径向基神经网络代码（pytorch）

具体内容：径向基网络函数(最小二乘法)python实现）_class rbfnetwork:-优快云博客

pytorch代码实现：

import torch
import torch.nn as nn
import matplotlib.pyplot as plt
import torch.optim as optim
import time
from sklearn.datasets import load_iris
from sklearn.cluster import KMeans
from sklearn.model_selection import train_test_split
import numpy as np

start = time.perf_counter()
class RBF(nn.Module):
    def __init__(self,centers_dim,out_dim,centers,sigma):
        super(RBF,self).__init__()
        self.flatten = nn.Flatten()#变成二维的
        self.centers_dim=centers_dim
        self.out_dim=out_dim
        self.centers = nn.Parameter(centers)
        self.sigma = nn.Parameter(sigma)
        self.linear = nn.Linear(self.centers_dim, self.out_dim)

    def forward(self,X):
        x= self.flatten(X)
        distance = torch.cdist(x, self.centers)
        gauss = torch.exp(-distance ** 2 / (2 * self.sigma ** 2))
        y=self.linear(gauss)
        return y
#数据运行
iris = load_iris()
X = iris.data  # 获取特征值
y = iris.target  # 获取特征值
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=10)
X_tensor = torch.tensor(X_train, dtype=torch.float32)  # 明确指定 dtype 以避免潜在的自动转换问题
y_tensor = torch.tensor(y_train, dtype=torch.long)  # 类别标签应使用整数类
X_test = torch.tensor(X_test, dtype=torch.float32)  # 明确指定 dtype 以避免潜在的自动转换问题
y_test = torch.tensor(y_test, dtype=torch.long)
centers_dim=10
input_dim=X_tensor.shape[1]
out_dim=3
#聚类产生聚类中心等
def P_centers(X_tensor,centers_dim):
    kmeans = KMeans(n_clusters=centers_dim)
    kmeans.fit(X_tensor)  # 找到一组适当的中心点
    centers = kmeans.cluster_centers_  # 用kmeans找中心点位
    centers=torch.tensor(centers,dtype=torch.float32)
    # 计算标准差
    distances = torch.cdist(X_tensor,centers)
    sigma = torch.std(distances, axis=0)  # 计算了所有聚类中心的距离标准差的平均值
    return centers,sigma
centers,sigma=P_centers(X_tensor,centers_dim)

rbf= RBF(centers_dim,out_dim,centers,sigma)
optimizer = optim.Adam(rbf.parameters(),lr=0.001)
loss_fun = nn.CrossEntropyLoss()#分类模型
start = time.time()
epochs = 10000
Loss=[]
for epoch in range(epochs):
    Y_pre = rbf(X_tensor)
    loss = loss_fun(Y_pre,y_tensor)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    print("epoch:{}\t  loss:{:>.9}".format(epoch+1,loss.item()))
    Loss.append(loss.item())
end = time.time()
print("time:",end-start)
plt.plot(Loss)
plt.xlabel("epoch")
plt.ylabel("Loss")
plt.show()

from sklearn.metrics import precision_score, recall_score, accuracy_score
def evaluation( y_test, y_predict):
    precision = precision_score(y_test, y_predict, average='macro')
    accuracy = accuracy_score(y_test, y_predict)
    recall = recall_score(y_test, y_predict, average='macro')
    print("accuracy:", accuracy)
    print(" precision:", precision)
    print(" recall:", recall)


#训练效果
print("!!!训练集！！！")
Y_pre= torch.max(Y_pre, 1)[1]
pred_y = Y_pre.data.numpy()
target_y = y_tensor.data.numpy()
# 衡量训练集准确率
evaluation(pred_y ,target_y)


#预测效果
print("!!!测试集！！！")
y_pre = rbf(X_test)
y_pre= torch.max(y_pre, 1)[1]
pred_y = y_pre.data.numpy()
target_y = y_test.data.numpy()
# 衡量测试集准确率
evaluation(pred_y ,target_y)

数据来源：Iris - UCI 机器学习存储库

运行结果：