def _build(self):
num_samples = [layer_info.num_samples for layer_info in self.layer_infos]
# positive examples pairs embeddings (self.outputs)
self.samples1, support_sizes1 = self.sample(self.inputs1, self.layer_infos) # samples :sampled node id
self.samples2, support_sizes2 = self.sample(self.inputs2, self.layer_infos)
self.outputs1, self.aggregators = self.aggregate(self.samples1, [self.features], self.dims, num_samples,
support_sizes1, concat=self.concat, model_size=self.model_size)
self.outputs2, _ = self.aggregate(self.samples2, [self.features], self.dims, num_samples,
support_sizes2, aggregators=self.aggregators, concat=self.concat,
model_size=self.model_size)
# negative examples embeddings
self.neg_sample, neg_support_sizes = self.sample(self.neg_samples, self.layer_infos,
self.number)
self.neg_outputs, _ = self.aggregate(self.neg_sample, [self.features], self.dims, num_samples,
neg_support_sizes, batch_size=self.number,
aggregators=self.aggregators,
concat=self.concat, model_size=self.model_size)
dim_mult = 2 if self.concat else 1
# unsupervised loss
self.link_pred_layer = BipartiteEdgePredLayer(dim_mult * self.dims[-1],
dim_mult * self.dims[-1], self.placeholders, act=tf.nn.sigmoid,
bilinear_weights=False,
name='edge_predict')
for aggregator in self.aggregators:
for var in aggregator.vars.values():
tf.add_to_collection("params",var)
self.outputs1 = tf.nn.l2_normalize(self.outputs1, 1)
self.outputs2 = tf.nn.l2_normalize(self.outputs2, 1)
self.neg_outputs = tf.nn.l2_normalize(self.neg_outputs, 1)
def _build(self):
self.outputs1 = tf.nn.embedding_lookup(self.target_embeds, self.inputs1)
self.outputs2 = tf.nn.embedding_lookup(self.context_weights, self.inputs2)
self.true_b = tf.nn.embedding_lookup(self.context_bias, self.inputs2)
self.neg_outputs = tf.nn.embedding_lookup(self.context_weights, self.neg_samples)
self.neg_b = tf.nn.embedding_lookup(self.context_bias, self.neg_samples)
self.link_pred_layer = BipartiteEdgePredLayer(self.embedding_dim, self.embedding_dim,
self.placeholders, bilinear_weights=False)
def build(self):
""" Wrapper for _build() """
with tf.variable_scope(self.name):
self._build()
# Build sequential layer model
self.activations.append(self.inputs)
for layer in self.layers:
hidden = layer(self.activations[-1])
self.activations.append(hidden)
self.embedding = self.activations[-1] # embedding matrix
self.embedding = tf.nn.l2_normalize(self.embedding, 1)
self.outputs1 = tf.nn.embedding_lookup(self.embedding, self.inputs1)
self.outputs2 = tf.nn.embedding_lookup(self.embedding, self.inputs2)
self.neg_outputs = tf.nn.embedding_lookup(self.embedding,
self.neg_samples)
self.link_pred_layer = BipartiteEdgePredLayer(self.output_dim,
self.output_dim,
self.placeholders,
act=tf.nn.sigmoid,
bilinear_weights=False,
name='edge_predict')
# Store model variables for easy access
variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope=self.name)
self.vars = {var.name: var for var in variables}
# Build metrics
self._loss()
self._accuracy()
self.opt_op = self.optimizer.minimize(self.loss)
self.p_probs = self.link_pred_layer.get_probs(self.outputs1,
self.outputs2)