Build Your First Model with PyTorch and Python

In this tutorial, we will learn how to build our first model using PyTorch and Python. PyTorch is a popular machine learning library that is widely used for building neural network models. It provides a dynamic computational graph that allows you to define and train your models with ease.

Before we start building our model, make sure you have PyTorch installed on your system. You can install PyTorch using pip:

pip install torch

Now, let’s get started by importing the necessary libraries:


import torch
import torch.nn as nn
import torch.optim as optim


Next, let’s define our model. In this tutorial, we will build a simple neural network with one hidden layer:


class NeuralNetwork(nn.Module):
def __init__(self):
super(NeuralNetwork, self).__init__()
self.hidden_layer = nn.Linear(10, 5)
self.output_layer = nn.Linear(5, 1)

def forward(self, x):
x = torch.relu(self.hidden_layer(x))
x = self.output_layer(x)
return x


Now that we have defined our model, let’s create an instance of it and define our loss function and optimizer:


model = NeuralNetwork()
criterion = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)


Finally, let’s train our model using some sample data:


X = torch.randn(100, 10)
y = torch.randn(100, 1)

for epoch in range(100):
optimizer.zero_grad()
output = model(X)
loss = criterion(output, y)
loss.backward()
optimizer.step()


And that’s it! We have successfully built and trained our first model using PyTorch and Python. I hope this tutorial was helpful for beginners getting started with PyTorch. Happy coding!