def embed_cluster(raw_args=None):
# parse arguments
args = parse_args(raw_args)
if args is None:
exit()
if args.dataset == 'fashion-mnist': args.dataset = "FashionMNIST"
elif args.dataset =='cifar10': args.dataset = "CIFAR10"
config_m = ConfigManager(args)
isinstance(config_m, ConfigManager)
if args.model_name == "VAE": # choose between VAE or ACGAN for the latent mapping
if not args.labeled: i = -1 # train VAE on all classes to get a common latent representation for visualization and calculation of wasserstein distance
else: i = 0 # train model on data splited according to label, starting with index 0, declare instance for VAE for each label
while i < (args.num_labels):
# reset the graph
tf.reset_default_graph()
# open session
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
vae = None
vae = VAE(sess,
label=i, # the difference with respect to non-label sensitive, by default label = -1, without label, built up a common latent representation of all instances from all classes
config_manager=config_m
)
# build graph
vae.build_model()
# show network architecture
if i < 1: # only once
utils_parent.show_all_variables()
# launch the graph in a session
vae.train()
print(" [*] Training finished!")
# visualize learned generator
vae.visualize_results(args.epoch - 1)
print(" [*] Fake Image saved!")
# save the transformed latent space into result dir
filepath = config_m.get_data_path_for_each_label(i) + config_m.z_name
if (not tf.gfile.Exists(filepath)) or args.labeled:
z, g_ind_y = vae.transform()
z = z.eval() # z is not used for cluster in the case of args.labeled=False, but should be used to calculate wasserstein distance, calculating z for all data can be very time consuming!
print(" [*] latent representation calculation finished!")
if not tf.gfile.Exists(filepath):
np.save(filepath, z)
np.save(config_m.get_data_path_for_each_label(i)+ config_m.y_name, g_ind_y)
print(" [*] latent representation and correponding class label saved!")
print(" [*] going to enter clustering (or not) ....")
#with tf.session
if args.cluster: # inside a tensorflow session, the behavior can be different, so better put cluster outside a tf session
print(" [*] clustering....")
# cluster latent space using VGMM: cluster the transformed latent space, and store the dictionary and prediction into result_path
if not args.labeled: # if not divided by label, cluster by label and merge
print(" [*] VAE training without label, run cluster for each label now")
filepath = config_m.get_data_path_for_each_label(-1) + config_m.cluster_index_json_name # "/cluster_dict.json"
if not tf.gfile.Exists(filepath):
ds = InputDataset(args.dataset, -1, args.num_labels) # FIXME: change InputDataset into singleton to avoid too initialization of InputDataset generate two different setting
data_manipulator.cluster_common_embeding_labelwise(ds.data_y, config_m.get_data_path_for_each_label(-1), num_labels=config_m.num_labels, num_clusters=config_m.num_clusters)
print(" [*] after VAE training without label information, cluster by each label and merge finished and saved!")
else: # data comes in, divided by label
data_manipulator.cluster_save2disk_label(config_m.get_data_path_for_each_label(i), z, config_m.num_clusters) # if data already comes by label, then run VGMM directly
print("vgmm cluster on label", i, "finished")
if not args.labeled:
i = args.num_labels # while
print("label", i, "finished")
i = i+1 # while
# while
# After for loop is finished
if args.labeled and args.cluster: # merge the clusters from each label
print(" [*] merging clusters from each label....")
# concatenate clustered data into one dict after clustering
data_dict, global_index = InputDataset.concatenate_data_from_dir(config_m)
# global_index is the final result of this routine
# save global index for cluster data
np.save(config_m.get_data_path()+ config_m.global_index_name, global_index, allow_pickle=True)
if args.plot:
print(" [*] creating T-SNE plot....")
visualization.T_SNE_Plot_with_datadict(data_dict=data_dict, num_clusters=config_m.num_clusters, result_path=config_m.get_data_path())
config_m.write_config_file()
print("* volatile configuration file written")
# not yet used
elif args.model_name == "ACGAN":
# declare instance for ACGANG
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
acgan = None
acgan = ACGAN(sess,
epoch=args.epoch,
batch_size=args.batch_size,
z_dim=args.z_dim,
dataset_name=args.dataset,
checkpoint_dir=args.checkpoint_dir,
result_dir=args.result_dir,
log_dir=args.log_dir)
# build graph
acgan.build_model()
# show network architecture
utils_parent.show_all_variables()
# launch the graph in a session
acgan.train()
print(" [*] Training finished!")
# visualize learned generator
acgan.visualize_results(args.epoch - 1)
print(" [*] Testing finished!")
else:
raise NotImplementedError
