刘老师的《Pytorch深度学习实践》第七讲：处理多维特征的输入 代码

import numpy as np
import torch

xy=np.loadtxt('diabetes.csv',delimiter=',',dtype=np.float32)
x_data=torch.from_numpy(xy[:, :-1])
y_data=torch.from_numpy(xy[:, [-1]])

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()

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

for epoch in range(1000):
    y_pred=model(x_data)
    loss=criterion(y_pred,y_data)
    print(epoch,loss.item())

    optimizer.zero_grad()
    loss.backward()

    optimizer.step()#更新

使用reduction=‘sum'显示出的loss一直不变，改为reduction='mean'后呈现出下降