请教训练过程中的一些结果

[aagq]

您好，如图所示，训练前几个Batch有metric-f1值，之后变nan，产生这样的结果正常吗？

[yumi-di]

同问

[YanqiangWang]

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

[Kevin9780]

请问一下您是如何获取data/entity_id.pkl、data/id_entity.pkl等文件的呢，我跑代码跑不了显示缺文件。