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?
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.
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
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
Here, are important differences between Keras and Tensorflow
|Keras is a high-level API which is running on top of TensorFlow, CNTK, and Theano.||TensorFlow is a framework that offers both high and low-level APIs.|
|Keras is easy to use if you know the Python language.||You need to learn the syntax of using various Tensorflow function.|
|Perfect for quick implementations.||Ideal for Deep learning research, complex networks.|
|Uses another API debug tool such as TFDBG.||You can use Tensor board visualization tools for debugging.|
|It started by François Chollet from a project and developed by a group of people.||It was developed by the Google Brain team.|
|Written in Python, a wrapper for Theano, TensorFlow, and CNTK||Written mostly in C++, CUDA, and Python.|
|Keras has a simple architecture that is readable and concise.||Tensorflow is not very easy to use.|
|In the Keras framework, there is a very less frequent need to debug simple networks.||It is quite challenging to perform debugging in TensorFlow.|
|Keras is usually used for small datasets.||TensorFlow used for high-performance models and large datasets.|
|Community support is minimal.||It is backed by a large community of tech companies.|
|It can be used for low-performance models.||It is use for high-performance models.|
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.
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
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.
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
Here, are some criteria which help you to select a specific framework:
|Development purpose||Library to Choose|
|You are a Ph.D. student||TensorFlow|
|You want to use Deep Learning to get more features||Keras|
|You work in an industry||TensorFlow|
|You have just started your 2-month internship||Keras|
|You want to give practice works to students||Keras|
|You don’t even know Python||Keras|
- Keras is a high-level API which is running on top of TensorFlow, CNTK, and Theano whereas TensorFlow is a framework that offers both high and low-level APIs.
- Keras is perfect for quick implementations while Tensorflow is ideal for Deep learning research, complex networks.
- Keras uses API debug tool such as TFDBG on the other hand, in, Tensorflow you can use Tensor board visualization tools for debugging.
- Keras has a simple architecture that is readable and concise while Tensorflow is not very easy to use.
- Keras is usually used for small datasets but TensorFlow used for high-performance models and large datasets.
- In Keras, community support is minimal while in TensorFlow It is backed by a large community of tech companies.
- Keras can be used for low-performance models whereas TensorFlow can be use for high-performance models.