tf.keras.losses.MeanAbsoluteError
Stay organized with collections
Save and categorize content based on your preferences.
Computes the mean of absolute difference between labels and predictions.
Inherits From: Loss
tf.keras.losses.MeanAbsoluteError(
reduction='sum_over_batch_size',
name='mean_absolute_error'
)
loss = mean(abs(y_true - y_pred))
Args |
reduction
|
Type of reduction to apply to the loss. In almost all cases
this should be "sum_over_batch_size" .
Supported options are "sum" , "sum_over_batch_size" or None .
|
name
|
Optional name for the loss instance.
|
Methods
call
View source
call(
y_true, y_pred
)
from_config
View source
@classmethod
from_config(
config
)
get_config
View source
get_config()
__call__
View source
__call__(
y_true, y_pred, sample_weight=None
)
Call self as a function.
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 2024-06-07 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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-06-07 UTC."],[],[]]