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

Args:

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:

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.