Deep learning is one of the fascinating and exciting subfields related to machine learning and artificial intelligence. Deep learning has led to great advances in technology such as computer vision, audio processing, and even self-driving cars. Deep learning works with a concept called neural networks. To implement the neural network, there are various libraries in the Python programming language, including Tensorflow and Theano. But according to many experts, the Keras library is the most popular library for designing and building neural network models.
In this article, we want to learn more about this library.
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Deep learning and neural networks
Cress is one of the popular open source deep learning libraries written in the Python programming language. Deep learning is one of the branches of artificial intelligence that solves complex problems by simulating the human brain.
As mentioned, deep learning uses a concept called neural networks. A neural network is like a function that receives input and output data and finds the relationship between these data. In order to do this with high accuracy, the neural network is made up of several layers. Each layer of these networks consists of nodes, each of which has a different function. Neural network can have from 2 layers to 200 layers.
When the number of neural network layers is large, it will be difficult to implement. To solve this problem, Keras was introduced.
What is Keras?
Cress is one of the open source deep learning frameworks in Python programming language. Google artificial intelligence researcher Francois Chollet introduced Cress for the first time in 2015. Today, major companies in the world, including Google, Netflix, Huawei and Uber, use Cress.
Keras is mostly compared with Theano and TensorFlow libraries. Cress is built on top of other libraries such as TensorFlow, Tiano, and Cognitive Toolkit. In other words, TensorFlow, Tiano and CNTK are like back-end languages and Keras is front-end.
CNTK is a deep learning framework developed by Microsoft. This framework uses libraries of programming languages such as Python, C# and C++ or machine learning tools.
Tiano and TensorFlow are powerful libraries, but they can be a bit complicated for building neural network models. Because they are low level and working with them requires high attention to detail.
This is where Cress, as a minimal structure, and by providing a simple environment, allows the programmer or deep learning expert to build deep learning models. In other words, Cress is an optimal choice for use in deep learning.
Advantages of using Keras
Keras is a powerful and dynamic framework. This library provides a high-level view of neural networks. In the official website of Keras it is said that:
Keras is designed for humans, not machines.
This means that Keras is easy to work with because humans can easily interact with it.
It is because of this simplicity and high level that the number of lines of code is significantly reduced. In fact, the programmer’s focus is only on the network architecture and no longer needs to pay attention to the details.
Additionally, Keras is used by a large community of developers and deep learning experts. For this reason, programmers have good support.
How to install Keras
As mentioned, Keras is implemented with TensorFlow or Theano library. You can use the following method to install these two libraries.
Anaconda is the easiest way to install any Python library, including Keras and TensorFlow. To install TensorFlow and Keras library with Anaconda, select not installed in the Environment section. Then type Tensorflow in the search box. Both the Keras and TensorFlow libraries are displayed for installation.
Different models of layer architecture in Keras
The neural network has a layered structure. Two different architectures are usually used in the design of the neural network model with Keras:
- Sequential Model
- Functional API Models
The sequential model is the simplest architectural model in Keras. When you use the Sequential library in the Keras library, layers are added as needed based on the project type. Of course, using shared layers or branching layers is not allowed.
Functional model is used for complex modeling. It is also more flexible than the previous model. In this model, branching or sharing of layers is allowed.
The steps of building a neural network model with Keras
To make a model with a cross, go through the following steps:
- Neural network definition: In this step, define the layers needed for the model and the communication between the layers to have a network of interconnected and targeted layers. The two main types of models in Cross are: sequential model and functional model. It is you who should decide which type to choose and determine the data flow between them.
- Compile: Compile the code, that is, convert the code into a language that can be understood by the machine. In Cress, the model.compile() command does this. In addition, in the compilation stage, another function called the loss function is also defined.
The loss function is used in machine learning and especially in deep learning to show the amount of mathematical error between the output of the neural network and the real data.
- Data matching: In this step, we match our model with the available data after compilation. The purpose of this step is to train the model based on real data.
- Network Evaluation: After training the model, it should be checked again to fix any errors or bugs in it.
- Implementation: This stage is the implementation stage where we use the built model after all the evaluations.