def __init__(self, args, X=None, Y=None, optimizer=None, use_gpu=-1, collect_output=False, activation=F.tanh):
## We only instantitate the descendant of this class
assert hasattr(self, "_all_models"), "Shoudln't instantitate this class."
## Default configuration
self._opt = optimizer
self._xp, use_gpu = UF.setup_gpu(use_gpu)
self._collect_output = collect_output
self._gpu_id = use_gpu
self._train_state = self._train_state = { "loss": 150, "epoch": 0 }
## Loading Classifier
if args.init_model:
serializer = ModelSerializer(args.init_model)
serializer.load(self, self._all_models, xp=self._xp)
self._model.report(sys.stderr, verbosity=1)
else:
args.input = len(X)
args.output = len(Y)
self._model = UF.select_model(args.model, self._all_models)(X, Y, args, xp=self._xp)
## Use GPU or not?
if use_gpu >= 0:
self._model = self._model.to_gpu(use_gpu)
## Setup Optimizer
if optimizer is not None:
self._opt.setup(self._model)
if args.init_model:
serializer.load_optimizer(self._opt)
