tf.keras.metrics.mean_squared_logarithmic_error
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Computes the mean squared logarithmic error between y_true
& y_pred
.
View aliases
Main aliases
tf.keras.losses.MSLE
, tf.keras.losses.mean_squared_logarithmic_error
, tf.keras.losses.msle
, tf.keras.metrics.MSLE
, tf.keras.metrics.msle
tf . keras . metrics . mean_squared_logarithmic_error (
y_true , y_pred
)
loss = mean(square(log(y_true + 1) - log(y_pred + 1)), axis=-1)
Standalone usage:
y_true = np . random . randint ( 0 , 2 , size = ( 2 , 3 ))
y_pred = np . random . random ( size = ( 2 , 3 ))
loss = tf . keras . losses . mean_squared_logarithmic_error ( y_true , y_pred )
assert loss . shape == ( 2 ,)
y_true = np . maximum ( y_true , 1e-7 )
y_pred = np . maximum ( y_pred , 1e-7 )
assert np . allclose (
loss . numpy (),
np . mean (
np . square ( np . log ( y_true + 1. ) - np . log ( y_pred + 1. )), axis =- 1 ))
Args
y_true
Ground truth values. shape = [batch_size, d0, .. dN]
.
y_pred
The predicted values. shape = [batch_size, d0, .. dN]
.
Returns
Mean squared logarithmic error values. shape = [batch_size, d0, ..
dN-1]
.
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 2024-01-23 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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-01-23 UTC."],[],[]]