Defined in tensorflow/_api/v1/losses/__init__.py.
Loss operations for use in neural networks.
Classes
class Reduction: Types of loss reduction.
Functions
absolute_difference(...): Adds an Absolute Difference loss to the training procedure.
add_loss(...): Adds a externally defined loss to the collection of losses.
compute_weighted_loss(...): Computes the weighted loss.
cosine_distance(...): Adds a cosine-distance loss to the training procedure. (deprecated arguments)
get_losses(...): Gets the list of losses from the loss_collection.
get_regularization_loss(...): Gets the total regularization loss.
get_regularization_losses(...): Gets the list of regularization losses.
get_total_loss(...): Returns a tensor whose value represents the total loss.
hinge_loss(...): Adds a hinge loss to the training procedure.
huber_loss(...): Adds a Huber Loss term to the training procedure.
log_loss(...): Adds a Log Loss term to the training procedure.
mean_pairwise_squared_error(...): Adds a pairwise-errors-squared loss to the training procedure.
mean_squared_error(...): Adds a Sum-of-Squares loss to the training procedure.
sigmoid_cross_entropy(...): Creates a cross-entropy loss using tf.nn.sigmoid_cross_entropy_with_logits.
softmax_cross_entropy(...): Creates a cross-entropy loss using tf.nn.softmax_cross_entropy_with_logits_v2.
sparse_softmax_cross_entropy(...): Cross-entropy loss using tf.nn.sparse_softmax_cross_entropy_with_logits.