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TensorFlow 2 version View source on GitHub

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

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(

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

regularization_loss Regularization loss.

Scalar loss value.