What Is Keras? An Explanatory Guide On The Deep Neural Network API
Keras – A deep neural network API, is a much more engaging topic for all deep learning enthusiasts. But what is this? If you’re looking for the answer, read further.
Today, machine learning and deep learning are everyone’s first choice. Like machine learning is a subset of artificial intelligence, deep learning is a subset of machine learning.
For deep learning, there are many libraries/frameworks that aid the programmer in deep learning projects. One such library is Keras. So, what in general is Keras? Why is its use in deep learning? This, along with other things we’re going to learn in today’s blog. So, without further ado, let’s start with a brief introduction to Keras.
What is Keras?
Keras is an open-source library, developed by a Google engineer, François Chollet. Keras is written in Python and runs over other frameworks, Tensorflow, CNTK, and Theano.
Due to this fact, Keras is easy to learn and use. Furthermore, it uses another library, Backend in order to be capable of dealing with low-level computation. Now, it is important to learn a bit about Backend, Theano, and Tensorflow.
So, let’s proceed with this.
Since Keras isn’t capable of dealing with any low-level computation, it uses another library Backend for doing so. These low-level computations include tensor products, convolution, etc.
The backend also manages all these with the aid of Theano or Tensorflow.
Theano is a widely used deep learning framework, developed by the MILA group. In common terms, Theano is a Python library that aids in 2D or 3D arrays (or multi-dimensional arrays) for the various mathematical operations using Python libraries.
Two such libraries are Scikit-learn and NumPy. Some popular functions that are performed by Theano are using general processing units (GPUs) for quick computation, build symbolic graphs, recognize unstable expressions, etc.
Tensorflow is not uncommon to anyone and is the most important deep learning tool.
Developed by Google, other researchers contribute their part in making this a more powerful and effective tool.
Microsoft Cognitive Toolkit (CNTK)
CNTK is another deep learning framework which is faster than Theano and Tensorflow. CNTK was developed by Microsoft.
It is basically put into use for the training purpose of deep learning models on a large scale. This training ensures that the models meet the required accuracy and effectiveness.
Some deep learning models are Classic Neutral Networks, Convolutional Neural Networks (CNN), etc.
Advantages of Keras
Easy to understand and deploy:
Keras helps the programmer to develop a simple neural network model with fewer lines of code.
Superior community support:
Keras boasts wide community support, especially by the AI community. This community provides easy to understand documentation for the general public.
Keras possess multiple backends
With Keras, you can incorporate a number of backends like Theano, Tensorflow, CNTK, etc.
Furthermore, apart from these, you can use other backends as per the needs of your project. Remember, each and every backend possesses its own advantages and disadvantages.
Keras support multiple GPUs:
You can train Keras with a number of GPUs. Due to built-in support for data parallelism, it is capable of training large sets of data with superior speed.
Disadvantages of Keras
Inability to deal with low-level API:
This is one of the most common disadvantages of Keras. Keras is capable of handling only high-level APIs which runs over other backend engines. These backend engines include CNTK, Theano, and Tensorflow.
Following are the important things you need to know about Keras.
There are two Keras models. These are:
Keras Sequential Model
In Keras Sequential Model, there is a sequential stack of layers which can be described easily. Each layer is defined using a single line of code. This model is suitable for creating simple to medium models.
Keras Functional API Model
Keras Functional API model is used for creating complex models. Some examples of such complex models are Multi-Input/Multi-Output models, DAGs, etc.
It is a bit similar to the Sequential model, but it is more flexible. In this model, we first create the layer, then we define the model, compiles it, and then fit and evaluate it.
So, with this, today’s blog on Keras – A Neutral Network API comes to an end. This blog defines every important concept of Keras that is useful for a beginner. There are many other advanced concepts you need to master but starting with these is the first step.