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()