直接上代码：
import torch, random import torch.nn as nn import torch.optim as optim torch.manual_seed(42) class RBFN(nn.Module): """ 以高斯核作为径向基函数 """ def __init__(self, centers, n_out=3): """ :param centers: shape=[center_num,data_dim] :param n_out: """ super(RBFN, self).__init__() self.n_out = n_out self.num_centers = centers.size(0) # 隐层节点的个数 self.dim_centure = centers.size(1) # self.centers = nn.Parameter(centers) # self.beta = nn.Parameter(torch.ones(1, self.num_centers), requires_grad=True) self.beta = torch.ones(1, self.num_centers)*10 # 对线性层的输入节点数目进行了修改 self.linear = nn.Linear(self.num_centers+self.dim_centure, self.n_out, bias=True) self.initialize_weights()# 创建对象时自动执行 def kernel_fun(self, batches): n_input = batches.size(0) # number of inputs A = self.centers.view(self.num_centers, -1).repeat(n_input, 1, 1) B = batches.view(n_input, -1).unsqueeze(1).repeat(1, self.num_centers, 1) C = torch.exp(-self.beta.mul((A - B).pow(2).sum(2, keepdim=False))) return C def forward(self, batches): radial_val = self.kernel_fun(batches) class_score = self.linear(torch.cat([batches, radial_val], dim=1)) return class_score def initialize_weights(self, ): """ 网络权重初始化 :return: """ for m in self.modules(): if isinstance(m, nn.Conv2d): m.weight.data.normal_(0, 0.02) m.bias.data.zero_() elif isinstance(m, nn.ConvTranspose2d): m.weight.data.normal_(0, 0.02) m.bias.data.zero_() elif isinstance(m, nn.Linear): m.weight.data.normal_(0, 0.02) m.bias.data.zero_() def print_network(self): num_params = 0 for param in self.parameters(): num_params += param.numel() print(self) print('Total number of parameters: %d' % num_params) # centers = torch.rand((5,8)) # rbf_net = RBFN(centers) # rbf_net.print_network() # rbf_net.initialize_weights() if __name__ =="__main__": data = torch.tensor([[0.25, 0.75], [0.75,0.75], [0.25,0.5], [0.5,0.5],[0.75,0.5], [0.25,0.25],[0.75,0.25],[0.5,0.125],[0.75,0.125]], dtype=torch.float32) label = torch.tensor([[-1,1,-1],[1,-1,-1],[-1,-1,1],[-1,-1,1],[-1,-1,1], [1,-1,-1],[-1,1,-1],[-1,1,-1],[1,-1,-1]], dtype=torch.float32) print(data.size()) centers = data[0:8,:] rbf = RBFN(centers,3) params = rbf.parameters() loss_fn = torch.nn.MSELoss() optimizer = torch.optim.SGD(params,lr=0.1,momentum=0.9) for i in range(10000): optimizer.zero_grad() y = rbf.forward(data) loss = loss_fn(y,label) loss.backward() optimizer.step() print(i,"\t",loss.data) # 加载使用 y = rbf.forward(data) print(y.data) print(label.data)说明：代码在https://goodgoodstudy.blog.csdn.net/article/details/105756137上进行了小修改（原代码应该是错的），并加了一个自己的实验。
