Keras API – Your Comprehensive Guide

Introduction

Keras is a Python-based Neural Network library that is compiled to run over a number of platforms such as TensorFlow and Theano. This high-level API is not able to handle computation at lower level and utilises Backend library for resolving it. The Keras api is largely used in machine learning as it gives complete access to facilities of cross platform and scalability of TensorFlow. Its in-depth focus on deep learning techniques enables the api to solve problems related to ML.
If you are aspiring to build a career in Machine Learning, you must learn about keras in machine learning.

Key features of Keras api

A popular library in deep learning, Keras focuses on easy and faster experimentation of the library neural network. Keras is backed by Theano and TensorFlow in the backend. Some of the popular features features that make Keras api python a credible library in machine learning are as follows:

• Modular format
The keras api tensorflow carries a modular format that makes it flexible, expressive, and agile for new developments and innovative research.

• Large set of pre-defined data
Keras uses a larger set of predefined data that provides the opportunity of directly loading and importing the data.

• Training from NumPy data
The predict() and evaluate() method in Keras uses the NumPY dataset. The evaluation is done based on a thorough testing of the data.
The NumPy array is used in Keras for evaluation and training of the models with the use of fit () method. This is deployed for data training and includes the arguments such as epochs, validation_data, and batch_size.

• Data pre-processing
Keras provides the user with different functions for the overall data preprocessing. For instance, ImageDataGenerator is imported by Keras.

• Python native library
The python library in Keras uses the concepts of Python and provides the user with a user friendly approach.

• Multiple layers
Keras has multiple parameters and layers that are used in configuring, training, and constructing data to facilitate implementation of operations. The functional api tensorflow allows users to obtain necessary output within the intermediate layer. It can also create an additional layer that fetches the output.

• Encoding
Encoding feature is offered by Keras with one_hot() function. It helps in encoding integers and tokenisation of data. The filtering function eliminates white spaces, filters any punctuations, and makes the text into lower case.

How does Keras work?

Keras is composed of multiple APIs and is able to define the neural network. The factors on which Keras work include the following:

• Model Subclassing:
In Keras, the model subclassing enables the implementation of all the factors from scratch that is useful in complex cases.

• Functional API:
The full featured API supports development of arbitrary architecture models that create a complex and flexible API.

• Sequential API:
This enables the creation of the model in a layer-by-layer approach to build a straightforward process. It is not restricted to single output or input layers.

You can sign up with our machine learning course online to learn more about how Keras work.

Components of Keras

Layer: Keras carries various corresponding layers such as output, input, and hidden layer. Users will find pre-built layers for easy creation of complex neural networks. The different layers of Keras include the following:
• Convolution Layers
• Core Layers
• Recurrent Layers
• Pooling Layers

Core Modules There are several in-built neural networks in Keras which include the following:
• Loss module:
The module includes loss functions such as poisson, mean_absolute_error, mean_squared_error and more.

• Regulaziers:
The module includes functions such as L1 regularizer, L2 regularizer and more.

• Activation module:
The function is important in the ANN and it provides activation of functions such as relu, softmax, and more.

• Optimizer module:
The optimiser module is deployed to train ML models.

Model The models of Keras are classified into two types:
• Sequential Model
It is the linear composition of layers of Kears and has the ability of representing neural networks that are available.
• Functional API
The tensorflow functional api helps to define complicated models like multi-output models, models carrying shared layers, and so on.

Advantages of using Keras

Here is a brief on the multiple advantages of Keras api:

• Faster deployment
Keras assures speedy deployment of models. This is one major reason why the API is widely used in the ML domain.

• Friendly UI
Keras also features a easy-to-use user interface that makes operation a breeze.

• Multiple backends
Keras has found support from multiple backends including CNTK, Theano, and TensorFlow.

• Support from multiple GPU
Keras enables training the model on one GPU as well as more than one GPUs that can support users with data parallelism.

• Pre-trained models
The deep learning models in Keras with pre-trained weights are capable of extracting features and making predictions.

Popular competitors of Keras

Some of the popular competitors of Keras are:

• Dataloop AI
• PyTorch
• Microsoft Cognitive Toolkit
• DataMelt
• Google Deep Learning Containers
• AWS Deep Learning AMIs

Conclusion

Keras boasts various usability benefits and also saves users a huge lot of time. It minimises user burden by providing different functions so that they can deal with datasets. If you are looking forward to learning about Keras or other M tools and applications, join DataSpace Academy. We not only offer an industry-leading ML curriculum but also the opportunity to develop hands-on training on the applications.