Keras vs. TensorFlow: Which one is the right fit for your Project?

Gaurav Roy CTO, Masters | BS-Cyber-Sec | MIT | LPU

9 min readDec 30, 2021

Machine Learning and Deep Learning have experienced unusual tours from bust to boom from the last decade. Simmering in research labs, these two verticals of artificial intelligence became a savior for many companies. Machine learning talks about the different approaches and algorithms to parse data and learn from them through various guises.

Machine learning became even more popular as its algorithm started to predict accurately. It helped companies understand their requirements and have also been able to gather insights from the collected data. Then Deep Learning came into the picture as a sub-domain of machine learning. What Deep Learning can do is even more fascinating.

Let’s get familiar with Machine Learning and Deep learning a little more

Deep Learning algorithms can imitate the working of the human brain. It creates patterns and feeds on data to make machines eligible to reap decisions on their own. Deep learning renders various types of AI functions that mimic the brain functionality, called artificial neural networks. Deep learning techniques leverage machine learning models with artificial intelligence to create neural nets. These networks can perform unsupervised learning from unstructured,semi-structured, and unlabelled data. As deep learning algorithms & models create deep networks mimicking the brain, it has an alternative name, deep neural learning.

Figure: Deep Neural Network Architecture

Nowadays, Deep Learning is almost everywhere. From virtual assistants in your smartphones and smart home devices to visual recognition, fraud detectors, self-driving cars, healthcare equipment, everything utilizes Deep Learning algorithms and methods.2016 was the year when Deep Learning showed some significant advancement, and since then, it all set on fuel and fire. By the end of 2022, Gartner predicts that more than 75% of enterprises and firms will start implementing DNNs cultivating classical ML techniques. (Source: Gartner:https://www.gartner.com/smarterwithgartner/gartner-predicts-the-future-of-ai-technologies/) There are a lot of Deep Learning libraries available. Therefore, it becomes difficult for beginners to choose from among them. This article will talk about two popular deep learning frameworks. You might have understood by now. Also, this article will dive deep into the differences between Keras and TensorFlow.

What is TensorFlow?

It is the most popular Deep Learning library that helps engineers, Deep neural scientists, and others to create deep learning algorithms and models. Google Brain team is the brainchild behind this open-source library that leverages dataflow programmers to deal with numerical computation & large-scale supervised and unsupervised learning. TensorFlow clusters together machine learning and deep learning models and renders them through large datasets to train these models to think and create sensible outcomes on their own. Developers and engineers use Python to implement this library, plus creating a suitable front-end for using the framework. The design and purpose of TensorFlow are mainly to run deep neural networks and train machines to learn and make prompt decisions. Companies also use TensorFlow for image recognition, hand-written character classification, recurrent neural networks, word embeddings, NLP for teaching machines to understand human languages, sequence-to-sequence models for machine translation, and PDE (partial differential equation) simulations. TensorFlow also helps in sales analysis and predict production units required at scale. Medical science and healthcare devices using AI also leverage TensorFlow to determine accurate solutions.

What is Keras?

Deep neural learning has been in a rage since 2017. With its growth, the complexity of all the dominant frameworks became a barrier for data science and machine learning engineers. Developers and engineers put forward many proposals for a simplified yet high-level API for building large neural networks and models. After going through a long research and adaptation phase, Keras became the choice of high-level neural network. Keras is an open-source deep neural network library developed by François Chollet, who is a Google engineer. He designed it to be fast, easy to implement, and modular by nature. François created Keras using Python that runs on top of Theano. Since then, Keras got adopted as the high-level API for developing deep learning algorithms. It also helps in the rapid prototyping of deep neural models.

Salient Features of TensorFlow and Keras –

Keras got developed on top of TensorFlow, so you might think both will have the same features. But that’s not the case. Let us now dig into the various characteristics of Keras and TensorFlow one by one.

Salient Features of Keras –

Here is the list of various features offered by Keras.

i. Keras is an API-based tool that is way more Pythonic.

ii. Modularity is a significant feature of Keras.

iii. User experience and smooth production of deep learning models are its key focus.

iv. Modeling and creating a deep neural network is easy yet robust.

v. It supports rapid and easy prototyping of models.

vi. Keras is a high-level API that supports multi-platform and multi-backend integrations.

vii. It supports the creation of recurrent and convolutional neural networks.

viii. Keras is flexible and hence, preferred in different domains like healthcare, corporate insights, sales predictions, customer support, virtual assistants, etc.

ix. Keras is expressive. Therefore, enterprises and research organizations use it for various research purposes.

x. Keras got developed from Python itself. Therefore, it is easy to explore, debug, and integrate.

xi. It helps in the rapid experimentation of projects that offer fast market-ready projects.

Salient Features of TensorFlow –

Here is the list of various features offered by TensorFlow.

i. It has extensive community support with developers.

ii. It supports customized and high-ordered gradients.

iii. It allows fast debugging via Python tools.

iv. It provisions different levels of extraction that can simulate human brain neural models.

v. It can also allow building and training complex neural models that can make decisions based on ML algorithms.

vi. It has several dynamic models that use Python to control the flow.

vii. It also has the flexibility to work with various other deep learning libraries and frameworks.

viii. It can also operate with Keras Functional API.

ix. It has well-written documentation of what to use. It makes developers inclined towards using this.

x. TensorFlow can uphold and integrate powerful add-on predefined models and libraries into its ecosystem.

xi. Integrating TensorFlow with other data science and machine learning libraries is easy. It is the reason why developers are finding comfort in using it.

Difference between Keras and TensorFlow

Although Keras can afford developers to use all the general-purpose deep learning operations and functionalities, it cannot provide as much as TensorFlow does. It is because Keras runs on top of TensorFlow. TensorFlow extends with its most advanced form of deep learning functions and operations. These operations become handy and beneficial while performing thorough research & development on new and exceptional deep neural models.

Here is a graph showing the Google Trends between Keras and TensorFlowwhen both came in their booming form (2016–2018 vs. Now).

Let us now take a comparative scenario to understand the differences between Keras and TensorFlow.

Keras vs. TensorFlow (GitHub Popularity)

Keras vs. TensorFlow from the Career perspective –

If you are planning to learn and implement deep learning models quickly, you should go with Keras. If you are into intense research and want to proceed with Deep learning innovative projects having large datasets, TensorFlow is for you. To choose between Keras and TensorFlow entirely tenants upon the features, functionalities, and tasks the frameworks can perform. Researchers, engineers, and data scientists have to pick their frameworks as per the requirements and demands of the project and its client. Therefore, learning both of them is a plus point.

Let us now witness the stat report of different online job platforms showing career opportunities.

Benefits of TensorFlow and Keras –

Here are some of the benefits of Keras.

· Using this, developers can minimize the number of user actions; so that the firm can deliver the prototype quickly.

· It can render actionable feedback as and when the user makes an error.

· It helps to produce a frequent, simplified, and optimized user interface for general use-cases.

· It helps in developing state-of-art models and creating new metrics and layers.

· Developers can deploy Keras on a wide range of devices.

· It is easy to learn and use.

· We can use Keras with Raspberry Pi and Android systems also.

Here are some of the benefits of Tensorflow.

· It can cater to as well as train models in live projects. It can serve these models as and when required.

· TensorFlow can utilize both GPU and CPU for training and modeling acceleration.

· It supports automated differentiation capabilities. It helps in gradient-based ML modeling.

· It aids developers in performing sub-part of a graph or neural network that helps in retrieving discrete data.

· Its compilation time is way faster than other deep learning libraries and frameworks.

Drawbacks of Keras and TensorFlow –

Here are some of the shortcomings of Keras.

· Keras is slow in executing and training deep learning models.

· It has fewer projects available online than that of TensorFlow.

· It has a complex architecture.

· Although it supports multi-GPU, it cannot utilize all of them.

· Sometimes it spontaneously shows low-level backend errors that are hard to debug.

Here are some of the shortcomings of TensorFlow

· Although TensorFlow is efficient, it does not render much speed as compared to other deep learning frameworks.

· It does not support Nvidia GPU.

· Working with TensorFlow is lengthy because developers need to know linear algebra and advanced calculus.

· Because of its low-level API structure, the learning curve of TensorFlow is steep.

· It has missing symbolic loops.

· It does not support OpenCL.

Suitability Aspects of Keras and TensorFlow –

Here are some of the points mentioned that show where Keras proves to be better than TensorFlow.

· Provides versatile backend support

· Accelerated prototyping and market-ready samples

· Projects with small datasets

· Beginner-friendly projects

Again, there are situations listed that show where TensorFlow proves better than Keras.

· Renders heavy projects with ease

· Can easily handle projects with a large data set

· Better suited for object detection

· Broad-spectrum of functionalities

Wrapping Up –

All the technological advancements and the industry future are moving towards automation. Deep learning is playing a significant role in taking control over various aspects like industrial sectors and research. Right from online shopping, booking tickets, or sending money over the internet, every vertical of technology is leveraging Machine learning and Deep learning models. It helps machines get a better insight to understand human interactions with the technology and take prompt decisions accordingly. Both Keras and TensorFlow have the potential to help developers work on deep learning projects, but this article showed some crisp distinctions between both of them. There is a formula that is popular among the developers -

tf.keras + tf (TensorFlow) = All you need to prosper in the Deep learning market

So, it is always beneficial to learn both. But, choosing from these two depends on the situation or the project you are into entirely. If you are looking for premade projects and want to re-use them, ProjectPro is the best provider for this. We save developers time and money by giving them reusable project solutions. ProjectPro caters to a vast collection of projects on Data Science and Big Data.