import torch     import random import numpy as np from torch.nn import init import torch.optim as optim import matplotlib.pyplot as plt import pandas as pd nethid=37 print(9//4*4) df=pd.read_excel('1.xlsx') w=df.values def data_iter(batch_size,features,lables):     num_example=len(features)     indices=list(range(num_example))     random.shuffle(indices)     for i in range(0,num_example,batch_size):         j = torch.LongTensor(indices[i: min(i + batch_size,num_example)])         yield   features.index_select(0,j),lables.index_select(0,j) class simple_net(torch.nn.Module):     def __init__(self,imn,hid,out):         super(simple_net,self).__init__()         self.layer=torch.nn.Linear(imn,hid)         self.layer1=torch.nn.Linear(hid,out)     def forward(self,x):         x=self.layer(x)         """x=torch.nn.functional.sigmoid(x)"""         x=torch.sigmoid(x)         x=self.layer1(x)
        return x num_example=50 net=simple_net(4,nethid,1) num_inputs=1
feature=w[:,1:5] feature=np.reshape(feature,(-1,4)) feature=torch.tensor(feature, dtype=torch.float) lable=w[:,6] lable=torch.tensor(lable,dtype=torch.float) batch_size=3 loss = torch.nn.MSELoss() optimizer = optim.SGD(net.parameters(), lr=0.000003) num=0 sum=0 for epoch in range(10000):     for X,y in data_iter(batch_size,feature,lable):         output = net(X)         l = loss(output, y.view(-1, 1))         optimizer.zero_grad() # 梯度清零，等价于net.zero_grad()         l.backward()         optimizer.step()     num=num+1     if epoch%16==0:         sum=0     print('epoch %d, loss: %f' % (epoch, l.item())) for epoch in range(155128):     for X,y in data_iter(1000,feature,lable):         output = net(X)         l = loss(output, y.view(-1, 1))         optimizer.zero_grad() # 梯度清零，等价于net.zero_grad()         l.backward()         optimizer.step()     num=num+1     if epoch%16==0:         sum=0     print('epoch %d, loss: %f' % (epoch, l.item()))