Multi-Layer Perceptron:

MLP is also referred as Artificial Neural Networks. This research article explores the implementation of MLP as a trusted source used in the coding realm and encouraged by Computational Mind.  It emphasizes on fitting with highly configurable multi-layer perceptron. This helps the user by doing all of the operations without writing a single line of code. The code can be found on their website in both TensorFlow as well as in PyTorch. Let’s get into the realm of coding as encouraged by MLP.

Overview

Before we talk about everything regarding MLP, we must have a clear understanding of what Multi-Layer Perceptron is. MLP is a class of feed improvisation Artificial Neural Network.

According to a research, if any of the Multi-Layer Perceptron possesses linear activation functionality in all its present neurons then it can be limited to a two-layer output model. That is, the liner function that will map all the available weighted inputs according to the yield of the neurons. Some of the neurons also use nonlinear functions that might give some problems.

 

Fitting with highly configurable multi-layer perceptron

Fitting curves, functions, and the surfaces that have a neural network, provides for the oldest machine learning problems that we can work on to achieve the accuracy of 100%.

There is only one neuron available in the input, as well as the output layer, respectively. This happens whenever the measurement of domains and co-domains are one. Here is one of the benefits that as you can select the hidden layer design loss functions, and with that you will get several choices when it comes to optimizer and training parameters.

This is one of the common problems faced when it comes to deep learning, and there are various solutions available on the internet.

Solution:

Most of the machine experts suggest that they should combine all of them into a single python script dataset generation. It works in most of the cases as functions that are fit are usually hardcoded. The architecture of the neural network is also hardcoded here. Hardcoding is a complex method and is sometimes difficult to understand. But don’t worry because the authors of these commonly coded solutions will tell you about the solution without providing any explanation.

Computational Mindset’s Solution

The authors of Computational Mindset suggest a method that is easy and more flexible. The main focus is for the users to experience all the combinations of MLP architecture without writing a single code line. If we put it in simple terms, the user can write only a single line, and in that single line, they will get the complete data that they are looking for which includes activation functions, training algorithms, as well as loss functions.

Four python scripts are used here and all four files have different features that the user can use.

Let’s talk more about these features and how useful it can be for programmers as well as for casual users.

 

Dataset Generation

The first feature specially focuses on the dataset generation. It generates the CSV file from the math object. It is the same object that is passed through the arguments. It is not hardcoded as it is passed as an argument.

The best part is that the CSV is not always to be generated synthetically. For instance, there could already be a file with curves data in the dataset. So, there is no need to generate the CSV file synthetically.

MLP Definition and Training

Coming to the next feature, it is specially designed for the neural networks. The Multi-Layer Perceptron hidden layer is configured with their activation functions. This will allow you to select any optimization algorithm, loss functions, and it will give you the option to choose any of the training parameters.

The feature emphasizes on machine learning training and the next part is where the neural networks will jump in.

Assumption

The prediction output is done by python script. An input dataset is loaded into the python script and from this the predictions are performed using a previously trained model. After this process, it will give you an output Excel file or the CSV that will contain the predictions. This process is time intensive and the script improves on this due to the process not requiring much coding.

Results:

The user can benefit from visualizing the performance of the model through comparing results for training dataset against the prediction dataset after the script has run. This provides you a clear understanding of the output and also measures the effectiveness of the MLP model.

Implementation:

The final part of the section focuses on the actual implementation of the code.

TensorFlow or PyTorch are the two main ways through which you can implement your desired codes.

Computational Mindset has code for both platforms and this allows you to check out the code in both of them. The code is also available in Github. There is also an article written where they explain clearly on how the entire python code works.

You can find four main typologies on the website. They are also available in the TensorFlow or PyTorch.

  • One variable real-valued function fitting
  • The parametric curve on plane fitting
  • A parametric curve in space fitting
  • Two variables real-valued functions fitting

For further information, visit our official website. There is a lot of information on it and you will also find the Github link to the code.

Point of View

So far, I have presented the implementation of the multi-layer perceptron technique by Computational Mindset. It is easy to use and a good way of running the code because there is either little or no need for coding intervention to run it. It can be easily implemented by TensorFlow or Pytorch. Detailed explanations are given regarding the four methods. If you want to know more about the method or how the code works, you can read the explanations. The above mentioned procedure and technical insight will help you with coding and encompasses the complete steps for the implementation of MLP.