def get_models(models):
"""
The models would be stored in S3, or perhaps for now just on the server in a file.
Eventually they might be in a database, but either way I need to reduce their size because they take a while to load.
That being said, I really should have a login capability where we use the user information to gather and load the necessary models.
I could have those models loaded already using celery
:return:
"""
models_loaded = []
for model in models:
if (model in MODEL_MAPS):
models_loaded.append(model)
if (model not in IDEANETS):
model_path = os.path.realpath(os.path.abspath(os.path.join(this_dir,MODEL_MAPS[model])))
IDEANETS[model] = lstm() # I can see the type of model to use being an input at some point.
IDEANETS[model].load_pickle(model_path)
app.logger.info("Model(s) loaded: " + str(model))
return models_loaded
def get_models(models):
"""
The models would be stored in S3, or perhaps for now just on the server in a file.
Eventually they might be in a database, but either way I need to reduce their size because they take a while to load.
That being said, I really should have a login capability where we use the user information to gather and load the necessary models.
I could have those models loaded already using celery
:return:
"""
models_loaded = []
for model in models:
if (model in MODEL_MAPS):
models_loaded.append(model)
if (model not in IDEANETS):
model_path = os.path.realpath(
os.path.abspath(os.path.join(this_dir, MODEL_MAPS[model])))
IDEANETS[model] = lstm(
) # I can see the type of model to use being an input at some point.
IDEANETS[model].load_pickle(model_path)
app.logger.info("Model(s) loaded: " + str(model))
return models_loaded
import os, inspect, sys, numpy
import cPickle as pkl
this_dir = os.path.realpath( os.path.abspath( os.path.split( inspect.getfile( inspect.currentframe() ))[0]))
ideanet_dir = os.path.realpath(os.path.abspath(os.path.join(this_dir,"../../../..")))
# params_directory = os.path.realpath(os.path.abspath(os.path.join(this_dir,"../params")))
if this_dir not in sys.path: sys.path.insert(0, this_dir)
if ideanet_dir not in sys.path: sys.path.insert(0, ideanet_dir)
pkl_file = os.path.realpath(os.path.abspath(os.path.join(this_dir,"./lstm_model.npz")))
from IdeaNets.models.lstm.scode.lstm_class import LSTM as lstm
params={}
params["data_directory"] = "/home/ying/Deep_Learning/Synapsify_data"
Lpickle = lstm(params=params)
Lpickle.preprocess()
Lpickle.build_model()
Lpickle.train_model()
Lpickle.test_model()
'''
This part of code is to test if npz file object works or not
Lpickle = lstm()
Lpickle.build_model()
Lpickle.train_model()
Lpickle.test_model()
'''
