PyTorch深度学习：多维特征的输入

问题：

若给出一组糖尿病数据，如何作出判断是否会得糖尿病

解决办法：

        使用多维特征输入。

与单维对比：

        写成矩阵相乘的形式以加快运算速率

  采用多层维度变化，采用非线性元素α（即激活函数），层数越多学习能力越强

神经网络的本质：寻找一种非线性的空间变换函数。

代码实现过程：

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


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)

loss_list = []
epoch_list = []

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()
    loss_list.append(loss.item())
    epoch_list.append(epoch)

plt.plot(epoch_list,loss_list)
plt.xlabel(epoch)
plt.ylabel(loss)
plt.show()