How to make a simple neural network on python

Learn how to build a simple neural network in Python with an example.

Creating a Simple Neural Network Using Python

A neural network is a type of machine learning algorithm that is modeled after a human brain. It is capable of learning from data and making predictions. In this article, we will create a simple neural network using the Python programming language.

We will use the popular scikit-learn library for creating our neural network. Scikit-learn is a popular library for machine learning and data science in Python. It provides simple and efficient tools for data analysis, as well as a wide variety of algorithms for classification and regression.

First, we need to import the necessary modules. We will use the numpy module for mathematical operations, and the sklearn module for creating our neural network. In the following code, we import the necessary modules:

import numpy as np
from sklearn.neural_network import MLPClassifier

Next, we create our neural network. We create an MLPClassifier object, which is a type of neural network classifier. Then we set the parameters for our neural network. In this example, we will use two hidden layers, with 100 neurons each. We will also use the logistic activation function for the output layer. Finally, we set the maximum number of iterations to 1000:

model = MLPClassifier(hidden_layer_sizes=(100, 100), activation='logistic', max_iter=1000)

Now, we need to train our model. We will use the fit() function to train our model on the data. In this example, we will use the Iris dataset, which contains measurements of 150 iris flowers. This dataset is available in the scikit-learn library:

from sklearn.datasets import load_iris

# Load the Iris dataset
iris = load_iris()

# Train the model
model.fit(iris.data, iris.target)

Finally, we can use our model to make predictions. We can use the predict() function to make predictions on new data. For example, if we have a flower with the following measurements:

# Measurements of a flower
flower_measurements = [5.0, 2.0, 3.5, 1.0]

We can use our model to predict the type of flower:

# Predict the type of flower
prediction = model.predict([flower_measurements])

# Print the prediction
print(prediction)

The model will output the predicted class of the flower, which is 0 in this example. This means that the model predicts that the flower is an Iris Setosa.

In this article, we have seen how to create a simple neural network using Python and the scikit-learn library. We have also seen how to use the model to make predictions. Neural networks are powerful machine learning algorithms, and they can be used for a variety of tasks, from image classification to predicting stock prices.