Introduction
I've heard about neural networks in programming. How can I create a simple one? 🧠
Neural networks are the foundation of deep learning. Let's create a simple one using Python and PyTorch!
Sounds fun! Let's focus on defining the network this time. 🚀
Step 1: Import Libraries
What libraries do we need?
We'll use PyTorch and torchvision for this example.
import torch
import torch.nn as nn
import torchvision
Step 2: Define the Neural Network
Now, let's create the neural network!
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
What does "nn.Module" mean?
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.
And what's " init "?
init is the constructor of the class. It sets up the structure of the neural network with the input, hidden, and output layers.
How about "forward"?
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.
Conclusion
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! 🎉