57 lines
1.5 KiB
Python
57 lines
1.5 KiB
Python
import argparse
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import torch
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from torch.nn import Softplus
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from pina import PINN as pPINN
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from problems.parametric_poisson import ParametricPoisson
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from pina.model import FeedForward
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class myFeature(torch.nn.Module):
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"""
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"""
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def __init__(self):
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super(myFeature, self).__init__()
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def forward(self, x):
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return torch.exp(- 2*(x['x'] - x['mu1'])**2 - 2*(x['y'] - x['mu2'])**2)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Run PINA")
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group = parser.add_mutually_exclusive_group(required=True)
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group.add_argument("-s", "-save", action="store_true")
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group.add_argument("-l", "-load", action="store_true")
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parser.add_argument("id_run", help="number of run", type=int)
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parser.add_argument("features", help="extra features", type=int)
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args = parser.parse_args()
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feat = [myFeature()] if args.features else []
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poisson_problem = ParametricPoisson()
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model = FeedForward(
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layers=[200, 40, 10],
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output_variables=poisson_problem.output_variables,
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input_variables=poisson_problem.input_variables,
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func=Softplus,
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extra_features=feat
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)
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pinn = pPINN(
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poisson_problem,
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model,
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lr=0.0006,
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regularizer=1e-6,
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lr_accelerate=None)
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if args.s:
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pinn.span_pts(2000, 'random', ['D'])
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pinn.span_pts(200, 'random', ['gamma1', 'gamma2', 'gamma3', 'gamma4'])
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pinn.train(10000, 10)
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pinn.save_state('pina.poisson_param')
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else:
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pinn.load_state('pina.poisson_param')
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