evaluation_metrics.py 文件源码

def precision_keras(y_true, y_pred):
    #convert probas to 0,1
    y_pred_ones = K.zeros_like(y_true)
    y_pred_ones[:, K.argmax(y_pred, axis=-1)] = 1

    #where y_ture=1 and y_pred=1 -> true positive
    y_true_pred = K.sum(y_true*y_pred_ones, axis=0)

    #for each class: how many where classified as said class
    pred_cnt = K.sum(y_pred_ones, axis=0)

    #precision for each class
    precision = K.switch(K.equal(pred_cnt, 0), 0, y_true_pred/pred_cnt)

    #return average f1 score over all classes
    return K.mean(precision)