01aeb17673
* NTK weighting + typo fixing * black code formatter + .rst docs --------- Co-authored-by: Dario Coscia <dariocos99@gmail.com>
66 lines
1.6 KiB
Python
66 lines
1.6 KiB
Python
import pytest
|
|
from pina import Trainer
|
|
from pina.solver import PINN
|
|
from pina.model import FeedForward
|
|
from pina.problem.zoo import Poisson2DSquareProblem
|
|
from pina.loss import NeuralTangentKernelWeighting
|
|
|
|
problem = Poisson2DSquareProblem()
|
|
condition_names = problem.conditions.keys()
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"model,alpha",
|
|
[
|
|
(
|
|
FeedForward(
|
|
len(problem.input_variables), len(problem.output_variables)
|
|
),
|
|
0.5,
|
|
)
|
|
],
|
|
)
|
|
def test_constructor(model, alpha):
|
|
NeuralTangentKernelWeighting(model=model, alpha=alpha)
|
|
|
|
|
|
@pytest.mark.parametrize("model", [0.5])
|
|
def test_wrong_constructor1(model):
|
|
with pytest.raises(ValueError):
|
|
NeuralTangentKernelWeighting(model)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"model,alpha",
|
|
[
|
|
(
|
|
FeedForward(
|
|
len(problem.input_variables), len(problem.output_variables)
|
|
),
|
|
1.2,
|
|
)
|
|
],
|
|
)
|
|
def test_wrong_constructor2(model, alpha):
|
|
with pytest.raises(ValueError):
|
|
NeuralTangentKernelWeighting(model, alpha)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"model,alpha",
|
|
[
|
|
(
|
|
FeedForward(
|
|
len(problem.input_variables), len(problem.output_variables)
|
|
),
|
|
0.5,
|
|
)
|
|
],
|
|
)
|
|
def test_train_aggregation(model, alpha):
|
|
weighting = NeuralTangentKernelWeighting(model=model, alpha=alpha)
|
|
problem.discretise_domain(50)
|
|
solver = PINN(problem=problem, model=model, weighting=weighting)
|
|
trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu")
|
|
trainer.train()
|