编程人员所需具备的能力

1、读文档的能力 2、基本架构的理解：cpu架构、主机架构、操作系统架构（微机原理，编译原理，数据结构）

P7 处理多维特征的输入

import numpy as np  
import torch  
  
xy = np.loadtxt('diabetes.csv.gz', delimiter=',', dtype=mp.float(32))  
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()  
optimizer = torch.optim.SGD(model.parameters(), lr=0.1)  
  
num_epochs = 100  
for epoch in range(num_epochs):  
    y_pred = model(x_data)  
    loss = criterion(y_pred, y_data)  
  
optimizer.zero_grad()  
loss.backward()  
optimizer.step()  
if (epoch+1) % 10 == 0:  
print('epoch:{},/loss:{:.4f}'.format(epoch+1, loss.item()))