Keras vs Theano

Overview

Keras

Stacks1.1K

Followers1.1K

Votes22

Theano

Stacks32

Followers65

Votes0

GitHub Stars10.0K

Forks2.5K

Keras vs Theano: What are the differences?

Execution Speed: Theano is known for its efficient computation of mathematical expressions, utilizing GPU for faster processing. On the other hand, Keras provides a high-level interface to design neural networks, making it more user-friendly but sacrificing some speed compared to Theano.
Flexibility: Theano offers more control and customization options, allowing users to fine-tune algorithms and parameters for specific tasks. In contrast, Keras abstracts complex operations, offering a simpler and more intuitive way to build models without delving into low-level details.
Community Support: Keras has gained significant popularity in the deep learning community due to its easy-to-use design and compatibility with other popular frameworks like TensorFlow. Theano, although powerful, has seen a decline in support and development in recent years, making it less appealing for new users.
Development Status: Keras is actively maintained and updated, with new features and improvements continuously being added to the framework. Theano, on the other hand, has been deprecated in favor of other deep learning libraries, such as TensorFlow, leading to a lack of new developments and updates.
Ease of Use: Keras focuses on simplicity and ease of use, allowing users to quickly prototype and build models with minimal code and effort. In contrast, Theano requires a deeper understanding of neural network concepts and implementation, making it more suitable for advanced users or researchers in the field.
Compatibility: Keras is designed to work seamlessly with TensorFlow, providing a unified platform for building and training neural networks. Theano, while powerful, may face compatibility issues with newer hardware or software environments due to its limited support and development.

In Summary, Keras and Theano vary in their execution speed, flexibility, community support, development status, ease of use, and compatibility with other frameworks.

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Manual

Advice on Keras, Theano

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	Theano
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/	Theano is a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray).
neural networks API;Allows for easy and fast prototyping;Convolutional networks support;Recurent networks support;Runs on GPU	-
Statistics
GitHub Stars -	GitHub Stars 10.0K
GitHub Forks -	GitHub Forks 2.5K
Stacks 1.1K	Stacks 32
Followers 1.1K	Followers 65
Votes 22	Votes 0
Pros & Cons
Pros 8 Quality Documentation 7 Supports Tensorflow and Theano backends 7 Easy and fast NN prototyping Cons 4 Hard to debug	No community feedback yet
Integrations
TensorFlow scikit-learn Python	NumPy Python

What are some alternatives to Keras, Theano?

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.

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.

Related Comparisons

Keras vs Theano: What are the differences?

Execution Speed: Theano is known for its efficient computation of mathematical expressions, utilizing GPU for faster processing. On the other hand, Keras provides a high-level interface to design neural networks, making it more user-friendly but sacrificing some speed compared to Theano.
Flexibility: Theano offers more control and customization options, allowing users to fine-tune algorithms and parameters for specific tasks. In contrast, Keras abstracts complex operations, offering a simpler and more intuitive way to build models without delving into low-level details.
Community Support: Keras has gained significant popularity in the deep learning community due to its easy-to-use design and compatibility with other popular frameworks like TensorFlow. Theano, although powerful, has seen a decline in support and development in recent years, making it less appealing for new users.
Development Status: Keras is actively maintained and updated, with new features and improvements continuously being added to the framework. Theano, on the other hand, has been deprecated in favor of other deep learning libraries, such as TensorFlow, leading to a lack of new developments and updates.
Ease of Use: Keras focuses on simplicity and ease of use, allowing users to quickly prototype and build models with minimal code and effort. In contrast, Theano requires a deeper understanding of neural network concepts and implementation, making it more suitable for advanced users or researchers in the field.
Compatibility: Keras is designed to work seamlessly with TensorFlow, providing a unified platform for building and training neural networks. Theano, while powerful, may face compatibility issues with newer hardware or software environments due to its limited support and development.

In Summary, Keras and Theano vary in their execution speed, flexibility, community support, development status, ease of use, and compatibility with other frameworks.

Keras vs Theano

Overview