This open source project is aimed to provide simple and ready-to-use tutorials for TensorFlow. The explanations are present in the wiki associated with this repository. Each tutorial has a source code and its documetation.
This is a great start, and I find your code extremely easy to read.
I might suggest some example code for more advanced methods of data ingestion. All the code I see uses feed_dict, which is certainly the most straightforward, but once you start to try to ingest larger out-of-core datasets or use distributed tf, things get exceptionally complicated (to me at least).
Some simple-ish code with queue runners and the new Dataset API would really be helpful.
Thank you so much for your feedback ... We will post very soon about the queue runners. However, since this open source project tries to provide simple codes, in the beginning, our emphasis is one feed_dict simple method although it is suboptimal.
However, the example code here is amongst the cleanest I've seen.
Try googling how to do simple regressions, and run some of that code. I think there is one other program out of the dozens out there that come anywhere close to this level of clarity.
In fact, I think you may have used that as a starting point, in which case extra kudos for going out and seeing the state of the art.
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[ 5.2 ms ] story [ 38.8 ms ] threadI might suggest some example code for more advanced methods of data ingestion. All the code I see uses feed_dict, which is certainly the most straightforward, but once you start to try to ingest larger out-of-core datasets or use distributed tf, things get exceptionally complicated (to me at least).
Some simple-ish code with queue runners and the new Dataset API would really be helpful.
However, the example code here is amongst the cleanest I've seen.
Try googling how to do simple regressions, and run some of that code. I think there is one other program out of the dozens out there that come anywhere close to this level of clarity.
In fact, I think you may have used that as a starting point, in which case extra kudos for going out and seeing the state of the art.