TensorFlow or Keras? Which one should I learn? – Imploding Gradients – Medium

#TensorFlow or #Keras? Which one should I learn?

  • With plenty of libraries out there for deep learning, one thing that confuses a beginner in this field the most is which library to choose.Deep Learning libraries/frameworks as per popularity(Source : Google)In this blog post, I am only going to focus on Tensorflow and Keras.
  • And if Keras is more user-friendly, why should I ever use TF for building deep learning models?
  • You can tweak TF much more as compared to Keras.FunctionalityAlthough Keras provides all the general purpose functionalities for building Deep learning models, it doesn’t provide as much as TF.
  • Absolutely, check the example below:Playing with gradients in TensorFlow (Credits : CS 20SI: TensorFlow for Deep Learning Research)Conclusion (TL;DR)if you are not doing some research purpose work or developing some special kind of neural network, then go for Keras (trust me, I am a Keras fan!!)
  • But as we all know that Keras is going to be integrated in TF, it is wiser to build your network using tf.contrib.Keras and insert anything you want in the network using pure TensorFlow.

Deep learning is everywhere. 2016 was the year where we saw some huge advancements in the field of Deep Learning and 2017 is all set to see many more advanced use cases. With plenty of libraries out…

TensorFlow or Keras? Which one should I learn?Deep learning is everywhere. 2016 was the year where we saw some huge advancements in the field of Deep Learning and 2017 is all set to see many more advanced use cases. With plenty of libraries out there for deep learning, one thing that confuses a beginner in this field the most is which library to choose.Deep Learning libraries/frameworks as per popularity(Source : Google)In this blog post, I am only going to focus on Tensorflow and Keras. This will give you a better insight about what to choose and when to choose either. Tensorflow is the most famous library used in production for deep learning models. It has a very large and awesome community. The number of commits as well the number of forks on TensorFlow Github repository are enough to define the wide-spreading popularity of TF (short for TensorFlow). However TensorFlow is not that easy to use. On the other hand, Keras is a high level API built on TensorFlow (and can be used on top of Theano too). It is more user-friendly and easy to use as compared to TF.If Keras is built on top of TF, what’s the difference between the two then? And if Keras is more user-friendly, why should I ever use TF for building deep learning models? The following points will clarify which one you should choose.Rapid prototypingIf you want to quickly build and test a neural network with minimal lines of code, choose Keras. With Keras, you can build…

TensorFlow or Keras? Which one should I learn? – Imploding Gradients – Medium