Keras vs scikit-learn vs TensorFlow

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Keras

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scikit-learn

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TensorFlow

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Keras vs TensorFlow vs scikit-learn: What are the differences?

Introduction

In this article, we will discuss the key differences between Keras and TensorFlow, and scikit-learn, which are popular machine learning libraries. Understanding these differences can help us choose the right tool for a particular task and enable us to utilize their strengths effectively.

  1. Ease of Use: Keras is a high-level deep learning library that runs on top of TensorFlow, making it easier to build and train deep learning models. It provides a simple and intuitive interface, allowing users to quickly prototype and experiment with different architectures. In contrast, TensorFlow is a lower-level library that requires more coding and provides greater flexibility for customization. Scikit-learn, on the other hand, is a general-purpose machine learning library that provides simple and consistent APIs for various algorithms, making it easy to implement and evaluate models.

  2. Supported Algorithms: TensorFlow is a comprehensive machine learning framework that supports both deep learning and traditional machine learning algorithms. It provides a wide range of pre-built deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), as well as tools for training and deploying them. Keras, being a part of TensorFlow, inherits all these capabilities. Scikit-learn, on the other hand, specializes in traditional machine learning algorithms and provides implementations for various supervised and unsupervised learning methods, such as regression, classification, clustering, and dimensionality reduction.

  3. Performance and Scalability: TensorFlow is optimized for large-scale distributed computing and can efficiently utilize multiple CPUs or GPUs. It supports distributed training across multiple machines, which is essential for training deep learning models on large datasets. Keras, being built on top of TensorFlow, inherits its performance and scalability benefits. Scikit-learn, being primarily designed for single-machine usage, may not scale well for very large datasets or complex models.

  4. Customization and Low-level Control: TensorFlow provides a low-level API that allows developers to have fine-grained control over the network architecture and training process. It enables the creation of custom layers, loss functions, and optimizers, making it suitable for research and advanced development. Keras, being a high-level library, sacrifices some of this flexibility in favor of simplicity and ease of use. Scikit-learn, similarly, provides a higher-level API with less customizability but focuses on providing a uniform interface for various algorithms.

  5. Community and Ecosystem: TensorFlow has a large and active community of developers, researchers, and enthusiasts, contributing to its extensive ecosystem. It has a rich set of tools, libraries, and frameworks built on top of it, making it easier to integrate with other technologies. Keras, being a part of TensorFlow, benefits from this ecosystem and community support. Scikit-learn also has a vibrant community and is widely adopted, providing a range of resources, tutorials, and third-party extensions. However, its focus is more on traditional machine learning algorithms compared to deep learning.

  6. Industry Adoption: TensorFlow and Keras have gained significant popularity and adoption in both the research and industrial communities. Many large companies and organizations use these libraries for developing and deploying deep learning models at scale. Scikit-learn, on the other hand, is widely used for traditional machine learning tasks and has become an industry standard for many common algorithms.

In Summary, Keras and TensorFlow are closely related, with Keras being a high-level API that runs on top of TensorFlow. They offer ease of use, extensive deep learning capabilities, and scalable performance, making them ideal choices for deep learning tasks. Scikit-learn, on the other hand, focuses on traditional machine learning algorithms, providing a simple and consistent interface for various supervised and unsupervised learning methods.

Decisions about Keras, scikit-learn, and TensorFlow

Pytorch is a famous tool in the realm of machine learning and it has already set up its own ecosystem. Tutorial documentation is really detailed on the official website. It can help us to create our deep learning model and allowed us to use GPU as the hardware support.

I have plenty of projects based on Pytorch and I am familiar with building deep learning models with this tool. I have used TensorFlow too but it is not dynamic. Tensorflow works on a static graph concept that means the user first has to define the computation graph of the model and then run the ML model, whereas PyTorch believes in a dynamic graph that allows defining/manipulating the graph on the go. PyTorch offers an advantage with its dynamic nature of creating graphs.

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Fabian Ulmer
Software Developer at Hestia · | 3 upvotes · 50.5K views

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.

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Xi Huang
Developer at University of Toronto · | 8 upvotes · 92.7K views

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.

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A large part of our product is training and using a machine learning model. As such, we chose one of the best coding languages, Python, for machine learning. This coding language has many packages which help build and integrate ML models. For the main portion of the machine learning, we chose PyTorch as it is one of the highest quality ML packages for Python. PyTorch allows for extreme creativity with your models while not being too complex. Also, we chose to include scikit-learn as it contains many useful functions and models which can be quickly deployed. Scikit-learn is perfect for testing models, but it does not have as much flexibility as PyTorch. We also include NumPy and Pandas as these are wonderful Python packages for data manipulation. Also for testing models and depicting data, we have chosen to use Matplotlib and seaborn, a package which creates very good looking plots. Matplotlib is the standard for displaying data in Python and ML. Whereas, seaborn is a package built on top of Matplotlib which creates very visually pleasing plots.

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Pros of Keras
Pros of scikit-learn
Pros of TensorFlow
  • 8
    Quality Documentation
  • 7
    Supports Tensorflow and Theano backends
  • 7
    Easy and fast NN prototyping
  • 25
    Scientific computing
  • 19
    Easy
  • 32
    High Performance
  • 19
    Connect Research and Production
  • 16
    Deep Flexibility
  • 12
    Auto-Differentiation
  • 11
    True Portability
  • 6
    Easy to use
  • 5
    High level abstraction
  • 5
    Powerful

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Cons of Keras
Cons of scikit-learn
Cons of TensorFlow
  • 4
    Hard to debug
  • 2
    Limited
  • 9
    Hard
  • 6
    Hard to debug
  • 2
    Documentation not very helpful

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What is Keras?

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

What is scikit-learn?

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

What is 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.

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What are some alternatives to Keras, scikit-learn, and TensorFlow?
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.
MXNet
A deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, it contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly.
JavaScript
JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles.
Git
Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency.
GitHub
GitHub is the best place to share code with friends, co-workers, classmates, and complete strangers. Over three million people use GitHub to build amazing things together.
See all alternatives