from . import KAN, ChebyLayer_v2, TaylorLayer
import os, torch, torch.nn as nn
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class KANSIN(KAN):
def __init__(
self,
nneuron_sin:int,
sigma:float = 1.0,
*args,
**kwargs
):
super().__init__(*args, **kwargs)
self.nneuron_sin = nneuron_sin
self.sigma = sigma
self.input = SineLayer(self.ninput, self.nneuron_sin, self.sigma)
self.kan_layers[0] = self.layer_type(self.nneuron_sin, self.hidden_neur, self.degree)
self.to(self.device)
if self.intro:
print('Adding sin layer at the beginning:')
keys_print = ['nneuron_sin', 'sigma']
for key in keys_print:
print(f" {key}: {getattr(self, key)}")
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def define_checkpoint(self):
checkpoint = super().define_checkpoint()
checkpoint["sigma"] = self.sigma
checkpoint["nneuron_sin"] = self.nneuron_sin
checkpoint["state_dict"] = self.state_dict()
return checkpoint
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class SineLayer(nn.Module):
def __init__(self, input_size, output_size, sigma=1.0):
super(SineLayer, self).__init__()
self.linear = nn.Linear(input_size, output_size)
self.sigma = sigma
self.init_weights()
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def init_weights(self):
nn.init.normal_(self.linear.weight, mean=0.0, std=self.sigma)
nn.init.zeros_(self.linear.bias)
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def forward(self, x):
return torch.sin( self.linear(x))