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MainROM_example.py
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MainROM_example.py
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"""
Stefania Fresca, MOX Laboratory, Politecnico di Milano
April 2020
"""
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import sys
sys.stdout = open('*.out', 'w')
import utils
from ROMNet import ROMNet
if __name__ == '__main__':
config = dict()
config['n'] = # reduced dimension - n
config['n_params'] = # number of parameters + 1 - n_{mu} + 1
config['lr'] = # starting learning rate - eta
config['omega_h'] =
config['omega_N'] =
config['batch_size'] = # batch_size
config['n_data'] = # number of training samples - N_{train} x N_t
config['N_h'] = # FOM dimension - N_h
config['N'] = # rPOD dimension - N
config['n_h'] =
config['N_t'] = # number of time instances - N_t
config['n_channels'] = # number of channels - d
config['compute_POD'] = # options: '', 'exact', 'randomized'
config['POD_mat'] = # POD matrix filename
config['train_mat'] = # training snapshot matrix
config['test_mat'] = # testing snapshot matrix
config['train_params'] = # training parameter matrix
config['test_params'] = # testing parameter matrix
config['checkpoints_folder'] = # checkpoints folder
config['graph_folder'] = # graphs folder
config['n_early_stopping'] = # number of epochs for early-stopping criterion
config['large_POD'] = # True if POD matrix in .h5 format
config['large'] = # True if snapshot matrices in .h5 format
config['restart'] = # True if restart
config['scaling'] = # True if data must be scaled
model = ROMNet(config)
model.build()
model.train_all() # input: total numer of epochs - N_{epochs}