- 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…
Continue reading “TensorFlow or Keras? Which one should I learn? – Imploding Gradients – Medium”
- Keras and TensorFlow are the state of the art in deep learning tools and with the keras package you can now access both with a fluent R interface.
- To prepare this data for training we one-hot encode the vectors into binary class matrices using the Keras function:
The core data structure of Keras is a model, a way to organize layers.
- We begin by creating a sequential model and then adding layers using the pipe ( ) operator:
The argument to the first layer specifies the shape of the input data (a length 784 numeric vector representing a grayscale image).
- Use the function to print the details of the model:
Next, compile the model with appropriate loss function, optimizer, and metrics:
Use the function to train the model for 30 epochs using batches of 128 images:
The object returned by includes loss and accuracy metrics which we can plot:
Evaluate the model’s performance on the test data:
Keras provides a vocabulary for building deep learning models that is simple, elegant, and intuitive.
- After you’ve become familiar with the basics, these articles are a good next step:
Keras provides a productive, highly flexible framework for developing deep learning models.
We are excited to announce that the keras package is now available on CRAN. The package provides an R interface to Keras, a high-level neural networks API developed with a focus on enabling fast experimentation. Keras has the following key features:
Continue reading “Keras for R”