使用tf。Keras中的指标?

我发现了一个相关的问题 github ,看来 tf.metrics Keras模型仍然不支持。但是,如果您对使用 tf.metrics.specificity_at_sensitivity ,我建议以下解决方法(灵感来自 BogdanRuzh's 解决方案):

def specificity_at_sensitivity(sensitivity, **kwargs):
    def metric(labels, predictions):
        # any tensorflow metric
        value, update_op = tf.metrics.specificity_at_sensitivity(labels, predictions, sensitivity, **kwargs)

        # find all variables created for this metric
        metric_vars = [i for i in tf.local_variables() if 'specificity_at_sensitivity' in i.name.split('/')[2]]

        # Add metric variables to GLOBAL_VARIABLES collection.
        # They will be initialized for new session.
        for v in metric_vars:
            tf.add_to_collection(tf.GraphKeys.GLOBAL_VARIABLES, v)

        # force to update metric values
        with tf.control_dependencies([update_op]):
            value = tf.identity(value)
            return value
    return metric


model.compile(loss='mean_squared_error',
              optimizer='sgd',
              metrics=[metrics.mae,
                       metrics.categorical_accuracy,
                       specificity_at_sensitivity(0.5)])

更新:

你可以用 model.evaluate 在培训后检索指标。

我不认为只有两个传入参数有严格的限制。py函数只有三个传入参数,但k选择默认值5。

def sparse_top_k_categorical_accuracy(y_true, y_pred, k=5):
    return K.mean(K.in_top_k(y_pred, K.cast(K.max(y_true, axis=-1), 'int32'), k), axis=-1)