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mf_dataset.py
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mf_dataset.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import deepxde as dde
def main():
fname_lo_train = "dataset/mf_lo_train.dat"
fname_hi_train = "dataset/mf_hi_train.dat"
fname_hi_test = "dataset/mf_hi_test.dat"
data = dde.data.MfDataSet(
fname_lo_train=fname_lo_train,
fname_hi_train=fname_hi_train,
fname_hi_test=fname_hi_test,
col_x=(0,),
col_y=(1,),
)
activation = "tanh"
initializer = "Glorot uniform"
regularization = ["l2", 0.01]
net = dde.maps.MfNN(
[1] + [20] * 4 + [1],
[10] * 2 + [1],
activation,
initializer,
regularization=regularization,
)
model = dde.Model(data, net)
model.compile("adam", lr=0.001, metrics=["l2 relative error"])
losshistory, train_state = model.train(epochs=80000)
dde.saveplot(losshistory, train_state, issave=True, isplot=True)
if __name__ == "__main__":
main()