parser.add_argument('--data_path', type=str, default="dataset") parser.add_argument('--output_dir', type=str, default="model") parser.add_argument('--dataset', type=str, default="midi") parser.add_argument('--init_from', type=str, default="") parser.add_argument('--clip_grads', type=int, default=5) parser.add_argument('--gpu', type=int, default=-1) args = parser.parse_args() if not os.path.exists(args.output_dir): os.mkdir(args.output_dir) if args.dataset == 'midi': # midi = dataset.load_midi_data('%s/midi/sample1.mid' % args.data_path) midi = dataset.load_midi_data('%s/midi/Suteki-Da-Ne.mid' % args.data_path) # midi = dataset.load_midi_data('%s/midi/example.mid' % args.data_path) # midi = dataset.load_midi_data('%s/midi/haydn_7_1.mid' % args.data_path) train_x = midi[:120].astype(np.float32) n_x = train_x.shape[1] n_hidden = [500] n_z = 2 n_y = n_x frames = train_x.shape[0] n_batch = 6 seq_length = frames / n_batch split_x = np.vsplit(train_x, n_batch)
parser = argparse.ArgumentParser() parser.add_argument('--data_path', type=str, default="dataset") parser.add_argument('--output_dir', type=str, default="model") parser.add_argument('--dataset', type=str, default="midi") parser.add_argument('--init_from', type=str, default="") parser.add_argument('--clip_grads', type=int, default=5) parser.add_argument('--gpu', type=int, default=-1) args = parser.parse_args() if not os.path.exists(args.output_dir): os.mkdir(args.output_dir) if args.dataset == 'midi': midi = dataset.load_midi_data('%s/midi/sample1.mid' % args.data_path) train_x = midi[:120].astype(np.float32) n_x = train_x.shape[1] n_hidden = [500] n_z = 2 n_y = n_x frames = train_x.shape[0] n_batch = 6 seq_length = frames / n_batch split_x = np.vsplit(train_x, n_batch) n_epochs = 500 continuous = False
parser = argparse.ArgumentParser() parser.add_argument('--data_path', type=str, default="dataset") parser.add_argument('--output_dir', type=str, default="model") parser.add_argument('--dataset', type=str, default="midi") parser.add_argument('--init_from', type=str, default="") parser.add_argument('--clip_grads', type=int, default=5) parser.add_argument('--gpu', type=int, default=-1) args = parser.parse_args() if not os.path.exists(args.output_dir): os.mkdir(args.output_dir) if args.dataset == 'midi': midi = dataset.load_midi_data('%s/midi/sample.mid' % args.data_path) train_x = midi[:120].astype(np.float32) n_x = train_x.shape[1] n_hidden = [500] n_z = 2 n_y = n_x frames = train_x.shape[0] n_batch = 6 seq_length = frames / n_batch split_x = np.vsplit(train_x, n_batch) n_epochs = 500 continuous = False