Advantages and Drawbacks of Keras

Keras is a high-level neural network API. It runs on the top of TensorFlow and Theano and is very famous in the field of Deep Learning. It is one of the best libraries used for neural networks that follows a minimalist philosophy. Keras is very powerful and useful for beginners who are starting with Deep Learning. Before starting with Keras, it is important to know the Advantages and Drawbacks of Keras. So let us start.

Advantages and Drawback of Keras

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Advantages of Keras

1. Simplicity

Keras is very easy and simple. It is a user-friendly API with easy to learn and code feature. It is very simple to start with Deep Learning using Keras. The functions in Keras are very simple. It becomes easy to design neural network models using Keras.

2. Backend Support

Keras does not operate with low-level computations. So, it supports the use of backends. It runs off the top of TensorFlow, Theano, and Microsoft CNTK. These are some libraries that Keras use for backend support. It provides the user the opportunity to choose among this backend support as per the requirement.

3. Pre-trained Models

Keras provides numerous pre-trained models. There are models besides the pre-trained weights. These models help users to simplify their tasks. These models enable the user to do fine-tuning, feature extraction, and prediction. While instantiating models, it allows weights to download automatically. There are various image classifications models:

  • VGG16
  • VGG19
  • Xception
  • NASNet
  • MobileNet
  • MobileNetV2
  • InceptionV3
  • InceptionResNetV2

4. Fast Experimentation

Keras is built to simplify the tasks of users. It enables users to complete their tasks with minimum efforts. Keras has the ability to built neural network models with fewer lines of code. It has good support for functions that enable users for fast deployment.

5. Great Community and Calibre Documentation

Keras has a large supportive community. It provides code on an open-platform. This community allows the researchers to publish their code and experimentation details for the public. This community never fails to respond to the queries of its users.

It also has standard documentation. It helps to use to deal with Keras. This documentation contains every detail of the functions and its tutorials. It is in a proper and sequential manner. The documentation is enriched with examples to make the task of the users easy.

Drawbacks of Keras

1. Some Improvable Features

There are some features in Keras that have space for improvement. It lacks some pre-trained models to use. Keras does not support features of dynamic chart creation.

2. Inefficient Errors

The errors given by the Keras library are not effective. There is a need for the errors to be easily identifiable. It is not very useful and helpful to detect the root cause of the error. It is difficult to debug in Keras.

3. Low-level API

Keras gives you low-level errors many times. The reason for these low-level errors is that there are some functionality and operation that Keras is not capable of.
Also, Keras is not capable to handle low-level computations. Therefore, it runs on the top of TensorFlow, Theano, and Microsoft CNTK.

Conclusion

Finally, we have seen the advantages and drawbacks of Keras. Keras is the best platform to start with Deep Learning. It is a high-level API that does not support low-level computations. It focuses on fast experimentation and is chosen by the beginners as well as by the researchers. Underlying the fact that a coin has two faces. Keras also has two sides: upside and downside. Before starting with Keras, it is important to note it’s pros and cons.

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