def _build(self):
self.hidden1 = GraphConvolutionSparse(
input_dim=self.input_dim,
output_dim=FLAGS.hidden1,
adj=self.adj,
features_nonzero=self.features_nonzero,
act=tf.nn.relu,
dropout=self.dropout,
logging=self.logging)(self.inputs)
self.belief1 = GraphConvolution(input_dim=FLAGS.hidden1,
output_dim=FLAGS.hidden2,
adj=self.adj,
act=tf.nn.sigmoid,
dropout=self.dropout,
logging=self.logging)(self.hidden1)
self.belief2 = GraphConvolution(input_dim=FLAGS.hidden1,
output_dim=FLAGS.hidden2,
adj=self.adj,
act=tf.nn.sigmoid,
dropout=self.dropout,
logging=self.logging)(self.hidden1)
self.uncertainty = GraphConvolution(input_dim=FLAGS.hidden1,
output_dim=FLAGS.hidden2,
adj=self.adj,
act=tf.nn.sigmoid,
dropout=self.dropout,
logging=self.logging)(self.hidden1)
self.belief3 = tf.nn.relu(1.0 - self.belief1 - self.belief2)
def _build(self):
self.hidden1 = GraphConvolutionSparse(
input_dim=self.input_dim,
output_dim=FLAGS.hidden1,
adj=self.adj,
features_nonzero=self.features_nonzero,
act=tf.nn.relu,
dropout=self.dropout,
logging=self.logging)(self.inputs)
self.alpha1 = GraphConvolution(input_dim=FLAGS.hidden1,
output_dim=FLAGS.hidden2,
adj=self.adj,
act=lambda x: x,
dropout=self.dropout,
logging=self.logging)(self.hidden1)
self.alpha2 = GraphConvolution(input_dim=FLAGS.hidden1,
output_dim=FLAGS.hidden2,
adj=self.adj,
act=lambda x: x,
dropout=self.dropout,
logging=self.logging)(self.hidden1)
self.alpha3 = GraphConvolution(input_dim=FLAGS.hidden1,
output_dim=FLAGS.hidden2,
adj=self.adj,
act=lambda x: x,
dropout=self.dropout,
logging=self.logging)(self.hidden1)
def _build(self):
self.hidden1 = GraphConvolutionSparse(
input_dim=self.input_dim,
output_dim=FLAGS.hidden1,
adj=self.adj,
features_nonzero=self.features_nonzero,
act=tf.nn.relu,
dropout=self.dropout,
logging=self.logging)(self.inputs)
self.belief = GraphConvolution(input_dim=FLAGS.hidden1,
output_dim=FLAGS.hidden2,
adj=self.adj,
act=tf.nn.sigmoid,
dropout=self.dropout,
logging=self.logging)(self.hidden1)
self.uncertain = GraphConvolution(input_dim=FLAGS.hidden1,
output_dim=FLAGS.hidden2,
adj=self.adj,
act=tf.nn.sigmoid,
dropout=self.dropout,
logging=self.logging)(self.hidden1)
self.basetate = GraphConvolution(input_dim=FLAGS.hidden1,
output_dim=FLAGS.hidden2,
adj=self.adj,
act=tf.nn.sigmoid,
dropout=self.dropout,
logging=self.logging)(self.hidden1)
self.pro_belief = self.belief + self.basetate * self.uncertain
def _build(self):
self.hidden1 = GraphConvolutionSparse(
input_dim=self.input_dim,
output_dim=FLAGS.hidden1,
adj=self.adj,
features_nonzero=self.features_nonzero,
act=tf.nn.relu,
dropout=self.dropout,
logging=self.logging)(self.inputs)
self.belief = GraphConvolution(input_dim=FLAGS.hidden1,
output_dim=FLAGS.hidden2,
adj=self.adj,
act=tf.nn.sigmoid,
dropout=self.dropout,
logging=self.logging)(self.hidden1)
self.uncertain = GraphConvolution(input_dim=FLAGS.hidden1,
output_dim=FLAGS.hidden2,
adj=self.adj,
act=tf.nn.sigmoid,
dropout=self.dropout,
logging=self.logging)(self.hidden1)
self.sigma_b = tf.Variable(tf.constant(-4.0),
dtype=tf.float32,
name="sigma_b")
self.sigma_u = tf.Variable(tf.constant(-4.0),
dtype=tf.float32,
name="sigma_u")
def _build(self):
self.hidden1 = GraphConvolutionSparse(
input_dim=self.input_dim,
output_dim=FLAGS.hidden1,
adj=self.adj,
features_nonzero=self.features_nonzero,
act=tf.nn.relu,
dropout=self.dropout,
logging=self.logging)(self.inputs)
self.embeddings = GraphConvolution(input_dim=FLAGS.hidden1,
output_dim=FLAGS.hidden2,
adj=self.adj,
act=lambda x: x,
dropout=self.dropout,
logging=self.logging)(self.hidden1)
self.z_mean = self.embeddings
self.reconstructions = InnerProductDecoder(input_dim=FLAGS.hidden2,
act=lambda x: x,
logging=self.logging)(
self.embeddings)
def _build(self):
self.hidden1 = GraphConvolutionSparse(
input_dim=self.input_dim,
output_dim=FLAGS.hidden1,
adj=self.adj,
features_nonzero=self.features_nonzero,
act=tf.nn.relu,
dropout=self.dropout,
logging=self.logging)(self.inputs)
self.belief = GraphConvolution(input_dim=FLAGS.hidden1,
output_dim=FLAGS.hidden2,
adj=self.adj,
act=tf.nn.sigmoid,
dropout=self.dropout,
logging=self.logging)(self.hidden1)
self.uncertain = GraphConvolution(input_dim=FLAGS.hidden1,
output_dim=FLAGS.hidden2,
adj=self.adj,
act=tf.nn.sigmoid,
dropout=self.dropout,
logging=self.logging)(self.hidden1)
# decode define
self.b = tf.minimum(self.belief + 0.0000001, 0.999999)
self.u = tf.minimum(self.uncertain + 0.0000001, 0.999999)
self.W = 2.0
self.r = self.W * self.b / self.u + 0.0000001
self.d = tf.minimum(
tf.nn.relu(1 - self.b - self.u) + 0.0000001, 0.999999)
self.s = self.W * self.d / self.u + 0.0000001
self.a = 0.5
self.alpha = self.r + self.W * self.a + 0.0000001
self.beta = self.s + self.W * (1 - self.a) + 0.0000001
self.uni = tf.random_uniform([self.n_samples, FLAGS.hidden2], 0.001,
0.999)
self.z_n_b = tf.minimum(
(1.0 - tf.pow(self.uni, tf.reciprocal(self.beta)) + 0.0000001),
0.999999)
self.z_n = tf.minimum(
tf.pow(self.z_n_b, tf.reciprocal(self.alpha)) + 0.0000001,
0.999999)
self.z = tf.distributions.Normal(loc=0.0, scale=1.0).quantile(self.z_n)
# self.z = self.z_mean + tf.random_normal([self.n_samples, FLAGS.hidden2]) * tf.exp(self.z_log_std)
self.reconstructions = InnerProductDecoder_Opinion(
input_dim=FLAGS.hidden2, act=tf.nn.sigmoid,
logging=self.logging)(self.z)
# KL-divergence
self.digmma_beta = tf.digamma(self.beta)
self.kl_1 = (1.0 - self.alpha_0 / self.alpha) * (
-0.5772 - self.digmma_beta - tf.reciprocal(self.beta)) + tf.log(
self.alpha * self.beta) + tf.lbeta([
self.alpha_0, self.beta_0
]) - 1.0 + tf.reciprocal(self.beta)
self.kl_2 = 0.0
for m in range(1, self.m + 1):
self.kl_2 += tf.reciprocal(m + self.alpha * self.beta) * tf.exp(
tf.lbeta(tf.stack([m / self.alpha, self.beta], axis=2)))
self.kl_d = self.kl_1 + self.beta * (self.beta_0 - 1.0) * self.kl_2
# omege
self.omega = self.alpha / (self.alpha + self.beta)