metric_binary_focal_crossentropy {keras3} | R Documentation |
Computes the binary focal crossentropy loss.
Description
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]
.
Usage
metric_binary_focal_crossentropy(
y_true,
y_pred,
apply_class_balancing = FALSE,
alpha = 0.25,
gamma = 2,
from_logits = FALSE,
label_smoothing = 0,
axis = -1L
)
Arguments
y_true |
Ground truth values, of shape |
y_pred |
The predicted values, of shape |
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 |
gamma |
A focusing parameter, default is |
from_logits |
Whether |
label_smoothing |
Float in |
axis |
The axis along which the mean is computed. Defaults to |
Value
Binary focal crossentropy loss value
with shape = [batch_size, d0, .. dN-1]
.
Examples
y_true <- rbind(c(0, 1), c(0, 0)) y_pred <- rbind(c(0.6, 0.4), c(0.4, 0.6)) loss <- loss_binary_focal_crossentropy(y_true, y_pred, gamma=2) loss
## tf.Tensor([0.32986466 0.20579838], shape=(2), dtype=float64)
See Also
Other losses:
Loss()
loss_binary_crossentropy()
loss_binary_focal_crossentropy()
loss_categorical_crossentropy()
loss_categorical_focal_crossentropy()
loss_categorical_hinge()
loss_cosine_similarity()
loss_ctc()
loss_dice()
loss_hinge()
loss_huber()
loss_kl_divergence()
loss_log_cosh()
loss_mean_absolute_error()
loss_mean_absolute_percentage_error()
loss_mean_squared_error()
loss_mean_squared_logarithmic_error()
loss_poisson()
loss_sparse_categorical_crossentropy()
loss_squared_hinge()
loss_tversky()
metric_binary_crossentropy()
metric_categorical_crossentropy()
metric_categorical_focal_crossentropy()
metric_categorical_hinge()
metric_hinge()
metric_huber()
metric_kl_divergence()
metric_log_cosh()
metric_mean_absolute_error()
metric_mean_absolute_percentage_error()
metric_mean_squared_error()
metric_mean_squared_logarithmic_error()
metric_poisson()
metric_sparse_categorical_crossentropy()
metric_squared_hinge()
Other metrics:
Metric()
custom_metric()
metric_auc()
metric_binary_accuracy()
metric_binary_crossentropy()
metric_binary_iou()
metric_categorical_accuracy()
metric_categorical_crossentropy()
metric_categorical_focal_crossentropy()
metric_categorical_hinge()
metric_cosine_similarity()
metric_f1_score()
metric_false_negatives()
metric_false_positives()
metric_fbeta_score()
metric_hinge()
metric_huber()
metric_iou()
metric_kl_divergence()
metric_log_cosh()
metric_log_cosh_error()
metric_mean()
metric_mean_absolute_error()
metric_mean_absolute_percentage_error()
metric_mean_iou()
metric_mean_squared_error()
metric_mean_squared_logarithmic_error()
metric_mean_wrapper()
metric_one_hot_iou()
metric_one_hot_mean_iou()
metric_poisson()
metric_precision()
metric_precision_at_recall()
metric_r2_score()
metric_recall()
metric_recall_at_precision()
metric_root_mean_squared_error()
metric_sensitivity_at_specificity()
metric_sparse_categorical_accuracy()
metric_sparse_categorical_crossentropy()
metric_sparse_top_k_categorical_accuracy()
metric_specificity_at_sensitivity()
metric_squared_hinge()
metric_sum()
metric_top_k_categorical_accuracy()
metric_true_negatives()
metric_true_positives()