Overview
What is Keras?
Keras is a Python deep learning library
Keras Review
Rapidly build neural network
Best wrapper library for TensorFlow and Theano
Best wrapper library for TensorFlow
Best wrapper library for TensorFlow and Theano.
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What is Keras?
Keras Technical Details
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Reviews and Ratings
(18)Attribute Ratings
Reviews
(1-6 of 6)- As the high level API, clean and neat, allowing the data scientists to develop the standard deep learning model very quickly (using the mature and existed algorithm module blocks)
- Seamlessly integrate with [a] couple of Deep Learning framework, although we only use TensorFlow, and since TF2.0 Keras has become the 1st class native API
- Keras model itself is not thread safe, and the documentation regarding how the multi thread works during the prediction time is not quite clear and enough, we still need [to] tweak the TensorFlow old (session + graph) way to support our Model As Service, in the high concurrent scenario, but with limited memory constraint.
- Some API and default implementation still has a lot [of] improvement room, for example, the checkpoint callback, only saves the "best" model, natively can not save all other metadata of the "best" model, we currently have to extend that by ourselves to meet our project requirement.
- Some advanced topic, like distributed training, documentation is not so clear and very hard to find the real code example.
Keras Review
- Until we have IDEs that can make an interpretation of our idea into code, I don't think making Deep Learning models could be made a lot simpler.
- It's makes the process easy for building the Neural Network.
- Doesn't require to have strong background in Deep Learning.
- I didn't face any issue so far.
- The only thing, you can't modify everything in this. So it's not recommended for constructing highly optimised algorithms.
Rapidly build neural network
- Easy to use. We can implement neural networks easily.
- There is a lot of built-in utility that makes the task easier.
- It also supports TensorFlow.
- We can't modify everything that we want to.
- Some built-in model can be included as a part of this library.
- Resource requirement is quite high for using this library.
Best wrapper library for TensorFlow and Theano
- Performs well when you are doing some implementation which requires neural network implementation and some other deep learning models
- It has lots of inbuilt tools which you can have clean your data before processing
- It supports TensorFlow as its backend, so it can easily use GPU
- As it is a kind of wrapper library it won't allow you to modify everything of its backend
- Unlike other deep learning libraries, it lacks a pre-defined trained model to use
- Errors thrown are not always very useful for debugging. Sometimes it is difficult to know the root cause just with the logs
Best wrapper library for TensorFlow
- Implementing neural networks and deep learning models is easy with this.
- Data processing is easy with Python and Keras. Keras helps a lot and has a good collection of functions to do data processing.
- It has good integration with other devices like Android.
- With Keras you don't have much power to configure your model. So, if it can be possible to do the customization to the deep level, then it will be good.
- It is only available for Python, doesn't have other language support.
- Would love to see dynamic chart creation, like PyTorch
Best wrapper library for TensorFlow and Theano.
- One of the reason to use Keras is that it is easy to use. Implementing neural network is very easy in this, with just one line of code we can add one layer in the neural network with all it's configurations.
- It provides lot of inbuilt thing like cov2d, conv2D, maxPooling layers. So it makes fast development as you don't need to write everything on your own. It comes with lot of data processing libraries in it like one hot encoder which also makes your development easy and fast.
- It also provides functionality to develop models on mobile device.
- As Keras works at a high level of abstraction, it limits the user to use it's own implemented algorithm. It doesn't give complete power to user to modify or implementing their own basic algorithm.
- Sometimes it is slow on GPU as compared to the pure TensorFlow.
- Other than the above two cons, I don't think it has any negatives.