FScoreLoss
- class ketos.neural_networks.dev_utils.losses.FScoreLoss(beta=1.0, **kwargs)[source]
Loss function based on the inverse of F-Score.
When instantiated, the resulting loss function expects the predictions in the ‘y_pred’ argument and the true labels in the ‘y_true’ argument.
- Args:
- beta:float
The relative weight of recall in relation to precision.
- Examples:
If beta = 1.0, recall has same weight as precision If beta = 0.5, recall has half the weight of precision If beta = 2.0, recall has twice the weight of precision
Methods
call
(y_true, y_pred)Invokes the Loss instance.
- call(y_true, y_pred)[source]
Invokes the Loss instance.
- Args:
- 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]
- Returns:
Loss values with the shape [batch_size, d0, .. dN-1].