Warning: This project is deprecated. TensorFlow Addons has stopped development, The project will only be providing minimal maintenance releases until May 2024. See the full announcement here or on github.

# tfa.losses.SigmoidFocalCrossEntropy

Implements the focal loss function.

Focal loss was first introduced in the RetinaNet paper (https://arxiv.org/pdf/1708.02002.pdf). Focal loss is extremely useful for classification when you have highly imbalanced classes. It down-weights well-classified examples and focuses on hard examples. The loss value is much higher for a sample which is misclassified by the classifier as compared to the loss value corresponding to a well-classified example. One of the best use-cases of focal loss is its usage in object detection where the imbalance between the background class and other classes is extremely high.

#### Usage:

````fl = tfa.losses.SigmoidFocalCrossEntropy()`
`loss = fl(`
`    y_true = [[1.0], [1.0], [0.0]],y_pred = [[0.97], [0.91], [0.03]])`
`loss`
`<tf.Tensor: shape=(3,), dtype=float32, numpy=array([6.8532745e-06, 1.9097870e-04, 2.0559824e-05],`
`dtype=float32)>`
```

Usage with `tf.keras` API:

````model = tf.keras.Model()`
`model.compile('sgd', loss=tfa.losses.SigmoidFocalCrossEntropy())`
```

`alpha` balancing factor, default value is 0.25.
`gamma` modulating factor, default value is 2.0.

Weighted loss float `Tensor`. If `reduction` is `NONE`, this has the same shape as `y_true`; otherwise, it is scalar.

`ValueError` If the shape of `sample_weight` is invalid or value of `gamma` is less than zero.

## Methods

### `from_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

Returns the config dictionary for a `Loss` instance.

### `__call__`

Invokes the `Loss` instance.

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.)

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.

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