tfa.losses.WeightedKappaLoss

Implements the Weighted Kappa loss function.

tfa.losses.WeightedKappaLoss(
    num_classes: int,
    weightage: Optional[str] = 'quadratic',
    name: Optional[str] = 'cohen_kappa_loss',
    epsilon: Optional[Number] = 1e-06,
    reduction: str = tf.keras.losses.Reduction.NONE
)

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.

Usage:

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)
loss
<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 ...>

Args
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.

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

Methods

from_config

@classmethod
from_config(
    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

get_config()

Returns the config dictionary for a Loss instance.

__call__

__call__(
    y_true, y_pred, sample_weight=None
)

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 ondN-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.