对糖尿病病人的病情进行预测，预测未来是否会加重。
输入有8个指标，也就是8个维度，输出只有一个维度即是否加重病情。

维度的变化其实就是线性代数中矩阵乘法的作用：$[m\times n]\cdot [n\times k]=[m\times k]$

import numpy as np
import torch
xy = np.loadtxt('diabetes.csv.gz', delimiter = ',', dtype = np.float32) # 注意使用32位
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() # 与nn.Function.sigmoid不同，用于构建计算图def forward(self, x):x = self.sigmoid(self.linear1(x))x = self.sigmoid(self.linear2(x))x = self.sigmoid(self.linear3(x))return xmodel = Model()criterion = torch.nn.BCELoss(reduction='mean') # 损失函数optimizer = torch.optim.SGD(model.parameters(), lr = 0.1) # 优化器for epoch in range(100):# Forwardy_pred = model(x_data)loss = criterion(y_pred, y_data)print(epoch, loss.item())# Backwardoptimizer.zero_grad() # 注意清零loss.backward()# Updateoptimizer.step()