Gal Curious

I've heard about neural networks in programming. How can I create a simple one? 🧠

Geek Smiling

Neural networks are the foundation of deep learning. Let's create a simple one using Python and PyTorch!

Gal Happy

Sounds fun! Let's focus on defining the network this time. 🚀

Step 1: Import Libraries

Gal Eager

What libraries do we need?

Geek Nodding

We'll use PyTorch and torchvision for this example.

import torch
import torch.nn as nn
import torchvision

Step 2: Define the Neural Network

Gal Excited

Now, let's create the neural network!

Geek Smiling

We'll create a simple feedforward neural network with one hidden layer. Let me explain each part of the code.

class SimpleNeuralNetwork(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(SimpleNeuralNetwork, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        out = self.fc1(x)
        out = self.relu(out)
        out = self.fc2(out)
        return out
Gal Curious

What does "nn.Module" mean?

Geek Smiling

nn.Module is the base class for all neural network modules in PyTorch. It provides useful functions and helps keep track of the model's parameters.

Gal Wondering

And what's " init "?

Geek Happy

init is the constructor of the class. It sets up the structure of the neural network with the input, hidden, and output layers.

Gal Question

How about "forward"?

Geek Nodding

The forward function defines how the input data flows through the network. It takes the input data, passes it through the layers, and returns the output.


Now you know how to define a simple neural network with Python and PyTorch! We’ve explained the importance of the nn.Module class, the constructor, and the forward function. Keep exploring and have fun with neural networks! 🎉