tf.compat.v1.losses.get_regularization

Returns a tensor whose value represents the total loss.

tf.compat.v1.losses.get_total_loss(
    add_regularization_losses=True, name='total_loss', scope=None
)

In particular, this adds any losses you have added with tf.add_loss() to any regularization losses that have been added by regularization parameters on layers constructors e.g. tf.layers. Be very sure to use this if you are constructing a loss_op manually. Otherwise regularization arguments on tf.layers methods will not function.

Args
`add_regularization_losses`	A boolean indicating whether or not to use the regularization losses in the sum.
`name`	The name of the returned tensor.
`scope`	An optional scope name for filtering the losses to return. Note that this filters the losses added with `tf.add_loss()` as well as the regularization losses to that scope.

Returns
A `Tensor` whose value represents the total loss.

Raises
`ValueError`	if `losses` is not iterable.

TensorFlow

tf.compat / v1 / v1.losses.get_regularization_losses

Args

Returns

Raises

TensorFlow

tf

tf.audio

tf.autograph

tf.bitwise

tf.compat

tf.config

tf.data

tf.debugging

tf.distribute

tf.dtypes

tf.errors

tf.estimator

tf.experimental

tf.feature_column

tf.graph_util

tf.image

tf.initializers

tf.io

tf.keras

tf.linalg

tf.lite

tf.lookup

tf.losses

tf.math

tf.metrics

tf.nest

tf.nn

tf.optimizers

tf.quantization

tf.queue

tf.ragged

tf.random

tf.raw_ops

tf.saved_model

tf.sets

tf.signal

tf.sparse

tf.strings

tf.summary

tf.sysconfig

tf.test

tf.tpu

tf.train

tf.version

tf.xla

tf.compat / v1 / v1.losses.get_regularization_losses

Args

Returns

Raises