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Creates a cross-entropy loss using tf.nn.sigmoid_cross_entropy_with_logits.
tf.losses.sigmoid_cross_entropy(
multi_class_labels, logits, weights=1.0, label_smoothing=0, scope=None,
loss_collection=tf.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
Args | |
---|---|
multi_class_labels
|
[batch_size, num_classes] target integer labels in
{0, 1} .
|
logits
|
Float [batch_size, num_classes] logits outputs of the network.
|
weights
|
Optional Tensor whose rank is either 0, or the same rank as
labels , and must be broadcastable to labels (i.e., all dimensions must
be either 1 , or the same as the corresponding losses dimension).
|
label_smoothing
|
If greater than 0 then smooth the labels.
|
scope
|
The scope for the operations performed in computing the loss. |
loss_collection
|
collection to which the loss will be added. |
reduction
|
Type of reduction to apply to loss. |
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
.