tf.compat.v1.losses.get_total_loss
Stay organized with collections
Save and categorize content based on your preferences.
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.
|
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates. Some content is licensed under the numpy license.
Last updated 2023-10-06 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2023-10-06 UTC."],[],[]]
View source on GitHub