metavar='S', help='random seed (default: 42)') parser.add_argument( '--log-interval', type=int, default=10, metavar='N', help='how many batches to wait before logging training status (default: 10)' ) args = parser.parse_args() if args.classifier: args.idx_label = None args.betas = tuple(np.array(args.betas, dtype=float)) nu.train_rnn(file_name=args.file_name, hidden_size=args.hidden_size, n_layers=args.n_layers, bidirectional=args.bidirectional, classifier=args.classifier, idx_label=args.idx_label, n_epochs_max=args.epochs, train_ratio=args.train_ratio, batch_size=args.batch_size, n_workers=args.n_workers, root_dir=args.root_dir, lr=args.lr, betas=args.betas, opt_level=args.opt_level, seed=args.seed, log_interval=args.log_interval)
''' >>>>>>> 18a8402a9b721b66ed28bcee5beabe6256a96076 # train RNNs for scat transformed data for file_name_scat in file_names_scat: meta = torch.load(os.path.join(root_dir, file_name_scat)) avg_len = meta['avg_len'] n_filter_octave = meta['n_filter_octave'] for hidden_size in hidden_sizes: for n_layers in n_layerss: for bidirectional in bidirectionals: try: print("training rnn for {}, avg_len:{}, n_filter_octave:{}, hidden_size:{}, n_layers:{}, bidirectional:{}" .format(file_name_scat, avg_len, n_filter_octave, hidden_size, n_layers, bidirectional)) nu.train_rnn(file_name_scat, [hidden_size, hidden_size], n_layers, bidirectional, classifier=False, n_epochs_max=n_epochs_max, train_ratio=train_ratio, batch_size=batch_size, n_workers=n_workers, root_dir=root_dir) except: print("exception occurred for {}, avg_len:{}, n_filter_octave:{}, hidden_size:{}, n_layers:{}, bidirectional:{}" .format(file_name_scat, avg_len, n_filter_octave, hidden_size, n_layers, bidirectional)) # train RNNs for raw data for file_name_data in file_names_data: for hidden_size in hidden_sizes: for n_layers in n_layerss: for bidirectional in bidirectionals: try: print("training rnn for {}, hidden_size:{}, n_layers:{}, bidirectional:{}".format(file_name_data, hidden_size, n_layers, bidirectional)) nu.train_rnn(file_name_data, [hidden_size, hidden_size], n_layers, bidirectional, classifier=False, n_epochs_max=n_epochs_max, train_ratio=train_ratio, batch_size=batch_size, n_workers=n_workers, root_dir=root_dir)
except: print("exception for avg_len:{}, n_filter_octave:{}".format(avg_len, n_filter_octave)) # train RNNs for scat transformed data for file_name_scat in file_names_scat: meta = torch.load(os.path.join(root_dir, file_name_scat)) avg_len = meta['avg_len'] n_filter_octave = meta['n_filter_octave'] for hidden_size in hidden_sizes: for n_layers in n_layerss: for bidirectional in bidirectionals: try: print("training rnn for {}, avg_len:{}, n_filter_octave:{}, hidden_size:{}, n_layers:{}, bidirectional:{}" .format(file_name_scat, avg_len, n_filter_octave, hidden_size, n_layers, bidirectional)) nu.train_rnn(file_name_scat, hidden_size, n_layers, bidirectional, classifier=True, n_epochs_max=n_epochs_max, train_ratio=train_ratio, batch_size=batch_size, n_workers=n_workers, root_dir=root_dir, lr=lr, betas=betas) except: print("exception for file_name_scat:{}, hidden_size:{}, n_layers:{}, bidirectional:{}".format(file_name_scat, hidden_size, n_layers, bidirectional)) # train RNNs for raw data for hidden_size in hidden_sizes: for n_layers in n_layerss: for bidirectional in bidirectionals: try: print("training rnn for {}, hidden_size:{}, n_layers:{}, bidirectional:{}".format(file_name_data, hidden_size, n_layers, bidirectional)) nu.train_rnn(file_name_data, hidden_size, n_layers, bidirectional, classifier=True, n_epochs_max=n_epochs_max, train_ratio=train_ratio, batch_size=batch_size, n_workers=n_workers, root_dir=root_dir, lr=lr, betas=betas) except: print("exception for file_name_data:{}, hidden_size:{}, n_layers:{}, bidirectional:{}".format(file_name_data, hidden_size, n_layers, bidirectional))