54 lines
1.9 KiB
Python
54 lines
1.9 KiB
Python
import pytest
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from pina import Trainer
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from pina.solver import PINN
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from pina.model import FeedForward
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from pina.loss import NeuralTangentKernelWeighting
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from pina.problem.zoo import Poisson2DSquareProblem
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# Initialize problem and model
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problem = Poisson2DSquareProblem()
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problem.discretise_domain(10)
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model = FeedForward(len(problem.input_variables), len(problem.output_variables))
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@pytest.mark.parametrize("update_every_n_epochs", [1, 10, 100, 1000])
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@pytest.mark.parametrize("alpha", [0.0, 0.5, 1.0])
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def test_constructor(update_every_n_epochs, alpha):
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NeuralTangentKernelWeighting(
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update_every_n_epochs=update_every_n_epochs, alpha=alpha
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)
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# Should fail if alpha is not >= 0
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with pytest.raises(ValueError):
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NeuralTangentKernelWeighting(
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update_every_n_epochs=update_every_n_epochs, alpha=-0.1
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)
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# Should fail if alpha is not <= 1
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with pytest.raises(ValueError):
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NeuralTangentKernelWeighting(alpha=1.1)
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# Should fail if update_every_n_epochs is not an integer
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with pytest.raises(AssertionError):
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NeuralTangentKernelWeighting(update_every_n_epochs=1.5)
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# Should fail if update_every_n_epochs is not > 0
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with pytest.raises(AssertionError):
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NeuralTangentKernelWeighting(update_every_n_epochs=0)
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# Should fail if update_every_n_epochs is not > 0
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with pytest.raises(AssertionError):
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NeuralTangentKernelWeighting(update_every_n_epochs=-3)
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@pytest.mark.parametrize("update_every_n_epochs", [1, 3])
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@pytest.mark.parametrize("alpha", [0.0, 0.5, 1.0])
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def test_train_aggregation(update_every_n_epochs, alpha):
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weighting = NeuralTangentKernelWeighting(
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update_every_n_epochs=update_every_n_epochs, alpha=alpha
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)
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solver = PINN(problem=problem, model=model, weighting=weighting)
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trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu")
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trainer.train()
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