Module: tf.losses

Loss operations for use in neural networks.

Note: All the losses are added to the GraphKeys.LOSSES collection by default.

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.