如何为Keras神经网络创建特异性自定义指标

Question

我在这里找到了这个功能 How to calculate F1 Macro in Keras?，但我不确定如何以相同的方式写出特异性？我正在为keras使用tensorflow后端。

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

我尝试了此解决方案，但它给出了错误，

def compute_binary_specificity(y_pred, y_true):
    """Compute the confusion matrix for a set of predictions.
    Returns
    -------
    out : the specificity
    """
    TN = np.logical_and(K.eval(y_true) == 0, K.eval(y_pred) == 0)
    FP = np.logical_and(K.eval(y_true) == 0, K.eval(y_pred) == 1)
    # as Keras Tensors
    TN = K.sum(K.variable(TN))
    FP = K.sum(K.variable(FP))
    specificity = TN / (TN + FP + K.epsilon())
    return specificity

错误：InvalidArgumentError：您必须使用dtype float和shape [？，140]输入占位符张量'dense_95_input'的值      [[[Node：density_95_input = Placeholderdtype = DT_FLOAT，shape = [?, 140]，_device =“ / job：localhost /副本：0 / task：0 / device：CPU：0”]]

并指向此处
    ---> TN = np.logical_and（K.eval（y_true）== 0，K.eval（y_pred）== 0）