[人工智能]全连接神经网络实现非线性回归（学习笔记一）

1.神经网络模型的搭建

定义一个基础的神经网络架构，同时新建一个net=Net(1, 100, 100, 10, 1)，包含三个隐藏层的神经网络：

import torch
import os
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"


class Net(torch.nn.Module):
    def __init__(self, n_feature, n_hidden, n_hidden1, n_hidden2, n_output):
        super(Net, self).__init__()
        self.hidden0 = torch.nn.Linear(n_feature, n_hidden)
        self.hidden1 = torch.nn.Linear(n_hidden, n_hidden1)
        self.hidden2 = torch.nn.Linear(n_hidden1, n_hidden2)
        self.predict = torch.nn.Linear(n_hidden2, n_output)

    def forward(self, x):
        x = torch.relu(self.hidden0(x))
        x = torch.relu(self.hidden1(x))
        x = torch.relu(self.hidden2(x))
        x = self.predict(x)
        return x
net = Net(1, 100, 100, 10, 1)
print(net)

输出结果为：

Net(
  (hidden0): Linear(in_features=1, out_features=100, bias=True)
  (hidden1): Linear(in_features=100, out_features=100, bias=True)
  (hidden2): Linear(in_features=100, out_features=100, bias=True)
  (predict): Linear(in_features=100, out_features=1, bias=True)
)

这里可以清楚的看到所搭建的神经网络的架构细节

2.神经网络实现非线性回归

import torch
import matplotlib.pyplot as plt
import os
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"

x = torch.unsqueeze(torch.linspace(-3, 5, 421), dim=1)
y = 0.3 * x.pow(2) + 0.2 * torch.rand(x.size())


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)
        self.predict = torch.nn.Linear(n_hidden, n_output)

    def forward(self, x):
        x = torch.relu(self.hidden(x))
        x = self.predict(x)
        return x


net = Net(1, 100, 1)
#print(net)
plt.ion()
plt.show()
optimizer = torch.optim.Adam(net.parameters(), lr=0.1)
loss_func = torch.nn.MSELoss()

for t in range(200):
    prediction = net(x)
    loss = loss_func(prediction, y)

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    if t % 5 == 0:
        plt.cla()
        plt.scatter(x, y)
        plt.plot(x, prediction.data.numpy(), 'r-', lw=3)
        plt.text(0.5, 0, 'Loss=%.4f' % loss.item(), fontdict={'size': 20, 'color': 'red'})
        plt.pause(0.1)
plt.ioff()
plt.show()

2.结果展示

3.关键代码解析

1.代码：

import os
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"

是为了解决下面的问题

OMP: Error #15: Initializing libiomp5md.dll, but found libiomp5md.dll already initialized.

2.代码：

x = torch.unsqueeze(torch.linspace(-3, 5, 421), dim=1)
y = 0.3 * x.pow(2) + 0.2 * torch.rand(x.size())

产生数据，x和y