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some simple optimize #252

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some simple optimize #252

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32 changes: 14 additions & 18 deletions kan/KAN.py
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Original file line number Diff line number Diff line change
Expand Up @@ -143,7 +143,7 @@ def __init__(self, width=None, grid=3, k=3, noise_scale=0.1, noise_scale_base=0.

# bias
bias = nn.Linear(width[l + 1], 1, bias=False, device=device).requires_grad_(bias_trainable)
bias.weight.data *= 0.
torch.nn.init.zeros_(bias.weight)
self.biases.append(bias)

self.biases = nn.ModuleList(self.biases)
Expand All @@ -154,12 +154,8 @@ def __init__(self, width=None, grid=3, k=3, noise_scale=0.1, noise_scale_base=0.
self.base_fun = base_fun

### initializing the symbolic front ###
self.symbolic_fun = []
for l in range(self.depth):
sb_batch = Symbolic_KANLayer(in_dim=width[l], out_dim=width[l + 1], device=device)
self.symbolic_fun.append(sb_batch)

self.symbolic_fun = nn.ModuleList(self.symbolic_fun)
self.symbolic_fun = nn.ModuleList(
[Symbolic_KANLayer(in_dim=width[l], out_dim=width[l + 1], device=device) for l in range(self.depth)])
self.symbolic_enabled = symbolic_enabled

self.device = device
Expand All @@ -185,7 +181,7 @@ def initialize_from_another_model(self, another_model, x):
>>> model_fine = KAN(width=[2,5,1], grid=10, k=3)
>>> print(model_fine.act_fun[0].coef[0][0].data)
>>> x = torch.normal(0,1,size=(100,2))
>>> model_fine.initialize_from_another_model(model_coarse, x);
>>> model_fine.initialize_from_another_model(model_coarse, x)
>>> print(model_fine.act_fun[0].coef[0][0].data)
tensor(-0.0030)
tensor(0.0506)
Expand Down Expand Up @@ -356,10 +352,10 @@ def set_mode(self, l, i, j, mode, mask_n=None):
None
'''
if mode == "s":
mask_n = 0.;
mask_n = 0.
mask_s = 1.
elif mode == "n":
mask_n = 1.;
mask_n = 1.
mask_s = 0.
elif mode == "sn" or mode == "ns":
if mask_n == None:
Expand All @@ -368,7 +364,7 @@ def set_mode(self, l, i, j, mode, mask_n=None):
mask_n = mask_n
mask_s = 1.
else:
mask_n = 0.;
mask_n = 0.
mask_s = 0.

self.act_fun[l].mask.data[j * self.act_fun[l].in_dim + i] = mask_n
Expand Down Expand Up @@ -814,7 +810,7 @@ def train(self, dataset, opt="LBFGS", steps=100, log=1, lamb=0., lamb_l1=1., lam
>>> model = KAN(width=[2,5,1], grid=5, k=3, noise_scale=0.1, seed=0)
>>> f = lambda x: torch.exp(torch.sin(torch.pi*x[:,[0]]) + x[:,[1]]**2)
>>> dataset = create_dataset(f, n_var=2)
>>> model.train(dataset, opt='LBFGS', steps=50, lamb=0.01);
>>> model.train(dataset, opt='LBFGS', steps=50, lamb=0.01)
>>> model.plot()
'''

Expand Down Expand Up @@ -958,7 +954,7 @@ def prune(self, threshold=1e-2, mode="auto", active_neurons_id=None):
>>> model = KAN(width=[2,5,1], grid=5, k=3, noise_scale=0.1, seed=0)
>>> f = lambda x: torch.exp(torch.sin(torch.pi*x[:,[0]]) + x[:,[1]]**2)
>>> dataset = create_dataset(f, n_var=2)
>>> model.train(dataset, opt='LBFGS', steps=50, lamb=0.01);
>>> model.train(dataset, opt='LBFGS', steps=50, lamb=0.01)
>>> model.prune()
>>> model.plot(mask=True)
'''
Expand Down Expand Up @@ -1063,7 +1059,7 @@ def suggest_symbolic(self, l, i, j, a_range=(-10, 10), b_range=(-10, 10), lib=No
>>> model = KAN(width=[2,5,1], grid=5, k=3, noise_scale=0.1, seed=0)
>>> f = lambda x: torch.exp(torch.sin(torch.pi*x[:,[0]]) + x[:,[1]]**2)
>>> dataset = create_dataset(f, n_var=2)
>>> model.train(dataset, opt='LBFGS', steps=50, lamb=0.01);
>>> model.train(dataset, opt='LBFGS', steps=50, lamb=0.01)
>>> model = model.prune()
>>> model(dataset['train_input'])
>>> model.suggest_symbolic(0,0,0)
Expand Down Expand Up @@ -1124,7 +1120,7 @@ def auto_symbolic(self, a_range=(-10, 10), b_range=(-10, 10), lib=None, verbose=
>>> model = KAN(width=[2,5,1], grid=5, k=3, noise_scale=0.1, seed=0)
>>> f = lambda x: torch.exp(torch.sin(torch.pi*x[:,[0]]) + x[:,[1]]**2)
>>> dataset = create_dataset(f, n_var=2)
>>> model.train(dataset, opt='LBFGS', steps=50, lamb=0.01);
>>> model.train(dataset, opt='LBFGS', steps=50, lamb=0.01)
>>> >>> model = model.prune()
>>> model(dataset['train_input'])
>>> model.auto_symbolic()
Expand All @@ -1139,7 +1135,7 @@ def auto_symbolic(self, a_range=(-10, 10), b_range=(-10, 10), lib=None, verbose=
>>> model = KAN(width=[2,5,1], grid=5, k=3, noise_scale=0.1, seed=0)
>>> f = lambda x: torch.exp(torch.sin(torch.pi*x[:,[0]]) + x[:,[1]]**2)
>>> dataset = create_dataset(f, n_var=2)
>>> model.train(dataset, opt='LBFGS', steps=50, lamb=0.01);
>>> model.train(dataset, opt='LBFGS', steps=50, lamb=0.01)
>>> >>> model = model.prune()
>>> model(dataset['train_input'])
>>> model.auto_symbolic(lib=['exp','sin','x^2'])
Expand Down Expand Up @@ -1184,11 +1180,11 @@ def symbolic_formula(self, floating_digit=2, var=None, normalizer=None, simplify
>>> model = KAN(width=[2,5,1], grid=5, k=3, noise_scale=0.1, seed=0, grid_eps=0.02)
>>> f = lambda x: torch.exp(torch.sin(torch.pi*x[:,[0]]) + x[:,[1]]**2)
>>> dataset = create_dataset(f, n_var=2)
>>> model.train(dataset, opt='LBFGS', steps=50, lamb=0.01);
>>> model.train(dataset, opt='LBFGS', steps=50, lamb=0.01)
>>> model = model.prune()
>>> model(dataset['train_input'])
>>> model.auto_symbolic(lib=['exp','sin','x^2'])
>>> model.train(dataset, opt='LBFGS', steps=50, lamb=0.00, update_grid=False);
>>> model.train(dataset, opt='LBFGS', steps=50, lamb=0.00, update_grid=False)
>>> model.symbolic_formula()
'''
symbolic_acts = []
Expand Down
3 changes: 1 addition & 2 deletions kan/KANLayer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -116,8 +116,7 @@ def __init__(self, in_dim=3, out_dim=2, num=5, k=3, noise_scale=0.1, scale_base=
# shape: (size, num)
self.grid = torch.einsum('i,j->ij', torch.ones(size, device=device), torch.linspace(grid_range[0], grid_range[1], steps=num + 1, device=device))
self.grid = torch.nn.Parameter(self.grid).requires_grad_(False)
noises = (torch.rand(size, self.grid.shape[1]) - 1 / 2) * noise_scale / num
noises = noises.to(device)
noises = (torch.rand(size, self.grid.shape[1], device=device) - 1 / 2) * noise_scale / num
# shape: (size, coef)
self.coef = torch.nn.Parameter(curve2coef(self.grid, noises, self.grid, k, device))
if isinstance(scale_base, float):
Expand Down