Artificial intelligence (AI) and machine learning (ML) have continued to gain lots of attention. Not only in the technology industry but also other industries are now paying more and more attention. Though these two fields share many similarities, there have been debates. The concern is on whether they are two sides of the same coin or manifest different concepts.
Google developed TensorFlow, in 2011 without using JavaScript. It is one of the popular frameworks among developers.
In March 2018, Google launched TensorFlow.js to perform ML and deep learning (DL) in JavaScript. This is without using server-side applications.
TensorFlow.js and Keras are both Machine learning libraries. Machine learning utilizes various models and calculations to respond to specific data queries.
Machine learning actions are as follows;
- Speech recognition
- Detecting spam emails
- Identify objects via computer vision
Machine learning calculations are very complex. This creates the need for machine learning libraries to navigate them. Library examples include; TensorFlow, Keras, sciKit learn, Theano, and Microsoft Cognitive Toolkit (CNTK)
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What Is Tensor Flow?
TensorFlow is an open-source deep learning software library developed and maintained by Google. The TensorFlow library provides a flexible numerical computation system. This system is programmable to execute a variety of machine learning algorithms. The design for TensorFlow is to deploy computations on many CPUs or GPUs and mobile operating systems with one API. It has many wrappers in different languages such as Python, C++, and Java.
Some popular companies that use TensorFlow are Uber Technologies, 9GAG, and StyleShare Inc.
What is TensorFlow.js?
TensorFlow js is an open-source tool that has 11.2K GitHub stars and 816 GitHub forks. Js tensorflow boosts hardware acceleration with its complete and flexible selection of tools. It has flexible and intuitive APIs to build and train models from scratch. It uses the low-level JavaScript machine learning library or the high-level layers API.
Nowadays, TensorFlow.js features Machine Learning-based JavaScript projects. The presence of its deep learning layers and comprehensive linear algebra make these possible.
TensorFlow provides better computational graph visualizations. It also offers extra benefits like frequent new releases, quick updates, and seamless performance. TensorFlow.javascript comes with some pre-existing machine learning models, which can re-train their data. It brings on the ability to deploy the machine learning models on any device platform, regardless of the language and location, whether on-premises, on the browser, or the cloud.
TensorFlow vs. TensorFlow.js
Both TensorFlow and TensorFlow.js identify as Machine Learning tools.
In comparison, TensorFlow has a broader approval than TensorFlow.javascript. This is because TensorFlow has mentions in 200 company stacks & 135 developer stacks. TensorFlow.js lists in 5 company stacks and three developer stacks.
TensorFlow.js is highly parallel and designed for use together with many back-end software like ASIC and GPU.
TensorFlow.js is a version of TensorFlow. A few variations are:
- TensorFlow Extended
- TensorFlow Lite for mobile devices
- TensorFlow Rust for Rust bindings
TensorFlow.js Node is a repository that provides TensorFlow execution in back-end JavaScript applications under the Node.js runtime. Node js machine learning supports Linux, Mac, Windows CPU, and GPU.
Key Features of TensorFlow
- Quick debugging with Python tools
- Has dynamic models with Python control flow
- Provides support for custom and higher-order gradients
- TensorFlow has many levels of abstraction to assist in building and training models.
- Regardless of the programming language or platform used, TensorFlow allows users to train and deploy models fast.
- TensorFlow has features such as Keras Functional API and Model to provide flexibility and control.
- It’s well-documented, relatively easy to understand, and easy to use with tensorflow js python
Benefits of TensorFlow
1. More Functionality
TensorFlow’s advance is specifically in high-level operations such as debugging, threading, and queues.
2. It’s Powerful!
TensorFlow is great for building anything revolving around processing data by running a series of mathematical operations. It’s useful in making neural nets.
3. It’s Versatile!
TensorFlow allows complete control over the model and the preprocessing logic as well. The TensorFlow Transform enables the user to define preprocessing pipelines.
It supports full passes over the data to ensure large-scale, efficient, and distributed data processing.
Additional benefits include:
- Large if not largest community of ML developers, researchers, and Tech companies.
- Thorough documentation and guidelines.
- Visualization with TensorBoard simplifies model design and debugging.
- Simplified model design and debugging by the use of Tensorflow Lite. It makes deployments on mobile and edge devices possible.
- js enables deployments in tensorflow JavaScript environments.
- TensorFlow supports distributed computing.
- TensorFlow serving provides a high-performance serving system for machine learning models. It’s flexible and designed for production environments.
- A static computation graph provides the ability to run on different devices (CPU / GPU / TPU), and the performance is great. It helps to execute subpart of a graph to retrieve discrete data
- It offers both Python and API’s, making it easier to work on
Use Cases of Tensorflow
- Building a custom model from scratch can take some time. One quicker alternative for inference is to import a pre-trained model.
- They can convert offline TensorFlow.js formats into TensorFlow or Keras models, and then use them for inference once loaded onto the browser.
- Refining an imported copy, or re-training a model.
- On TensorFlow, it’s possible to transfer learning to augment an existing model trained offline.
- Through a technique called Image Retraining, the transfer uses a small amount of data collected in the browser. It makes it possible to train an accurate model quickly, using little data.
- Create models directly in the browser.
- TensorFlow.js can define, train, and run models entirely in the browser using Javascript and a high-level layers API.
Disadvantages of TensorFlow
High barrier to entry for beginners
It has a complicated system design, with over 1 million lines of source code on GitHub. This makes it difficult to understand the framework entirely.
Frequently changed APIs
Tensorflow’s API iterates rapidly, and backward compatibility has not gone through proper evaluation. As a result, many open-sourced projects are becoming incompatible with the latest version of TensorFlow.
Provides a variety of implementations for the same functionality,
These numerous options make it hard for users to make a choice.
It has no GPU support for Nvidia and only has language support
The user must have a fundamental knowledge of advanced calculus, linear algebra, and some machine learning experience.
What is Keras?
Keras is a high-level neural network Application Programming Interface (API) written in Python. It has 11.2K GitHub stars and 816 GitHub forks and wraps around the functionalities of other ML and DL libraries, including TensorFlow, Theano, and CNTK.
The design is modular, user-friendly, and extensible.
Keras serves as a library to construct deep learning algorithms because it provides fast experimentation with deep neural networks.
It only handles high-level computations and hands over any low-level computations to the back end. An example of back-end software is ASIC or GPU.
Even though Keras is now adopted and integrated into TensorFlow in 2017, the Keras library can operate separately and independently.
Features of Keras
- It focuses on user experience.
- Has multi-backend options and is multi-platform.
- Easy production of models
- Facilitates easy and fast prototyping
- Has recurrent, convolutional network support
- Keras is expressive, flexible, and suited for innovative research.
- It is a Python-based framework and hence easy to debug and explore.
- Has highly modular neural networks library written in Python
- Its development focuses on allowing fast experimentation
Benefits of Keras
- Keras makes navigating TensorFlow easier, resulting in fewer models offering the wrong conclusions.
- Keras is modular and user-friendly, making it easy to experiment with deep neural networks.
- Keras builds and trains neural networks, therefore is great for fast prototyping, quality research, and production
- Keras makes it possible to get clear, actionable feedback for most errors. This is because the consistent UX in Keras is simple and optimized for use cases.
- In modular composition, Keras’ models have few restrictions when connecting configurable building blocks
- Highly flexible and extendable. Using Keras, it’s possible to write custom blocks for new research and create new layers, loss functions, metrics, and whole models.
- Keras minimizes the cognitive load and reduces the number of user actions.
- Modules on Keras; Neural layers, functions, schemes, and optimizers are all independent, making it possible to combine them to create new models.
- Keras works with Python
Disadvantages of Keras
Since Keras runs on top of TensorFlow, CNTK, and Theano, it functions more like a deep learning interface rather than a deep learning framework. The excess packaging doesn’t allow much flexibility.
Keras has a layer-by-layer encapsulation to provide a consistent user interface and condense the many different back-ends. This makes it hard for users to add new operations or change the underlying architecture of the model.
Keras encapsulation slows down the execution process, and this can hide potential bugs.
Comparison of Individual Capabilities Between TensorFlow and Keras
Definition
TensorFlow is an open-source math software library used for dataflow programming or machine learning applications such as neural networks.
Keras is an open-source neural network library written in Python, designed to facilitate fast experimentation with deep neural networks. It can run on top of TensorFlow.
Origin
TensorFlow library is a product by the Google Brain team and a free software library. The library design is to be open-source and can be in Python, C++, and CUDA (Nvidia’s language for programming GPUs)
Francois Chollet, a Google engineer, developed Keras. He based it on four principles; Modularity, Minimalism, Extensibility, and Python.
It’s a Python library for deep learning and can run on top of Theano, TensorFlow, and Microsoft Cognitive Toolkit (CNTK)
Speed
TensorFlow is fast and best suited for high performance. Keras’ performance is lower than TensorFlow.
Level of API
- Keras is a high-level API and is popular because it’s easy to use, has syntactic simplicity, and enables fast development.
- The TensorFlow framework provides both high-level and low-level APIs.
- TensorFlow.js gets power from WebGL and offers a high-level layer API for defining models and a low-level API for linear algebra and automatic differentiation.
- TensorFlow.js allows importing TensorFlow SavedModels and Keras models.
Architecture
- Keras has pure, concise architecture and is more readable.
- TensorFlow can be challenging to use but provides Keras a framework that makes work easier.
Debugging
- Keras has simple networks that require minimal debugging. On TensorFlow, debugging is a bit difficult.
Dataset
- TensorFlow fits for use in high-performance models and extensive datasets that need fast execution. Keras is slower and suitable for small datasets.
Popularity
- Following the rising growth in the deep learning industry, Keras is more popular than TensorFlow because of its simple design.
Uses
- Keras is best suited for rapid prototyping, small datasets, and multiple back-end support
- TensorFlow is preferable for large datasets, high performance, functionality, and visual object detection
Key Differences at A Glance
- Keras is a high-level API running on top of TensorFlow, CNTK, and Theano, but TensorFlow is a framework that provides both high and low-level APIs.
- Keras is suitable for quick implementations, while Tensorflow is perfect for Deep learning research and complex networks.
- Keras uses API debug tools like TFDBG, while Tensorflow allows the use of Tensor board visualization tools for debugging.
- Keras has a simple, concise, readable architecture, but Tensorflow is complex to use.
- Keras works best for small datasets, but TensorFlow is perfect for high-performance models and large datasets.
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