381.2370, dtype=torch.float64, grad_fn=) loss: tensor(332.9759, dtype=torch.float64, grad_fn=) loss: tensor(302.4142, dtype=torch.float64, grad_fn=) loss: tensor(281.3954, dtype=torch.float64, grad_fn=) loss: tensor(266.1516, dtype=torch.float64, grad_fn=) loss: tensor(254.6720, dtype=torch.float64, grad_fn=) loss: tensor(245.7872, dtype=torch.float64, grad_fn=) torch.Size([350, 1])

3.2 采用pytorch自带模块进行建模、迭代训练

''' pytorch 搭建神经网络模型-预测天气预报 author:leecj2015 time:2021-09-02 ''' #one-step :Import remaining functions import numpy as np import pandas as pd import matplotlib.pyplot as plt import torch import torch.optim as optim import datetime #second-step: load model(import data) data=pd.read_csv('temp.csv') yaers=data['year'] months=data['month'] days=data['day'] ''' ip() 函数用于将可迭代的对象作为参数，将对象中对应的元素打包成一个个元组，然后返回由这些元组组成的列表。 如果各个迭代器的元素个数不一致，则返回列表长度与最短的对象相同，利用 * 号操作符，可以将元组解压为列表。 zip([iterable, ...]) eg: >>>a = [1,2,3] >>> b = [4,5,6] >>> c = [4,5,6,7,8] >>> zipped = zip(a,b) # 打包为元组的列表 [(1, 4), (2, 5), (3, 6)] >>> zip(a,c) # 元素个数与最短的列表一致 [(1, 4), (2, 5), (3, 6)] >>> zip(*zipped) # 与 zip 相反，*zipped 可理解为解压，返回二维矩阵式 [(1, 2, 3), (4, 5, 6)] ''' datas=[str(int(year))+'-'+str(int(month))+'-'+str(int(day)) for year,month ,day in zip(yaers,months,days)] datas=[datetime.datetime.strptime(date,'%Y-%m-%d') for date in datas] #采用pyplot将数据用表格形式呈现，便于观测 #设置布局为2行，2列，每个框大小为10*10 fig,((x1,x2),(x3,x4))=plt.subplots(nrows=2,ncols=2,figsize=(10,10)) plt.style.use('fivethirtyeight') #显示整个400多个真实值actual x1.plot(datas,data['actual']) x1.set_xlabel('') x1.set_ylabel('Temp') x1.set_title('Max Tem') #显示整个400多个真实值temp_1 x2.plot(datas,data['temp_1']) x2.set_xlabel('') x2.set_ylabel('Temp') x2.set_title('temp1 Max Tem') #显示整个400多个真实值temp_2 x3.plot(datas,data['temp_2']) x3.set_xlabel('') x3.set_ylabel('Temp') x3.set_title('temp2 Max Tem') #显示整个400多个真实值random x4.plot(datas,data['random']) x4.set_xlabel('') x4.set_ylabel('Temp') x4.set_title('random Max Tem') plt.show() #独热编码 data=pd.get_dummies(data) #取出标签，作为标签。 labels=np.array(data['actual']) #去掉标签 data=data.drop('actual',axis=1) data_list=list(data.columns) data=np.array(data) #模型建立 from sklearn import preprocessing input_data=preprocessing.StandardScaler().fit_transform(data) input_size=input_data.shape[1] hidden_size=128 output_size=1 batch_size=16 my_nn=torch.nn.Sequential( torch.nn.Linear(input_size,hidden_size), torch.nn.Sigmoid(), torch.nn.Linear(hidden_size,output_size), ) cost=torch.nn.MSELoss(reduction='mean') optimizer=torch.optim.Adam(my_nn.parameters(),lr=0.001) #训练网络 losses=[] for i in range(100): batch_loss=[] for s in range(0,len(input_data),batch_size): end=s+batch_size if s+batch_size'data':test_dates,'predictions':predict.reshape(-1)}) #真实值 plt.plot(tab_data['data'],tab_data['actual'],'b-',label='actual') #预测值 plt.plot(predictions_data['data'],predictions_data['predictions'],'ro',label='predict') plt.legend() plt.xlabel('Data') plt.ylabel('Maximum Tem') plt.title("Actual and Predicted") plt.show()

示例：pandas 是基于NumPy 的一种工具，该工具是为了解决数据分析任务而创建的。

总结

提示：采用pytorch进行2021年天气气温预测，熟悉神经网络模型基本训练。 完整源代码可以私信我



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