Keras vs Tensorflow: Must Know Differences!

What is Tensor flow?

TensorFlow is an open-source deep learning library that is developed and maintained by Google. It offers dataflow programming which performs a range of machine learning tasks. It was built to run on multiple CPUs or GPUs and even mobile operating systems, and it has several wrappers in several languages like Python, C++, or Java.

In this tutorial, you will learn:

• What is Tensor flow?
• What is Keras?
• Features of Tensorflow
• Features of Keras
• Difference Between TensorFlow and Keras
• Advantages of Tensor flow
• Advantages of Keras
• Disadvantages of Tensor flow
• Disadvantages of Keras
• Which framework to select?

What is Keras?

KERAS is an Open Source Neural Network library written in Python that runs on top of Theano or Tensorflow. It is designed to be modular, fast and easy to use. It was developed by François Chollet, a Google engineer. It is a useful library to construct any deep learning algorithm.

Features of Tensorflow

Here are important features of Tensorflow:

• Faster debugging with Python tools
• Dynamic models with Python control flow
• Support for custom and higher-order gradients
• TensorFlow offers multiple levels of abstraction, which helps you to build and train models.
• TensorFlow allows you to train and deploy your model quickly, no matter what language or platform you use.
• TensorFlow provides the flexibility and control with features like the Keras Functional API and Model
• Well-documented so easy to understand
• Probably the most popular easy to use with Python

Features of Keras

Here are important features of Keras:

• Focus on user experience.
• Multi-backend and multi-platform.
• Easy production of models
• Allows for easy and fast prototyping
• Convolutional networks support
• Recurrent networks support
• Keras is expressive, flexible, and apt for innovative research.
• Keras is a Python-based framework that makes it easy to debug and explore.
• Highly modular neural networks library written in Python
• Developed with a focus on allows on fast experimentation

Difference Between TensorFlow and Keras

Here, are important differences between Kera and Tensorflow

Advantages of Tensor flow

Here, are pros/benefits of Tensor flow

• Offers both Python and API's that makes it easier to work on
• Should be used to train and serve models in live mode to real customers.
• The TensorFlow framework supports both CPU and GPU computing devices
• It helps us execute subpart of a graph which helps you to retrieve discrete data
• Offers faster compilation time compared to other deep learning frameworks
• It provides automatic differentiation capabilities that benefit gradient-based machine learning algorithms.

Advantages of Keras

Here, are pros/benefits of Keras:

• It minimizes the number of user actions need for frequent use cases
• Provide actionable feedback upon user error.
• Keras provides a simple, consistent interface optimized for common use cases.
• It helps you to write custom building blocks to express new ideas for research.
• Create new layers, metrics, and develop state-of-the-art models.
• Offer an easy and fast prototyping

Disadvantages of Tensor flow

Here, are cons/drawbacks of using Tensor flow:

• TensorFlow does not offer speed and usage compared to other python frameworks.
• No GPU support for Nvidia and only language support:
• You need a fundamental knowledge of advanced calculus and linear algebra, along with an experience of machine learning.
• TensorFlow has a unique structure, so it's challenging to find an error and difficult to debug.
• It is a very low level as it offers a steep learning curve.

Disadvantages of Keras

Here, are cons/drawback of using Keras framework

• It is a less flexible and more complex framework to use
• No RBM (Restricted Boltzmann Machines) for example
• Fewer projects available online than TensorFlow
• Multi-GPU, not 100% working

Which framework to select?

Here, are some criteria which help you to select a specific framework: