def load(cls, data_store):
if type(data_store) is LocalFileSystem:
matrix = data_store.read_pickle_file(
filename=SIMILARITY_MATRIX_FILENAME)
movie_names = data_store.read_pickle_file(
filename=MOVIE_LIST_FILENAME)
if type(data_store) is S3DataStore:
data_store.download_file(SIMILARITY_MATRIX_FILENAME,
"/tmp/" + SIMILARITY_MATRIX_FILENAME)
data_store.download_file(MOVIE_LIST_FILENAME,
"/tmp/" + MOVIE_LIST_FILENAME)
temp_data_store = LocalFileSystem("/tmp/")
matrix = temp_data_store.read_pickle_file(
filename=SIMILARITY_MATRIX_FILENAME)
movie_names = temp_data_store.read_pickle_file(
filename=MOVIE_LIST_FILENAME)
return ImdbRecSys(matrix=matrix, movie_names=movie_names)
def load(cls, data_store):
if type(data_store) is LocalFileSystem:
word_class_dict = data_store.read_pickle_file(
filename=WORD_CLASS_DICT_FILENAME)
net = tflearn.input_data(
shape=[None, int(word_class_dict["num_input"])])
net = tflearn.fully_connected(net, 8)
net = tflearn.fully_connected(net, 8)
net = tflearn.fully_connected(net,
int(word_class_dict["num_output"]),
activation='softmax')
net = tflearn.regression(net)
model = tflearn.DNN(net)
dl_model = data_store.read_dl_model(data=model,
filename=MODEL_FILENAME)
if type(data_store) is S3DataStore:
data_store.download_file(MODEL_FILENAME + ".index",
"/tmp/" + MODEL_FILENAME + ".index")
data_store.download_file(MODEL_FILENAME + ".meta",
"/tmp/" + MODEL_FILENAME + ".meta")
data_store.download_file(
MODEL_FILENAME + ".data-00000-of-00001",
"/tmp/" + MODEL_FILENAME + ".data-00000-of-00001")
data_store.download_file(WORD_CLASS_DICT_FILENAME,
"/tmp/" + WORD_CLASS_DICT_FILENAME)
temp_data_store = LocalFileSystem("/tmp/")
word_class_dict = temp_data_store.read_pickle_file(
filename=WORD_CLASS_DICT_FILENAME)
net = tflearn.input_data(
shape=[None, int(word_class_dict["num_input"])])
net = tflearn.fully_connected(net, 8)
net = tflearn.fully_connected(net, 8)
net = tflearn.fully_connected(net,
int(word_class_dict["num_output"]),
activation='softmax')
net = tflearn.regression(net)
model = tflearn.DNN(net)
dl_model = temp_data_store.read_dl_model(data=model,
filename=MODEL_FILENAME)
return ChatbotModel(words=word_class_dict["words"],
classes=word_class_dict["classes"],
num_input=word_class_dict["num_input"],
num_output=word_class_dict["num_output"],
dl_model=dl_model,
response=word_class_dict["response"])
