def reload(self, custom_objects={}):
"""
Load keras multitask DNN from disk.
"""
filename = Model.get_model_filename(self.model_dir)
filename, _ = os.path.splitext(filename)
json_filename = "%s.%s" % (filename, "json")
h5_filename = "%s.%s" % (filename, "h5")
with open(json_filename) as file_obj:
model = model_from_json(file_obj.read(), custom_objects=custom_objects)
model.load_weights(h5_filename)
self.model_instance = model
def load(self, model_dir):
"""
Load keras multitask DNN from disk.
"""
filename = Model.get_model_filename(model_dir)
filename, _ = os.path.splitext(filename)
json_filename = "%s.%s" % (filename, "json")
h5_filename = "%s.%s" % (filename, "h5")
with open(json_filename) as file_obj:
model = model_from_json(file_obj.read())
model.load_weights(h5_filename)
self.raw_model = model
def reload(self):
"""
Load keras multitask DNN from disk.
"""
filename = Model.get_model_filename(self.model_dir)
filename, _ = os.path.splitext(filename)
json_filename = "%s.%s" % (filename, "json")
h5_filename = "%s.%s" % (filename, "h5")
with open(json_filename) as file_obj:
model = model_from_json(file_obj.read())
model.load_weights(h5_filename)
self.raw_model = model
def save(self, out_dir):
"""
Saves underlying keras model to disk.
"""
super(KerasModel, self).save(out_dir)
model = self.get_raw_model()
filename, _ = os.path.splitext(Model.get_model_filename(out_dir))
# Note that keras requires the model architecture and weights to be stored
# separately. A json file is generated that specifies the model architecture.
# The weights will be stored in an h5 file. The pkl.gz file with store the
# target name.
json_filename = "%s.%s" % (filename, "json")
h5_filename = "%s.%s" % (filename, "h5")
# Save architecture
json_string = model.to_json()
with open(json_filename, "wb") as file_obj:
file_obj.write(json_string)
model.save_weights(h5_filename, overwrite=True)
def save(self, out_dir):
"""
Saves underlying keras model to disk.
"""
super(KerasModel, self).save(out_dir)
model = self.get_raw_model()
filename, _ = os.path.splitext(Model.get_model_filename(out_dir))
# Note that keras requires the model architecture and weights to be stored
# separately. A json file is generated that specifies the model architecture.
# The weights will be stored in an h5 file. The pkl.gz file with store the
# target name.
json_filename = "%s.%s" % (filename, "json")
h5_filename = "%s.%s" % (filename, "h5")
# Save architecture
json_string = model.to_json()
with open(json_filename, "wb") as file_obj:
file_obj.write(json_string)
model.save_weights(h5_filename, overwrite=True)
def reload(self):
"""Loads sklearn model from joblib file on disk."""
self.model_instance = load_from_disk(
Model.get_model_filename(self.model_dir))
def load(self, model_dir): """Loads sklearn model from joblib file on disk.""" self.raw_model = load_from_disk(Model.get_model_filename(model_dir))
def load(self, model_dir):
"""Loads sklearn model from joblib file on disk."""
self.raw_model = load_from_disk(Model.get_model_filename(model_dir))
def reload(self): """Loads sklearn model from joblib file on disk.""" self.model_instance = load_from_disk(Model.get_model_filename(self.model_dir))
