params=config, ); save_yaml_file(yaml_str, os.path.join(config.experiment_root, 'settings.yaml')); with log_timing(log, 'training network'): train.main_loop(); def get_default_config_path(): return os.path.join(os.path.dirname(__file__),'train_convnet.cfg'); if __name__ == '__main__': config = load_config(default_config=get_default_config_path(), reset_logging=False); if not config.get('only_extract_results', False): train_convnet(config); scan_for_best_performance(config.experiment_root, 'valid_y_misclass'); scan_for_best_performance(config.experiment_root, 'valid_ptrial_misclass_rate') values = extract_results(config.experiment_root, mode='misclass'); print np.multiply(100, [ # 1 - values['test_y_misclass'], # 1 - values['test_wseq_misclass_rate'], # 1 - values['test_wtrial_misclass_rate']]); 1 - values['frame_misclass'], 1 - values['sequence_misclass'], 1 - values['trial_misclass']]);
with log_timing(log, 'training network'): train.main_loop() def get_default_config_path(): return os.path.join(os.path.dirname(__file__), 'train_convnet.cfg') if __name__ == '__main__': config = load_config(default_config=get_default_config_path(), reset_logging=False) if not config.get('only_extract_results', False): train_convnet(config) scan_for_best_performance(config.experiment_root, 'valid_y_misclass') scan_for_best_performance(config.experiment_root, 'valid_ptrial_misclass_rate') values = extract_results(config.experiment_root, mode='misclass') print np.multiply( 100, [ # 1 - values['test_y_misclass'], # 1 - values['test_wseq_misclass_rate'], # 1 - values['test_wtrial_misclass_rate']]); 1 - values['frame_misclass'], 1 - values['sequence_misclass'], 1 - values['trial_misclass'] ])
log.info('done'); def get_default_config_path(): return os.path.join(os.path.dirname(__file__),'train_sda_mlp.cfg'); if __name__ == '__main__': # config = load_config(default_config='../../train_sda.cfg', reset_logging=False); config = load_config(default_config=get_default_config_path(), reset_logging=False); hyper_params = { }; params = merge_params(config, hyper_params); if not config.get('only_extract_results', False): train_mlp(params); scan_for_best_performance(params.experiment_root, 'valid_y_misclass'); scan_for_best_performance(params.experiment_root, 'valid_ptrial_misclass_rate') values = extract_results(config.experiment_root, mode='misclass'); print np.multiply(100, [ # 1 - values['test_y_misclass'], # 1 - values['test_wseq_misclass_rate'], # 1 - values['test_wtrial_misclass_rate']]); 1 - values['frame_misclass'], 1 - values['sequence_misclass'], 1 - values['trial_misclass']]);
# config.experiment_root = '/Users/stober/git/deepbeat/deepbeat/output/gpu/sda/exp2.14all/'; for i in xrange(13): hyper_params = { 'experiment_root' : os.path.join(config.experiment_root, 'subj'+str(i+1)), 'subjects' : [i] # NOTE: layerX_content should still point to global sda/ folder }; if config.global_sda == False: hyper_params['layer0_content'] = os.path.join(hyper_params['experiment_root'], 'sda', 'sda_layer0_tied.pkl'); hyper_params['layer1_content'] = os.path.join(hyper_params['experiment_root'], 'sda', 'sda_layer1_tied.pkl'); hyper_params['layer2_content'] = os.path.join(hyper_params['experiment_root'], 'sda', 'sda_layer2_tied.pkl'); hyper_params['layer3_content'] = os.path.join(hyper_params['experiment_root'], 'sda', 'sda_layer3_tied.pkl'); params = merge_params(config, hyper_params); if os.path.exists(os.path.join(params.experiment_root, 'epochs')): print 'skipping existing path: {}'.format(params.experiment_root); continue; train_mlp(params); # generate plot.pdfs # plot_batch(config.experiment_root); # print best peformance values for i in xrange(13): scan_for_best_performance(os.path.join(config.experiment_root, 'subj'+str(i+1)));