tf.keras.losses.Huber
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Computes the Huber loss between y_true
& y_pred
.
Inherits From: Loss
tf.keras.losses.Huber(
delta=1.0,
reduction='sum_over_batch_size',
name='huber_loss'
)
Used in the notebooks
for x in error:
if abs(x) <= delta:
loss.append(0.5 * x^2)
elif abs(x) > delta:
loss.append(delta * abs(x) - 0.5 * delta^2)
loss = mean(loss, axis=-1)
See: Huber loss.
Args |
delta
|
A float, the point where the Huber loss function changes from a
quadratic to linear.
|
reduction
|
Type of reduction to apply to loss. Options are "sum" ,
"sum_over_batch_size" or None . Defaults to
"sum_over_batch_size" .
|
name
|
Optional name for the 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.
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Last updated 2024-06-07 UTC.
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