Introduction to Multi-Layer Perceptron

Multi-layer perceptron (MLP) is a category of feedforward artificial neural network (ANN). It is based on a learning algorithm that enables it to learn a task or function under supervision. In addition to it, the algorithm of Multi-Layer Perceptron is capable of approaching an XOR operator and many other non-linear functions.

While there are various approaches available on internet with respect to application of Multi-Layer Perceptron (MLP) in many coding languages, this article includes the review of a potent approach underscored by the authors of Computational Mindset. The focus of this approach is on fitting with highly configurable multi-layer perceptron. The major advantage of this MLP approach is that the user can perform several operations without lettering a single code-line. The code of their approach can be utilized through their website in both PyTorch and TensorFlow.

An Overview of Multi-Layer Perceptron

Before delving deep into the application of this algorithm, it is crucial to have an overview of Multi-Layer Perceptron (MLP).

Multi-Layer Perceptron is a particular type of Artificial Neural Network (ANN) that holds immense importance in research and industry related to Machine Learning (ML). Multi-Layer Perceptron (MLP), apart from input and output layer, also contains one or more than one hidden layers. An input layer receives the signal whereas, an output layer makes prediction or decision about the outcome of received signal through input. The hidden layers are the computational engines that serve the process of computation of signals. Moreover, unlike Single-Layer Perceptron, it can learn both linear and non-linear functions.

In addition to it, for general understanding, Multi-Layer Perceptron (MLP) operates by learning linear and non-linear functions through examples and make a prediction when provided a new set of data accordingly. On theoretical ground, it is suggested that if Multi-Layer Perceptron (MLP) has linear function in present neurons, it can be reduced to a two-layer model.

Fitting with Highly Configurable Multi-Layer Perceptron

In order to achieve 100 percent accuracy in machine learning process, we have to deal with typical and oldest problems of machine learning like curves, surface and fitting functions. As the dimension of domains and co-domains is one, input and output, both will have one neuron. Furthermore, there will be several choices available for the user with respect to training contours and optimization.

Multi-Layer Perceptron is a deep-artificial neural network with common errors in the process of deep learning. This can be solved through various techniques focused on deep-learning.

The Common Technique as Solution to Common Problems in Deep Learning

A common technique suggested by most experts in the field of machine learning is eclecticism. This means combining them all into a single python script. The reason why it functions in most of the cases is that functions are hardcoded and hence, fit. In addition to it, the architectural structure of neural network too is hardcoded. Hardcoding can be difficult to understand. Furthermore, it can be inflexible or complex. These common solutions are without any specific and detailed explanation.

Solution Provided by Computational Mindset

The solution provided by the authors of Computational Mindset could be utilized easily considering the flexibility it possesses. The major objective of this approach is to ease the burden of code-writing while facilitating the user to perform several operations with Multi-Layer Perceptron (MLP).

In other words, through this approach, the user can simply write a line and receive the complete data he is seeking. It contains training and learning algorithms along with loss and activation functions.

The solution provided by Computational Mindset’s authors includes python scripts. The number of python scripts used here are four with different features. These features can be effectively used by the user.

For further exploration of this solution, please continue reading this article.

Dataset Generation

The dataset generation includes generation of Comma-Separated Values (CSV) file from the math object. The object is passed through arguments, therefore, it is not hardcoded.

The best part about it is that you do not have to generate Comma-Separated Values (CSV) file synthetically. This is because there could a file already with curves in dataset.

Multi-Layer Perceptron Definition and Training

This feature is based on machine learning where neural networks will be involved. Multi-Layer Perceptron (MLP) consists of hidden layer which is configured with activation functions. Moreover, the training of MLP will allow choosing any loss functions, optimization algorithm and other parameters of training.

Prediction Output

The dataset in the form of input is loaded into python script which uses previous model to make predictions. These predictions are delivered in excel of CSV file. This process, however, demands time. Moreover, the script ameliorates on this as it does not require much coding.

Visualization of Performance

Through visualization of performance by drawing a visualized comparison chart between results of training dataset and prediction dataset, the user can effectively analyze the performance of Multi-Layer Perceptron Model.

This can be helpful in understanding the outcomes and possible application of MLP model in other related arenas.

Application of MLP Code

This part includes reviewing actual application of the code.

The user can mainly apply this code either through TensorFlow or PyTorch. You can check out the code for both softwares provided at Computational Mindset. Moreover, GitHub can also be of great help as the code is available there along with a specific article which comprehensively explains the nitty-gritty of scripts. In other words, it explains the python code to its entirety.

The main four typologies of fitting are available on the website of Computational Mindset. These are also available in PyTorch or TensorFlow.

  1. One Variable Real Valued Function Fitting
  2. Parametric Curve on Plane Fitting
  3. Parametric Curve in Space Fitting
  4. Two Variables Real Valued Function Fitting

You can also find the GitHub link from there.

Concluding Statement

The approach to run the code mentioned by the authors of Computational Mindset is indeed an interesting one. Moreover, this approach is not only relatively easy to apply but is also with less complexities in term of coding intervention.

You can find the thorough explanations of all four methods along with availability of code in PyTorch and TensorFlow on the website.