def __init__(self,
n_class,
loss_type='Dice',
softmax=True,
loss_func_params=None,
name='loss_function'):
super(LossFunction, self).__init__(name=name)
assert n_class > 0, \
"Number of classes for segmentation loss should be positive."
self._num_classes = n_class
self._softmax = bool(softmax)
# set loss function and function-specific additional params.
self._data_loss_func = LossSegmentationFactory.create(loss_type)
self._loss_func_params = \
loss_func_params if loss_func_params is not None else dict()
data_loss_function_name = self._data_loss_func.__name__
if data_loss_function_name.startswith('cross_entropy') \
or 'xent' in data_loss_function_name:
tf.logging.info(
'Cross entropy loss function calls '
'tf.nn.sparse_softmax_cross_entropy_with_logits '
'which always performs a softmax internally.')
self._softmax = False
def __init__(self,
n_class,
loss_type='Dice',
softmax=True,
loss_func_params=None,
name='loss_function'):
super(LossFunction, self).__init__(name=name)
assert n_class > 0, \
"Number of classes for segmentation loss should be positive."
self._num_classes = n_class
self._softmax = bool(softmax)
# set loss function and function-specific additional params.
self._data_loss_func = LossSegmentationFactory.create(loss_type)
self._loss_func_params = \
loss_func_params if loss_func_params is not None else dict()
if self._data_loss_func.__name__ == 'cross_entropy':
tf.logging.info(
'Cross entropy loss function calls '
'tf.nn.sparse_softmax_cross_entropy_with_logits '
'which always performs a softmax internally.')
self._softmax = False
def make_callable_loss_func(self, type_str):
self._data_loss_func = LossSegmentationFactory.create(type_str)