class Neuro_net(torch.nn.Module):
  """神经网络"""
  def __init__(self, n_feature, n_hidden_layer, n_output):
    super(Neuro_net, self).__init__()
    self.hidden_layer = torch.nn.Linear(n_feature, n_hidden_layer)
    self.output_layer = torch.nn.Linear(n_hidden_layer, n_output)

  def forward(self, input):
    hidden_out = torch.relu(self.hidden_layer(input))
    out = self.output_layer(hidden_out)
    return out
  
net = Neuro_net(2, 10, 2)
print(net)

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

# -*- coding: utf-8 -*-
# @Time  : 2019/11/5 10:43
# @Author : Chen
# @File  : neural_network_impl.py
# @Software: PyCharm
 
import torch
import torch.nn.functional as F
 
#data
x = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1)
y = x.pow(2) + 0.2 * torch.rand(x.size())
 
 
#第一种搭建方法：Module
# 其中，init中定义每个神经层的神经元个数，和神经元层数；
# forward是继承nn.Moudle中函数，来实现前向反馈（加上激励函数）
class Net(torch.nn.Module):
  def __init__(self):
    #继承__init__函数
    super(Net, self).__init__()
    #定义每层的形式
    #隐藏层线性输出feature->hidden
    self.hidden = torch.nn.Linear(1, 10)
    #输出层线性输出hidden->output
    self.predict = torch.nn.Linear(10, 1)
 
  #实现所有层的连接关系。正向传播输入值，神经网络分析输出值
  def forward(self, x):
    #x首先在隐藏层经过激励函数的计算
    x = F.relu(self.hidden(x))
    #到输出层给出预测值
    x = self.predict(x)
    return x
 
net = Net()
print(net)
 
print('\n\n')
 
#快速搭建：Sequential
#模板：net2 = torch.nn.Sequential()
 
net2 = torch.nn.Sequential(
  torch.nn.Linear(1, 10),
  torch.nn.ReLU(),
  torch.nn.Linear(10, 1)
)
print(net2)