# tfa.losses.PinballLoss

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

`loss = maximum(tau * (y_true - y_pred), (tau - 1) * (y_true - y_pred))`

In the context of regression, this loss yields an estimator of the tau conditional quantile.

#### Usage:

``````pinball = tfa.losses.PinballLoss(tau=.1)
loss = pinball([0., 0., 1., 1.], [1., 1., 1., 0.])

# loss = max(0.1 * (y_true - y_pred), (0.1 - 1) * (y_true - y_pred))
#      = (0.9 + 0.9 + 0 + 0.1) / 4

print('Loss: ', loss.numpy())  # Loss: 0.475
``````

Usage with the `compile` API:

``````model = tf.keras.Model(inputs, outputs)
model.compile('sgd', loss=tfa.losses.PinballLoss(tau=.1))
``````

`tau` (Optional) Float in [0, 1] or a tensor taking values in [0, 1] and shape = `[d0,..., dn]`. It defines the slope of the pinball loss. In the context of quantile regression, the value of tau determines the conditional quantile level. When tau = 0.5, this amounts to l1 regression, an estimator of the conditional median (0.5 quantile).
`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.

`fn` The loss function to wrap, with signature ```fn(y_true, y_pred, **kwargs)```.
`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 this custom training tutorial for more details.
`name` (Optional) name for the loss.
`**kwargs` The keyword arguments that are passed on to `fn`.

## 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.