np.mean(P_val), np.mean(R_val), np.mean(ap_val) ] learning_rate = lr.adapt(mAP_val) values = [ i, l, l_rank, l_att_span, l_att_global, l_att_dist, mF1_val, mAP_val, learning_rate ] logger.add(values) print( '{} loss: {} l_rank: {} l_att_span: {} l_att_global: {} l_att_dist: {} mF1: {} mAP: {} lr: {}' .format(*values)) print('Precision: {} Recall: {}'.format(mP_val, mR_val)) logger.save() print('learning rate', learning_rate) if is_save and mAP_val >= logger.get_max('mAP'): saver.save(sess, save_path + '/model_ES.ckpt') def evaluate_df(): ap_tst, predictions_mll, labels_mll = evaluate( iterator_test, [img_ids_test, features_test, seen_labels_test], model.features, model.logits, sess, model) F1_3_tst, P_3_tst, R_3_tst = evaluate_k( 3, iterator_test, [img_ids_test, features_test, seen_labels_test], model.features, model.logits, sess, model, predictions_mll, labels_mll) F1_5_tst, P_5_tst, R_5_tst = evaluate_k( 5, iterator_test, [img_ids_test, features_test, seen_labels_test],