论文写完了也不让实习，糟糕的环境就不用吐槽了，感觉被生活蹂躏的我身心疲惫，能做的只有忍下去，每天一个人这么将就的活着。没事我还能忍，既然如此，暑假的我还是踏踏实实学习，只希望将来能不辜负自己受的这些苦难，让我爱的人都能幸福的活着，加油。

#1.神将网络天气预测,线性回归
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch
import torch.optim as optim
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline

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

print('数据维度：',features.shape)

#处理时间数据
import datetime
years=features['year']
months=features['month']
days=featuures['day']
dates=[str(int(year))+'-'+str(int(month))+'-'+str(int(day)) for year,month,day in zip(years,month,days)]
dates=[datetime.datetime.strptime(date,'%Y=%m-%d') for date in dates]
#画图展示
plt.style.use('fivethirtyeight')#指定默认风格
fig,((ax1,ax2,ax3,ax4))=plt.subplot(nrows=2,ncols=2,figsize=(10,10))
fig.autofmt_xdate(rotation=45)#设置布局
#画出4个子图
ax1.plot(dates,features['actual'])
ax1.set_xlabel('');ax1.set_ylabel('Temperature');ax1.set_title('Max Temp')
ax2.plot(dates,features['temp_1'])
ax2.set_xlabel('');ax2.set_ylabel('Temperature');ax2.set_title('Previous Max Temp')
ax3.plot(dates,features['temp_2'])
ax3.set_xlabel('Date');ax3.set_ylabel('Temperature');ax3.set_title('Two Days Prior Max Temp')
ax4.plot(dates,features['friend'])
ax4.set_xlabel('Date');ax4.set_ylabel('Temperature');ax4.set_title('Friend Estimate')
plt.tight_layout(pad=2)
#独热编码
features=pd.get_dummies(features)#自动将字符串形式的换成独热编码
labels=np.array(features['actual'])#标签
features=features.drop('actual',axis=1)#特征
features=np.array(features)#datafrom>>>>ndarray
feature_list=list(features.columns)#名字
from sklearn import preprocessing
input_features=preprocessing.StandardScalar().fit_transform(features)#特征数据标准化
#构建模型
x=torch.tensor(input_features,dtype=float)
y=torch.tensor(labels,dtype=float)
weights=torch.randn((14,128),dtype=float,requires_grad=True)
biases=torch.randn(128,dtype=float,requires_grad=True)
weights2=torch.randn((128,1),dtype=float,requires_grad=True)
biases2=torch.randn(1,dtype=float,requires_grad=True)
learning_rate=0.01
losses=[]
for i in range(1000):
    hidden=x.mm(weights)+biases#计算隐层
    hidden=torch.relu(hidden)#加入激活层
    prediction=hidden.mm(weights)+biases2#预测结果
    loss=torch.mean((predictions-y)**2)#计算损失
    losses.append(loss.data.numpy())
    if i%100==0:
        print('loss',loss)
    loss.backward()#反向传播
    #更新参数
    weights.data.add_(-learning_rate*weights.grad.data)
    biases.data.add_(-learning_rate*biases.grad.data)
    weights2.data.add_(-learning_rate*weights.grad.data)
    biases2.data.add_(-learning_rate*biases.grad.data)
    #每次迭代完都记得清空
    weights.grad.data.zero_()
    biases.grad.data.zero_()
    weights2.grad.data.zero_()
    biases2.grad.data.zero_()
    

#2.更简单的方式构建网络
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,out_size))#定义网络结构
cost=torch.nn.MSELoss(reduction='mean')#均方误差
optimizer=torch.optim.Adam(my_nn.parameters(),lr=0.001)#对网络参数进行优化的优化器设置
#训练网络
losses=[]
for i in range(1000):
    batch_loss=[]
    for start in range(0.len(input_features),batch_size):
        end=start+batch_size if star+batch_size<len(inpu_features) else len(input_featurs)
        xx=torch.tensor(input_features[start:end],dtype=torch.flaot,requires_grad=True)
        yy=torch.tensor(labels[start:end],dtype=torch.float,requires_grad=True)
        prediction=my_nn(xx)
        loss=cost(prediction,yy)
        optimizer.zero_grad()
        loss.backward(retain_graph=True)#表示此代码可以重复执行
        optimizer.step()#更新操作
        batch_loss.append(loss,data.numpy())
    
    if i%100==0:
        losses.append(np.mean(batch_loss))
        print(i,np.mean(batch_loss))