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 $ [-\infty, \log 2] $, where $ \log 2 $ means the random prediction.


kappa_loss = tfa.losses.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)
<tf.Tensor: shape=(), dtype=float32, numpy=-1.1611925>

Usage with tf.keras API:

model = tf.keras.Model()
model.compile('sgd', loss=tfa.losses.WeightedKappaLoss(num_classes=4))

<... outputs should be softmax results if you want to weight the samples, just multiply the outputs by the sample weight ...>

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'.
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 $ lies in $ [-1, 1] $. Defaults to 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']



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

config Output of get_config().

A Loss instance.


View source

Returns the config dictionary for a Loss instance.


Invokes the Loss instance.

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 ondN-1: all loss functions reduce by 1 dimension, usually axis=-1.)

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.)

ValueError If the shape of sample_weight is invalid.