tf.keras.losses.Huber

Computes the Huber loss between y_true and y_pred.

Main aliases

tf.losses.Huber

Compat aliases for migration

See Migration guide for more details.

tf.keras.losses.Huber(
    delta=1.0, reduction=losses_utils.ReductionV2.AUTO, name='huber_loss'
)

For each value x in error = y_true - y_pred:

loss = 0.5 * x^2                  if |x| <= d
loss = 0.5 * d^2 + d * (|x| - d)  if |x| > d

l = tf.keras.losses.Huber()
loss = l([0., 1., 1.], [1., 0., 1.])
print('Loss: ', loss.numpy())  # Loss: 0.333

Usage with the compile API:

model = tf.keras.Model(inputs, outputs)
model.compile('sgd', loss=tf.keras.losses.Huber())

Args
`delta`	A float, the point where the Huber loss function changes from a quadratic to linear.
`reduction`	(Optional) Type of `tf.keras.losses.Reduction` to apply to loss. Default value is `AUTO`. `AUTO` indicates that the reduction option will be determined by the usage context. For almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When used with `tf.distribute.Strategy`, outside of built-in training loops such as `tf.keras` `compile` and `fit`, using `AUTO` or `SUM_OVER_BATCH_SIZE` will raise an error. Please see https://www.tensorflow.org/tutorials/distribute/custom_training for more details on this.
`name`	Optional name for the op.

Methods

@classmethod
from_config(
    config
)

Instantiates a Loss from its config (output of get_config()).

Args
`config`	Output of `get_config()`.

Returns
A `Loss` instance.

get_config()

__call__(
    y_true, y_pred, sample_weight=None
)

Invokes the Loss instance.

Args
`y_true`	Ground truth values. shape = `[batch_size, d0, .. dN]`
`y_pred`	The predicted values. shape = `[batch_size, d0, .. dN]`
`sample_weight`	Optional `sample_weight` acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If `sample_weight` is a tensor of size `[batch_size]`, then the total loss for each sample of the batch is rescaled by the corresponding element in the `sample_weight` vector. If the shape of `sample_weight` is `[batch_size, d0, .. dN-1]` (or can be broadcasted to this shape), then each loss element of `y_pred` is scaled by the corresponding value of `sample_weight`. (Note on`dN-1`: all loss functions reduce by 1 dimension, usually axis=-1.)

Returns
Weighted loss float `Tensor`. If `reduction` is `NONE`, this has shape `[batch_size, d0, .. dN-1]`; otherwise, it is scalar. (Note `dN-1` because all loss functions reduce by 1 dimension, usually axis=-1.)

Raises
`ValueError`	If the shape of `sample_weight` is invalid.