e, total_train_c_loss_mean, total_train_c_loss_std, total_train_accuracy_mean, total_train_accuracy_std, total_val_c_loss_mean, total_val_c_loss_std, total_val_accuracy_mean, total_val_accuracy_std, total_test_c_loss_mean, total_test_c_loss_std, total_test_accuracy_mean, total_test_accuracy_std ]) save_path = train_saver.save( sess, "{}/{}_{}.ckpt".format(saved_models_filepath, args.experiment_title, e)) pbar_e.update(1) val_saver.restore( sess, "{}/best_val_{}_{}.ckpt".format(saved_models_filepath, args.experiment_title, best_val_epoch)) total_test_c_loss_mean, total_test_c_loss_std, total_test_accuracy_mean, total_test_accuracy_std = \ experiment.run_testing_epoch(total_test_batches=total_test_batches, sess=sess) print( "Epoch {}: test_loss_mean: {}, test_loss_std: {}, test_accuracy_mean: {}, test_accuracy_std: {}" .format(best_val_epoch, total_test_c_loss_mean, total_test_c_loss_std, total_test_accuracy_mean, total_test_accuracy_std)) save_statistics(logs_filepath, [ "Test error on best validation model", -1, -1, -1, -1, -1, -1, -1, -1, total_test_c_loss_mean, total_test_c_loss_std, total_test_accuracy_mean, total_test_accuracy_std ])
# tf.summary.scalar("acc_train", total_accuracy) print("Epoch {}: train_loss: {}, train_accuracy: {}".format( e, total_c_loss, total_accuracy)) total_val_c_loss, total_val_accuracy = experiment.run_validation_epoch( total_val_batches=total_val_batches, sess=sess) # tf.summary.scalar("loss_val", total_val_c_loss) # tf.summary.scalar("loss_val", total_val_accuracy) print("Epoch {}: val_loss: {}, val_accuracy: {}".format( e, total_val_c_loss, total_val_accuracy)) if total_val_accuracy >= best_val: #if new best val accuracy -> produce test statistics best_val = total_val_accuracy total_test_c_loss, total_test_accuracy = experiment.run_testing_epoch( total_test_batches=total_test_batches, sess=sess) print("Epoch {}: test_loss: {}, test_accuracy: {}".format( e, total_test_c_loss, total_test_accuracy)) else: total_test_c_loss = -1 total_test_accuracy = -1 save_statistics(experiment_name, [ e, total_c_loss, total_accuracy, total_val_c_loss, total_val_accuracy, total_test_c_loss, total_test_accuracy ]) save_path = saver.save( sess, "saved_models/{}_{}.ckpt".format(experiment_name, e)) pbar_e.update(1)