# tf.keras.losses.CategoricalFocalCrossentropy

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/keras-team/keras/tree/v2.14.0/keras/losses.py#L933-L1070) |

Computes the alpha balanced focal crossentropy loss.

Inherits From: [`Loss`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/keras/losses/Loss)

#### View aliases


**Main aliases**

[`tf.losses.CategoricalFocalCrossentropy`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/CategoricalFocalCrossentropy)

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    tf.keras.losses.CategoricalFocalCrossentropy(
        alpha=0.25,
        gamma=2.0,
        from_logits=False,
        label_smoothing=0.0,
        axis=-1,
        reduction=losses_utils.ReductionV2.AUTO,
        name='categorical_focal_crossentropy'
    )

Use this crossentropy loss function when there are two or more label
classes and if you want to handle class imbalance without using
`class_weights`. We expect labels to be provided in a `one_hot`
representation.

According to [Lin et al., 2018](https://arxiv.org/pdf/1708.02002.pdf), it
helps to apply a focal factor to down-weight easy examples and focus more on
hard examples. The general formula for the focal loss (FL)
is as follows:

`FL(p_t) = (1 − p_t)^gamma * log(p_t)`

where `p_t` is defined as follows:
`p_t = output if y_true == 1, else 1 - output`

`(1 − p_t)^gamma` is the `modulating_factor`, where `gamma` is a focusing
parameter. When `gamma` = 0, there is no focal effect on the cross entropy.
`gamma` reduces the importance given to simple examples in a smooth manner.

The authors use alpha-balanced variant of focal loss (FL) in the paper:
`FL(p_t) = −alpha * (1 − p_t)^gamma * log(p_t)`

where `alpha` is the weight factor for the classes. If `alpha` = 1, the
loss won't be able to handle class imbalance properly as all
classes will have the same weight. This can be a constant or a list of
constants. If alpha is a list, it must have the same length as the number
of classes.

The formula above can be generalized to:
`FL(p_t) = alpha * (1 − p_t)^gamma * CrossEntropy(y_true, y_pred)`

where minus comes from `CrossEntropy(y_true, y_pred)` (CE).

Extending this to multi-class case is straightforward:
`FL(p_t) = alpha * (1 − p_t)^gamma * CategoricalCE(y_true, y_pred)`

In the snippet below, there is `# classes` floating pointing values per
example. The shape of both `y_pred` and `y_true` are
`[batch_size, num_classes]`.

#### Standalone usage:

    y_true = [[0., 1., 0.], [0., 0., 1.]]
    y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]
    # Using 'auto'/'sum_over_batch_size' reduction type.
    cce = tf.keras.losses.CategoricalFocalCrossentropy()
    cce(y_true, y_pred).numpy()
    0.23315276

    # Calling with 'sample_weight'.
    cce(y_true, y_pred, sample_weight=tf.constant([0.3, 0.7])).numpy()
    0.1632

    # Using 'sum' reduction type.
    cce = tf.keras.losses.CategoricalFocalCrossentropy(
        reduction=tf.keras.losses.Reduction.SUM)
    cce(y_true, y_pred).numpy()
    0.46631

    # Using 'none' reduction type.
    cce = tf.keras.losses.CategoricalFocalCrossentropy(
        reduction=tf.keras.losses.Reduction.NONE)
    cce(y_true, y_pred).numpy()
    array([3.2058331e-05, 4.6627346e-01], dtype=float32)

Usage with the `compile()` API:

    model.compile(optimizer='adam',
                  loss=tf.keras.losses.CategoricalFocalCrossentropy())

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| ## Args ||
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| `alpha` | A weight balancing factor for all classes, default is `0.25` as mentioned in the reference. It can be a list of floats or a scalar. In the multi-class case, alpha may be set by inverse class frequency by using `compute_class_weight` from `sklearn.utils`. |
| `gamma` | A focusing parameter, default is `2.0` as mentioned in the reference. It helps to gradually reduce the importance given to simple (easy) examples in a smooth manner. |
| `from_logits` | Whether `output` is expected to be a logits tensor. By default, we consider that `output` encodes a probability distribution. |
| `label_smoothing` | Float in \[0, 1\]. When \> 0, label values are smoothed, meaning the confidence on label values are relaxed. For example, if `0.1`, use `0.1 / num_classes` for non-target labels and `0.9 + 0.1 / num_classes` for target labels. |
| `axis` | The axis along which to compute crossentropy (the features axis). Defaults to -1. |
| `reduction` | Type of [`tf.keras.losses.Reduction`](https://www.tensorflow.org/versions/r2.14/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/r2.14/api_docs/python/tf/distribute/Strategy), except via [`Model.compile()`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/keras/Model#compile) and [`Model.fit()`](https://www.tensorflow.org/versions/r2.14/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. Defaults to 'categorical_focal_crossentropy'. |

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## Methods

### `from_config`

[View source](https://github.com/keras-team/keras/tree/v2.14.0/keras/losses.py#L287-L301)

    @classmethod
    from_config(
        config
    )

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

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

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| Returns ||
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| A [`keras.losses.Loss`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/keras/losses/Loss) instance. ||

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### `get_config`

[View source](https://github.com/keras-team/keras/tree/v2.14.0/keras/losses.py#L1064-L1070)

    get_config()

Returns the config dictionary for a `Loss` instance.

### `__call__`

[View source](https://github.com/keras-team/keras/tree/v2.14.0/keras/losses.py#L102-L163)

    __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 ||
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| 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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