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Keras

Keras is an open-source, high-level neural network library written in Python that can be run on Theano, CNTK, or TensorFlow. It was developed by Franco Chollet, an engineer at Google.

It is used for developing deep learning models. Python uses libraries from different programming languages such as C++, C. Also, Keras offers the opportunity for mobile platform development on both iOS and Android based smartphones for users who want to implement deep learning models.

As a flexible, scalable and user-friendly neural network library, Keras supports CNNs separately. It cannot solve low-level calculations, so it uses the Backend library to solve this.

Built on top of TensorFlow 2, Keras benefits from the distribution features of the TensorFlow platform. Keras models need to be transferred to JavaScript to run directly in the browser; and to TF Lite to run on iOS, Android, and embedded devices.

With its ease of use and focus on user experience, Keras is a deep learning solution preferred by many universities and developers. Keras is the most used deep learning library among the top 5 winning teams on Kaggle. Because it facilitates new experiments, many developers prefer Keras. Currently, 250000 developers are using Keras. The number of people preferring Keras is increasing day by day. Tech giants such as Microsoft, NVIDIA, Google, and Amazon support Keras and are playing an active role in its growth.
 

Why Keras?

There are many deep learning libraries available today. But why should you prefer Keras over other libraries?

The biggest reason to use Keras is its user-friendly structure. Keras also offers its users ease of learning and ease of model creation.

Being compatible with TensorFlow, CNTK, Theano, MXNet and PlaidML is just one of the reasons why it is preferred. Furthermore, Keras is supported by technology giants such as Google, Microsoft, Amazon, Apple, Nvidia and Uber.
 

What are the Features of Keras?

Some features of Keras, a neural network library, are as follows:

  • Supports multi-platform.
  • Allows all developers to code.
  • Allows any architecture to be created.
  • Runs both on GPU and CPU.
  • Designed to work fast and easy with Python.
  • Keras is a good practice to reduce cognitive load and maintain accuracy of models.
  • It's a high-level library for developing deep learning models.
  • The necessary modules for building a neural network are available in an easy-to-use interface for the end user. So, the application is quite simple and handy.
  • Writing a new module in Keras is easy.

 

What are the Advantages of Keras?

Some advantages of Keras, a neural network library, are as follows:

  • Network models are easy to understand.
  • It enables faster implementation of network models.
  • User can use CNTK, TensorFlow and Theano according to their needs.
  • It detects errors quickly when encountered any error.
  • Keras is flexible. This feature provides versatility to all developers.
  • It is easy to test.
  • Has a large supporting community.

 

What are the Disadvantages of Keras?

  1. Keras is dependent on low-level languages like Theano And TensorFlow.
  2. Keras is not flexible enough to create custom operations.
  3. Keras is a new neural network library. Its first version was released in 2015. Therefore, there are changes to be updated on it.

 

How to Build a Model in Keras?

The necessary steps to build a model in Keras are as follows:

  1. Network definition: In this step, different layers in the model and the connections between them are defined. Keras has two main models, sequential and functional. Once the type of model is decided, the data flow between them is defined.
  2. Network compilation: In the network compilation step, the codes are compiled in a way that machine can understand.
  3. Network evaluation: In this step, after the model is created, it is tested whether there is any problem or error in the model.
  4. Prediction: It is the stage where the model is reviewed to make predictions on new data.

 

Keras Layers

Keras has a wide range of layer types. These layers have been predefined. However, it also supports writing your own layers. The basic layers of Keras are:

  1. Dense
  2. Activation
  3. Dropout
  4. Lambda
  5. Layer

 

How to Install Keras?

Keras is compatible with TensorFlow 2. In order to start using Keras, you need to install TensorFlow 2.

Here from this link, you can find out how to install TensorFlow. Also, Keras/TensorFlow is compatible with:

  • Python 3.7–3.10
  • Ubuntu 16.04 or higher
  • Windows 7 or higher
  • macOS 10.12.6 (Sierra) or higher.

 

Conclusion

You can check out the code repository and Keras tutorials to start using Keras, a high-level neural network library. From there, you can move on to other tutorials and finally explore the Keras examples.

When you have any questions about Keras, there is also a large community to answer your questions or get ideas in the development stage. You can ask your questions to the Keras community from the links below.

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