tf.keras.losses.Huber | TensorFlow v2.16.1 (original) (raw)

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
Playing CartPole with the Actor-Critic method Parametrized Quantum Circuits for Reinforcement Learning

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.

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.