Sparse implementation of Focal Loss.

Inherits From: FocalLoss

This is the same as FocalLoss, except the labels are expected to be class ids instead of 1-hot encoded vectors. See FocalLoss class documentation defined in this same file for more details.

Example usage:

y_true = [1, 2]
y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]
gamma = 2
focal_loss = SparseFocalLoss(gamma, 3)
focal_loss(y_true, y_pred).numpy()
# Calling with 'sample_weight'.
focal_loss(y_true, y_pred, sample_weight=tf.constant([0.3, 0.7])).numpy()

gamma Focal loss gamma, as described in class docs.
num_classes Number of classes.
class_weight A weight to apply to the loss, one for each class. The weight is applied for each input where the ground truth label matches.



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

config Output of get_config().

A Loss instance.


Returns the config dictionary for a Loss instance.


View source

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.