Understanding Tensor Flow Input Pipelines Part 1

TFLogo

Hello World!

Alright; so this whole input pipeline thing in pretty much every framework is the most undocumented thing in the universe.  So this article is about demystifying it.  We can break down the process into a few key steps:

  1. Acquire & Label Data
  2. Process Label Files for Record Conversions
  3. Process Label Files for Training a Specific Network Interface
  4. Train the Specific Network Interface

This is part 1.  We will focus on the 3rd item in this list; processing the files into TF Records.  Note you can find more associated code in the TensorFlow section of this git repository: https://github.com/drcrook1/CIFAR10

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Docker, Tensor Flow and Scientific Computing

Hello World,

So this blog post is to get you operational with Docker, image and volume management with a pivot towards scientific computing and tensor flow.  So I am working on building a Jupyter Notebook for the local mahcine learning meetup to learn the ins and outs of Tensor Flow and deploy this thing up to Azure.  Part of getting this to work is not only managing the Docker Containers, but also the data on the volumes so when we deploy up to Azure and somebody opens up the notebook it comes pre-loaded with all the necessary tutorial data.

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