pytorch搭建简单网络

pytorch搭建一个简单神经网络

 import torch
 import torch.nn as nn

 # 定义数据
 # x:输入数据
 # y:标签
 x = torch.Tensor([[0.2, 0.4], [0.2, 0.3], [0.3, 0.4]])
 y = torch.Tensor([[0.6], [0.5], [0.7]])

 class MyNet(nn.Module):
     def __init__(self):
         # 调用基类构造函数
         super(MyNet, self).__init__()
         # 容器，使用时顺序调用各个层
         self.fc = nn.Sequential(
             # 定义三层
             # 输入层
             nn.Linear(2, 4),
             # 激活函数
             nn.Sigmoid(),
             # 隐藏层
             nn.Linear(4, 4),
             nn.Sigmoid(),
             # 输出层
             nn.Linear(4, 1),
         )
         # 优化器
         # params:优化对象
         # lr:学习率
         self.opt = torch.optim.Adam(params=self.parameters(), lr=0.001)
         # 损失函数,均方差
         self.mls = torch.nn.MSELoss()

     def forward(self, inputs):
         # 前向传播
         return self.fc(inputs)

     def train(self, x, y):
         # 训练
         # 得到输出结果
         out = self.forward(x)
         # 计算误差
         loss = self.mls(out, y)
         # print('loss', loss)
         # 梯度置零
         self.opt.zero_grad()
         # 误差反向传播
         loss.backward()
         # 更新权重
         self.opt.step()

     def test(self, x):
         # 测试，就是前向传播的过程
         return self.forward(x)

 net = MyNet()
 for i in range(10000):
     net.train(x, y)
 x = torch.Tensor([[0.4, 0.1]])
 out = net.test(x)
 print(out)  # 输出结果 tensor([[0.5205]], grad_fn=<AddmmBackward>)

训练集较少，可能结果不是很好，主要是结构，毕竟刚开始接触这个pytorch