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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.
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.
Pros of Keras
- Quality Documentation5
- Easy and fast NN prototyping4
- Supports Tensorflow and Theano backends3
Pros of Theano
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Cons of Keras
- Hard to debug3
