Keras is a simple and deep learning API (Application Programming Interface) that was developed to implement neural networks. It is a high-level API that shows the relevant information to the user and hides the backend computation. This API also supports the backends like TensorFlow, PlaidML, Theano, etc. Here are some of the advantages of Python Keras over its competitors.
Easy And Understandable:
Keras is an easy and user-friendly API with simplicity as well. It is very satisfactory for implementing deep learning algorithms and reduces developer cognitive load to focus on your important task.
It is used to build a model of neural networks without engaging more space of code lines. The coding, functions, and parameters are easy to understand and we can work with them efficiently.
Multiple Backend Support:
This API Keras provides various backend support which TensorFlow, Microsoft CNTK, and Theano are the most familiar. There are some libraries in Keras used for the backend.
Keras supports the backend only because it does not operate with low-level computations. These backends work according to the difficulty level of the different projects. In Keras, it is a piece of cake to change the backend only by replacing the other name.
Keras provides several deep learning with pre-trained models. The simplification of function can be done with the help of these models.
Pre-trained models also give allowance to the user for doing feature extraction and prediction. Some of the pre-trained models available in Keras are-
- ResNet, ResNetV2
Appreciable Community and Standard Documentation:
Keras has a huge community. This community allows many developers, data scientists, or other researchers to publish their own codes publicly. There are many competitions which are done in Keras and most of the developers and researchers prefer to compete here.
Keras also has quality or standardized documentation. These documentations acts as a mentor to deal with Keras in a well-organized manner. Documentation is the one that contains examples and details of function and its tutorial in a sequential way.
GPU Support and Quick Analysis:
Keras gives the opportunity to do the tasks with minimum hardship and to reduce the extra time-taking efforts. It provides the ability to build neural network models in minimum lines of code and hence increases the working speed.
We can train our model in two ways – data parallelism and device parallelism. Keras provides built-in data parallelism and allows to train the model whether it is on a single or multi-GPUs (Graphics Processing Unit). It is very efficient for processing large numbers of data.
We hope this article has helped you to understand and clear your doubts regarding Keras.
Here, at Augmento labs, we have 18+ years of experience in IT, and we are here to communicate the large amount of information we have accumulated from working in the industry. Our main purpose is to help guide you through the process and utilize cloud computing.
Our team of experts will help you move up the maturity chain, and we guarantee consistent growth and productivity. Reach us for a quick consultation now.