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_integer("num_classes", num_classes, 1, Rel.GT,
self.cls_name)
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 = P.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)
def __init__( self):
super(NetSoftmaxWithCrossEntropy, self).__init__()
logits = Tensor(np.array([[1,1,10],
[1,10,1],
[10,1,1]]).astype(np.float32))
self.logits = Parameter(initializer(logits, logits.shape()), name ='logits')
labels = Tensor(np.array([2,1,0]).astype(np.int32))
self.labels = Parameter(initializer(labels, labels.shape()), name ='labels')
self.SoftmaxWithCrossEntropy = P.SparseSoftmaxCrossEntropyWithLogits(True)
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 = P.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):
super(SoftmaxCrossEntropyWithLogits, self).__init__(reduction)
self.is_grad = is_grad
self.sparse = sparse
self.softmax_cross_entropy = P.SoftmaxCrossEntropyWithLogits()
self.one_hot = P.OneHot()
self.on_value = Tensor(1.0, mstype.float32)
self.off_value = Tensor(0.0, 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)
def __init__(self, is_grad=False):
super(Net, self).__init__()
self.SparseSoftmaxCrossEntropyWithLogits = P.SparseSoftmaxCrossEntropyWithLogits(is_grad=is_grad)
# shapes of logits and labels mismatch
('SoftmaxCrossEntropyWithLogits3', {
'block': (P.SoftmaxCrossEntropyWithLogits(), {
'exception': ValueError,
'error_keywords': ['SoftmaxCrossEntropyWithLogits']
}),
'desc_inputs': [
Tensor(np.ones([5]).astype(np.float32)),
Tensor(np.ones([3]).astype(np.float32))
],
'skip': ['backward']
}),
# input is scalar
('SparseSoftmaxCrossEntropyWithLogits0', {
'block': (P.SparseSoftmaxCrossEntropyWithLogits(), {
'exception': TypeError,
'error_keywords': ['SparseSoftmaxCrossEntropyWithLogits']
}),
'desc_inputs': [5.0, 5.0],
'skip': ['backward']
}),
# logits is Tensor(bool)
('SparseSoftmaxCrossEntropyWithLogits1', {
'block': (P.SparseSoftmaxCrossEntropyWithLogits(), {
'exception': TypeError,
'error_keywords': ['SparseSoftmaxCrossEntropyWithLogits']
}),
'desc_inputs': [
Tensor(np.ones([5]).astype(np.bool_)),
Tensor(np.ones([5]).astype(np.bool_))
