tf.nn.scale_regularization_loss

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Scales the sum of the given regularization losses by number of replicas.

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.nn.scale_regularization_loss, `tf.compat.v2.nn.scale_regularization_loss`

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.compute_average_loss(
        per_example_loss,
        sample_weight=sample_weight,
        global_batch_size=GLOBAL_BATCH_SIZE)

    # Add scaled regularization losses.
    loss += tf.scale_regularization_loss(tf.nn.l2_loss(weights))
    return loss

Args
regularization_loss	Regularization loss.

Returns
Scalar loss value.