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TensorFlow vs Caffe: What are the differences?

TensorFlow: 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; Caffe: A deep learning framework. It is a deep learning framework made with expression, speed, and modularity in mind.

TensorFlow and Caffe can be categorized as "Machine Learning" tools.

Caffe is an open source tool with 29.2K GitHub stars and 17.6K GitHub forks. Here's a link to Caffe's open source repository on GitHub.

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Pros of Caffe
Pros of TensorFlow
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    • 29
      High Performance
    • 17
      Connect Research and Production
    • 14
      Deep Flexibility
    • 11
      Auto-Differentiation
    • 10
      True Portability
    • 4
      Powerful
    • 4
      High level abstraction
    • 4
      Easy to use

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

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      What is Caffe?

      It is a deep learning framework made with expression, speed, and modularity in mind.

      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 Caffe?
      What companies use TensorFlow?
      See which teams inside your own company are using Caffe or TensorFlow.
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      What tools integrate with Caffe?
      What tools integrate with TensorFlow?

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      What are some alternatives to Caffe and TensorFlow?
      Torch
      It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.
      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.
      Caffe2
      Caffe2 is deployed at Facebook to help developers and researchers train large machine learning models and deliver AI-powered experiences in our mobile apps. Now, developers will have access to many of the same tools, allowing them to run large-scale distributed training scenarios and build machine learning applications for mobile.
      Keras
      Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
      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.
      See all alternatives