Overview

TensorFlow

Stacks3.9K

Followers3.5K

Votes106

GitHub Stars192.3K

Forks74.9K

Keras

Stacks1.1K

Followers1.1K

Votes22

Keras vs TensorFlow: What are the differences?

# Introduction

1. **Architecture**: Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. TensorFlow, on the other hand, is an open-source deep learning framework developed by Google. While Keras offers a user-friendly interface for building neural networks, TensorFlow provides more flexibility in terms of architecture customization.
2. **Backend Support**: Keras allows for easy switching between different backend engines such as TensorFlow, Theano, and CNTK. In contrast, TensorFlow has its own backend engine, which is optimized for performance and provides robust support for distributed computing across multiple devices. This makes TensorFlow more suitable for large-scale machine learning projects.
3. **Ease of Use**: Keras focuses on simplicity and ease of use, with intuitive APIs that allow developers to quickly prototype deep learning models. On the other hand, TensorFlow requires more code for the same tasks, making it less user-friendly for beginners. However, TensorFlow's low-level APIs offer greater control and flexibility for advanced users.
4. **Community Support**: TensorFlow has a larger and more active community compared to Keras, resulting in more resources, tutorials, and third-party contributions. This makes it easier to find solutions to common problems and stay up-to-date with the latest developments in the field of deep learning.
5. **Deployment**: Keras models are easier to deploy due to their lightweight nature and higher level of abstraction. TensorFlow, being a more complex framework, requires additional steps for deployment and productionization. This makes Keras a better choice for rapid prototyping and quick deployment in production environments.
6. **Extensibility**: TensorFlow offers more extensive support for customized operations and extensions through its low-level APIs, allowing for more advanced research and development. Keras, while more beginner-friendly, may be limited in terms of extending and customizing neural network architectures beyond what is provided in its high-level APIs.

In Summary, Keras and TensorFlow differ in architecture, backend support, ease of use, community support, deployment, and extensibility, making them suitable for different use cases based on the specific requirements of a project.

🔥 Trending in Development & Training Tools on StackShare

CUDA AI Development Training Tools

It provides everything you need to develop GPU-accelerated applications

Try it View Docs Alternatives

Try

215

Advice on TensorFlow, Keras

Developer at DCSIL

Oct 11, 2020

Decided

For data analysis, we choose a Python-based framework because of Python's simplicity as well as its large community and available supporting tools. We choose PyTorch over TensorFlow for our machine learning library because it has a flatter learning curve and it is easy to debug, in addition to the fact that our team has some existing experience with PyTorch. Numpy is used for data processing because of its user-friendliness, efficiency, and integration with other tools we have chosen. Finally, we decide to include Anaconda in our dev process because of its simple setup process to provide sufficient data science environment for our purposes. The trained model then gets deployed to the back end as a pickle.

99.3k views99.3k

Comments

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

4.64k views4.64k

Comments

Detailed Comparison

TensorFlow	Keras
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.	Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
-	neural networks API;Allows for easy and fast prototyping;Convolutional networks support;Recurent networks support;Runs on GPU
Statistics
GitHub Stars 192.3K	GitHub Stars -
GitHub Forks 74.9K	GitHub Forks -
Stacks 3.9K	Stacks 1.1K
Followers 3.5K	Followers 1.1K
Votes 106	Votes 22
Pros & Cons
Pros 32 High Performance 19 Connect Research and Production 16 Deep Flexibility 12 Auto-Differentiation 11 True Portability Cons 9 Hard 6 Hard to debug 2 Documentation not very helpful	Pros 8 Quality Documentation 7 Supports Tensorflow and Theano backends 7 Easy and fast NN prototyping Cons 4 Hard to debug
Integrations
JavaScript	scikit-learn Python

What are some alternatives to TensorFlow, Keras?

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.

TensorFlow.js

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

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.