TensorFlow:是否有度量标准来计算和更新前k个精度? [英] TensorFlow: Is there a metric to calculate and update top k accuracy?

问题描述

当前的tf.contrib.metrics.streaming_accuracy仅能计算前1个精度，而不是前k个.作为解决方法，这是我一直在使用的:

The current tf.contrib.metrics.streaming_accuracy is only able to calculate the top 1 accuracy, and not the top k. As a workaround, this is what I've been using:

tf.reduce_mean(tf.cast(tf.nn.in_top_k(predictions=predictions, targets=labels, k=5), tf.float32))

但是，这并没有给我一种方法来计算每个批次的平均流精度，这对于获得稳定的评估准确性很有用.我目前正在使用numpy输出手动计算流式前5位的准确性，但这意味着我将无法在张量板上可视化该指标.

However, this does not give me a way to calculate the streaming accuracies averaged across each batch, which would be useful in getting a stable evaluation accuracy. I am currently manually calculating this streaming top 5 accuracy through using its numpy output, but this means I won't be able to visualize this metric on tensorboard.

是否可以通过创建precision_update函数来实现更简单的实现，或者是否已有已经执行此操作的函数?

Is there a way to have a simpler implementation by creating an accuracy_update function, or is there an existing function that already does this?

谢谢.

推荐答案

您可以将tf.contrib.metrics.streaming_accuracy的使用替换为较低级别的

You could replace your use of tf.contrib.metrics.streaming_accuracy by the lower-level tf.metrics.mean, which is by the way ultimately used by streaming_accuracy -- you will find a similarity in their respective documentations.

例如(未经测试)

tf.metrics.mean(tf.nn.in_top_k(predictions=predictions, targets=labels, k=5))

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