方法一：采用torch.nn.Module模块

import torchimport torch.nn.functional as F#法1class Net(torch.nn.Module):    def __init__(self,n_feature,n_hidden,n_output):        super(Net,self).__init__()        self.hidden = torch.nn.Linear(n_feature,n_hidden)        self.predict = torch.nn.Linear(n_hidden,n_output)    def forward(self,x):        x = F.relu(self.hidden(x))        out = self.predict(x)        return xnet1 = Net(2,10,2)print(net1)

打印的结果：

Net(  (hidden): Linear(in_features=2, out_features=10, bias=True)  (predict): Linear(in_features=10, out_features=2, bias=True))

方法二：类似keras的sequencial搭建网络的方法

net2 = torch.nn.Sequential(    torch.nn.Linear(2,10),    torch.nn.ReLU(),    torch.nn.Linear(10,2),)print(net2)

打印结果：

Sequential(

  (0): Linear(in_features=2, out_features=10, bias=True)

  (1): ReLU()

  (2): Linear(in_features=10, out_features=2, bias=True)

)