Loss operations for use in neural networks.

Note:All the losses are added to the`GraphKeys.LOSSES`

collection by default.

`class Reduction`

: Types of loss reduction.

`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`

.

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Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/compat/v1/losses