def build_model(self):
num_units = self.FLAGS.num_units
gru_net_ins = GRU()
self.sequence_embedding, self.positive_embedding, \
self.behavior_embedding_result_dense, self.positive_embedding_result_dense, \
self.mask_index, self.label_ids, \
self.seq_length, user_embedding, self.time = self.embedding.get_embedding(num_units)
with tf.variable_scope("EnhanceUserPreferenceIntentEncoder"):
user_preference_temp = gru_net_ins.gru_net_initial(hidden_units=num_units,
input_length=self.mask_index,
input_data=self.behavior_embedding_result_dense,
initial_state=user_embedding)
self.user_preference = gather_indexes(batch_size=self.now_bacth_data_size,
seq_length=self.FLAGS.max_len,
width=self.FLAGS.num_units,
sequence_tensor=user_preference_temp,
positions=tf.add(self.mask_index, -1))
with tf.variable_scope("OutputLayer"):
self.predict_behavior_emb = layer_norm(self.user_preference)
# self.mf_auc = tf.reduce_mean(tf.to_float((tf.reduce_sum(tf.multiply(tf.expand_dims(self.predict_behavior_emb, 1),
# tf.expand_dims(self.positive_embedding_result_dense, 1) - self.negative_embedding_result_dense), 2)) > 0))
l2_norm = tf.add_n([
tf.nn.l2_loss(self.sequence_embedding),
tf.nn.l2_loss(self.positive_embedding)
])
regulation_rate = self.FLAGS.regulation_rate
item_lookup_table_T = tf.transpose(self.embedding.item_emb_lookup_table)
logits = tf.matmul(self.predict_behavior_emb, item_lookup_table_T)
log_probs = tf.nn.log_softmax(logits)
label_ids = tf.reshape(self.label_ids, [-1])
one_hot_labels = tf.one_hot(label_ids, depth=self.embedding.item_count+3, dtype=tf.float32)
self.loss_origin = -tf.reduce_sum(log_probs * one_hot_labels, axis=[-1])
lstur_loss = regulation_rate * l2_norm + tf.reduce_mean(self.loss_origin)
with tf.name_scope("LearningtoRankLoss"):
self.loss = lstur_loss
tf.summary.scalar("l2_norm", l2_norm)
tf.summary.scalar("Training Loss", self.loss)
tf.summary.scalar("Learning_rate", self.learning_rate)
trainable_params = tf.trainable_variables()
gradients = tf.gradients(self.loss, trainable_params)
clip_gradients, _ = tf.clip_by_global_norm(gradients, self.FLAGS.max_gradient_norm)
self.train_op = self.opt.apply_gradients(zip(clip_gradients, trainable_params))
self.summery()
class LSTUR(Hybird_Baseline_Models):
def build_model(self):
self.gru_net_ins = GRU()
with tf.variable_scope('ShortTermIntentEncoder'):
self.short_term_intent_temp = self.gru_net_ins.gru_net_initial(hidden_units=self.num_units,
initial_state=self.user_embedding,
input_data=self.behavior_list_embedding_dense,
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.predict_behavior_emb = layer_norm(self.short_term_intent)
self.output()
def build_model(self):
num_units = self.FLAGS.num_units
num_heads = self.FLAGS.num_heads
num_blocks = self.FLAGS.num_blocks
dropout_rate = self.FLAGS.dropout
attention_net = Attention()
gru_net_ins = GRU()
self.sequence_embedding, self.positive_embedding, self.negative_embedding, \
self.behavior_embedding_result_dense, self.positive_embedding_result_dense, \
self.negative_embedding_result_dense, self.mask_index, self.label_ids, \
self.seq_length, user_embedding, time = self.embedding.get_embedding(num_units)
with tf.variable_scope("LongTermIntentEncoder"):
long_term_intent_temp = attention_net.self_attention(
enc=self.behavior_embedding_result_dense,
num_units=128,
num_heads=num_heads,
num_blocks=num_blocks,
dropout_rate=dropout_rate,
is_training=True,
reuse=False,
key_length=self.seq_length,
query_length=self.seq_length)
self.long_term_intent = gather_indexes(
batch_size=self.now_bacth_data_size,
seq_length=self.FLAGS.max_len,
width=self.FLAGS.num_units,
sequence_tensor=long_term_intent_temp,
positions=self.mask_index)
# average pooling for long_term_intent_temp
self.long_term_preference = tf.reduce_mean(long_term_intent_temp,
axis=1)
with tf.variable_scope('ShortTermIntentEncoder'):
short_term_intent_temp = gru_net_ins.gru_net(
hidden_units=num_units,
input_data=self.behavior_embedding_result_dense,
input_length=self.mask_index)
self.short_term_intent = gather_indexes(
batch_size=self.now_bacth_data_size,
seq_length=self.FLAGS.max_len,
width=self.FLAGS.num_units,
sequence_tensor=short_term_intent_temp,
positions=tf.add(self.mask_index, -1))
with tf.variable_scope("EnhancePreferenceIntentEncoder"):
user_enhance_preference_temp = gru_net_ins.gru_net_initial(
hidden_units=num_units,
input_length=self.mask_index,
input_data=long_term_intent_temp,
initial_state=self.long_term_preference)
self.user_enhance_preference = gather_indexes(
batch_size=self.now_bacth_data_size,
seq_length=self.FLAGS.max_len,
width=self.FLAGS.num_units,
sequence_tensor=user_enhance_preference_temp,
positions=tf.add(self.mask_index, -1))
with tf.variable_scope("EnhanceUserPreferenceIntentEncoder"):
user_enhance_preference_temp_user = gru_net_ins.gru_net_initial(
hidden_units=num_units,
input_length=self.mask_index,
input_data=long_term_intent_temp,
initial_state=user_embedding)
self.user_enhance_preference_user = gather_indexes(
batch_size=self.now_bacth_data_size,
seq_length=self.FLAGS.max_len,
width=self.FLAGS.num_units,
sequence_tensor=user_enhance_preference_temp_user,
positions=tf.add(self.mask_index, -1))
with tf.variable_scope("PreferenceEncoder"):
user_preference_temp = gru_net_ins.gru_net(
hidden_units=num_units,
input_data=long_term_intent_temp,
input_length=self.mask_index)
self.user_preference = gather_indexes(
batch_size=self.now_bacth_data_size,
seq_length=self.FLAGS.max_len,
width=self.FLAGS.num_units,
sequence_tensor=user_preference_temp,
positions=tf.add(self.mask_index, -1))
with tf.variable_scope("OutputLayer"):
self.predict_behavior_emb = self.user_enhance_preference_user
self.predict_behavior_emb = layer_norm(self.predict_behavior_emb)
self.mf_auc = tf.reduce_mean(
tf.to_float((tf.reduce_sum(
tf.multiply(
tf.expand_dims(self.predict_behavior_emb, 1),
tf.expand_dims(self.positive_embedding_result_dense, 1)
- self.negative_embedding_result_dense), 2)) > 0))
l2_norm = tf.add_n([
tf.nn.l2_loss(self.sequence_embedding),
tf.nn.l2_loss(self.positive_embedding),
tf.nn.l2_loss(self.negative_embedding)
])
regulation_rate = self.FLAGS.regulation_rate
item_lookup_table_T = tf.transpose(
self.embedding.item_emb_lookup_table)
logits = tf.matmul(self.predict_behavior_emb, item_lookup_table_T)
log_probs = tf.nn.log_softmax(logits)
label_ids = tf.reshape(self.label_ids, [-1])
one_hot_labels = tf.one_hot(label_ids,
depth=500000,
dtype=tf.float32)
self.loss_origin = -tf.reduce_sum(log_probs * one_hot_labels,
axis=[-1])
lstur_loss = regulation_rate * l2_norm + tf.reduce_mean(
self.loss_origin)
with tf.name_scope("LearningtoRankLoss"):
self.loss = lstur_loss
tf.summary.scalar("l2_norm", l2_norm)
tf.summary.scalar("Training Loss", self.loss)
tf.summary.scalar("Learning_rate", self.learning_rate)
trainable_params = tf.trainable_variables()
gradients = tf.gradients(self.loss, trainable_params)
clip_gradients, _ = tf.clip_by_global_norm(
gradients, self.FLAGS.max_gradient_norm)
self.train_op = self.opt.apply_gradients(
zip(clip_gradients, trainable_params))
self.summery()
