# tf.keras.losses.Loss

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/keras-team/keras/tree/v2.13.1/keras/losses.py#L41-L219) |

Loss base class.

#### View aliases


**Main aliases**

[`tf.losses.Loss`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/Loss)
**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

\`tf.compat.v1.keras.losses.Loss\`

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    tf.keras.losses.Loss(
        reduction=losses_utils.ReductionV2.AUTO, name=None
    )

To be implemented by subclasses:

- `call()`: Contains the logic for loss calculation using `y_true`, `y_pred`.

Example subclass implementation:

    class MeanSquaredError(Loss):

      def call(self, y_true, y_pred):
        return tf.reduce_mean(tf.math.square(y_pred - y_true), axis=-1)

When using a Loss under a [`tf.distribute.Strategy`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/distribute/Strategy), except passing it
to [`Model.compile()`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/Model#compile) for use by [`Model.fit()`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/Model#fit), please use reduction
types 'SUM' or 'NONE', and reduce losses explicitly. Using 'AUTO' or
'SUM_OVER_BATCH_SIZE' will raise an error when calling the Loss object
from a custom training loop or from user-defined code in [`Layer.call()`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/layers/Layer#call).
Please see this custom training
[tutorial](https://www.tensorflow.org/tutorials/distribute/custom_training)
for more details on this.

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| ## Args ||
|---|---|
| `reduction` | Type of [`tf.keras.losses.Reduction`](https://www.tensorflow.org/versions/r2.13/api_docs/python/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 under a [`tf.distribute.Strategy`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/distribute/Strategy), except via [`Model.compile()`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/Model#compile) and [`Model.fit()`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/Model#fit), using `AUTO` or `SUM_OVER_BATCH_SIZE` will raise an error. Please see this custom training [tutorial](https://www.tensorflow.org/tutorials/distribute/custom_training) for more details. |
| `name` | Optional name for the instance. |

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## Methods

### `call`

[View source](https://github.com/keras-team/keras/tree/v2.13.1/keras/losses.py#L180-L194)

    @abc.abstractmethod
    call(
        y_true, y_pred
    )

Invokes the `Loss` instance.

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| Args ||
|---|---|
| `y_true` | Ground truth values. shape = `[batch_size, d0, .. dN]`, except sparse loss functions such as sparse categorical crossentropy where shape = `[batch_size, d0, .. dN-1]` |
| `y_pred` | The predicted values. shape = `[batch_size, d0, .. dN]` |

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| Returns ||
|---|---|
| Loss values with the shape `[batch_size, d0, .. dN-1]`. ||

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### `from_config`

[View source](https://github.com/keras-team/keras/tree/v2.13.1/keras/losses.py#L164-L174)

    @classmethod
    from_config(
        config
    )

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

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

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| Returns ||
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| A `Loss` instance. ||

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### `get_config`

[View source](https://github.com/keras-team/keras/tree/v2.13.1/keras/losses.py#L176-L178)

    get_config()

Returns the config dictionary for a `Loss` instance.

### `__call__`

[View source](https://github.com/keras-team/keras/tree/v2.13.1/keras/losses.py#L101-L162)

    __call__(
        y_true, y_pred, sample_weight=None
    )

Invokes the `Loss` instance.

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| Args ||
|---|---|
| `y_true` | Ground truth values. shape = `[batch_size, d0, .. dN]`, except sparse loss functions such as sparse categorical crossentropy where shape = `[batch_size, d0, .. dN-1]` |
| `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.) |

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| 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.) ||

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

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