New evolving techniques in architecture and mathematics have given birth to new algorithms.

In such times, it has become extremely important for one to be fully aware of all the approaches used to implement in different coding languages.

One such approach is Multi-Layer Perceptrons, also known as MLP. Before we go into the intricacies, let’s find out what is a Multi-Layer Perceptron.

Multi-Layer Perceptrons is an extensive artificial neural network (ANN). It is composed of three layers;

  • Input Layer: It is there to receive the signal.
  • Output Layer: This layer makes a decision for the input.
  • Hidden Layer: Set between the input and output layer, this layer is the heart of MLP. With the help of a hidden layer, MLP can support any persistent function.

 

Still didn’t get it? No worries. The team of Computational Mindset is here to help you learn about application of MLP.

In this article we will pay special attention to installation with a highly adaptable Multi-Layer Perceptron. Without going through the painstaking process of writing down the whole code, you can solve supervised learning problems in the form of PyTorch and TensorFlow.

What does the Theory say?

 

According to the theorists, any number of layers can be cut down into a two-layer input output model if a Multi Layer Perceptron contains a linear activation function in all neurons. However, some neurons make use of a nonlinear activation function for the purpose of monitoring the frequency of action potentials and biological neurons.

Installing with extremely configurable Multi-Layer Perceptron

The adjustment and fitting of curves, functions and the surface are one of the most popular machine learning issues and complications that we need to be worked on in order to reach 100% accuracy.

In MLP, both input and output layers consist of one neuron. That is because the dimension of domain and codomain is one. At this stage, you will be given a choice to choose hidden layer architecture, loss function, several training parameters as well as optimizator.

Since it is one of the most occurring problems, the internet is flooding with solutions.

The Mainstream Solution

If you search on google the solution for the aforementioned problem, you will come across numerous techniques. Most of these machine learning professionals will recommend putting all of them in a single Python Script Database Generation. Usually, this method works. But not always. Since the architecture of the neural network is hardcoded, it cannot be very specific or particular in its function and thus, users may find it difficult to understand the complexity of hardcoding. Not only this, but many experts may fail to provide the solution without explanation required for sufficient learning.

Computational Mindset’s Strategy

Computational Mindset brings the ultimate solution for your problem. They take special care to provide you with an amazing experience to explore different combinations of MLP architecture,training algorithms, your own activation functions and loss functions without wasting your time to write lengthy codes. Instead, you can work only on the command line of the  four Python Script. Each one of them has different features.

Let’s dive in deeper to inspect these features.

Dataset Generation

This feature, as the name suggests, generates a CSV file from a mathematical entity. It is passed through the arguments and therefore, it is not hardcoded.

The plus point here is that it does not always need to be generated synthetically such as withdrawing curves from data in Excel files, from data loggers linked to electronic sensors, from the output of measuring instruments etc.

The Multi-Layer Perceptron Architecture Definition and Training

This feature involves neural networks. In this, the Multi-Layer Perceptron hidden layer architecture is regulated with their activation functions. Here, you are given an advantage from training which allows you to use any optimization algorithm coupled with loss functions as well as training parameters.

Prediction

This feature is related to the previous one. Here, the predicted output is calculated for you.

An input dataset containing data from a previously trained model, is entered into the python script. Thereafter, the predictions are given.

Lastly, the output is given in the form of Excel file or CSV containing the predictions. Although the efficiency of a script is increased during the process because extensive coding is not involved, it may require some time to preform the task.

Visualization of the Result

Last but not the least, visualization of the result is an amazing feature. By using this, machine professionals can compare initial dataset with prediction curve. It helps users to get more familiar with the output resulting in effective implementation of Multi-Layer Perceptron.

An Overview of Application

As mentioned before, there are two ways through which the user can apply this code; TensorFlow and PyTorch.

Fortunately, Computational Mindset supports coding in both cases.

We know how laborious it can become to search through these methods and watch YouTube videos or surf content on Wikipedia. That is why Computational Mindsets have designed their website to accommodate all your concerns and queries only to explain you in the easiest way possible. The whole sketch of code is there on Github. Moreover, a full fledged article is also present to guide you about the usage of different scripts according to their respective functions.

 

Generally, there are four main types of classifications available. Be it PyTorch or TensorFlow, you can find classifications for both.

  1. One-variable real-valued function fitting with TensorFlow/PyTorch
  2. Parametric curve on Plane fitting with TensorFlow/PyTorch
  3. Parametric curve in Space fitting with TensorFlow/PyTorch
  4. Two-variables real-valued function fitting with TensorFlow/PyTorch

 

Head to their website for great in-depth study of the above methods. You won’t be disappointed!

 

All in all, Computational Mindsets offer scrupulous methods of coding and implementation of MLP in a systematic way. Extensive and well structured explanations are available on their website to help machine experts to conduct coding in a productive manner.

We hope you find this article useful. So don’t waste your time and check out their website today.