def build_model(self):
self.gru_net_ins = GraphRNN()
self.gated_gnn_model = modified_gated_GNN()
with tf.variable_scope('user_behavior_emb'):
user_behavior_list_embedding = self.behavior_list_embedding_dense
with tf.variable_scope('neighbor_emb', reuse=tf.AUTO_REUSE):
structure_emb = self.gated_gnn_model.generate_graph_emb(
init_emb=user_behavior_list_embedding,
now_batch_size=self.now_bacth_data_size,
num_units=self.num_units,
adj_in=self.adj_in,
adj_out=self.adj_out,
step=self.FLAGS.graph_step
) # batch_size, max_len, num_units * 2
with tf.variable_scope('ShortTermIntentEncoder', reuse=tf.AUTO_REUSE):
grnn_inputs = tf.concat(
[user_behavior_list_embedding, structure_emb], axis=2)
user_behavior_list_embedding = self.gru_net_ins.modified_grnn_net(
hidden_units=self.num_units,
input_data=grnn_inputs,
input_length=tf.add(self.seq_length, -1))
self.short_term_intent = gather_indexes(
batch_size=self.now_bacth_data_size,
seq_length=self.max_len,
width=self.num_units,
sequence_tensor=user_behavior_list_embedding,
positions=self.mask_index - 1)
self.short_term_intent = self.short_term_intent
self.predict_behavior_emb = layer_norm(self.short_term_intent)
self.output()
def build_model(self):
self.gru_net_ins = GraphRNN()
self.gated_gnn_model = ordered_gated_GNN()
with tf.variable_scope('user_behavior_emb'):
user_behavior_list_embedding = self.behavior_list_embedding_dense
with tf.variable_scope('neighbor_emb', reuse=tf.AUTO_REUSE):
structure_emb = self.gated_gnn_model.generate_graph_emb(
init_emb=user_behavior_list_embedding,
now_batch_size=self.now_bacth_data_size,
num_units=self.num_units,
adj_in=self.adj_in,
adj_out=self.adj_out,
eid_emb_in=self.in_eid_embedding,
eid_emb_out=self.out_eid_embedding,
mask_adj_in=self.mask_adj_in,
mask_adj_out=self.mask_adj_out,
step=self.FLAGS.graph_step
) # batch_size, max_len, num_units * 2
with tf.variable_scope('ShortTermIntentEncoder'):
# in_emb, out_emb = array_ops.split(value=structure_emb, num_or_size_splits=2, axis=2)
#
# structure_emb = in_emb+out_emb
# structure_emb = tf.layers.dense(structure_emb,units = self.num_units)
grnn_inputs = tf.concat(
[user_behavior_list_embedding, structure_emb], axis=2)
self.short_term_intent_temp = self.gru_net_ins.simple_grnn_net(
hidden_units=self.num_units,
input_data=grnn_inputs,
input_length=tf.add(self.seq_length, -1))
self.short_term_intent = gather_indexes(
batch_size=self.now_bacth_data_size,
seq_length=self.max_len,
width=self.num_units,
sequence_tensor=self.short_term_intent_temp,
positions=self.mask_index - 1)
self.short_term_intent = self.short_term_intent
self.predict_behavior_emb = layer_norm(self.short_term_intent)
self.output()
class OrderedGatRnnRec(MTAMRec_model):
def build_model(self):
self.gru_net_ins = GraphRNN()
self.gnn_model = OrderedGAT()
with tf.variable_scope('user_behavior_emb'):
user_behavior_list_embedding = self.behavior_list_embedding_dense
with tf.variable_scope('graph_emb', reuse=tf.AUTO_REUSE):
structure_emb = self.gnn_model.generate_graph_emb(
init_emb=user_behavior_list_embedding,
key_length=self.seq_length,
num_units=self.num_units,
mask_adj=self.mask_adj,
eid_emb=self.eid_embedding,
num_head=1,
step=1) # batch_size, max_len, num_units * 2
with tf.variable_scope('ShortTermIntentEncoder'):
# in_emb, out_emb = array_ops.split(value=structure_emb, num_or_size_splits=2, axis=2)
#
# structure_emb = in_emb+out_emb
structure_emb = tf.layers.dense(structure_emb,
units=self.num_units)
rnn_inputs = tf.concat(
[user_behavior_list_embedding, structure_emb], axis=2)
self.short_term_intent_temp = self.gru_net_ins.simple_grnn_net(
hidden_units=self.num_units,
input_data=rnn_inputs,
input_length=tf.add(self.seq_length, -1))
self.short_term_intent = gather_indexes(
batch_size=self.now_bacth_data_size,
seq_length=self.max_len,
width=self.num_units,
sequence_tensor=self.short_term_intent_temp,
positions=self.mask_index - 1)
self.short_term_intent = self.short_term_intent
self.predict_behavior_emb = layer_norm(self.short_term_intent)
self.output()
