def main():
# basic logging setup for tf
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
tf.logging.set_verbosity(tf.logging.INFO)
# init necessary args
args = init_model_args()
"""
train_dataset_path_list = get_dataset_path_list(
train_dataset_path,
sub_str="track2_train_time.txt"
)
"""
train_dataset_path_list = [args.training_path]
val_dataset_path_list = [args.validation_path]
print("training path list: {}".format(train_dataset_path_list))
print("training path list: {}".format(val_dataset_path_list))
save_model_dir = args.save_model_dir
print("saving model in ... {}".format(save_model_dir))
optimizer = args.optimizer
learning_rate = args.lr
print("we use {} as optimizer".format(optimizer))
print("learning rate is set as {}".format(learning_rate))
batch_size = args.batch_size
embedding_size = args.embedding_size
num_epochs = args.num_epochs
print("batch size: {}".format(batch_size))
print("embedding size: {}".format(embedding_size))
task = args.task
track = args.track
print("track: {}, task: {}".format(track, task))
model = RecommendModelHandler(train_dataset_path=train_dataset_path_list,
val_dataset_path=val_dataset_path_list,
save_model_dir=save_model_dir,
num_epochs=num_epochs,
optimizer=optimizer,
batch_size=batch_size,
embedding_size=embedding_size,
task=task,
track=track,
learning_rate=args.lr)
model.train()
def main():
# basic logging setup for tf
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
tf.logging.set_verbosity(tf.logging.INFO)
# init necessary args
# args = init_model_args()
#train_dataset_path_list = get_dataset_path_list(train_dataset_path, sub_str="track2_train_time.txt")
train_dataset_path_list = "../../../data/track2/final_track2_train.txt"
val_dataset_path_list = "../../../data/track2/final_track2_train.txt"
print("training path list: {}".format(train_dataset_path_list))
print("training path list: {}".format(val_dataset_path_list))
save_model_dir = "../../../data/"
print("saving model in ... {}".format(save_model_dir))
optimizer = "adam"
learning_rate = 0.0005
print("we use {} as optimizer".format(optimizer))
print("learning rate is set as {}".format(learning_rate))
batch_size = 128
embedding_size = 8
num_epochs = 1
print("batch size: {}".format(batch_size))
print("embedding size: {}".format(embedding_size))
task = "finish"
track = 2
print("track: {}, task: {}".format(track, task))
model = RecommendModelHandler(train_dataset_path=train_dataset_path_list,
val_dataset_path=val_dataset_path_list,
save_model_dir=save_model_dir,
num_epochs=num_epochs,
optimizer=optimizer,
batch_size=batch_size,
embedding_size=embedding_size,
task=task,
track=track,
learning_rate=learning_rate)
model.train()
def train_and_predict(task):
train_dataset_path_list = ['track2_data/final_track2_train_part.txt']
val_dataset_path_list = ['track2_data/final_track2_train_part.txt']
test_dataset_path_list = ['track2_data/final_track2_test_part.txt']
save_model_dir = 'trained/{}'.format(task)
num_epochs = 10
optimizer = 'adam'
batch_size = 256
embedding_size = 20
track = 2
if task == 'like':
lr = 0.0005
else:
lr = 0.0001
model = RecommendModelHandler(
train_dataset_path=train_dataset_path_list,
val_dataset_path=val_dataset_path_list,
test_dataset_path=test_dataset_path_list,
save_model_dir=save_model_dir,
num_epochs=num_epochs,
optimizer=optimizer,
batch_size=batch_size,
embedding_size=embedding_size,
task=task,
track=track,
learning_rate=lr)
model.train()
model.predict()