PyTorch神经网络气温预测

导入包

import torch
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch.optim as optim
import warnings
warnings.filterwarnings("ignore")
%matplotlib inline

观察数据

features=pd.read_csv('temps.csv')
features.head()

OUT:

print('数据维度：',features.shape)

OUT: 数据维度： (348, 9)

 时间数据格式转换：方便可视化以及其他操作

import datetime

years=features['year']
months=features['month']
days=features['day']

#datetime格式
dates=[str(int(year))+'-'+str(int(month))+'-'+str(int(day)) for year,month,day in zip(years,months,days)]
dates=[datetime.datetime.strptime(date,'%Y-%m-%d') for date in dates]

date[0:5]

 OUT: 

 数据可视化

# 准备画图
# 指定默认风格
plt.style.use('fivethirtyeight')

# 设置布局
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize = (10,10))
fig.autofmt_xdate(rotation = 45)

# 标签值
ax1.plot(dates, features['actual'])
ax1.set_xlabel(''); ax1.set_ylabel('Temperature'); ax1.set_title('Max Temp')

# 昨天
ax2.plot(dates, features['temp_1'])
ax2.set_xlabel(''); ax2.set_ylabel('Temperature'); ax2.set_title('Previous Max Temp')

# 前天
ax3.plot(dates, features['temp_2'])
ax3.set_xlabel('Date'); ax3.set_ylabel('Temperature'); ax3.set_title('Two Days Prior Max Temp')

# 我的逗逼朋友
ax4.plot(dates, features['friend'])
ax4.set_xlabel('Date'); ax4.set_ylabel('Temperature'); ax4.set_title('Friend Estimate')

plt.tight_layout(pad=2)

 OUT： 

 数据中week一列是字符，进行独热编码转换成数值得形式

#独热编码
features=pd.get_dummies(features)
features.head(5)

 OUT： 

 处理数据：设置标签与特征，标准化之后数据收敛会更快一些

#定义标签
labels=np.array(features['actual'])

#在特征中去掉标签
features=features.drop('actual',axis=1)

#名字单独保存
feature_list=list(features.columns)

#转换成合适得格式
features=np.array(features)

#数据处理标准化
from sklearn import preprocessing
input_features=preprocessing.StandardScaler().fit_transform(features)

 构建网络模型:

input_size=input_features.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(1000):
    batch_loss=[]
    #MINI-batch方法来进行训练
    for start in range(0,len(input_features),batch_size):
        end=start+batch_size if start+batch_size 
 OUT:
 
 
 
 预测训练结果
 
#预测值
x=torch.tensor(input_features,dtype=torch.float)
predict=my_nn(x).data.numpy()

# 转换日期格式
dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]
dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in dates]

# 创建一个表格来存日期和其对应的标签数值
true_data = pd.Dataframe(data = {'date': dates, 'actual': labels})

# 同理，再创建一个来存日期和其对应的模型预测值
months = features[:, feature_list.index('month')]
days = features[:, feature_list.index('day')]
years = features[:, feature_list.index('year')]

test_dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]

test_dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in test_dates]

predictions_data = pd.Dataframe(data = {'date': test_dates, 'prediction': predict.reshape(-1)}) 

# 真实值
plt.plot(true_data['date'], true_data['actual'], 'b-', label = 'actual')

# 预测值
plt.plot(predictions_data['date'], predictions_data['prediction'], 'ro', label = 'prediction')
plt.xticks(rotation = '60'); 
plt.legend()

# 图名
plt.xlabel('Date'); plt.ylabel('Maximum Temperature (F)'); plt.title('Actual and Predicted Values');
 
 
 
 总结：这是一个简单用pytorch构建一个最简单神经网络得例子，用于熟悉pytorch代码编写