Keras AI: Everything you need to know about this network library

Artificial Intelligence (AI) plays a key role in the development and use of video games (or other applications), as well as web services, devices or machines in today’s digital world. From this perspective, an important area of ​​research is the so-called neural networks, which explore in depth the basic concepts of “virtual thinking.”

Using Keras AI greatly simplifies the implementation of these networks. Discover what this open source library hides and how to make building neural networks easier.

What is Keras AI?

Keras AI is an open source library that enables the development of neural networks. From it you can program other intelligent applications. Keras is built into TensorFlow and is easy to use due to its simple interface. Other features include tensor calculation, mathematical graphs, sessions and more.

TensorFlow can be customized using its core API, giving you full flexibility and control over your applications and allowing you to bring your ideas to life in a relatively short period of time.

Keras AI Features

Keras AI is intuitive, modular, easily extensible, and designed to work with Python. According to its creators, the API is “designed for humans, not machines” and “uses best practices to reduce cognitive load.”

You can create new modules by combining modules from a neural layer, optimizer, initialization scheme, activation function, or regularization scheme. It is very easy to add new modules, as well as new classes and functions. Templates are defined in Python code rather than a separate template configuration file.

Why use Keras AI?

The popularity of Keras AI is related to its advantages. This API is easy to use and learn. Another advantage is that it is popular because it supports many production deployment options.

It supports at least five backend engines: TensorFlow, CNTK, Theano, MXNet and PlaidML. It also supports multiple GPUs and distributed training. This library is also compatible with Google, Microsoft, Amazon, Apple, Nvidia or Uber.

Backends

Keras AI does not directly perform low-level operations such as tensor products or convolutions. This type of report is based on a server engine. Several of these engines are supported, but the most popular is Google’s TensorFlow.

The Keras API ships with TensorFlow in tf.keras format. As of version 2.0, this was the core TensorFlow API.

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Keras AI Models

Models are the core of Keras AI data structures. There are two main types: serialized templates and template classes used with a functional API. A sequential model is a model that stacks layers linearly. The layers are very easy to explain. Each layer definition requires one line of code.

Compilation (which defines the learning process) also requires a line of code. Tuning (training), evaluating (computational costs and statistics), and estimating the results of the trained model each require one line of code. The Keras sequential model is simple, but the topology of the model is limited.

Therefore, the Keras AI functional API is useful for creating complex models such as Multi-Input models, Multi-Output models, Directed Acyclic Graphs (DAGs), and Shared Layer models. This functional API uses the same layers as the sequential model, but provides more flexibility in combining them.

First the layers are defined and then the model is created, built and trained. Estimation and forecasting work in the same way as ordinal models.

Keras AI layer

KeraS AI provides a variety of predefined layer types. Among the most important are density, activation, elimination and lambda. The different convolutional layers range from 1D to 3D and include the most common transformations for each dimension. Inspired by the work of the visual cortex, 2D convolutions are frequently used in image recognition.

Pooling layers range from 1D to 3D and include common options such as maximum pooling and average pooling. Locally connected layers act as convolutional layers, but their weights are not shared. Among the recurring layers is a simple layer, a closed LSTM.

It is very useful for a variety of applications, including language processing. Finally, the sound layer prevents overfitting.

Keras AI Datasets

Keras AI collects the seven most common sample data sets in deep learning through the keras class. These include cifar10 and cifar100 for small color images, IMDB movie reviews, Reuters news headlines, MNIST manuscript numbers, MNIST fashion images, and Boston real estate prices.

Keras Applications and Examples

Keras AI also includes 10 popular models called Keras applications that are pre-built on ImageNet. It can be used to predict image classification, extract features, and configure models for different classes. In total, the Keras template repository contains more than 40 example templates, such as video, text, sequence, or generative templates.

Keras models can be deployed on a variety of platforms. This is an advantage over other deep learning frameworks. These platforms include iOS using CoreML, Android using the TensorFlow Android runtime, web browser using Keras.js and WebDNN, Google Cloud using TensorFlow-Serving, Python on the web application server, and JVM using DL4J models.. imported and Raspberry Pi.

How to learn to use Keras AI?

To use Keras, first read the official documentation, explore the code repository on GitHub, and install server engines such as Keras AI and TensorFlow. Alternatively, you can try the official Sequential Pattern tutorial and explore a variety of examples. This is the first step to learn about the Deep Learning API.

But the best way to learn Keras is through the DataScientest course. This library, along with Tensorflow, CNN-RNN and GAN, forms the basis of the Deep Learning module of the Data Scientist course.

Through this training, you will learn Python programming, DataViz, machine learning, databases, and artificial intelligence systems. After completing this course, you will be able to work as a data scientist.

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How to draw a model in Keras?

Keras is a library for working with models. Provides building blocks for developing complex deep learning models. Unlike proprietary frameworks, this open source software does not perform simple low-level tasks on its own.

Instead, use the library as a relevant machine learning framework for this purpose. In Keras AI, it acts as a kind of reverse engine. The idea is modular, so that Keras users can integrate the layers they need for the neural network they are developing without having to understand or manage the actual servers of their chosen framework.

As mentioned above, Keras AI uses three tools: TensorFlow, Theano, and Microsoft Cognitive Toolkit. There is a list of interfaces to use that provide quick and intuitive access to that server. There is no need to choose a single framework as you can easily switch between different servers.

You can also choose different servers from the three solutions mentioned here. This must be specified in a configuration file and can use three functions: placeholders, variables, and functions.