|---| | [ View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.21.0/tensorflow/python/ops/nn_impl_distribute.py#L27-L67) | Scales the sum of the given regularization losses by number of replicas. #### View aliases **Compat aliases for migration** See [Migration guide](https://www.tensorflow.org/guide/migrate) for more details. [`tf.compat.v1.nn.scale_regularization_loss`](https://www.tensorflow.org/api_docs/python/tf/nn/scale_regularization_loss)
tf.nn.scale_regularization_loss( regularization_loss ) ### Used in the notebooks | Used in the guide | Used in the tutorials | |---|---| | - [Distributed training with TensorFlow](https://www.tensorflow.org/guide/distributed_training) | - [Custom training with tf.distribute.Strategy](https://www.tensorflow.org/tutorials/distribute/custom_training) - [Custom training loop with Keras and MultiWorkerMirroredStrategy](https://www.tensorflow.org/tutorials/distribute/multi_worker_with_ctl) - [Parameter server training with ParameterServerStrategy](https://www.tensorflow.org/tutorials/distribute/parameter_server_training) | Usage with distribution strategy and custom training loop: with strategy.scope(): def compute_loss(self, label, predictions): per_example_loss = tf.keras.losses.sparse_categorical_crossentropy( labels, predictions) # Compute loss that is scaled by sample_weight and by global batch size. loss = tf.nn.compute_average_loss( per_example_loss, sample_weight=sample_weight, global_batch_size=GLOBAL_BATCH_SIZE) # Add scaled regularization losses. loss += tf.nn.scale_regularization_loss(tf.nn.l2_loss(weights)) return loss


| ## Args || |---|---| | `regularization_loss` | Regularization loss. |



| ## Returns || |---|---| | Scalar loss value. ||