def train(name, epoch=20000, debug=False, batch_size=128, sample_iter=100, model_save_iter=1000, variation=True, recover_dir=None, test=True ): experiment_dir = None if not debug: experiment_dir = EXPERIMENT_ROOT / name experiment_dir.mkdir(parents=True, exist_ok=True) prepareLogger(experiment_dir/'train_log.txt') print('train') else: model_save_iter = -1 sample_iter = 1 epoch = 2 batch_size = 2 test=True print('debug train') loader = load_ferg() pprl = Pprl(loader, variation=variation, debug=debug) if recover_dir is not None: pprl.load_weights(recover_dir) pprl.train(experiment_dir=experiment_dir, total_iter=epoch, batch_size=batch_size, sample_iter=sample_iter, model_save_iter=model_save_iter) if test: pprl.evaluate(batch_size=batch_size)
def train(name, epoch=20000, debug=False, batch_size=128, sample_iter=100, model_save_iter=1000, variation=True, recover_dir=None, test=True ): name = 'ae_gan' + name experiment_dir = EXPERIMENT_ROOT / name experiment_dir.mkdir(parents=True, exist_ok=True) prepareLogger(experiment_dir/'train_log.txt') print('train') print(f'{name} aegan without reconstruction loss') loader = load_ferg() ae_gan = AeGan(loader, variation=variation, debug=debug) if recover_dir is not None: ae_gan.load_weights(recover_dir) ae_gan.train(experiment_dir, total_iter=epoch, batch_size=batch_size, sample_iter=sample_iter, model_save_iter=model_save_iter) if test: acc_y_on_x = ae_gan.evaluate_y_on_x() acc_p_on_x = ae_gan.evaluate_p_on_x() print(f'acc_y_on_x = {acc_y_on_x}, acc_p_on_x={acc_p_on_x}')
def test(name, iter_no=100, debug=False, batch_size=128, epoch=20): experiment_dir = EXPERIMENT_ROOT / name prepareLogger() print('test') loader = load_ferg() pprl = Pprl(loader, debug=debug) pprl.load_weights(experiment_dir=experiment_dir, iter_no=iter_no) pprl.evaluate(batch_size=batch_size, num_epochs=epoch)
def main(gpu, epoch, debug): os.environ["CUDA_VISIBLE_DEVICES"] = gpu loader = load_ferg() ae = Ae(loader, debug=debug) ae.train(num_epochs=epoch) acc_y_on_x = ae.evaluate_y_on_x() acc_p_on_x = ae.evaluate_p_on_x() print(f'acc_y_on_x = {acc_y_on_x}, acc_p_on_x={acc_p_on_x}')
def test(name, iter_no=100, debug=False, num_epochs=20, batch_size=128): experiment_dir = EXPERIMENT_ROOT / name prepareLogger() print('test') loader = load_ferg() aegan_qp = AeganQp(loader, debug=debug) aegan_qp.load_weights(experiment_dir=experiment_dir, iter_no=iter_no) aegan_qp.evaluate(batch_size=batch_size, num_epochs=num_epochs)
def test(name, iter_no=100, debug=False, num_epochs=20): name = 'gan_qp' + name experiment_dir = EXPERIMENT_ROOT / name prepareLogger() print('test') loader = load_ferg() gan_qp = GanQp(loader, debug=debug) gan_qp.load_weights(experiment_dir=experiment_dir, iter_no=iter_no) acc_y_on_x = gan_qp.evaluate_y_on_x(num_epochs=num_epochs) acc_p_on_x = gan_qp.evaluate_p_on_x(num_epochs=num_epochs) print(f'acc_y_on_x = {acc_y_on_x}, acc_p_on_x={acc_p_on_x}')
def train(name, epoch=20000, debug=False, batch_size=128, sample_iter=100, model_save_iter=1000, variation=True, recover_dir=None, test=True): experiment_dir = None if not debug: experiment_dir = EXPERIMENT_ROOT / name experiment_dir.mkdir(parents=True, exist_ok=True) prepareLogger(experiment_dir / 'train_log.txt') print('train') else: model_save_iter = -1 sample_iter = -1 epoch = 2 batch_size = 2 test = False print('debug train') loader = load_ferg() gan_qp = GanQp(loader, variation=variation, debug=debug) if recover_dir is not None: gan_qp.load_weights(recover_dir) gan_qp.train(experiment_dir=experiment_dir, total_iter=epoch, batch_size=batch_size, sample_iter=sample_iter, model_save_iter=model_save_iter) if test: acc_y_on_x = gan_qp.evaluate_y_on_x() acc_p_on_x = gan_qp.evaluate_p_on_x() print(f'acc_y_on_x = {acc_y_on_x},\ acc_p_on_x={acc_p_on_x}')
def show(): print('show') loader = load_ferg() gan_qp = GanQp(loader) gan_qp.summary()
def show(): print('show') loader = load_ferg() pprl = Pprl(loader) pprl.summary()
def show(): print('show') loader = load_ferg() ae_gan = AeGan(loader) ae_gan.summary()