Keras vs TensorFlow.js

Overview

Keras

Stacks1.1K

Followers1.1K

Votes22

TensorFlow.js

Stacks184

Followers378

Votes18

GitHub Stars19.0K

Forks2.0K

Keras vs TensorFlow.js: What are the differences?

## Introduction
This markdown compares the key differences between Keras and TensorFlow.js.

1. **High-level vs. Low-level Abstraction**: Keras is a high-level neural networks API, making it easier to build neural networks with simple, concise code, whereas TensorFlow.js is a lower-level library that provides more flexibility and control over the implementation of machine learning models.
   
2. **Backend Support**: Keras can run on top of different backends such as TensorFlow, Theano, or CNTK, giving users the freedom to choose the most suitable backend for their tasks. On the other hand, TensorFlow.js is specifically designed for running machine learning models in the browser with JavaScript, making it ideal for web applications.
   
3. **Deployment**: Keras models can be easily deployed on various platforms like cloud servers, mobile devices, and embedded systems, offering a wide range of deployment options. In contrast, TensorFlow.js is primarily focused on deployment within the browser, enabling real-time interactions with machine learning models directly on web pages.
   
4. **Python vs. JavaScript**: Keras is primarily built using Python, a popular language among data scientists and machine learning practitioners, simplifying the development process with its extensive libraries and tools. TensorFlow.js, however, leverages JavaScript, enabling developers to create and execute machine learning models directly in the browser without requiring server-side processing.
   
5. **Community and Ecosystem**: Keras benefits from a large and active community due to its integration with TensorFlow, allowing users to access a wide range of resources, tutorials, and pre-trained models. TensorFlow.js, being a newer framework, has a growing community focused on web-based machine learning applications, with an emphasis on interactive and visual experiences.
   
6. **Performance and Efficiency**: While Keras offers high performance and efficiency for developing and training neural networks, TensorFlow.js may have limitations in terms of speed and computational resources due to its reliance on web browsers for execution, especially when dealing with large-scale or complex models.

In Summary, Keras and TensorFlow.js differ in abstraction level, backend support, deployment options, programming languages, community resources, and performance capabilities.

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Manual

Advice on Keras, TensorFlow.js

Adithya

Student at PES UNIVERSITY

May 11, 2020

Needs advice

I have just started learning some basic machine learning concepts. So which of the following frameworks is better to use: Keras / TensorFlow/PyTorch. I have prior knowledge in python(and even pandas), java, js and C. It would be nice if something could point out the advantages of one over the other especially in terms of resources, documentation and flexibility. Also, could someone tell me where to find the right resources or tutorials for the above frameworks? Thanks in advance, hope you are doing well!!

107k views107k

Comments

philippe

Research & Technology & Innovation | Software & Data & Cloud | Professor in Computer Science

Sep 13, 2020

Review

Hello Amina, You need first to clearly identify the input data type (e.g. temporal data or not? seasonality or not?) and the analysis type (e.g., time series?, categories?, etc.). If you can answer these questions, that would be easier to help you identify the right tools (or Python libraries). If time series and Python, you have choice between Pendas/Statsmodels/Serima(x) (if seasonality) or deep learning techniques with Keras.

Good work, Philippe

4.65k views4.65k

Comments

Fabian

Software Developer at DCSIL

Feb 11, 2021

Decided

For my company, we may need to classify image data. Keras provides a high-level Machine Learning framework to achieve this. Specifically, CNN models can be compactly created with little code. Furthermore, already well-proven classifiers are available in Keras, which could be used as Transfer Learning for our use case.

We chose Keras over PyTorch, another Machine Learning framework, as our preliminary research showed that Keras is more compatible with .js. You can also convert a PyTorch model into TensorFlow.js, but it seems that Keras needs to be a middle step in between, which makes Keras a better choice.

55.4k views55.4k

Comments

Detailed Comparison

Keras	TensorFlow.js
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/	Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API
neural networks API;Allows for easy and fast prototyping;Convolutional networks support;Recurent networks support;Runs on GPU	-
Statistics
GitHub Stars -	GitHub Stars 19.0K
GitHub Forks -	GitHub Forks 2.0K
Stacks 1.1K	Stacks 184
Followers 1.1K	Followers 378
Votes 22	Votes 18
Pros & Cons
Pros 8 Quality Documentation 7 Easy and fast NN prototyping 7 Supports Tensorflow and Theano backends Cons 4 Hard to debug	Pros 6 Open Source 5 NodeJS Powered 2 Deploy python ML model directly into javascript 1 Cost - no server needed for inference 1 Runs Client Side on device
Integrations
TensorFlow scikit-learn Python	JavaScript TensorFlow

What are some alternatives to Keras, TensorFlow.js?

TensorFlow

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

PyTorch

PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.

Kubeflow

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.

MLflow

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

H2O

H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.

PredictionIO

PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.

Gluon

A new open source deep learning interface which allows developers to more easily and quickly build machine learning models, without compromising performance. Gluon provides a clear, concise API for defining machine learning models using a collection of pre-built, optimized neural network components.

Related Comparisons

Keras vs TensorFlow.js: What are the differences?

## Introduction
This markdown compares the key differences between Keras and TensorFlow.js.

1. **High-level vs. Low-level Abstraction**: Keras is a high-level neural networks API, making it easier to build neural networks with simple, concise code, whereas TensorFlow.js is a lower-level library that provides more flexibility and control over the implementation of machine learning models.
   
2. **Backend Support**: Keras can run on top of different backends such as TensorFlow, Theano, or CNTK, giving users the freedom to choose the most suitable backend for their tasks. On the other hand, TensorFlow.js is specifically designed for running machine learning models in the browser with JavaScript, making it ideal for web applications.
   
3. **Deployment**: Keras models can be easily deployed on various platforms like cloud servers, mobile devices, and embedded systems, offering a wide range of deployment options. In contrast, TensorFlow.js is primarily focused on deployment within the browser, enabling real-time interactions with machine learning models directly on web pages.
   
4. **Python vs. JavaScript**: Keras is primarily built using Python, a popular language among data scientists and machine learning practitioners, simplifying the development process with its extensive libraries and tools. TensorFlow.js, however, leverages JavaScript, enabling developers to create and execute machine learning models directly in the browser without requiring server-side processing.
   
5. **Community and Ecosystem**: Keras benefits from a large and active community due to its integration with TensorFlow, allowing users to access a wide range of resources, tutorials, and pre-trained models. TensorFlow.js, being a newer framework, has a growing community focused on web-based machine learning applications, with an emphasis on interactive and visual experiences.
   
6. **Performance and Efficiency**: While Keras offers high performance and efficiency for developing and training neural networks, TensorFlow.js may have limitations in terms of speed and computational resources due to its reliance on web browsers for execution, especially when dealing with large-scale or complex models.

In Summary, Keras and TensorFlow.js differ in abstraction level, backend support, deployment options, programming languages, community resources, and performance capabilities.

Keras vs TensorFlow.js

Overview