基于Python神经网络实现在数据拟合中应用（源码+数据）.rar

import torch inP = torch.Tensor([[-1.0, -1.0], [-1.0, 0.0], [-1.0, 1.0], [0.0, -1.0], [0.0, 0.0], [0.0, 1.0], [1.0, -1.0], [1.0, 0.0], [1.0, 1.0]]) bias1 = torch.Tensor([-3.12442959586638, 0.596253074987332, 1.08691596975814, -0.87833091886749, -1.04194161485478, -1.71097022352059, -2.49446233907584, -3.03073789969431, -1.78808522534959, 3.4743708479762]) bias2 = torch.Tensor([-1.50606651555681]) weight = torch.Tensor([4.763361096, 1.582268146, 1.296479134, 2.625841744, 2.951680086, -5.903043918, -0.000188714, 3.644154802, 0.790408045, 7.839779683]) ''' bias - 偏置 weight - 权重 ''' class Net(torch.nn.Module): def __init__(self): super().__init__() self.layer1 = torch.nn.Linear(2, 10) self.layer2 = torch.nn.Linear(10, 1) def forward(self, x): x = torch.tanh((self.layer1(x) + bias1) * weight) return self.layer2(x) + bias2 if __name__ == '__main__': model = Net() for i in inP: print(model(i).item())