def main(_):
params = ModelParams()
for key, value in params.__dict__.items():
print(key, "=", value)
if params.alg_name == "dnn":
model = dnn.model_estimator(params)
elif params.alg_name == "dnn_pool":
model = dnn_pool.model_estimator(params)
elif params.alg_name == "dnn_cate":
model = dnn_cate.model_estimator(params)
elif params.alg_name == "deepfm":
model = deepfm.model_estimator(params)
elif params.alg_name == "deepfm_pool":
model = deepfm_pool.model_estimator(params)
elif params.alg_name == "deepfm_cate":
model = deepfm_cate.model_estimator(params)
elif params.alg_name == "din":
model = din.model_estimator(params)
elif params.alg_name == "dinfm":
model = dinfm.model_estimator(params)
elif params.alg_name == "dien":
model = dien.model_estimator(params)
elif params.alg_name == "dnn_emb":
model = dnn_emb.model_estimator(params)
elif params.alg_name == "dnn_autoint":
model = dnn_autoint.model_estimator(params)
else:
model = dnn.model_estimator(params)
print("alg_name = %s is error" % params.alg_name)
exit(-1)
if params.action_type == "train":
start_time = time.time()
train_files = data_load.get_file_list(params.train_path)
predict_files = data_load.get_file_list(params.predict_path)
print("--------------train------------")
trained_model_path = op.model_fit(model, params, train_files,
predict_files)
end_time = time.time()
print("model_save training time: %.2f s" % (end_time - start_time))
# save model_pb path to a file
f = tf.gfile.GFile(params.model_pb + "/test", 'w')
f.write(str(trained_model_path, encoding="utf-8"))
print("--------------predict------------")
op.model_predict(trained_model_path, predict_files, params)
elif params.action_type == "pred":
print("--------------predict------------")
predict_files = data_load.get_file_list(params.predict_path)
op.model_predict(
'/Users/R.Stalker/PycharmProjects/deep_learing_estimator/files/model_save_pb/deepfm',
predict_files, params)
else:
print("action_type = %s is error !!!" % params.action_type)
def main(_):
params = ModelParams()
for key, value in params.__dict__.items():
print(key, "=", value)
print("---delete old data...")
delete_dt = my_utils.shift_date_time(params.dt, -1)
print("---delete_dt:", delete_dt)
print(params.train_path[:-9] + delete_dt)
print(params.predict_path[:-9] + delete_dt)
shutil.rmtree(params.train_path[:-9] + delete_dt, ignore_errors=True)
shutil.rmtree(params.predict_path[:-9] + delete_dt, ignore_errors=True)
if params.alg_name == "dnn":
model = dnn.model_estimator(params)
elif params.alg_name == "deepfm":
model = deepfm.model_estimator(params)
elif params.alg_name == "deepfm_pool":
model = deepfm_pool.model_estimator(params)
elif params.alg_name == "din":
model = din.model_estimator(params)
elif params.alg_name == "dnn_pool":
model = dnn_pool.model_estimator(params)
elif params.alg_name == "dinfm":
model = dinfm.model_estimator(params)
elif params.alg_name == "dien":
model = dien.model_estimator(params)
elif params.alg_name == "dnn_autoint":
model = dnn_autoint.model_estimator(params)
else:
model = dnn.model_estimator(params)
print("alg_name = %s is error" % params.alg_name)
exit(-1)
if params.mode == "train":
start_time = time.time()
train_files = data_load.get_file_list(params.train_path)
predict_files = data_load.get_file_list(params.predict_path)
print("--------------train------------")
trained_model_path = op.model_fit(model, params, train_files,
predict_files)
end_time = time.time()
# save model_pb path to a file
f = tf.gfile.GFile(params.model_pb[:-9] + "latest_model_path", 'w')
f.write(str(trained_model_path, encoding="utf-8"))
print("model_save training time: %.2f s" % (end_time - start_time))
print("--------------predict------------")
op.model_predict(trained_model_path, predict_files, params)
elif params.mode == "eval":
print("--------------predict------------")
predict_files = data_load.get_file_list(params.predict_path)
op.model_predict(params.model_pb, predict_files, params)
else:
print("action_type = %s is error !!!" % params.mode)
def main(_):
handle_arguments()
check_arguments()
if FLAGS.clear_existing_model_dir:
try:
shutil.rmtree(FLAGS.model_dir)
except Exception as e:
print(e, "at clear_existing_model_dir")
else:
print("existing model cleaned at %s" % FLAGS.model_dir)
if FLAGS.alg_name == "dnn":
model = dnn.model_estimator(FLAGS)
elif FLAGS.alg_name == "deepfm":
model = deepfm.model_estimator(FLAGS)
elif FLAGS.alg_name == "din":
model = din.model_estimator(FLAGS)
elif FLAGS.alg_name == "dinfm":
model = dinfm.model_estimator(FLAGS)
elif FLAGS.alg_name == "autoint":
model = autoint.model_estimator(FLAGS)
else:
print("ERROR!!! alg_name = %s is not exit!" % FLAGS.alg_name)
exit(-1)
if FLAGS.task_mode == "train":
# model.evaluate(input_fn=lambda: data_load.input_fn(FLAGS.eval_data, FLAGS))
model.train(input_fn=lambda: data_load.input_fn(FLAGS.train_data, FLAGS))
model_op.model_save_pb(FLAGS, model)
elif FLAGS.task_mode == "eval":
model.evaluate(input_fn=lambda: data_load.input_fn(FALGS.eval_data, FLAGS))
elif FLAGS.task_mode == "infer":
# preds = model.predict(input_fn=lambda: data_load.input_fn(FLAGS.eval_data, FLAGS), predict_keys=["item_embedding"])
# f = open(FLAGS.test_data, "rb")
# with open(FLAGS.infer_result, "w") as fo:
# for line, p in zip(f, preds):
# id = line.decode("utf-8").split(" ")[10]
# # print(p)
# # fo.write("%f\n" % (prob["embedding"]))
# emb = ','.join(["%.6f" % f for f in list(p["item_embedding"])])
# fo.write(id + "|" + id + "|" + emb + "\n")
pass
elif FLAGS.task_mode == "debug": # make some fake data for debugging !!!开发中，暂时不可用
# Flags.debug_data = "data/tfrecord/debug/"
# Flags.cont_field_count = 3
# Flags.cate_field_count = 5
# Flags.multi_cate_field_count = 2
# Flags.multi_cate_field_list = [(m1, 3), (m2, 5)]
# Flags.total_field_count = 10
# Flags.target_att_1vN_list = []
# Flags.target_att_NvN_list = []
pass
else:
print("Task_mode Error!")