基于预测值的Keras自定义召回指标 [英] Keras custom recall metric based on predicted values
问题描述
我想在keras中实现一个自定义指标,假设最高k%的最可能y_pred_probs
是真实的,从而计算召回率.
I would like to implement a custom metric in keras that calculates the recall assuming that the top k% most probable y_pred_probs
's are true.
在numpy
中,我将按以下步骤进行操作.对y_preds_probs进行排序.然后取第c2个索引处的值.注意k=0.5
将给出中间值.
In numpy
I would do it as follows. Sort the y_preds_probs. Then take the value at the k
th index. Note k=0.5
would give the median value.
kth_pos = int(k * len(y_pred_probs))
threshold = np.sort(y_pred_probs)[::-1][kth_pos]
y_pred = np.asarray([1 if i >= threshold else 0 for i in y_pred_probs])
答案来自:精确度和召回率的Keras自定义决策阈值非常接近,但是假定用于确定哪个y_pred
被假定为真的阈值是已知的.我想将这些方法结合起来,并在可能的情况下实现基于Keras后端中的k
和y_pred
查找阈值.
The answer from: Keras custom decision threshold for precision and recall is quite close but assumes that the threshold for deciding which y_pred
's are assumed true is already known. I would like to combine the approaches and implement finding the threshold_value based on k
and y_pred
's in Keras backend if possible.
def recall_at_k(y_true, y_pred):
"""Recall metric.
Computes the recall over the whole batch using threshold_value from k-th percentile.
"""
###
threshold_value = # calculate value of k-th percentile of y_pred here
###
# 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
推荐答案
感谢您引用我之前的回答.
Thanks for citing my previous answer.
在这种情况下,如果您正在使用tensorflow后端,我建议您使用此
In this case, if you are using tensorflow backend, I would suggest you to use this tensorflow function :
tf.nn.in_top_k(
predictions,
targets,
k,
name=None
)
它输出一个布尔张量,如果答案属于前k个,则输出张量;如果答案不属于前k,则输出一个张量.
It outputs a tensor of bools, 1 if the answer belongs to top k and 0 if it doesn't.
如果您需要更多信息,我已经链接了tensorflow文档.希望对您有所帮助. :-)
If you need more info, I have linked the tensorflow documentation. I hope it helps. :-)
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