tf.keras.losses.Huber

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

Used in the tutorials

Formula:

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.

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.