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. As there is a famous saying, “the larger, the better.” But when it comes to large data sets, determining insights from them through deep learning algorithms and mining them becomes tricky. The two most popular deep learning frameworks that machine learning and deep learning engineers prefer are TensorFlow and Keras. Since the initial release of Keras and TensorFlow in the year 2015, both became the most widely-known Deep learning frameworks. Both these frameworks are easy to use and have simpler APIs than their predecessors. Both Keras and TensorFlow help developers increase the functionality across data and more control over training machine learning models. .The bar graph shows the growth of both Keras and TensorFlow 3 years after its initial release.
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 and models create deep networks mimicking the brain, it has an alternative name, deep neural learning.
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 has all been 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 machine learning techniques. There are a lot of deep learning frameworks available. Therefore, it becomes difficult for beginners to choose between the two. In this article, we will highlight some of the important differences between Keras and Tensorflow to help you make the right choice for your use case.
Keras vs Tensorflow – A Quick Overview
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.
Keras vs TensorFlow – Features Comparison
Keras was 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.
Key Features of Keras
- Keras is an API-based tool that is way more Pythonic.
- Modularity is a significant feature of Keras.
- User experience and smooth production of deep learning models are its key focus.
- Modeling and creating a deep neural network is easy yet robust.
- It supports rapid and easy prototyping of models.
- Keras is a high-level API that supports multi-platform and multi-backend integrations.
- It supports the creation of recurrent and convolutional neural networks.
- Keras is flexible and hence, preferred in different domains like healthcare, corporate insights, sales predictions, customer support, virtual assistants, etc.
- Keras is expressive. Therefore, enterprises and research organizations use it for various research purposes.
- Keras got developed from Python itself. Therefore, it is easy to explore, debug, and integrate.
- It helps in the rapid experimentation of projects that offer fast market-ready projects.
Key Features of Tensorflow
- It has extensive community support with developers.
- It supports customized and high-ordered gradients.
- It allows fast debugging via Python tools.
- It provisions different levels of extraction that can simulate human brain neural models.
- It can also allow building and training complex neural models that can make decisions based on ML algorithms.
- It has several dynamic models that use Python to control the flow.
- It also has the flexibility to work with various other deep learning libraries and frameworks.
- It can also operate with Keras Functional API.
- It has well-written documentation of what to use. It makes developers inclined towards using this.
- TensorFlow can uphold and integrate powerful add-on predefined models and libraries into its ecosystem.
- Integrating TensorFlow with other data science and machine learning libraries is easy. It is the reason why developers are finding comfort in using it.
Keras vs Tensorflow – Exploring the Advantages
Advantages of using 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.
Advantages of using Tensorflow
- 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.
Keras vs Tensorflow – The Shortcomings Unleashed
Disadvantages 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.
Disadvantages 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.
Keras vs Tensorflow -Which one to Choose When?
Keras proves to be better than Tensorflow in –
- Providing versatile backend support
- Accelerated prototyping and market-ready samples
- Working with Beginner-friendly projects with small datasets.
There are circumstances where TensorFlow proves better than Keras –
- Rendering heavy projects with ease
- Ease of handling projects with large data sets
- Better suited for object detection
- Offers Broad-spectrum of functionalities
Keras vs Tensorflow – GitHub Popularity
Keras vs. TensorFlow- The Job Market
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 of the project. Therefore, learning both of them is a plus point. Let us now witness the stat report of different online job platforms showing career opportunities.
Tensorflow vs Keras – What’s the Difference?
Although Keras provides 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 TensorFlow –
Keras vs Tensorflow -Side-by-Side Comparison
| KERAS | TENSORFLOW |
| Keras has a high-level API architecture. | TensorFlow has a low-level API architecture. |
| It runs on top of Theano, CNTK (Microsoft Cognitive Toolkit), and TensorFlow. | TensorFlow got created using the C++ language and Compute Unified Device Architecture (CUDA). |
| It is the best option for rapid prototyping and fast implementation of any deep learning project. | It is an ideal solution for deep learning research, new experimentation, and creating complex neural networks. |
| Keras has become a handy tool for Python developers or those who already know Python. | TensorFlow is slightly time-consuming to learn because it has distinct syntaxes that one has to learn. |
| Keras is user-friendly and easy to understand. | TensorFlow is comparatively challenging to comprehend. |
| Using Keras, developers can render radio prototyping efficiently. | TensorFlow does not support feasible radio prototyping. |
| François Chollet started developing Keras, and then the community took it further. | The Google Brain team developed TensorFlow. |
| The internal architecture of Keras is simple and easy to comprehend. | The internal architecture of TensorFlow is complex and hard to comprehend. |
| Keras provides easy debugging options. | Debugging becomes lengthy when it comes to TensorFlow. |
| Keras does not come with an additional debugger. | TensorFlow comes with a specialized debugger. |
| Keras is useful when the project has small datasets. | TensorFlow turns out to be a robust framework when the project demands high-performance models with large datasets. |
| Keras is an API-based abstraction layer that uses TensorFlow as its backend. | TensorFlow is efficient, but because large datasets feed into it might make it slow. |
| Keras uses TensorFlow as its underlying engine, hence fast. | TensorFlow is faster when the model is in the training process. |
| Keras supports Python with the interface of the R language. | TensorFlow supports C++, Java, Python, JavaScript, Go, and Swift. |
| Keras is beginner-friendly. | TensorFlow is not a beginner’s friendly framework. |
| The primary purpose of Keras is to create a quick prototype and is slower as compared to TensorFlow. | TensorFlow is competent enough to apply in large projects. |
| Companies using Keras are Google, Nvidia, Apple, Amazon, Microsoft, Uber, Netflix, etc. | Companies using TensorFlow are Google, Qualcomm, AMD, LinkedIn, Bloomberg, Snapchat, PayPal, Airbnb, etc. |
| Companies like Google and Apple use Keras to create efficient models, prototypes, and deep learning backend engines. | Tech and fintech companies, along with financial institutions, utilize TensorFlow to enhance their fraud detection systems. To make efficient hardware architecture, processors, chipsets, and models, Snapdragon uses deep learning models created using TensorFlow. |
| arXiv is a research paper submission portal on the internet. According to this, Keras did not receive that much research popularity and mentions. | On the other hand, the study shows that many researchers are using TensorFlow for writing research papers & creating innovative projects. It got more mentions than Keras. |
Keras vs Tensorflow – A Battle of the Best
All the technological advancements 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 industry.
So, it is always beneficial to learn both. But, choosing from these two depends on the situation or the project you are working on. If you are looking for reusable projects using Keras and Tensorflow, ProjectPro has 100+ solved end-to-end big data and data science projects that come with solution code, explanatory videos, documentation, downloadable datasets, and 24×7 support.
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