Iris classification using neural network The problem concerns the Jan 3, 2023 · We’ve completed the model from end-to-end using a deep learning framework! We went through a sort EDA, feature selection and preparation, built the model, fitted the model, and evaluated the Oct 3, 2019 · This report focuses on IRIS plant classification using Neural Network. Oct 19, 2023 · Learn powerful CNN techniques for Iris flower classification using PyTorch! Dive into the code, understand the process, and build your own model. Classification is a machine learning technique used to predict group membership for data instances. This project aims to develop a machine learning model to classify iris flowers into different species based on their sepal and petal characteristics. Classification of IRIS data Aug 27, 2025 · A beginner-friendly deep learning project where I built an Artificial Neural Network (ANN) with TensorFlow/Keras to classify iris flowers into Setosa, Versicolor, and Virginica based on their features. There are many other types of activation functions in use today. These networks have been trained on millions of images, so they gained a powerful ability of extracting the rightful features from input images, which results in accurate classification. A perceptron is a single-layer neural network. The Jun 23, 2020 · I’m going to cover how to create a simple neural network using PyTorch for a simple classification problem with Iris data set. This powerful method is Artificial Neural Network based particle swarm optimization algorithm (ANNPSO), produces good results within a short In this work, the study of gender classification using Artificial Neural Networks (ANN) based on iris recognition. Model Hosting: Use Remote Python Script Snap from ML Core Snap Pack to deploy python script to host the model and schedule an Ultra Task to provide API. Explore and run machine learning code with Kaggle Notebooks | Using data from Iris Species Neural networks are capable of classifying data with many dimensions, finding patterns in data that is very difficult for humans to see. This iris identification system consists of localization of the iris region, normalization, feature extraction and then classification as a final stage. As a deep learning enthusiasts, it will be good to learn about how to use Keras for training a multi-class classification neural network. Recent developments brought scientists to create NNs with more connections than a human brain. Oct 1, 2017 · The neural network models are widely used in regression, classification, and other types of analysis. While challenges remain, ongoing research and technological advancements continue to push the boundaries, promising even more reliable and efficient systems in the future. Jan 1, 2024 · Request PDF | On Jan 1, 2024, D. Let’s try to do a binary classification of iris by a perceptron. In this notebook, we’ll implement an MLP from Classification is a machine learning technique used to predict group membership for data instances. Nov 28, 2021 · We now turn to state-of-the-art Convolutional Neural Network (CNN) architectures, implemented using TensorFlow 2 [5], to see how these techniques performing in the classification of Iris-CV images. Readme Activity 0 stars Description Almost 100 years have passed, Iris dataset is now one of the best-known datasets for people who study Machine Learning and data science. Written for Oxford Brooke's U08280 Advanced AI class. The problem concerns the identification of Iris plant species on the basis of plant attribute measurements. While a 'softmax' activation function is used in the output layer. Sigmoid Function The Sigmoid function is commonly used in logistic regression and artificial neurons. For this classification problem we will build a simple feed-forward full-connected artificial neural network. Using the scikit-learn library, we'll explore how MLP can Oct 21, 2024 · Abstract This paper presents a hybrid quantum-classical machine learning model for classification tasks, integrating a 4-qubit quantum circuit with a classical neural network. The project involves experimenting with different activation functions (Sigmoid, Tanh, ReLU), loss functions (Mean Squared Error, Cross-Entropy, Hinge), and optimizers (RMSProp, Adam). Multi-class Classification - Classifies among the three species of the Iris dataset. Aug 15, 2023 · In this post, we will discuss the use of simple feedforward neural networks (FNNs) to solve multi-class classification tasks, on Iris dataset, specifically, identifying the species of Iris flowers based on their sepal and petal measurements. Some applications of deep learning models are used to solve regression or classification problems. The objective of this project is to build a neural network for multiclass classification on the famous Iris dataset using PyTorch. The model achieves 93% accuracy on the test data and includes exploratory data analysis (EDA) with visualizations. This video will help you to get started with Neural Network with Iris Data Classification. This project uses the pytorch deep learning framework to implement a classification task for the Iris dataset based on a neural network algorithm and is perfect for beginners to neural network algorithms. The problem concerns the identification of IRIS plant species on the basis of plant attribute measurements. Aug 17, 2020 · This paper explores an efficient technique that uses convolutional neural network (CNN) and support vector machine (SVM) for feature extraction and classification respectively to increase the This paper presents an approach to classify iris plant species using artificial neural networks (ANNs) based on measurements of sepal and petal dimensions. The pipeline uses Vertex AI SDK to train, create an endpoint and monitor the classification results of a Neural Network applied to the Iris dataset. Oct 3, 2019 · To simplify the problem of classification neural networks are being introduced. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects. Classification for the classical "Iris" dataset using Neural Networks, more specifically, a Multi-Layer Perceptron (MLP). Nowadays, it's hard to even find the infamous Iris Classification solved without using such tools. Backpropagation ANN demonstrated maximum training performance of 96. The 'softmax' ensures that the Tutorial 11: Artificial neural network using iris Dataset | Multiclass classification using ANN Fahad Hussain 41. Use cross-entropy loss. Iris Classification using Neural Networks This repository contains a neural network model built with PyTorch to classify Iris flowers into three species: Setosa, Versicolor, and Virginica. About Iris Classification ML is a machine learning pipeline built on Google Cloud Platform. The dataset (iris. Aim: To Identify a person’s gender from an iris image when such identification is related to security surveillance systems and forensics applications. Determining age using iris image is a popular strategy in all the time. Oct 26, 2020 · Human age group classification has great utilization in different fields. In this paper we present an approach based on perceptron Neural Network to classified Iris Plant on the basis of the following measurements: sepal length, sepal width, petal length, and petal width. Please Feb 6, 2020 · Perceptron Neural Network is the first model of Artificial Neural Network implemented to simplify some problems of classification. In terms of biometrics technologies, it’s not surprising that iris recognition has also seen an increasing adoption of purely data-driven approaches at all stages of the recognition A neural network model built with TensorFlow/Keras to classify Iris flowers into three species: Setosa, Versicolor, and Virginica. Today we are going to develop the model that is going to classify the iris flowers for … Feb 22, 2025 · We will build a neural network to classify the famous Iris dataset, which contains three classes of flowers based on their petal and sepal dimensions. It synergizes the unique properties of quantum bits or qubits with the various techniques in vogue in neural networks. The project includes data preprocessing, model training, evaluation, and data visualization in both 2D and 3D using PCA. Step 1: Install Dependencies Apr 7, 2023 · The PyTorch library is for deep learning. The outputs of the layer are the recognition results after the intensity detection. In this video, we are going to create a simple neural network using Keras for classifying different classes in the iris dataset. The MLP has the following structure- 4 input nodes -> 9 hidden nodes -> 3 output nodes 'Rectified Linear Unit' or 'ReLU' activation function is used for the hidden neurons. It includes data preprocessing, model training, and evaluation. Oct 12, 2024 · In this article, we will learn how we can create neural network models to perform classification on IRIS Dataset. In this blog post, we'll embark on a journey through a Python code snippet that taps into the prowess of neural networks, specifically the Multi-Layer Perceptron (MLP). This project explores the use of different neural network architectures on the Iris dataset to classify flower species. Google Colaboratory I will be creating a 2 layer neural network. About Iris flower classification using deep neural network. The dataset consists of 150 samples from each of three species of Iris flowers (Iris setosa, Iris virginica, and Iris versicolor), with 50 samples for each Dec 16, 2017 · To summarize, we covered a sample case study for the classification of Iris flower species based on the features that were used to train our neural network. Platform - Google Colab. It is a core principle of deep learning. Includes preprocessing, model training, and 2D/3D visualizations with PCA. Compared with previous deep iris recognition network, the network architecture has three characteristics: (1) Compared with most existing training and phase adjustment algorithms, it is end-to-end trainable. This dataset is very small, with only a 150 samples. A simple stand alone project of deep learning using PyTorch to solve classification problem. "# Iris Flower Classification This project is a Python implementation of the Iris Flower Classification using a simple single-layer neural network with softmax activation and cross-entropy loss. A Canny Edge Detection Feb 14, 2023 · Download Citation | On Feb 14, 2023, F. Introduction:In the vast field of machine learning, the classification of iris flowers based on their sepal and petal measurements remains a fascinating challenge. The classification results are Oct 30, 2020 · Neuron is a minimum unit of neural network. Our questions are: How to classify those two species? When given a new Iris, how to predict which Iris category it belongs to? Data Set The data set we will use is modified from the Iris In this work, IRIS flower classification using Neural Network. The notebook provides a step-by-step guide for implementation. Jul 4, 2021 · Iris flower – Photo taken by Sheila Swayze from Unsplash We all start our machine learning journey by implementing a small classification or regression problem. O Aranuwa and others published Classification Model for Multi-Classes Iris Image Using Deep Learning Neural Networks | Find, read and cite all the research Aug 1, 2024 · To improve the accuracy and efficiency of iris recognition systems, this research proposes an innovative approach for iris recognition, focusing on efficient segmentation and classification using Convolutional neural networks with Sheaf Attention Networks (CSAN). We will als o tune the accuracy by tuning these many parameters like Vary learning rate, Varying the number of epochs, Random weight initialization, Using SGD optimizer, Using activation functions: sigmoid, tanh. The model is trained to predict the species of Iris flowers based on four features: sepal length, sepal width, petal length, and petal width. No prior machine learning experience needed! Iris flower classification is a classic machine learning problem that involves classifying iris flowers into three species (setosa, versicolor, and virginica) based on the features of their flowers, such Neural networks are capable of classifying data with many dimensions, finding patterns in data that is very difficult for humans to see. Below is the link to my code on colab notebook. This dataset is a multi-class classification setting with four numeric features. The problem concerns To contribute a share in this vast ocean of vital research on pattern detection and recognition, attempts have been made to implement an algorithm for pattern classification with an objective to give state-of-the-art results for the IRIS flower dataset. Abstract : Classification is one of the most important approach of ML and it is a technique used to predict group membership for data instances. x1 is sepal Objective Many coders jump straight into using machine learning frameworks (PyTorch, TensorFlow, Keras, Theano) without understanding what's happening "under the hood". Dataset link: https://raw. Simple 3 layer artificial neural network for classification of iris plants. If you are keen to know more about real-time analytics using deep learning methodologies such as neural networks and multi-layer perceptrons, you can refer to the book Big Data Analytics The results showed that training the feed-forward neural network by GSA is better than training it by PSO in an iris recognition system. This thorough investigation offers insightful information about the efficacy of various algorithms In this work, IRIS flower classification using Neural Network. Sep 14, 2021 · Abstract Recently, Convolutional neural networks (CNN) have shown a growth due to their ability of learning different level image representations that helps in image classification in different fields. Today I will implement a multiclass classification neural network using pure numpy. We will proceed through the following steps: 1. This report focuses on IRIS Flower classification using K-Nearest neighbor and Random Forest. We will create and train a neural network with Linear layers and we will employ a Softmax activation function and the Adam optimizer. Aug 1, 2024 · To improve the accuracy and efficiency of iris recognition systems, this research proposes an innovative approach for iris recognition, focusing on efficient segmentation and classification using Convolutional neural networks with Sheaf Attention Networks (CSAN). The numbers indicating the size of Iris classification with scikit-learn ¶ Here we use the well-known Iris species dataset to illustrate how SHAP can explain the output of many different model types, from k-nearest neighbors, to neural networks. This paper introduces an iris classification system using FFNNGSA and FFNNPSO. Iris Dataset Overview Neural Network Architecture Mathematical Formulation Visualization of IrisNet Code Walkthrough Import Libraries and Load Data Convert to Tensors and Split Data Define Neural Network Initialize Weights Iris Dataset Overview Neural Network Architecture Mathematical Formulation Visualization of IrisNet Code Walkthrough This project demonstrates how to build and train a simple feedforward neural network using TorchSharp in C# to classify the Iris dataset. This project showcases iris flower classification using machine learning. The eye image data were collected from the IIT Delhi IRIS Database. After completing this step-by-step tutorial, you will know: How to load data from […] This project paper refers to experiments towards the classification of Iris plants with back propagation neural networks (BPNN). ABSTRACT Classification is a machine learning technique used to predict group membership for data instances. The quantum circuit is designed to encode the features of the Iris dataset using angle embedding and entangling gates, thereby capturing complex feature relationships that are difficult for classical models alone. In this tutorial, you will discover how to use PyTorch to develop and evaluate neural network models for multi-class classification problems. Sep 27, 2020 · In this short article we will have a look on how to use PyTorch with the Iris data set. gi May 31, 2021 · In this work, the study of gender classification using Artificial Neural Networks (ANN) based on iris recognition. . Jan 21, 2025 · About This repository contains a tutorial on building a neural network for classifying the Iris dataset into three classes: Iris Setosa, Versicolour, and Virginica. Written in Java Artificial Neural Networks (ANN) are widely used for pattern recognition and classification owing to the fact that they can approximate complex systems that are difficult to model using conventional modelling techniques such as mathematical modelling. The network analyzes four measurements (sepal length, sepal width, petal length, and petal width; all in cm) to make its predictions. Using the Iris dataset, it demonstrates neural networks' capabilities in handling simple classification tasks effectively. The problem concerns the identification of IRIS flower species on the basis of flower attribute measurements. The research employs supervised Jan 1, 2020 · The network model has high parameter efficiency and speed. A [2 5]. The tutorial covers Apr 6, 2025 · This article will provide the clear cut understanding of Iris dataset and how to do classification on Iris flowers dataset using python and sklearn. Data Download. Various algorithms are available for classification like decision tree, Navie Bayes etc. Explore and run machine learning code with Kaggle Notebooks | Using data from Iris Species Iris-Classification-Using-Neural-Networks This project demonstrates how to train a neural network on the Iris dataset using TensorFlow and Keras. Because this is a small dataset with only a few features we The development of a model to categorize Iris blossoms using an Artificial Neural Network (ANN) and Support Vector Machine (SVM) is the main goal of this paper. It's important we take the time to understand neural network fundamentals. The project also includes an interactive Streamlit app for real-time predictions with a clean and attractive UI. Jul 23, 2025 · The Iris dataset is often used as a beginner's dataset to understand classification and clustering algorithms in machine learning. We’ll use the Iris dataset, a classic in Aug 4, 2018 · In this short article we will take a quick look on how to use Keras with the familiar Iris data set. Feb 6, 2020 · The convolutional layer learns local patterns of given data in convolutional neural networks. About Classifies irises in different species based on their petal mensurations using a neural network in Matlab. Jan 2, 2025 · Artificial Neural Network for Iris Classification Introduction This project implements a neural network from scratch to help a gardener classify Iris flowers into three species: Setosa, Versicolor, and Virginica. GitHub Gist: instantly share code, notes, and snippets. About IRIS flower classification using Artificial Neural Network using tensor flow. Iris recognition technology achieves 97% accuracy for gender classification using artificial neural networks (ANN). Every iris has unique properties, which are the Explore and run machine learning code with Kaggle Notebooks | Using data from Iris Species Oct 21, 2024 · The objective of this task is to build a multiclass classification model using PyTorch to classify three Iris species (setosa, versicolor, and virginica) based on four flower features (sepal length, sepal width, petal length, petal width). About Neural Network for Iris Classification Using backpropagation. In this Explore and run machine learning code with Kaggle Notebooks | Using data from Iris Species Iris plant classifier using Neural Network Associative Classification system is more accurate than classifier build using CBA as shown in experimental result because weights are adjusted according to the class association rules and best one is used to classify the data. Aug 13, 2024 · In this blog, we’ll walk through how to build a multi-class classification model using PyTorch, one of the most popular deep-learning frameworks. This paper focuses on IRIS plant classification using Neural Network. Model Testing: Test the model with a few samples. The model is built using TensorFlow/Keras. This report focuses on IRIS plant classification using Neural Network. Methods: In this paper, a robust iris gender-identification method based on a deep convolutional neural network is introduced. The IIT Delhi IRIS Database includes 1120 images from 224 individuals for gender prediction studies. It includes data preprocessing, model building, training, evaluation, and making predictions on new data. Iris Classification with Keras Neural Network This project demonstrates how to build and train a neural network model using the Keras library to classify the Iris dataset. The process involved preparing data, constructing the model, and iterating through training and evaluation. To give you an idea, it is estimated that an average brain has 86 billion neurons and 100 billion synapses. The dataset used for this project is the famous Iris dataset. Abstract—Iris recognition has been an active research area during last few decades, because of its wide applications in security, from airports to homeland security border control. We learned how to preprocess the data, define a model with the appropriate output layer for multiclass problems, train the model, and make predictions. This also called an activation function in neural networks. com Introduction Artificial Neural Networks (ANNs) are extremely powerful. Question In the last section, we learn about perceptron. The network will predict the species of Iris flowers based on four features: sepal length, sepal width, petal length, and petal width. Achieved high accuracy and clear insights into dataset patterns. To simplify the problem of classification neural networks are being introduced. 6K subscribers 204 Nov 15, 2006 · This paper presents an idea of quantum neural networks along with the training algorithm and its convergence property. The code is written in Python using the Keras library. Aug 6, 2022 · Below is a function that will create a baseline neural network for the iris classification problem. (2) Grad-cam has class recognition and high resolution. An example application of Fisher's Iris data set, a benchmark classification problem has also been presented. In this tutorial, you'll learn how to implement a convolutional layer to classify the Iris dataset in a simple way. Aug 1, 2018 · Machine learning-Iris classification Hai Everyone, Welcome to the part two of the machine learning tutorial. Sep 26, 2024 · This repository contains two projects focused on classifying the Iris dataset using neural networks built with PyTorch. To summarize, the Iris flower dataset has been extensively employed for the assessment and comparison of diverse machine learning methodologies, such as conventional classifiers, ensemble approaches, neural networks, and sophisticated techniques. It's a beginner-friendly example of data science and classification techniques. If you are keen to know more about real-time analytics using deep learning methodologies such as neural networks and multi-layer perceptrons, you can refer to the book Big Data Analytics Aug 1, 2024 · To improve the accuracy and efficiency of iris recognition systems, this research proposes an innovative approach for iris recognition, focusing on efficient segmentation and classification using Convolutional neural networks with Sheaf Attention Networks (CSAN). Explore the code, Jupyter Notebook, and Mar 11, 2022 · Model Building: Use Remote Python Script Snap from ML Core Snap Pack to deploy python script to train neural networks model on Iris flower dataset. In this paper, we propose an end-to-end deep learning framework for iris recognition based on residual convolutional neural Classification of the Iris dataset using Artificial Neural Networks (ANN). It helps to extract the features of input data to provide the output. The tutorial covers: Preparing the data Defining and fitting the model Apr 5, 2022 · Credits to Wirestock @ Freepik. The goal is to classify Iris flowers among three species (Setosa, Versicolor or Virginica) from measurements of length and width of sepals and petals. Jul 3, 2020 · This simple example shows that creating a neural network model with PyTorch and working on according to needs is easy and flexible. 4% in gender classification tasks. An example application of Fisher’s Iris data set, a benchmark classification problem has also been presented. Feb 6, 2020 · In this paper we present an approach based on perceptron Neural Network to classified Iris Plant on the basis of the following measurements: sepal length, sepal width, petal length, and petal width. GitHub is where people build software. The diagram below represents a neural network (multilayered perceptron) with L-layers where each layer l (1 ≤ l ≤ L) contains kl neurons. Nov 15, 2006 · This paper presents an idea of quantum neural networks along with the training algorithm and its convergence property. We build a multilayer deep neural net first and train it using Iris data This project uses a neural network to classify iris species (Setosa, Versicolour, Virginica) based on sepal and petal dimensions. Scikit tool is used for implementation purpose. data) is loaded using Pandas, containing information about sepal The integration of Convolutional Neural Networks into iris recognition systems has significantly advanced the field, offering improved accuracy and robustness. The Iris dataset contains 150 samples of iris flowers with 4 features: sepal length, sepal width, petal length, and petal width. Explore and run machine learning code with Kaggle Notebooks | Using data from Iris Flower Dataset Neural Network for Iris Dataset This repository contains the code for implementing an Artificial Neural Network (ANN) on the Iris dataset using Google Colab. No prior machine learning experience needed! Iris flower classification is a classic machine learning problem that involves classifying iris flowers into three species (setosa, versicolor, and virginica) based on the features of their flowers, such Oct 25, 2020 · In this post, you will learn about how to train a neural network for multi-class classification using Python Keras libraries and Sklearn IRIS dataset. We will compare networks with the regular Dense layer with different number of nodes and we will employ a Softmax activation function and the Adam optimizer. This repository contains a simple neural network model built using PyTorch to classify iris species based on the well-known Iris dataset. The Iris flowers dataset is one of the best-known datasets found in the classification literature. One of the most popular and best known databases of the neural network application is the Iris plant dataset which is obtained from UCI Machine Learning Repository and created by R. This project implements a neural network using PyTorch to classify the Iris dataset into different species. The authors employ a multilayer feed-forward network trained with a back-propagation algorithm to analyze patterns in the Iris dataset, aiming to enhance prediction accuracy for unknown data in the future. Iris Classification Problem Along this notebook we'll explain how to use the power of cloud computing with Google Colab for a classical example – The Iris Classification Problem – using the popular Iris flower dataset. Feb 24, 2025 · Simple Example for Multiclass Classification with Keras This workflow trains a fully connected feedforward neural network with 4-8-3 units per layers to classify iris flowers. - seyed2d/Iris-Classification Iris Classification Using Neural Network This project builds a Neural Network (NN) model to classify the Iris dataset into three categories: Setosa, Versicolor, and Virginica. This readme is introduced in Chinese (including most of the comments in the code). How to use it to classify data? To answer this question, let’s look at an example first: there is a data set which consists of two species of Iris. - Ashie32/Iris-classification-using-ANN Mar 5, 2024 · Deep learning-based methods, in particular using various Convolutional Neural Network (CNN) architectures, have been driving remarkable improvements in many computer vision applications over the last decade. Balakrishnan and others published Iris image classification using convolutional neural network for human identity detection | Find, read and cite all the research About IRIS flower classification using Artificial Neural Network using tensor flow. First Feb 29, 2012 · This paper focuses on IRIS plant classification using Neural Network. May 24, 2024 · In this blog post, I’ll guide you through building a simple neural network using PyTorch to classify Iris species — an introductory machine learning problem that involves categorizing flowers Along this notebook we'll explain how to use the power of cloud computing with Google Colab for a classical example – The Iris Classification Problem – using the popular Iris flower dataset. Although significant advancement has been made in classification related areas of neural networks, various issues in applying neural networks Sep 4, 2024 · In this tutorial, we built a neural network using TensorFlow to perform multiclass classification on the Iris dataset. The classification models include: Binary Classification - Classifies whether the species is Setosa or Non-Setosa. Mar 7, 2022 · Poojitha V, Shilpi Jain, "A Collecation of IRIS Flower Using Neural Network CLusterimg tool in MATLAB", International Journal on Computer Science And Engineering (IJCSE). Dec 14, 2024 · In this article, we implemented a simple feedforward neural network using PyTorch to solve a binary classification problem. The proposed One of the most popular and best known databases of the neural network application is the Iris plant dataset which is obtained from UCI Machine Learning Repository and created by R. By using the features of the iris flowers, researchers and data scientists can classify each sample into one of the three species. This project demonstrates the classification of the Iris dataset using an Artificial Neural Network (ANN) implemented with TensorFlow. Abstract Introduction: One attractive research area in the computer science field is soft biometrics. In this work, IRIS flower classification using Neural Network. We'll use the Conv1D layer of Keras API. We use a random set of 130 for training and 20 for testing the models. Mar 13, 2025 · Iris Classification using a Neural Network. Different features and algorithms have been proposed for iris recognition in the past. In this post, we'll briefly learn how to classify the Iris dataset with the 'neuralnet' package in R. The screenshot below shows a preview of this dataset, there are three types of Iris flowers: setosa, versicolor, and virginica. More advanced problems (classifying image data with CNNs etc Mar 25, 2023 · Deep learning approaches also play an important part in iris-based human identity recognition, harnessing the capacity of neural networks to increase accuracy and reliability of the identification process. Mar 1, 2012 · This paper focuses on IRIS plant classification using Neural Network. It's one way to convert continous values into more of a binary value. On the other hand, the largest NN in 2022, "Megatron-Turing NGL 530B (MT-NGL)", a Implementing a Multi-Layer Perceptron from Scratch Author : Mobin Nesari Prepared for : The Artificial Neural Network Graduate course 2023 Shahid Beheshti University Introduction A Multi-Layer Perceptron (MLP) is a type of artificial neural network that is commonly used for supervised learning tasks, such as binary and multi-class classification. I will use the iris dataset. It creates a simple, fully connected network with one hidden layer that contains eight neurons. The dataset is sourced from Kaggle. The model is trained and evaluated using standard deep learning techniques. Based on the human brain process, the neural network algorithm loosely imitates the learning method of biological neural networks. Iris flower classification using the diffractive neural network a Network consists of one layer. The model is expected to achieve an accuracy of over 95% and This example illustrates how a self-organizing map neural network can cluster iris flowers into classes topologically, providing insight into the types of flowers and a useful tool for further analysis.