创建这样的自定义指标：
感谢@Marcin进行编辑 ：创建返回带有
threshold_valueas参数的所需度量的函数
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 precisiondef 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
现在您可以在其中使用它们
model.compile(..., metrics = [precision_threshold(0.1), precision_threshold(0.2),precision_threshold(0.8), recall_threshold(0.2,...)])
我希望这有帮助 ：）
