def main(test_mode=False): log_fname = "logs/train.log" if os.path.isfile(log_fname): os.remove(log_fname) log_hdl = logging.FileHandler(log_fname) log_hdl.setFormatter(logging.Formatter('%(message)s')) LOGGER.addHandler(log_hdl) data = utils.load_data_2d(test_mode=test_mode, valid_pct=0.1, cropping=False) # way to map between string labels and int labels y_map = utils.get_y_map(data) data['y']['train'] = utils.convert_y(data['y']['train'], y_map) data['y']['valid'] = utils.convert_y(data['y']['valid'], y_map) # run experiments y_test, model, performance, optimizer = exp.resnet(data) y_test = utils.convert_y(y_test, y_map) utils.write_results('results/resnet.csv', y_test) import IPython IPython.embed()
def main(test_mode=False): log_fname = "logs/train13.log" if os.path.isfile(log_fname): os.remove(log_fname) log_hdl = logging.FileHandler(log_fname) log_hdl.setFormatter(logging.Formatter('%(message)s')) LOGGER.addHandler(log_hdl) data = utils.load_data(test_mode=test_mode, cropping=True) # way to map between string labels and int labels y_map = utils.get_y_map(data) data['y']['train'] = utils.convert_y(data['y']['train'], y_map) data['y']['valid'] = utils.convert_y(data['y']['valid'], y_map) # run experiments lr_pred, lr_model = exp.lr_baseline(data) lr_y_test = utils.convert_y(lr_pred['test'], y_map) utils.write_results('results/lr_baseline.csv', lr_y_test)