def load(cls, path, nlp, max_length=100):
with (path / 'config.json').open() as file_:
model = model_from_json(file_.read())
with (path / 'model').open('rb') as file_:
lstm_weights = pickle.load(file_)
embeddings = get_embeddings(nlp.vocab)
model.set_weights([embeddings] + lstm_weights)
return cls(model, max_length=max_length)
def load(cls, path, nlp, max_length=100):
with (path / "config.json").open() as file_:
model = model_from_json(file_.read())
with (path / "model").open("rb") as file_:
lstm_weights = pickle.load(file_)
embeddings = get_embeddings(nlp.vocab)
model.set_weights([embeddings] + lstm_weights)
return cls(model, max_length=max_length)
def load(cls, path, nlp, max_length=100):
print('> max_length: {}'.format(max_length))
print('> path: {}'.format(path))
with (path / "config.json").open() as file_:
model = model_from_json(file_.read())
with (path / "model").open("rb") as file_:
lstm_weights = pickle.load(file_)
embeddings = get_embeddings(nlp.vocab)
model.set_weights([embeddings] + lstm_weights)
return cls(model, max_length=max_length)
def load(cls, path, char_index, max_length, frozen):
xprint('SentimentAnalyser.load: path=%s max_length=%d' % (path, max_length))
with open(os.path.join(path, 'config.json'), 'rt') as f:
model = model_from_json(f.read())
with open(os.path.join(path, 'model'), 'rb') as f:
lstm_weights = pickle.load(f)
if frozen:
embeddings, char_index, index_char = get_char_embeddings()
lstm_weights = [embeddings] + lstm_weights
model.set_weights(lstm_weights)
return cls(char_index, model, max_length=max_length)
def load_model(path, nlp):
with (path / 'model_config.json').open() as file_:
_json = file_.read()
config_dict = json.loads(_json)
image_embedding_function = config_dict.get(
IMAGE_EMBEDDING_FUNCTION_KEY, None)
if IMAGE_EMBEDDING_FUNCTION_KEY in config_dict:
del config_dict[IMAGE_EMBEDDING_FUNCTION_KEY]
model = model_from_config(config_dict)
with (path / 'model_weights').open('rb') as file_:
lstm_weights = pickle.load(file_)
embeddings = get_embeddings(nlp.vocab)
model.set_weights([embeddings] + lstm_weights)
return model, config_dict, image_embedding_function
def reader(input_path):
f = open(input_path, 'rb')
data = pickle.load(f)
print(data)
print(len(data))
