如果在二进制交叉熵中猜测不正确，则加权样本

Question

keras 或 tensorflow 中是否有办法在仅对错误分类的样本赋予额外权重的情况下。 IE。类权重和样本权重的组合，但仅将样本权重应用于二元类中的一个结果？

Answer 1

是的，这是可能的。您可以在下面找到一个示例，说明如何在 true positives、false positives、truenegatives 等上添加额外权重：

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