tf.keras.losses.binary_focal_crossentropy

Computes the binary focal crossentropy loss.

View aliases

Main aliases

tf.keras.metrics.binary_focal_crossentropy

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

According to Lin et al., 2018, it helps to apply a focal factor to down-weight easy examples and focus more on hard examples. By default, the focal tensor is computed as follows:

focal_factor = (1 - output) ** gamma for class 1 focal_factor = output ** gamma for class 0 where gamma is a focusing parameter. When gamma = 0, there is no focal effect on the binary crossentropy loss.

If apply_class_balancing == True, this function also takes into account a weight balancing factor for the binary classes 0 and 1 as follows:

weight = alpha for class 1 (target == 1) weight = 1 - alpha for class 0 where alpha is a float in the range of [0, 1].

Args
y_true	Ground truth values, of shape (batch_size, d0, .. dN).
y_pred	The predicted values, of shape (batch_size, d0, .. dN).
apply_class_balancing	A bool, whether to apply weight balancing on the binary classes 0 and 1.
alpha	A weight balancing factor for class 1, default is 0.25 as mentioned in the reference. The weight for class 0 is 1.0 - alpha.
gamma	A focusing parameter, default is 2.0 as mentioned in the reference.
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 by squeezing them towards 0.5, that is, using 1. - 0.5 * label_smoothing for the target class and 0.5 * label_smoothing for the non-target class.
axis	The axis along which the mean is computed. Defaults to -1.

Returns
Binary focal crossentropy loss value with shape = [batch_size, d0, .. dN-1].

Example:

y_true = [[0, 1], [0, 0]]
y_pred = [[0.6, 0.4], [0.4, 0.6]]
loss = keras.losses.binary_focal_crossentropy(
       y_true, y_pred, gamma=2)
assert loss.shape == (2,)
loss
array([0.330, 0.206], dtype=float32)