pytorch之 bulid_nn_with_2_method

 import torch
 import torch.nn.functional as F

 # replace following class code with an easy sequential network
 class 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)   # hidden layer
         self.predict = torch.nn.Linear(n_hidden, n_output)   # output layer

     def forward(self, x):
         x = F.relu(self.hidden(x))      # activation function for hidden layer
         x = self.predict(x)             # linear output
         return x

 net1 = Net(1, 10, 1)

 # easy and fast way to build your network
 net2 = torch.nn.Sequential(
     torch.nn.Linear(1, 10),
     torch.nn.ReLU(),
     torch.nn.Linear(10, 1)
 )

 print(net1)     # net1 architecture
 """
 Net (
   (hidden): Linear (1 -> 10)
   (predict): Linear (10 -> 1)
 )
 """

 print(net2)     # net2 architecture
 """
 Sequential (
   (0): Linear (1 -> 10)
   (1): ReLU ()
   (2): Linear (10 -> 1)
 )
 """