Tensorflow——使用tf。总结1.2估计器API-Tensorflow-Usingtf.summarywith1.2EstimatorAPI

4  

Just for whoever have this question in the future, the selected solution doesn't work for me (see my comments in the selected solution).

只是对于将来有这个问题的人来说，所选的解决方案对我不起作用(请参阅我在所选解决方案中的评论)。

Actually, with TF 1.2 Estimator API, one doesn't need to have summary_hook. I just have tf.summary.scalar("loss", loss) in the model_fn, and run the code without summary_hook. The loss is recorded and shown in the tensorboard. I'm not sure if TF API was changed after this and similar questions.

实际上，使用TF 1.2 Estimator API，不需要使用summary_hook。我只有tf.summary。在model_fn中，标量(“丢失”，丢失)，并运行代码，而不使用摘要挂钩。损失被记录并显示在租船板上。我不确定TF API是否在这个问题和类似的问题之后被修改。

5  

Use tf.train.Scaffold() and pass tf.merge_all as following

使用tf.train. scaffold()并通过tf。merge_all如下

summary_hook = tf.train.SummarySaverHook(
    save_secs=2,
    output_dir=MODEL_DIR,
    scaffold=tf.train.Scaffold(summary_op=tf.summary.merge_all()))

0  

with Tensorflow ver-r1.3

与Tensorflow ver-r1.3

Add your summary ops in your estimator model_fn

在估计器model_fn中添加汇总操作

example :

例子:

tf.summary.histogram(tensorOp.name, tensorOp)

If you feel writing summaries may consume time and space, you can control the writing frequency of summaries, in your Estimator run_config

如果您认为编写摘要可能会消耗时间和空间，那么可以在估计器run_config中控制编写摘要的频率

run_cOnfig= tf.contrib.learn.RunConfig()
run_cOnfig= run_config.replace(model_dir=FLAGS.model_dir)
run_cOnfig= run_config.replace(save_summary_steps=150)

Note: this will affect the overall summary writer frequency for TensorBoard logging, of your estimator (tf.estimator.Estimator)

注意:这将影响您的估计器(tf.estimator.Estimator)的TensorBoard日志记录的总编写者频率。