The Ins and Outs of TensorFlow 2.0

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The Ins and Outs of TensorFlow 2.0 
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• In January 2019, Google officially announced
  that the new iteration of TensorFlow will be
  available in September 2019. Since the
  announcement of this news, there were umpteen
  questions popping on the minds of TensorFlow
  developers. Some were wondering that the latest
  iteration TensorFlow 2.0 would have a large jump
  similar to AngularJS v2 vs AngularJS v1. While
  others were thinking that the graphs would work
  properly in TensorFlow 2.0. 
• In this informative piece, we will walk through
  the changes that are worth noticing in TensorFlow
  2.0. Lets get started. 

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Noticing in TensorFlow 2.0

• Clean API
• Complete Control Over Variables
• Graph Mode Functions

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Clean API

• TensorFlow 2.0 has been redesigned and in the new
  iteration many APIs have shifted to separate
  repositories or removed including tf.app,
  tf.gans, tf.flags, tf.contrib, and tf.logging.
  While some APIs of TensorFlow 1.0 have been
  replaced with their TensorFlow 2.0 counterparts
  such as tf.keras.optimizers, tf.keras.metrics,
  and tf.summary. In TensorFlow 2.0, tf.keras is an
  advanced API that is strongly recommended. 

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Complete Control Over Variables

• In TensorFlow 1.0, data scientists used to keep a
  tab on the variables for future use. In other
  words, the older version banks heavily upon
  implicit global namespaces. So, if you were not
  present during the initial development stage, you
  would struggle to recover something that you
  never knew existed. However, TensorFlow 2.0 has
  enabled you to overcome all such hassles. 
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Graph Mode Functions

• The tf.function() in TensorFlow 2.0 enables data
  scientists to execute functions as a single
  graph. This operation allows TF 2.0 to leverage
  the benefits of graph mode such as optimized
  functions for kernel fusion or node pruning,
  resulting in enhanced portability of functions -
  import or export. 
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Wrapping Up

• This informative piece walks you through the ins
  and outs of the latest iteration TensorFlow 2.0.
  It comes with many advanced features with a focus
  on simplicity, developer productivity, and ease
  of use. Though in this piece, we discussed only a
  few of them. Hope you find them useful. If you
  want to make the most of TensorFlow 2.0, its
  wise to hire TensorFlow developers from a trusted
  partner. 

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