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# tf.keras.losses.Huber

## Class `Huber`

Computes the Huber loss between `y_true` and `y_pred`.

### Aliases:

• Class `tf.compat.v1.keras.losses.Huber`
• Class `tf.compat.v2.keras.losses.Huber`
• Class `tf.compat.v2.losses.Huber`
• Class `tf.losses.Huber`

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
``````

where d is `delta`. See: https://en.wikipedia.org/wiki/Huber_loss

#### Usage:

``````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/alpha/tutorials/distribute/training_loops for more details on this.
• `name`: Optional name for the op.

## `__init__`

View source

``````__init__(
delta=1.0,
reduction=losses_utils.ReductionV2.AUTO,
name='huber_loss'
)
``````

Initialize self. See help(type(self)) for accurate signature.

## Methods

### `__call__`

View source

``````__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.

### `from_config`

View source

``````from_config(
cls,
config
)
``````

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

#### Args:

• `config`: Output of `get_config()`.

#### Returns:

A `Loss` instance.

### `get_config`

View source

``````get_config()
``````