Neural Network Simulator in Python and QT

In this article, we will learn how to create a Neural Network Simulator from scratch using Python and QT (PySide6) with
Stochastic Gradient Descent (SGD), Momentum, and ADAM optimization algorithms.

Introduction

Neural networks are a powerful tool for performing complex tasks such as image recognition, natural language processing,
and more. By simulating a neural network, we can gain a better understanding of how it works and improve its performance.

Implementation

First, we need to install PySide6, a Python binding for the Qt application framework. We can do this using pip:

pip install PySide6

Next, we will create a neural network class that implements SGD, Momentum, and ADAM optimization algorithms. The class will
have methods for training the network, forward and backward propagation, and more.

class NeuralNetwork:
    def __init__(self, input_size, hidden_size, output_size):
        # initialize network parameters
        pass

    def forward(self, x):
        # forward propagation
        pass

    def backward(self, x, y):
        # backward propagation
        pass

    def train(self, x_train, y_train, epochs, learning_rate, optimizer):
        # train the network
        pass

Usage

Now, we can create an instance of the NeuralNetwork class and train it on some data:

nn = NeuralNetwork(input_size=784, hidden_size=128, output_size=10)
nn.train(x_train, y_train, epochs=10, learning_rate=0.001, optimizer='SGD')

Conclusion

In this article, we have learned how to create a Neural Network Simulator from scratch using Python and QT with SGD, Momentum,
and ADAM optimization algorithms. By experimenting with different hyperparameters and optimization algorithms, we can improve
the performance of our neural network and gain a better understanding of how it works.