def train_model(self):
for epoch in range(self.num_epochs):
# Generate training instances
user_input, item_input_pos, item_input_neg = data_gen._get_pairwise_all_data(
self.dataset)
total_loss = 0.0
training_start_time = time()
num_training_instances = len(user_input)
for num_batch in np.arange(
int(num_training_instances / self.batch_size)):
bat_users,bat_items_pos,bat_items_neg =\
data_gen._get_pairwise_batch_data(user_input,\
item_input_pos, item_input_neg, num_batch, self.batch_size)
feed_dict = {
self.users: bat_users,
self.pos_items: bat_items_pos,
self.node_dropout: [0.1],
self.mess_dropout: [0.1],
self.neg_items: bat_items_neg
}
loss, _ = self.sess.run((self.loss, self.optimizer),
feed_dict=feed_dict)
total_loss += loss
print("[iter %d : loss : %f, time: %f]" %
(epoch + 1, total_loss / num_training_instances,
time() - training_start_time))
if epoch % self.verbose == 0:
Evaluate.test_model(self, self.dataset)
def train_model(self):
for epoch in range(self.num_epochs):
# Generate training instances
if self.ispairwise.lower() == "true":
user_input, item_input_pos, item_input_neg = data_gen._get_pairwise_all_data(self.dataset)
else:
user_input, item_input, lables = data_gen._get_pointwise_all_data(self.dataset, self.num_negatives)
total_loss = 0.0
training_start_time = time()
num_training_instances = len(user_input)
for num_batch in np.arange(int(num_training_instances / self.batch_size)):
if self.ispairwise.lower() == "true":
bat_users, bat_items_pos, bat_items_neg = \
data_gen._get_pairwise_batch_data(user_input,
item_input_pos, item_input_neg, num_batch, self.batch_size)
feed_dict = {self.user_input: bat_users, self.item_input: bat_items_pos,
self.item_input_neg: bat_items_neg}
else:
bat_users, bat_items, bat_lables = \
data_gen._get_pointwise_batch_data(user_input,
item_input, lables, num_batch, self.batch_size)
feed_dict = {self.user_input: bat_users, self.item_input: bat_items,
self.lables: bat_lables}
loss, _ = self.sess.run((self.loss, self.optimizer), feed_dict=feed_dict)
total_loss += loss
print("[iter %d : loss : %f, time: %f]" % (
epoch + 1, total_loss / num_training_instances, time() - training_start_time))
if epoch % self.verbose == 0:
Evaluate.test_model(self, self.dataset)