tf.keras.metrics.categorical_focal_crossentropy

Computes the categorical focal crossentropy loss.

View aliases

Main aliases

tf.keras.losses.categorical_focal_crossentropy, tf.losses.categorical_focal_crossentropy, tf.metrics.categorical_focal_crossentropy

Compat aliases for migration

See Migration guide for more details.

`tf.compat.v1.keras.losses.categorical_focal_crossentropy`, `tf.compat.v1.keras.metrics.categorical_focal_crossentropy`

tf.keras.metrics.categorical_focal_crossentropy(
    y_true,
    y_pred,
    alpha=0.25,
    gamma=2.0,
    from_logits=False,
    label_smoothing=0.0,
    axis=-1
)

Standalone usage:

y_true = [[0, 1, 0], [0, 0, 1]]
y_pred = [[0.05, 0.9, 0.05], [0.1, 0.85, 0.05]]
loss = tf.keras.losses.categorical_focal_crossentropy(y_true, y_pred)
assert loss.shape == (2,)
loss.numpy()
array([2.63401289e-04, 6.75912094e-01], dtype=float32)

Args
y_true	Tensor of one-hot true targets.
y_pred	Tensor of predicted targets.
alpha	A weight balancing factor for all classes, default is 0.25 as mentioned in the reference. It can be a list of floats or a scalar. In the multi-class case, alpha may be set by inverse class frequency by using compute_class_weight from sklearn.utils.
gamma	A focusing parameter, default is 2.0 as mentioned in the reference. It helps to gradually reduce the importance given to simple examples in a smooth manner. When gamma = 0, there is no focal effect on the categorical crossentropy.
from_logits	Whether y_pred is expected to be a logits tensor. By default, we assume that y_pred encodes a probability distribution.
label_smoothing	Float in [0, 1]. If > 0 then smooth the labels. For example, if 0.1, use 0.1 / num_classes for non-target labels and 0.9 + 0.1 / num_classes for target labels.
axis	Defaults to -1. The dimension along which the entropy is computed.

Returns
Categorical focal crossentropy loss value.