Visualizing TensorFlow Graphs in Jupyter Notebooks

Visualizing #TensorFlow Graphs in #Jupyter Notebooks  #dataviz #DataVisualization #Python

  • TensorFlow operations form a computation graph.
  • Visualizing the graph can help both in diagnosing issues with the computation itself, but also in understanding how certain operations in TensorFlow work and how are things put together.
  • We’ll take a look at a few different ways of visualizing TensorFlow graphs, and most importantly, show how to do it in a very simple and time-efficient way.
  • Now onto the specifics, we’ll take a look at the following visualization techniques:

    First, let us create a simple TensorFlow graph.

  • Regular operations such as creating a placeholder with tf.placeholder will create a node in the so called default graph.

Prerequisites: This article assumes you are familiar with the basics of Python, TensorFlow, and Jupyter notebooks. We won’t use any of the advanced TensorFlo…
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  • The tensorflow package will be built against the default version of python found in the PATH .
  • The tensorflow package provides access to the complete TensorFlow API from within R.
  • The tensorflow package provides code completion and inline help for the TensorFlow API when running within the RStudio IDE.
  • The TensorFlow API is composed of a set of Python modules that enable constructing and executing TensorFlow graphs.

tensorflow – TensorFlow for R
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