Попытка повторить результат через некоторое время привела к ошибке:
“c:\Users\frost\KAN\pykan-master>python.exe Examp8.py
Traceback (most recent call last):
File ”c:\Users\frost\KAN\pykan-master\Examp8.py“, line 1, in <module>
from kan import *
File ”c:\Users\frost\KAN\pykan-master\kan\__init__.py“, line 1, in <module>
from MultKAN import *
ModuleNotFoundError: No module named ‘MultKAN’”
Естественно, что при правильном выполнении сообщений такого вида не было. Время последнего редактирования файла программы на 4 минуты меньше времени спасения рисунка. Программа:
from kan import * import os import numpy as np import torch import matplotlib.pyplot as plt datasets = [] n_peak = 5 # Without errors for 5, 1 or 2 errors at line 71 n_num_per_peak = 100 n_sample = n_peak * n_num_per_peak x_grid = torch.linspace(-1,1,steps=n_sample) x_centers = 2/n_peak * (np.arange(n_peak) - n_peak/2+0.5) x_sample = torch.stack([torch.linspace(-1/n_peak,1/n_peak,steps=n_num_per_peak)+center for center in x_centers]).reshape(-1,) y = 0. for center in x_centers: y += torch.exp(-(x_grid-center)**2*300) y_sample = 0. for center in x_centers: y_sample += torch.exp(-(x_sample-center)**2*300) plt.plot(x_grid.detach().numpy(), y.detach().numpy()) plt.scatter(x_sample.detach().numpy(), y_sample.detach().numpy()) #[2] plt.subplots(1, 5, figsize=(15, 2)) plt.subplots_adjust(wspace=0, hspace=0) #plt.show() for i in range(1,6): plt.subplot(1,5,i) group_id = i - 1 plt.plot(x_grid.detach().numpy(), y.detach().numpy(), color='black', alpha=0.1) plt.scatter(x_sample[group_id*n_num_per_peak:(group_id+1)*n_num_per_peak].detach().numpy(), y_sample[group_id*n_num_per_peak:(group_id+1)*n_num_per_peak].detach().numpy(), color="black", s=2) plt.xlim(-1,1) plt.ylim(-1,2) #plt.show() #[3] ys = [] # setting bias_trainable=False, sp_trainable=False, sb_trainable=False is important. # otherwise KAN will have random scaling and shift for samples in previous stages model = KAN(width=[1,1], grid=200, k=3, noise_scale=0.1, sp_trainable=False, sb_trainable=False, base_fun='zero') for group_id in range(n_peak): dataset = {} dataset['train_input'] = x_sample[group_id*n_num_per_peak:(group_id+1)*n_num_per_peak][:,None] dataset['train_label'] = y_sample[group_id*n_num_per_peak:(group_id+1)*n_num_per_peak][:,None] dataset['test_input'] = x_sample[group_id*n_num_per_peak:(group_id+1)*n_num_per_peak][:,None] dataset['test_label'] = y_sample[group_id*n_num_per_peak:(group_id+1)*n_num_per_peak][:,None] model.fit(dataset, opt = 'LBFGS', steps=100, update_grid=False); y_pred = model(x_grid[:,None]) ys.append(y_pred.detach().numpy()[:,0]) #[4] plt.subplots(1, 5, figsize=(15, 2)) plt.subplots_adjust(wspace=0, hspace=0) for i in range(1,n_peak+1): plt.subplot(1,5,i) group_id = i - 1 plt.plot(x_grid.detach().numpy(), y.detach().numpy(), color='black', alpha=0.1) plt.plot(x_grid.detach().numpy(), ys[i-1], color='black') # The place of error for 1 or 2 plt.xlim(-1,1) plt.ylim(-1,2) plt.show()
