Loss Functions and Metrics
Losses for imbalanced classes
- class atomai.losses_metrics.focal_loss(alpha=0.5, gamma=2, with_logits=True)[source]
Loss function for classification tasks with large data imbalance. Focal loss (FL) is define as: FL(p_t) = -alpha*((1-p_t)^gamma))*log(p_t), where p_t is a cross-entropy loss for binary classification. For more details, see https://arxiv.org/abs/1708.02002.
- Parameters
alpha (float) – “balance” coefficient,
gamma (float) – “focusing” parameter (>=0),
with_logits (bool) – indicates if the sigmoid operation was applied at the end of a neural network’s forward path.
- class atomai.losses_metrics.dice_loss(eps=1e-07)[source]
Computes the Sørensen–Dice loss. Adapted with changes from https://github.com/kevinzakka/pytorch-goodies
Metics for imbalanced classes
- class atomai.losses_metrics.IoU(true, pred, activation=True, thresh=0.5)[source]
Computes mean of the Intersection over Union. Adapted with changes from https://github.com/kevinzakka/pytorch-goodies
- Parameters
true (
Tensor
) – labels (aka ground truth)pred (
Tensor
) – model predictionsactivation (
bool
) – applies softmax/sigmoid to predictionsthresh (
float
) – image binary threshold level for predictions