tf.contrib.learn.BaseEstimator.evaluate（）：“步骤”的含义是什么？参数？ [英] tf.contrib.learn.BaseEstimator.evaluate(): What is the meaning of the "steps" parameter?

问题描述

根据 https：//www.tensorflow。 org / versions / r0.9 / api_docs / python / contrib.learn.html 中，tf.contrib.learn.BaseEstimator.evaluate函数可以采用steps参数。该参数的解释如下：

According to https://www.tensorflow.org/versions/r0.9/api_docs/python/contrib.learn.html, the tf.contrib.learn.BaseEstimator.evaluate function can take in a steps parameter. The parameter is explained as follows:

steps：要评估模型的步骤数。如果为None，则永远评估
。

steps: Number of steps for which to evaluate model. If None, evaluate forever.

如何进行评估？以我的理解，训练过的模型应该只评估一次（即step = 1），然后针对目标标签计算损失，对吧？

How can evaluation have steps? In my understanding, a trained model should be "evaluated" only once (i.e. steps=1), and then calculate the loss against the target labels, right?

谢谢！

推荐答案

您还可以在生成数据的函数上运行经过训练的模型。在这种情况下，您可以生成无限量的数据，并且不想永远运行评估。 （您也可以拥有大量数据，并且批次数量较小，因此再次花费的时间太长）。无论哪种情况，您都只想对一部分人口样本评估模型。

You can also run the trained model on a function that generates data. In which case you can generate an infinite amount of data and don't want to run your evaluation forever. (You can also have a lot of data and small batch size so again it'll take too long). In either case you want to only evaluate the model for some sample of the population.

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