tf.contrib.losses.sigmoid_cross_entropy()

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

Creates a cross-entropy loss using tf.nn.sigmoid_cross_entropy_with_logits.

weight acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If weight 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] target labels in (0, 1).
• weight: 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 predictions doesn't match that of targets or if the shape of weight is invalid or if weight is None.