Thank you
This is all the code
If you need all the data, I can give it to you.
import pandas as pd    
import numpy as np    
import matplotlib.pyplot as plt    
import torch˽    
import torch.nn as nn    
import warnings    
warnings.filterwarnings('ignore')    

train = pd.read_csv("../../Data/3-sofa公共自行车使用量预测/train.csv",index_col='id')    
test = pd.read_csv("../../Data/3-sofa公共自行车使用量预测/test.csv",index_col='id')    
submit = pd.read_csv("../../Data/3-sofa公共自行车使用量预测/sample_submit.csv")    

train.columns    

OUt
Index([‘city’, ‘hour’, ‘is_workday’, ‘weather’, ‘temp_1’, ‘temp_2’, ‘wind’,    ‘y’],    dtype=‘object’)
train.head()    


# 取出训练集的y
y_train = train.pop('y')

train = torch.FloatTensor(train.values)
test = torch.FloatTensor(test.values)
y_train = torch.FloatTensor(y_train.values)

net = torch.nn.Sequential(
    torch.nn.Linear(7,100),
    torch.nn.Sigmoid(),
    torch.nn.Linear(100,1),
)
optimizer = torch.optim.Adam(net.parameters(),lr=0.02)
loss_func = torch.nn.MSELoss()

for i in range(60):
    prediction = net(train)
    loss = loss_func(prediction,y_train)
    # 梯度归零
    optimizer.zero_grad()
    # 计算梯度
    loss.backward()
    # 更新结点
    optimizer.step()
    if i % 20 == 0:
        print(loss)

OUt˽˽
tensor(4836.1333, grad_fn=<MseLossBackward>)
tensor(2624.3867, grad_fn=<MseLossBackward>)
tensor(2307.5830, grad_fn=<MseLossBackward>)

y_train[:10]

tensor([15., 48., 21., 11., 39., 12., 11., 67., 77.,  2.])

net(train[:10])

Out
tensor([[51.4017],
[49.5164],
[48.2066],
[49.3453],
[49.4258],
[48.9165],
[48.8166],
[49.5939],
[50.7129],
[49.8309]])