if nb_trg_labels > 0: train_auc_list.append(train_target_AUC) tC_loss_list.append(tC_loss) np.savetxt(os.path.join(DA_model_folder, 'trg_train_auc.txt'), train_auc_list) np.savetxt(os.path.join(DA_model_folder, 'trg_clf_loss.txt'), tC_loss_list) print_green( 'AUC: T-test {0:.4f}, T-valid {1:.4f}, T-train {2:.4f}, S-test: {3:.4f}' .format(test_target_AUC, val_target_AUC, train_target_AUC, test_source_AUC)) print_yellow( 'Loss: D:{:.4f}, S:{:.4f}, T:{:.4f}, Iter:{:}'.format( M_loss, sC_loss, tC_loss, iteration)) plot_LOSS( DA_model_folder + '/loss_{}.png'.format(DA_model_name), M_loss_list, sC_loss_list, tC_loss_list) plot_loss( DA_model_folder, M_loss_list, M_loss_list, DA_model_folder + '/MMD_loss_{}.png'.format(DA_model_name)) plot_src_trg_AUCs( DA_model_folder + '/AUC_src_{}.png'.format(DA_model_name), train_auc_list, val_auc_list, test_auc_list, src_test_list) plot_AUCs( DA_model_folder + '/AUC_trg_{}.png'.format(DA_model_name), train_auc_list, val_auc_list, test_auc_list) else: print_green( 'AUC: T-test {0:.4f}, T-valid {1:.4f}, S-test: {2:.4f}'. format(test_target_AUC, val_target_AUC, test_source_AUC)) print_yellow('Loss: D:{:.4f}, S:{:.4f}, Iter:{:}'.format(
# save model if iteration % 10000 == 0: saver.save(sess, model_folder + '/model', global_step=iteration) print_red('Update model') # save results loss_trn_list, loss_val_list, loss_norm_list, loss_anomaly_list, auc_list =\ np.append(loss_trn_list, loss_trn), np.append(loss_val_list, loss_val),\ np.append(loss_norm_list, loss_norm), np.append(loss_anomaly_list, loss_anomaly), np.append(auc_list, AE_auc) np.savetxt(model_folder + '/train_loss.txt', loss_trn_list) np.savetxt(model_folder + '/val_loss.txt', loss_val_list) np.savetxt(model_folder + '/norm_loss.txt', loss_norm_list) np.savetxt(model_folder + '/anomaly_loss.txt', loss_anomaly_list) plot_LOSS(model_folder + '/loss-{}.png'.format(model_name), 0, loss_trn_list, loss_val_list, loss_norm_list, loss_anomaly_list) np.savetxt(model_folder + '/AE_auc.txt', auc_list) plot_AUC(model_folder + '/auc-{}.png'.format(model_name), auc_list) if best_loss_val > loss_val: best_loss_val = loss_val saver.save(sess, model_folder + '/best') print_red('update best:{}'.format(model_name)) np.savetxt(model_folder + '/AE_stat.txt', recon_errs) np.savetxt(model_folder + '/best_auc.txt', [AE_auc, MP_auc]) plot_hist(model_folder + '/hist-{}.png'.format(model_name), recon_errs[:int(len(recon_errs) / 2)], recon_errs[int(len(recon_errs) / 2):]) save_recon_images(model_folder + '/recon-{}.png'.format(model_name),