Scales the sum of the given regularization losses by number of replicas.
View aliases
Compat aliases for migration
See Migration guide for more details.
tf.nn.scale_regularization_loss(
regularization_loss
)
Used in the notebooks
Used in the guide | Used in the tutorials |
---|---|
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
Returns | |
---|---|
Scalar loss value. |