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model.py
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model.py
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from layers import *
import tensorflow.compat.v1 as tf
#import tensorflow as tf
flags = tf.app.flags
FLAGS = flags.FLAGS
class Model(object):
"""
Model base class
"""
def __init__(self, **kwargs):
allowed_kwargs = {'name', 'logging'}
for kwarg in kwargs.keys():
assert kwarg in allowed_kwargs, 'Invalid keyword argument: ' + kwarg
for kwarg in kwargs.keys():
assert kwarg in allowed_kwargs, 'Invalid keyword argument: ' + kwarg
name = kwargs.get('name')
if not name:
name = self.__class__.__name__.lower()
self.name = name
logging = kwargs.get('logging', False)
self.logging = logging
self.vars = {}
def _build(self):
raise NotImplementedError
def build(self):
""" Wrapper for _build() """
with tf.variable_scope(self.name):
self._build()
variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope = self.name)
self.vars = {var.name: var for var in variables}
def fit(self):
pass
def predict(self):
pass
class GCNModelAE(Model):
"""
2-layer GCN-based Graph Autoencoder
"""
def __init__(self, placeholders, num_features, features_nonzero, **kwargs):
super(GCNModelAE, self).__init__(**kwargs)
self.inputs = placeholders['features']
self.input_dim = num_features
self.features_nonzero = features_nonzero
self.adj = placeholders['adj']
self.adj_layer2 = placeholders['adj_layer2']
self.dropout = placeholders['dropout']
self.sampled_nodes = placeholders['sampled_nodes']
self.build()
def _build(self):
self.hidden = GraphConvolutionSparse(input_dim = self.input_dim,
output_dim = FLAGS.hidden,
adj = self.adj,
features_nonzero = self.features_nonzero,
act = tf.nn.relu,
dropout = self.dropout,
logging = self.logging)(self.inputs)
self.z_mean = GraphConvolution(input_dim = FLAGS.hidden,
output_dim = FLAGS.dimension,
adj = self.adj_layer2,
act = lambda x: x,
dropout = self.dropout,
logging = self.logging)(self.hidden)
self.reconstructions = InnerProductDecoder(fastgae = FLAGS.fastgae, # Whether to use FastGAE
sampled_nodes = self.sampled_nodes, # FastGAE subgraph
act = lambda x: x,
logging = self.logging)(self.z_mean)
# Pairwise exponential L2 distance term, used in the modularity-inspired loss
self.clusters = DistanceDecoder(fastgae = FLAGS.fastgae, # Whether to use FastGAE
sampled_nodes = self.sampled_nodes, # FastGAE subgraph
act = lambda x: x,
logging = self.logging)(self.z_mean)
class GCNModelVAE(Model):
"""
2-layer GCN-based Variational Graph Autoencoder
"""
def __init__(self, placeholders, num_features, num_nodes, features_nonzero, **kwargs):
super(GCNModelVAE, self).__init__(**kwargs)
self.inputs = placeholders['features']
self.input_dim = num_features
self.features_nonzero = features_nonzero
self.n_samples = num_nodes
self.adj = placeholders['adj']
self.adj_layer2 = placeholders['adj_layer2']
self.dropout = placeholders['dropout']
self.sampled_nodes = placeholders['sampled_nodes']
self.build()
def _build(self):
self.hidden = GraphConvolutionSparse(input_dim = self.input_dim,
output_dim = FLAGS.hidden,
adj = self.adj,
features_nonzero = self.features_nonzero,
act = tf.nn.relu,
dropout = self.dropout,
logging = self.logging)(self.inputs)
self.z_mean = GraphConvolution(input_dim = FLAGS.hidden,
output_dim = FLAGS.dimension,
adj = self.adj_layer2,
act = lambda x: x,
dropout = self.dropout,
logging = self.logging)(self.hidden)
self.z_log_std = GraphConvolution(input_dim = FLAGS.hidden,
output_dim = FLAGS.dimension,
adj = self.adj_layer2,
act = lambda x: x,
dropout = self.dropout,
logging = self.logging)(self.hidden)
self.z = self.z_mean + tf.random_normal([self.n_samples, FLAGS.dimension]) * tf.exp(self.z_log_std)
self.reconstructions = InnerProductDecoder(fastgae = FLAGS.fastgae, # Whether to use FastGAE
sampled_nodes = self.sampled_nodes, # FastGAE subgraph
act = lambda x: x,
logging = self.logging)(self.z_mean)
# Pairwise exponential L2 distance term, used in the modularity-inspired loss
self.clusters = DistanceDecoder(fastgae = FLAGS.fastgae, # Whether to use FastGAE
sampled_nodes = self.sampled_nodes, # FastGAE subgraph
act = lambda x: x,
logging = self.logging)(self.z)
class GCNModelVAE3D(Model):
"""
3-layer GCN-based Variational Graph Autoencoder
"""
def __init__(self, placeholders, num_features, num_nodes, features_nonzero, **kwargs):
super(GCNModelVAE3D, self).__init__(**kwargs)
self.inputs = placeholders['features']
self.input_dim = num_features
self.features_nonzero = features_nonzero
self.n_samples = num_nodes
self.adj = placeholders['adj']
self.adj_layer2 = placeholders['adj_layer2']
self.dropout = placeholders['dropout']
self.sampled_nodes = placeholders['sampled_nodes']
self.build()
def _build(self):
self.hidden2 = GraphConvolutionSparse(input_dim = self.input_dim,
output_dim = FLAGS.hidden2,
adj = self.adj,
features_nonzero = self.features_nonzero,
act = tf.nn.relu,
dropout = self.dropout,
logging = self.logging)(self.inputs)
self.hidden = GraphConvolution(input_dim = FLAGS.hidden2,
output_dim = FLAGS.hidden,
adj = self.adj_layer2,
act = lambda x: x,
dropout = self.dropout,
logging = self.logging)(self.hidden2)
self.z_mean = GraphConvolution(input_dim = FLAGS.hidden,
output_dim = FLAGS.dimension,
adj = self.adj_layer2,
act = lambda x: x,
dropout = self.dropout,
logging = self.logging)(self.hidden)
self.z_log_std = GraphConvolution(input_dim = FLAGS.hidden,
output_dim = FLAGS.dimension,
adj = self.adj_layer2,
act = lambda x: x,
dropout = self.dropout,
logging = self.logging)(self.hidden)
self.z = self.z_mean + tf.random_normal([self.n_samples, FLAGS.dimension]) * tf.exp(self.z_log_std)
self.reconstructions = InnerProductDecoder(sampled_nodes = self.sampled_nodes, # FastGAE subgraph
act = lambda x: x,
logging = self.logging)(self.z_mean)
# Pairwise exponential L2 distance term, used in the modularity-inspired loss
self.clusters = DistanceDecoder(sampled_nodes = self.sampled_nodes, # FastGAE subgraph
act = lambda x: x,
logging = self.logging)(self.z)