Inspired by the job-interview with Joel Grus, the goal of this challenge is to write a tensorflow (or other deep/machine learning) program that learns Fizzbuzz and correctly prints out the answers to the positive integers less than 1000.
You can assume there are files named
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.
- 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.
- You must utilize
train.csvin the training set and check your output against
test.csv. You cannot use
- 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).
- 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.
- This is a popularity contest, so the submission with the most votes after one week wins.