Need advice about which tool to choose?Ask the StackShare community!

OpenNN

5
16
+ 1
0
TensorFlow

3K
3.1K
+ 1
80
Add tool

TensorFlow vs OpenNN: What are the differences?

Developers describe TensorFlow as "Open Source Software Library for Machine Intelligence". 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. On the other hand, OpenNN is detailed as "*A neural networks C++/Python library *". It is a free neural networks library for advanced analytics. It has solved many real-world applications in energy, marketing, health and more.

TensorFlow and OpenNN belong to "Machine Learning Tools" category of the tech stack.

TensorFlow is an open source tool with 136K GitHub stars and 78.1K GitHub forks. Here's a link to TensorFlow's open source repository on GitHub.

Get Advice from developers at your company using Private StackShare. Sign up for Private StackShare.
Learn More
Pros of OpenNN
Pros of TensorFlow
    Be the first to leave a pro
    • 26
      High Performance
    • 16
      Connect Research and Production
    • 13
      Deep Flexibility
    • 9
      Auto-Differentiation
    • 9
      True Portability
    • 3
      High level abstraction
    • 2
      Powerful
    • 2
      Easy to use

    Sign up to add or upvote prosMake informed product decisions

    Cons of OpenNN
    Cons of TensorFlow
      Be the first to leave a con
      • 9
        Hard
      • 6
        Hard to debug
      • 1
        Documentation not very helpful

      Sign up to add or upvote consMake informed product decisions

      - No public GitHub repository available -

      What is OpenNN?

      It is a free neural networks library for advanced analytics. It has solved many real-world applications in energy, marketing, health and more.

      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.

      Need advice about which tool to choose?Ask the StackShare community!

      What companies use OpenNN?
      What companies use TensorFlow?
        No companies found
        See which teams inside your own company are using OpenNN or TensorFlow.
        Sign up for Private StackShareLearn More

        Sign up to get full access to all the companiesMake informed product decisions

        What tools integrate with OpenNN?
        What tools integrate with TensorFlow?

        Sign up to get full access to all the tool integrationsMake informed product decisions

        Blog Posts

        TensorFlowPySpark+2
        1
        637
        PythonDockerKubernetes+14
        11
        2169
        Dec 4 2019 at 8:01PM

        Pinterest

        JenkinsKubernetesTensorFlow+4
        5
        3016
        What are some alternatives to OpenNN 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.
        scikit-learn
        scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
        Keras
        Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
        CUDA
        A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
        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.
        See all alternatives