def _optimize_store(self, node, node_embeddings):
if not self.gradient_stores:
return tf.no_op()
losses = []
for gradient_store, node_embedding in zip(self.gradient_stores,
node_embeddings):
embedding_gradient = tf.nn.embedding_lookup(gradient_store, node)
with tf.control_dependencies([embedding_gradient]):
clear_gradient_op = \
utils_embedding.embedding_update(gradient_store, node, 0)
with tf.control_dependencies([clear_gradient_op]):
losses.append(
tf.reduce_sum(
tf.multiply(node_embedding, embedding_gradient)))
return self.store_optimizer.minimize(tf.add_n(losses))
def _optimize_store(self, node, node_embeddings):
if not self.gradient_stores:
return tf.zeros([]), tf.no_op()
losses = []
clear_ops = []
for gradient_store, node_embedding in zip(self.gradient_stores,
node_embeddings):
embedding_gradient = tf.nn.embedding_lookup(gradient_store, node)
with tf.control_dependencies([embedding_gradient]):
clear_ops.append(
utils_embedding.embedding_update(gradient_store, node, 0))
losses.append(tf.reduce_sum(node_embedding * embedding_gradient))
store_loss = tf.add_n(losses)
with tf.control_dependencies(clear_ops):
return store_loss, self.store_optimizer.minimize(store_loss)
def _update_store(self, node, node_embeddings):
update_ops = []
for store, node_embedding in zip(self.stores, node_embeddings):
update_ops.append(
utils_embedding.embedding_update(store, node, node_embedding))
return tf.group(*update_ops)
