loss_display_step=train_params["loss_display_step"], saver_step=train_params["saver_step"], z_rotate=train_params["z_rotate"], encoder=encoder, decoder=decoder, encoder_args=enc_args, decoder_args=dec_args, ) conf.experiment_name = experiment_name conf.held_out_step = 5 # how often to evaluate/print(out loss on) # held_out data (if they are provided in ae.train()). conf.class_name = class_name conf.use_fps = flags.use_fps conf.n_sample_points = flags.n_sample_points conf.n_samp_out = [2048, 3] conf.training_epochs = flags.training_epochs conf.save(osp.join(train_dir, "configuration")) # build AE Model reset_tf_graph() ae = PointNetAE(conf.experiment_name, conf) # train the AE (save output to train_stats.txt) buf_size = 1 # Make 'training_stats' file to flush each output line regarding training. fout = open(osp.join(conf.train_dir, "train_stats.txt"), "a", buf_size) train_stats = ae.train(pc_data_train, conf, log_file=fout, held_out_data=pc_data_val) fout.close()
learning_rate=train_params['learning_rate'], train_dir=train_dir, loss_display_step=train_params['loss_display_step'], saver_step=train_params['saver_step'], z_rotate=train_params['z_rotate'], encoder=encoder, decoder=decoder, encoder_args=enc_args, decoder_args=dec_args) conf.experiment_name = experiment_name conf.held_out_step = 5 # How often to evaluate/print out loss on # held_out data (if they are provided in ae.train()). conf.class_name = class_name conf.use_fps = flags.use_fps conf.n_sample_points = flags.n_sample_points conf.n_samp_out = [2048, 3] conf.save(osp.join(train_dir, 'configuration')) # Build AE Model reset_tf_graph() ae = PointNetAutoEncoder(conf.experiment_name, conf) # Train the AE (save output to train_stats.txt) buf_size = 1 # Make 'training_stats' file to flush each output line regarding training. fout = open(osp.join(conf.train_dir, 'train_stats.txt'), 'a', buf_size) train_stats = ae.train(pc_data_train, conf, log_file=fout, held_out_data=pc_data_val) fout.close()