PyTorch Tutorial: Linear Regression
In this tutorial, we will learn how to perform linear regression using PyTorch.
Step 1: Import PyTorch
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest"></script>
Step 2: Create Data
import torch import numpy as np # Create some data X = torch.tensor([[1.0], [2.0], [3.0], [4.0]]) y = torch.tensor([[2.0], [4.0], [6.0], [8.0]])
Step 3: Create a Model
# Define a linear regression model
class LinearRegression(torch.nn.Module):
def __init__(self):
super(LinearRegression, self).__init__()
self.linear = torch.nn.Linear(1, 1)
def forward(self, x):
return self.linear(x)
Step 4: Train the Model
<pre# Instantiate the model
model = LinearRegression()
# Define the loss function and optimizer
criterion = torch.nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
# Train the model
for epoch in range(100):
# Forward pass
y_pred = model(X)
# Compute the loss
loss = criterion(y_pred, y)
# Backward pass
optimizer.zero_grad()
loss.backward()
optimizer.step()
Step 5: Make Predictions
# Make predictions new_X = torch.tensor([[5.0], [6.0]]) new_y = model(new_X) print(new_y)
That’s it! You have now successfully performed linear regression using PyTorch.
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