# tfa.losses.WeightedKappaLoss

Implements the Weighted Kappa loss function.

Weighted Kappa loss was introduced in the Weighted kappa loss function for multi-class classification of ordinal data in deep learning. Weighted Kappa is widely used in Ordinal Classification Problems. The loss value lies in [-inf, log 2], where log 2 means the random prediction.

#### Usage:

``````kappa_loss = WeightedKappaLoss(num_classes=4)
y_true = tf.constant([[0, 0, 1, 0], [0, 1, 0, 0],
[1, 0, 0, 0], [0, 0, 0, 1]])
y_pred = tf.constant([[0.1, 0.2, 0.6, 0.1], [0.1, 0.5, 0.3, 0.1],
[0.8, 0.05, 0.05, 0.1], [0.01, 0.09, 0.1, 0.8]])
loss = kappa_loss(y_true, y_pred)
print('Loss: ', loss.numpy())  # Loss: -1.1611923
``````

Usage with `tf.keras` API:

``````# outputs should be softmax results
# if you want to weight the samples, just multiply the outputs
# by the sample weight.
model = tf.keras.Model(inputs, outputs)
model.compile('sgd', loss=tfa.losses.WeightedKappa(num_classes=4))
``````

`num_classes` Number of unique classes in your dataset.
`weightage` (Optional) Weighting to be considered for calculating kappa statistics. A valid value is one of ['linear', 'quadratic']. Defaults to `quadratic` since it's mostly used.
`name` (Optional) String name of the metric instance.
`epsilon` (Optional) increment to avoid log zero, so the loss will be log(1 - k + epsilon), where k belongs to [-1, 1], usually you can use the default value which is 1e-6.
`dtype` (Optional) Data type of the metric result. Defaults to `tf.float32`.

`ValueError` If the value passed for `weightage` is invalid i.e. not any one of ['linear', 'quadratic']

## 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.