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Inspired by the [job-interview with Joel Grus](http://joelgrus.com/2016/05/23/fizz-buzz-in-tensorflow/), the goal of this challenge is to write a [tensorflow](https://www.tensorflow.org/) (or other deep/machine learning) program that *learns* [Fizzbuzz](https://en.wikipedia.org/wiki/Fizz_buzz#Programming_interviews) and correctly prints out the answers to the positive integers less than 1000. 
You can assume there are files named `train.csv` and `test.csv` and each contain a sorted list of sequential integers and the fizzbuzz answer:
```
 ...
 100, buzz
 101, 101
 102, fizz
 103, 103
 104, 104
 105, buzz
 ...
 150000, fizzbuzz
```
`test.csv` spans 1-1000 and `train.csv` spans 1001-150000.
## Rules
1. You must not hard-code the rules to Fizzbuzz anywhere in your program. The output must be from a machine learned representation that is learned *while running the code*.
2. You must utilize `train.csv` in the training set and check your output against `test.csv`. You cannot use `test.csv` during training.
3. You must get all outputs correct from `test.csv` (but as is case with deep-learning, we'll allow your code to fail this rule no more than 5% of the time).
4. You may use any language and any external module (eg. python/tensorflow) as long they explicitly perform some kind of learning. Reference both the language and the module in the title of your post.
5. This is a popularity contest, so the submission with the most votes after one week wins.
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