# tf.nn.scale_regularization_loss

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/nn_impl.py#L477-L514) |

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)

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    tf.nn.scale_regularization_loss(
        regularization_loss
    )

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

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

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

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