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Caffe

49
53
+ 1
0
Keras

870
902
+ 1
12
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Keras vs Caffe: What are the differences?

Keras: Deep Learning library for Theano and TensorFlow. Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/; Caffe: A deep learning framework. It is a deep learning framework made with expression, speed, and modularity in mind.

Keras and Caffe can be primarily classified as "Machine Learning" tools.

Some of the features offered by Keras are:

  • neural networks API
  • Allows for easy and fast prototyping
  • Convolutional networks support

On the other hand, Caffe provides the following key features:

  • Extensible code
  • Speed
  • Community

Keras and Caffe are both open source tools. Keras with 44.7K GitHub stars and 17K forks on GitHub appears to be more popular than Caffe with 29.2K GitHub stars and 17.6K GitHub forks.

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Pros of Caffe
Pros of Keras
    Be the first to leave a pro
    • 5
      Quality Documentation
    • 4
      Easy and fast NN prototyping
    • 3
      Supports Tensorflow and Theano backends

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    Cons of Caffe
    Cons of Keras
      Be the first to leave a con
      • 3
        Hard to debug

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      What is Caffe?

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

      What is Keras?

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

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

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

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      What are some alternatives to Caffe and Keras?
      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.
      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.
      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