Weight samples if incorrect guessed in binary cross entropy

Question

Is there a way in keras or tensorflow to give samples an extra weight if they are incorrectly classified only. Ie. a combination of class weight and sample weight but only apply the sample weight for one of the outcomes in a binary class?

Answer 1

Yes, it's possible. Below you may find an example of how to add additional weight on true positives , false positives , true negatives, etc:

def reweight(y_true, y_pred, tp_weight=0.2, tn_weight=0.2, fp_weight=1.2, fn_weight=1.2):
    # Get predictions
    y_pred_classes = K.greater_equal(y_pred, 0.5)
    y_pred_classes_float = K.cast(y_pred_classes, K.floatx())

    # Get misclassified examples
    wrongly_classified = K.not_equal(y_true, y_pred_classes_float)
    wrongly_classified_float = K.cast(wrongly_classified, K.floatx())

    # Get correctly classified examples
    correctly_classified = K.equal(y_true, y_pred_classes_float)
    correctly_classified_float = K.cast(wrongly_classified, K.floatx())

    # Get tp, fp, tn, fn
    tp = correctly_classified_float * y_true
    tn = correctly_classified_float * (1 - y_true)
    fp = wrongly_classified_float * y_true
    fn = wrongly_classified_float * (1 - y_true)

    # Get weights
    weight_tensor = tp_weight * tp + fp_weight * fp + tn_weight * tn + fn_weight * fn

    loss = K.binary_crossentropy(y_true, y_pred)
    weighted_loss = loss * weight_tensor
    return weighted_loss