实体消歧训练时间

[sleepsophia]

请问实体消歧的训练,大约需要多久

[panchunguang]

一个模型20多小时 在 2019-10-12 14:15:02，"sleepsophia" <notifications@github.com> 写道： 请问实体消歧的训练,大约需要多久 — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub, or unsubscribe.

[zhangyanbo2007]

本人基本跑通，有需要交流加我15821444815，谢谢！

[Lanme]

@panchunguang 实体消歧的训练，metric_f1=nan？应该是里面的pred_num=0

[Jonny-Smith-GitHub]

@panchunguang 实体消歧的训练，metric_f1=nan？应该是里面的pred_num=0

您好，我也遇到了这个问题，找了一下代码，是metric_f1函数，但想请教一个问题：这个函数需要传两个参数y_pred和y_true，而在调用处，没有传参数就调用了，有点不太理解。

[YanqiangWang]

@panchunguang 实体消歧的训练，metric_f1=nan？应该是里面的pred_num=0

from keras import backend as K
def metrics_f1(y_true, y_pred):
    def recall(y_true, y_pred):
        """Recall metric.
        Only computes a batch-wise average of recall.
        Computes the recall, a metric for multi-label classification of
        how many relevant items are selected.
        """
        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
        possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
        recall = true_positives / (possible_positives + K.epsilon())
        return recall
 
    def precision(y_true, y_pred):
        """Precision metric.
        Only computes a batch-wise average of precision.
        Computes the precision, a metric for multi-label classification of
        how many selected items are relevant.
        """
        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
        predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
        precision = true_positives / (predicted_positives + K.epsilon())
        return precision
    precision = precision(y_true, y_pred)
    recall = recall(y_true, y_pred)
    return 2*((precision*recall)/(precision+recall+K.epsilon()))

使用这个代码计算f1值，把原代码中的metrics_f1方法替换掉，就不会出现nan的问题了