def __init__(self,
predictor=None,
lossfun=softmax_cross_entropy.softmax_cross_entropy,
accfun=accuracy.accuracy,
device=-1,
**sk_params
):
"""
:param predictor (~chainer.links.Chain):
:param lossfun: loss function
:param accfun: accuracy function. When `None` is set, accuracy is not
calculated during the training and `loassfun` is used for `score`.
:param device (int): GPU device id. -1 indicates to use CPU.
:param sk_params (dict): dict of parameters. This is used for
`GridSearchCV` and `RandomizedSearchCV` internally.
"""
super(SklearnBaseWrapper, self).__init__()
if predictor is None:
# Temporal counter measure to pass `check_estimator`,
# sklearn need to support default constructor
# TODO: Should dynamically asign n_out, instead of using magic parameter.
predictor = chainer.links.Linear(None, self._default_n_out)
if isinstance(predictor, chainer.Link):
# print('[DEBUG] predictor instance')
with self.init_scope():
self.predictor = predictor
self.predictor_constructor = predictor.__class__
elif is_function(predictor) or issubclass(predictor, chainer.Link):
# print('[DEBUG] predictor is constructor')
self.predictor_constructor = predictor
else:
print("[ERROR] predictor should be either Chain class instance or"
"function which returns Chain class instance")
assert False
self.lossfun = lossfun
self.accfun = accfun
self.compute_accuracy = accfun is not None
self.y = None
self.loss = None
self.accuracy = None
self.inputs = None
# Ensure initialization, necessary for GridSearch
self.device = -1
if hasattr(self, 'predictor'):
self.predictor.to_cpu()
self.update_device(device)
self.sk_params = sk_params
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