--- a
+++ b/MainROM.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'] = 3                                                                # n
+    config['n_params'] = 3                                                         # n_{\mu} + 1
+    config['lr'] = 0.0001                                                          # starting learning rate
+    config['omega_h'] = 0.5
+    config['omega_n'] = 0.5
+    config['batch_size'] = 40
+    config['n_data'] = 49000                                                       # N_{train} * N_t
+    config['N_h'] = 4096                                                           # N
+    config['n_h'] = 8                                                              # N = [n_h, n_h, 64]
+    config['N_t'] = 1000                                                           # N_t
+    config['train_mat'] = 'data/scar/S_train.mat'                                  # training snapshot matrix
+    config['test_mat'] = 'data/scar/S_test.mat'                                    # testing snapshot matrix
+    config['train_params'] = 'data/scar/params_train.mat'                          # training parameter matrix
+    config['test_params'] = 'data/scar/params_test.mat'                            # testing parameter matrix
+    config['checkpoints_folder'] = 'checkpoints'
+    config['graph_folder'] = 'graphs'
+    config['large'] = False                                                        # True if data are saved in .h5 format
+    config['zero_padding'] = False                                                 # True if you must use zero padding
+    config['p'] = 0                                                                # size of zero padding
+    config['restart'] = False                                                      # True if you want to restart training
+
+    model = ROMNet(config)
+    model.build()
+    model.train_all(10000)                                                         # number of epochs