62 lines
2.3 KiB
Python
62 lines
2.3 KiB
Python
import torch
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import matplotlib.pyplot as plt
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import torch.nn.functional as F
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class Net(torch.nn.Module): #继承 torch 的module
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def __init__(self, n_feature, n_hidden, n_output):
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super(Net, self).__init__() #继承_init_功能
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#定理每层用什么样的形式
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self.hidden1 = torch.nn.Linear(n_feature, n_hidden) #隐藏层线性输出
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self.hidden2 = torch.nn.Linear(n_hidden, n_hidden) #输出层线性输出
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self.hidden3 = torch.nn.Linear(n_hidden, n_hidden) #输出层线性输出
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self.predict = torch.nn.Linear(n_hidden, n_output) #输出层线性输出
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def forward(self, x): #这同时也是module中的forward功能
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#正向传播输入值，神经网络分析出输出值
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x = F.relu(self.hidden1(x)) #激励函数（隐藏层的线性值）
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x = F.relu(self.hidden2(x))
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x = F.relu(self.hidden3(x))
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x = self.predict(x) #输出值
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return x
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def main():
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x = torch.unsqueeze(torch.linspace(-1, 1, 100),
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dim=1) #x data(tensor),shape=(100,1)
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y = -x.pow(3) + 2 * x.pow(2) + 0.2 * torch.rand(x.size())
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#y=math.sin（x)+o.2*torch.rand(x.size())
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net = Net(n_feature=1, n_hidden=20, n_output=1)
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#optimizer是训练的工具
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optimizer = torch.optim.SGD(net.parameters(), lr=0.01) #传入net的所有参数，学习率
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loss_func = torch.nn.MSELoss() #预测值和真实值的误差计算公式（均方差）
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for t in range(2000):
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prediction = net(x) #喂给net训练数据x，输出预测值
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loss = loss_func(prediction, y) #计算两者的误差
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optimizer.zero_grad() #清空上一步的残余更新参数值
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loss.backward() #误差反向传播，计算参数更新值
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optimizer.step() #将参数更新值施加到net的parameters上
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if t % 5 == 0:
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#plot and show learning process
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plt.cla()
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plt.scatter(x.data.numpy(), y.data.numpy())
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plt.scatter(x.data.numpy(), prediction.data.numpy())
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plt.text(0.5,
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0,
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'Loss=%.4f' % loss.data.numpy(),
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fontdict={
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'size': 20,
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'color': 'red'
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})
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plt.show()
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if __name__ == "__main__":
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main()
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