tf.compat.v1.losses.sigmoid_cross_entropy

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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=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

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.

Weighted loss Tensor of the same type as logits. If reduction is NONE, this has the same shape as logits; otherwise, it is scalar.

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.