TensorFlow: An Easy Method for Building an MLP

Developing a neural network requires time and technical expertise. However, frameworks can help to make the process easier. In this article, we will discuss neural networks, Multi-Layer Perceptrons (MLPs) and how to create an MLP in TensorFlow using Google Colaboratory (Colab). We will explore how frameworks can be used to simplify the process of creating machine learning applications.

What exactly is a multilayer perceptron?

In deep learning, the Multilayer Perceptron (MLP) is the most complex neural network design. MLPs form the basis of neural networks and are computational models which strive to replicate the pattern recognition abilities of the human brain. Although they are the foundation of neural networks, most MLPs are developed to demonstrate the effectiveness of machine learning.

In 1958, Frank Rosenbluth proposed the pioneering concept of a Machine Learning (ML) model, termed a Single-Layer Perceptron (SLP). As the most basic element of a neural network, an SLP is utilised to distribute knowledge. Having a relatively simple structure, this type of network can only identify linear patterns when left to operate autonomously. Despite its apparent simplicity, an SLP is comprised of numerous minute components, similar to the structure of an atom.

A Multilayer Perceptron (MLP) is a type of Neural Network composed of multiple Single-Layer Perceptrons (SLPs) that are linked together. The complexity of an MLP is determined by the number of SLPs that are connected to it, which affects its ability to investigate the interdependencies between the layers.

In what ways do perceptrons do their tasks?

When you assign a computational task to a machine, you are creating a perceptron. The importance of each input is evaluated and used to assign a weight to each input. The behaviour of the machine is influenced by the aggregate of all inputs.

In this paper, we have examined the behaviour of the perceptron, taking into account all inputs and processes involved in its learning model. Ultimately, we have determined the perceptron’s behaviour.

The four main components of a perceptron are the input value or input layer, the weight, the net summation, and the activation function.

Quantity fed in

The open end of the network feeds data into the input layer, which subsequently passes it to the perceptron for processing. The input value and weight associated with a person’s perception are derived from the individual’s own perception.


All of the inputs are given weights. This data informs the perceptron how much weight each input has on the overall.

Subtracting everything else

In the majority of cases, a basic perceptron will be supplied with multiple inputs. The inputs are then multiplied by their respective weights, and this product is added to the bias. This sum is used as the initial value in the function that performs the actual task.

Doing the job of activation

At this stage of the perceptron process, a decision is made regarding the activation of the neuron. The output of the neuron is determined by evaluating the total value of the summation.

Multi-layer perceptron algorithm implementation

Here we outline how to put a multilayer perceptron model into action.

To begin, open your Google Colab notebook.

Choose a blank notebook and give it a name.

The second phase involves bringing in preexisting code bases and components.

Executing the following instructions will bring the necessary library into your Google Colab workspace.

Step 3: Pick and save a data set

For this demonstration, we will make use of the MNIST dataset which is conveniently integrated into TensorFlow, allowing us to instantly employ it for training and testing purposes.

Converting images to floating-point values is the fourth step.

In order to generate precise predictions, it is essential to convert the pixel values to floating-point numbers. Utilising grayscale values to reduce the size and simplify the calculations can be advantageous. Pixel values can range from 0 to 256, with any number other than 0 being considered to be 255. By dividing all of the numbers by 255, we can obtain a range of 0 to 1.

The training dataset comprises a total of 60,000 records, while the test dataset includes 10,000 records. Additionally, each image in the dataset has a resolution of 2828.

Fifth, make sense of the numbers via visualisation.

Create the input, hidden, and output layers (6th step)

When planning the layers, keep the following in mind.

  • The sequential approach to constructing models in a multilayer perceptron permits us to build them layer by layer in accordance with our needs; however, this methodology is limited to layer stacks that contain a single input and single output.
  • The “Flatten” operation allows for the flattening of the input without altering the batch size. When the inputs lack a feature axis, the output after the flattening will have a shape of (batch size, 1).
  • The activation process makes use of the sigmoid activation function.
  • To construct a fully linked model, the first two thick layers are kept secret.
  • The last thick layer, called “output,” contains 10 neurons responsible for classifying the picture.

Model compilation is Step 7.

In this situation, we employ the compile command to complete the process, which involves utilising a loss function, optimizers, and measurements. Specifically, the optimizer adam is employed in combination with the loss function sparse categorical crossentropy.

Model fitting is Step 8.

Here are a few things to keep in mind as you proceed:

  • The number of epochs defines how many times the model will iterate forward and backward.
  • It is the total number of samples that make up a batch. Unless otherwise specified, a batch size of 32 will be utilised.
  • Split is a value that can range from 0 to 1, which is used to indicate the portion of the training data that should be segregated after each epoch for the purpose of evaluating the loss and other metrics of the model. This data will not be used for the training of the model.

The Model’s Accuracy, Step 9

By examining the test data samples, we determined that the model had a 92 percent accuracy rate.

In this introduction to the multilayer perceptron, we explored how an MLP functions and the steps required to create one. For developers and ML professionals who wish to learn how to use TensorFlow for MLP, this article is an ideal starting point. Considering the importance of perceptrons and TensorFlow to ML projects, this is a highly valuable resource.

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