糖尿病数据集复现-pytorch

import torch
import numpy as np
#加载数据集
xy = np.loadtxt('./diadata.csv',delimiter=',',dtype=np.float32,skiprows=1)
# 此处csv文件为自己再excel中根据老师表格自己创建，成功导入,由于标题是字符串，故使用skiprows=1将第一行删掉，成功导入
x_data = torch.from_numpy(xy[0:,:-1]) #-1表示不要y这一列
y_data = torch.from_numpy(xy[:,[-1]]) #只取y这一列
#print(x_data)
#print(y_data)
#建立模型
class MyModel(torch.nn.Module):
    def __init__(self):
        super(MyModel,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
#类的实例化
model = MyModel()
#loss和优化器
criterion = torch.nn.BCELoss(size_average = True)
optimzer = torch.optim.SGD(model.parameters(),lr = 0.1)
#训练
for epoch in range(100):
    y_pred = model(x_data)
    loss = criterion(y_pred,y_data)
    print(epoch,loss.item())
    optimzer.zero_grad()
    #backward
    loss.backward()
    #updata
    optimzer.step()

此部分为训练结果展示