9:00 – BRC
- Installed following TF installation guide.
- Found issues with the install instructions almost immediately. Found this link with a suggestion that I followed to get it installed.
- Almost immediately found that the Hello World example succeeded with a list of errors. Apparently its a known issue for the release candidate which was just fixed in the nightly build as per this link.
- I haven’t had a chance to try it yet, but found a good Reddit link for a brief TF tutorial.
- I went through the process of trying to get my IntelliJ project to connect and be happy with the Python interpreter in my Anaconda install, and although I was able to RUN the TF tutorials, it was still acting really wacky for features like code completion. Given Phil was able to get up and running with no problems doing a direct pip install to local Python, I scrapped my intent to run through Anaconda and did the local install. Tada! Everything is working fine now.
- Unsupervised Learning (Clustering)
- Our plan is to implement our unsupervised learning for the IH customer in an automated fashion by writing a MR app dispatched by MicroService that populates a Protobuf matrix for TensorFlow.
- The trick about this is that there is no built in density-based clustering algorithm native for TF like the DBSCAN we used on last sprint’s deliverable. TF supports K-Means “out of the box” but with the high number of dimensions in our data set this isn’t ideal. Here is a great article explaining why.
- However, one possible method of successfully utilizing K-Means (or improving the scalability of DBSCAN is to convert our high dimensional data to polar coordinates. We’ll be investigating this once we’ve comfortable with TensorFlow’s matrix math operations.
- Proposal Work
- Spent a fun hour of my day converting a bunch of content from previous white-papers and RFI documents into a one-page write-up of our Cognitive Computing capabilities. Ironically the more we have to write these the easier it gets because I’ve already written it all before. Also more importantly as time goes by more and more of the content describes things we’ve actually done instead of things we have in mind to do.