Error running code from the manual

[diego-uva]

Good afternoon,

I have started using the KAN library and I got a couple of errors running the code from the https://kindxiaoming.github.io/pykan/intro.html#hello-kan manual page.

The first is that the manual has not yet been updated and the train method is used instead of fit.

The second one was when executing this code from the manual page:

from kan import *
# create a KAN: 2D inputs, 1D output, and 5 hidden neurons. cubic spline (k=3), 5 grid intervals (grid=5).
model = KAN(width=[2,5,1], grid=5, k=3, seed=0)
# create dataset f(x,y) = exp(sin(pi*x)+y^2)
f = lambda x: torch.exp(torch.sin(torch.pi*x[:,[0]]) + x[:,[1]]**2)
dataset = create_dataset(f, n_var=2)
dataset['train_input'].shape, dataset['train_label'].shape
# plot KAN at initialization
model(dataset['train_input']);
model.plot(beta=100)
# train the model
model.fit(dataset, opt="LBFGS", steps=20, lamb=0.01, lamb_entropy=10.) #fit instead train
model.plot()
model.prune() #The error in this line!
model.plot(mask=True)

The error is:

	"name": "AttributeError",
	"message": "'float' object has no attribute 'to'",
	"stack": "---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[13], line 1
----> 1 model.prune()
      2 model.plot(mask=True)

File c:\\Users\\hp\\.conda\\envs\\kan\\lib\\site-packages\\kan\\MultKAN.py:970, in MultKAN.prune(self, node_th, edge_th)
    969 def prune(self, node_th=1e-2, edge_th=3e-2):
--> 970     self = self.prune_node(node_th, log_history=False)
    971     #self.prune_node(node_th, log_history=False)
    972     self.forward(self.cache_data)

File c:\\Users\\hp\\.conda\\envs\\kan\\lib\\site-packages\\kan\\MultKAN.py:942, in MultKAN.prune_node(self, threshold, mode, active_neurons_id, log_history)
    938         model2.symbolic_fun[i].out_dim_mult = num_mult
    940         width_new.append([num_sum, num_mult])
--> 942     model2.act_fun[i] = model2.act_fun[i].get_subset(active_neurons_up[i], active_neurons_down[i])
    943     model2.symbolic_fun[i] = self.symbolic_fun[i].get_subset(active_neurons_up[i], active_neurons_down[i])
    945 model2.cache_data = self.cache_data

File c:\\Users\\hp\\.conda\\envs\\kan\\lib\\site-packages\\kan\\KANLayer.py:305, in KANLayer.get_subset(self, in_id, out_id)
    283 def get_subset(self, in_id, out_id):
    284     '''
    285     get a smaller KANLayer from a larger KANLayer (used for pruning)
    286     
   (...)
    303     (2, 3)
    304     '''
--> 305     spb = KANLayer(len(in_id), len(out_id), self.num, self.k, base_fun=self.base_fun, device=self.device)
    306     spb.grid.data = self.grid[in_id]
    307     spb.coef.data = self.coef[in_id][:,out_id]

File c:\\Users\\hp\\.conda\\envs\\kan\\lib\\site-packages\\kan\\KANLayer.py:132, in KANLayer.__init__(self, in_dim, out_dim, num, k, noise_scale, scale_base, scale_sp, base_fun, grid_eps, grid_range, sp_trainable, sb_trainable, save_plot_data, device, sparse_init)
    129 else:
    130     mask = 1.
--> 132 scale_base = scale_base.to(device)
    133 self.scale_base = torch.nn.Parameter(torch.ones(in_dim, out_dim, device=device) * scale_base * mask).requires_grad_(sb_trainable)  # make scale trainable
    134 #else:
    135 #self.scale_base = torch.nn.Parameter(scale_base.to(device)).requires_grad_(sb_trainable)

AttributeError: 'float' object has no attribute 'to'"

Best regards.

Diego.