View source on GitHub |
Creates a cross-entropy loss using tf.nn.sigmoid_cross_entropy_with_logits.
tf.compat.v1.losses.sigmoid_cross_entropy(
multi_class_labels,
logits,
weights=1.0,
label_smoothing=0,
scope=None,
loss_collection=ops.GraphKeys.LOSSES,
reduction=Reduction.SUM_BY_NONZERO_WEIGHTS
)
weights
acts as a coefficient for the loss. If a scalar is provided,
then the loss is simply scaled by the given value. If weights
is a
tensor of shape [batch_size]
, then the loss weights apply to each
corresponding sample.
If label_smoothing
is nonzero, smooth the labels towards 1/2:
new_multiclass_labels = multiclass_labels * (1 - label_smoothing)
+ 0.5 * label_smoothing
Returns | |
---|---|
Weighted loss Tensor of the same type as logits . If reduction is
NONE , this has the same shape as logits ; otherwise, it is scalar.
|
Raises | |
---|---|
ValueError
|
If the shape of logits doesn't match that of
multi_class_labels or if the shape of weights is invalid, or if
weights is None. Also if multi_class_labels or logits is None.
|
eager compatibility
The loss_collection
argument is ignored when executing eagerly. Consider
holding on to the return value or collecting losses via a tf.keras.Model
.