def __init__(self):
"""initialize the parameters, prepare the data and build the network"""
self.n_node, self.linked_nodes = utils.read_edges(
config.train_filename, config.test_filename, config.n_node)
self.discriminator = None
self.generator = None
print self.n_node
self.n_node = config.n_node # If self.n_node is smaller than config.n_node, there is isolated nodes.
self.root_nodes = [i for i in range(self.n_node)]
print("start reading initial embeddings")
# read the initial embeddings
self.node_embed_init_d = utils.read_emd(
filename=config.pretrain_emd_filename_d,
n_node=config.n_node,
n_embed=config.n_embed)
self.node_embed_init_g = utils.read_emd(
filename=config.pretrain_emd_filename_g,
n_node=config.n_node,
n_embed=config.n_embed)
print("finish reading initial embeddings")
# use the BFS to construct the trees
print("Constructing Trees")
if config.import_tree:
self.trees = pickle.load(open(config.tree_path, 'r'))
else:
if config.app == "recommendation":
self.mul_construct_trees_for_recommend(self.user_nodes)
else: # classification
self.mul_construct_trees(self.root_nodes)
pickle.dump(self.trees, open(config.tree_path, 'w'))
config.max_degree = utils.get_max_degree(self.linked_nodes)
self.build_gan()
self.initialize_network()
def __init__(self):
"""initialize the parameters, prepare the data and build the network"""
self.n_node, self.linked_nodes = utils.read_edges(
config.train_filename, config.test_filename)
self.root_nodes = [i for i in range(self.n_node)]
self.discriminator = None
self.generator = None
assert self.n_node == config.n_node
print("start reading initial embeddings")
# read the initial embeddings
self.node_embed_init_d = utils.read_emd(
filename=config.pretrain_emd_filename_d,
n_node=config.n_node,
n_embed=config.n_embed)
self.node_embed_init_g = utils.read_emd(
filename=config.pretrain_emd_filename_g,
n_node=config.n_node,
n_embed=config.n_embed)
print("finish reading initial embeddings")
# use the BFS to construct the trees
print("Constructing Trees")
if config.app == "recommendation":
self.mul_construct_trees_for_recommend(self.user_nodes)
else: # classification
self.mul_construct_trees(self.root_nodes)
config.max_degree = utils.get_max_degree(self.linked_nodes)
self.build_gan()
self.config = tf.ConfigProto()
self.config.gpu_options.allow_growth = True
self.init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
self.sess = tf.Session(config=self.config)
self.sess.run(self.init_op)
def __init__(self):
"""initialize the parameters, prepare the data and build the network"""
self.n_node, self.linked_nodes = utils.read_edges(
config.train_filename, config.test_filename)
self.root_nodes = [i for i in range(self.n_node)]
self.discriminator = None
self.generator = None
assert self.n_node == config.n_node
print("start reading initial embeddings")
# read the initial embeddings
self.node_embed_init_d = utils.read_emd(
filename=config.pretrain_emd_filename_d,
n_node=config.n_node,
n_embed=config.n_embed)
self.node_embed_init_g = utils.read_emd(
filename=config.pretrain_emd_filename_g,
n_node=config.n_node,
n_embed=config.n_embed)
print("finish reading initial embeddings")
# use the BFS to construct the trees
print("Constructing Trees")
if config.app == "recommendation":
self.mul_construct_trees_for_recommend(self.user_nodes)
else: # classification
self.mul_construct_trees(self.root_nodes)
config.max_degree = utils.get_max_degree(self.linked_nodes)
self.generator = Generator(lambda_gen=config.lambda_gen,
node_emd_init=self.node_embed_init_g)
self.discriminator = Discriminator(
lambda_dis=config.lambda_dis, node_emd_init=self.node_embed_init_d)
self.generator.cuda()
self.discriminator.cuda()
self.all_score = None
def __init__(self):
"""initialize the parameters, prepare the data and build the network"""
self.n_node, self.linked_nodes = utils.read_edges(
config.train_filename, config.test_filename)
self.root_nodes = [i for i in range(self.n_node)
] #root nodes for each node
self.discriminator = None #none right now
self.generator = None
assert self.n_node == config.n_node # if equal
print("start reading initial embeddings")
# read the initial embeddings
self.node_embed_init_d = utils.read_emd(
filename=config.pretrain_emd_filename_d,
n_node=config.n_node,
n_embed=config.n_embed)
self.node_embed_init_g = utils.read_emd(
filename=config.pretrain_emd_filename_g,
n_node=config.n_node,
n_embed=config.n_embed)
print("finish reading initial embeddings")
# use the BFS to construct the trees
print("Constructing Trees")
if config.app == "recommendation":
self.mul_construct_trees_for_recommend(self.user_nodes)
else: # classification
self.mul_construct_trees(self.root_nodes)
print("after storing the data")
config.max_degree = utils.get_max_degree(self.linked_nodes)
print("config.max")
self.build_gan()
print("build gan")
self.config = tf.ConfigProto() #device_count = {"GPU": 0, "CPU":1})
print("config self")
self.config.gpu_options.allow_growth = True
print("config gpu")
#tf.group An operation that executes all its input
self.init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
print("init op")
self.sess = tf.Session(config=self.config)
#self.sess = tf.Session()
print("before run")
self.sess.run(self.init_op)