def __init__(self, sparse=False, reduction='none'):
super(SoftmaxCrossEntropyWithLogits, self).__init__(reduction)
self.sparse = validator.check_bool(sparse, "sparse")
self.reduction = reduction
self.softmax_cross_entropy = _selected_ops.SoftmaxCrossEntropyWithLogits(
)
self.one_hot = P.OneHot()
self.on_value = Tensor(1.0, mstype.float32)
self.off_value = Tensor(0., mstype.float32)
self.is_cpugpu = context.get_context('device_target') in ["CPU", "GPU"]
self.sparse_softmax_cross_entropy = P.SparseSoftmaxCrossEntropyWithLogits(
)
def __init__(self,
is_grad=True,
sparse=False,
reduction=None,
smooth_factor=0,
num_classes=2):
super(SoftmaxCrossEntropyWithLogits, self).__init__(reduction)
self.is_grad = is_grad
self.sparse = sparse
validator.check_number_range("smooth_factor", smooth_factor, 0, 1,
Rel.INC_BOTH, self.cls_name)
self.smooth_factor = smooth_factor
self.num_classes = num_classes
self.softmax_cross_entropy = _selected_ops.SoftmaxCrossEntropyWithLogits(
)
self.one_hot = P.OneHot()
self.on_value = Tensor(1.0 - self.smooth_factor, mstype.float32)
self.off_value = Tensor(
1.0 * self.smooth_factor / (self.num_classes - 1), mstype.float32)
self.is_cpugpu = context.get_context('device_target') in ["CPU", "GPU"]
if self.is_cpugpu:
self.sparse_softmax_cross_entropy = P.SparseSoftmaxCrossEntropyWithLogits(
is_grad=self.is_grad)
