tf.contrib.losses.sigmoid_cross_entropy

tf.contrib.losses.sigmoid_cross_entropy(
    logits,
    multi_class_labels,
    weights=1.0,
    label_smoothing=0,
    scope=None
)

Defined in tensorflow/contrib/losses/python/losses/loss_ops.py.

See the guide: Losses (contrib) > Loss operations for use in neural networks.

Creates a cross-entropy loss using tf.nn.sigmoid_cross_entropy_with_logits. (deprecated)

THIS FUNCTION IS DEPRECATED. It will be removed after 2016-12-30. Instructions for updating: Use tf.losses.sigmoid_cross_entropy instead. Note that the order of the predictions and labels arguments has been changed.

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 size [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:

• logits: [batch_size, num_classes] logits outputs of the network .
• multi_class_labels: [batch_size, num_classes] labels in (0, 1).
• weights: Coefficients for the loss. The tensor must be a scalar, a tensor of shape [batch_size] or shape [batch_size, num_classes].
• label_smoothing: If greater than 0 then smooth the labels.
• scope: The scope for the operations performed in computing the loss.

Returns:

A scalar Tensor representing the loss value.

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.