--- a
+++ b/MainROMExample.py
@@ -0,0 +1,41 @@
+"""
+
+Stefania Fresca, MOX Laboratory, Politecnico di Milano
+April 2019
+
+"""
+
+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
+    config['n_params'] =                                                           # number of parameters (time excluded)
+    config['lr'] =                                                                 # starting learning rate
+    config['omega_h'] =
+    config['omega_n'] =
+    config['batch_size'] =
+    config['n_data'] =                                                             # N_{train} * N_t
+    config['N_h'] =                                                                # FOM dimension
+    config['n_h'] =                                                                # N_h = [n_h, n_h, 64]
+    config['N_t'] =                                                                # N_t
+    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'] = ''
+    config['graph_folder'] = ''
+    config['large'] =                                                              # True if data are saved in .h5 format
+    config['zero_padding'] =                                                       # True if you must use zero padding
+    config['p'] =                                                                  # size of zero padding
+    config['restart'] =
+
+    model = ROMNet(config)
+    model.build()
+    model.train_all(10000)                                                         # number of epochs