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Keras

828
847
+ 1
12
Theano

26
47
+ 1
0
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Keras vs Theano: 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/; Theano: Define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano is a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray). Using Theano it is possible to attain speeds rivaling hand-crafted C impleme.

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

Keras and Theano are both open source tools. It seems that Keras with 42.5K GitHub stars and 16.2K forks on GitHub has more adoption than Theano with 8.83K GitHub stars and 2.49K GitHub forks.

Decisions about Keras and Theano
Fabian Ulmer
Software Developer at Hestia · | 3 upvotes · 4.7K 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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Pros of Keras
Pros of Theano
  • 5
    Quality Documentation
  • 4
    Easy and fast NN prototyping
  • 3
    Supports Tensorflow and Theano backends
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    Cons of Keras
    Cons of Theano
    • 3
      Hard to debug
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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 Theano?

      Theano is a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray).

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      What companies use Keras?
      What companies use Theano?

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      What tools integrate with Keras?
      What tools integrate with Theano?

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      What are some alternatives to Keras and Theano?
      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.
      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.
      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.
      scikit-learn
      scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
      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.
      See all alternatives
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