面对多维输入的数据时：
根据数据集构造从多维到一维的计算图：

import numpy as np
import torch
import matplotlib.pyplot as plt

#Prepare dataset
xy = np.loadtxt(r'C:\Users\22843\pythonProject3\data\diabetes.csv.gz',delimiter=',',dtype=np.float32)

x_data = torch.from_numpy(xy[:,:-1])  #第一个‘：’是指读取所有行，第二个‘：’是指从第一列开始，
y_data = torch.from_numpy(xy[:,[-1]]) #[-1]得到的是矩阵

#design model using class
class Model(torch.nn.Module):
    def __init__(self):
        super(Model,self).__init__()
        self.linear1 = torch.nn.Linear(8,6)
        self.linear2 = torch.nn.Linear(6,4)
        self.linear3 = torch.nn.Linear(4,1)
        self.sigmoid = torch.nn.Sigmoid()

    def forward(self,x):
        x = self.sigmoid(self.linear1(x))
        x = self.sigmoid(self.linear2(x))
        x = self.sigmoid(self.linear3(x))
        return x

model = Model()

#Construct loss and optimizer
criterion = torch.nn.BCELoss(reduction='mean')
optimizer = torch.optim.SGD(model.parameters(),lr=0.1)

epoch_list = []
loss_list = []

#Trainnig cycle forward,backward,update
for epoch in range(100):
    #forward
    y_pred = model(x_data)
    loss = criterion(y_pred,y_data)
    print(epoch,loss.item())
    epoch_list.append(epoch)
    loss_list.append(loss.item())
    #backward
    optimizer.zero_grad()
    loss.backward()
    #update
    optimizer.step()

plt.plot(epoch_list,loss_list)
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.show()