精密Keras定制判决门限和召回

Question

我正在使用Keras（与Tensorflow后端）的二元分类，我已经得到了约76％的精度和70％的召回。现在我想尝试玩决定门槛。据我所知Keras使用决策阈值0.5。 Keras有没有办法使用自定义阈值进行决策精确度和召回？

谢谢你的时间！

Answer 1

创建自定义指标是这样的：

编辑感谢@Marcin：创建功能与threshold_value为参数

def precision_threshold(threshold=0.5): 
    def precision(y_true, y_pred): 
     """Precision metric. 
     Computes the precision over the whole batch using threshold_value. 
     """ 
     threshold_value = threshold 
     # Adaptation of the "round()" used before to get the predictions. Clipping to make sure that the predicted raw values are between 0 and 1. 
     y_pred = K.cast(K.greater(K.clip(y_pred, 0, 1), threshold_value), K.floatx()) 
     # Compute the number of true positives. Rounding in prevention to make sure we have an integer. 
     true_positives = K.round(K.sum(K.clip(y_true * y_pred, 0, 1))) 
     # count the predicted positives 
     predicted_positives = K.sum(y_pred) 
     # Get the precision ratio 
     precision_ratio = true_positives/(predicted_positives + K.epsilon()) 
     return precision_ratio 
    return precision 

def recall_threshold(threshold = 0.5): 
    def recall(y_true, y_pred): 
     """Recall metric. 
     Computes the recall over the whole batch using threshold_value. 
     """ 
     threshold_value = threshold 
     # Adaptation of the "round()" used before to get the predictions. Clipping to make sure that the predicted raw values are between 0 and 1. 
     y_pred = K.cast(K.greater(K.clip(y_pred, 0, 1), threshold_value), K.floatx()) 
     # Compute the number of true positives. Rounding in prevention to make sure we have an integer. 
     true_positives = K.round(K.sum(K.clip(y_true * y_pred, 0, 1))) 
     # Compute the number of positive targets. 
     possible_positives = K.sum(K.clip(y_true, 0, 1)) 
     recall_ratio = true_positives/(possible_positives + K.epsilon()) 
     return recall_ratio 
    return recall 

现在你可以在

model.compile(..., metrics = [precision_threshold(0.1), precision_threshold(0.2),precision_threshold(0.8), recall_threshold(0.2,...)]) 

使用这些返回所需的指标

我希望这有助于:)