快速搭建神经网络

在搭建神经网络的过程中，我们也经常使用 Sequential() 函数帮助我们快速搭建神经网络，通常使用两种不同方式搭建的神经网络没有区别。

1. 准备包

import torch
import torch.nn.functional as F

2.常见搭建神经网络的方法

# 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)

注：net = Net ('param') ，神经网络的参数可以在神经网络搭建好后直接传入

2. 快速搭建神经网络的方法

# 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)
)

在快速搭建神经网路的过程中，激活函数直接作为一层传入，不需要参数

3. 显示结果

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)
)
"""