import torch

import torch.nn.functional as F  # 包含激励函数

# 建立神经网络

# 先定义所有的层属性(__init__()), 然后再一层层搭建(forward(x))层于层的关系链接

class Net(torch.nn.Module):     # 继承 torch 的 Module

    def __init__(self, n_feature, n_hidden, n_output):

        super(Net, self).__init__()     # 继承 __init__ 功能

        # 定义每层用什么样的形式

        self.hidden = torch.nn.Linear(n_feature, n_hidden)   # hidden layer  # 隐藏层线性输出

        self.predict = torch.nn.Linear(n_hidden, n_output)   # output layer  # 输出层线性输出 ==== 定义层数

    def forward(self, x):       # 这同时也是 Module 中的 forward 功能

        # 正向传播输入值, 神经网络分析出输出值

        x = F.relu(self.hidden(x))      # activation function for hidden layer  # 激励函数(隐藏层的线性值)

        x = self.predict(x)             # linear output     # 输出值

        return x

net1 = Net(1, 10, 1)

# easy and fast way to build your network

net2 = torch.nn.Sequential(

    torch.nn.Linear(1, 10),     # 第一层

    torch.nn.ReLU(),            # 激活层

    torch.nn.Linear(10, 1)      # 输出层

)

print(net1)     # net1 architecture   ==  显示神经网络结构

"""

Net (

  (hidden): Linear (1 -> 10)

  (predict): Linear (10 -> 1)

)

"""

print(net2)     # net2 architecture

"""

Sequential (

  (0): Linear (1 -> 10)

  (1): ReLU ()

  (2): Linear (10 -> 1)

)

"""