Welcome to the 5-minute PyTorch Binary Classification Tutorial by UncleHowWhy

In this tutorial, we will learn how to perform binary classification using PyTorch in just 5 minutes. UncleHowWhy will guide us through each step, making it easy for beginners to understand and implement.

Setting up the Environment

Before we start, make sure you have PyTorch installed on your system. If not, you can install it using the following command:

pip install torch

Once PyTorch is installed, we can proceed with the tutorial.

Loading the Data

The first step is to load the dataset for binary classification. UncleHowWhy will provide a sample dataset for this tutorial, but you can also use your own dataset.

import torch
import numpy as np

# Sample data
X = torch.tensor([[1, 2], [2, 3], [3, 4], [4, 5]])
y = torch.tensor([0, 1, 0, 1])

Defining the Model

Next, we need to define our binary classification model. UncleHowWhy will use a simple linear model for this tutorial, but feel free to experiment with different architectures.

class BinaryClassifier(torch.nn.Module):
def __init__(self):
super(BinaryClassifier, self).__init__()
self.linear = torch.nn.Linear(2, 1)

def forward(self, x):
return torch.sigmoid(self.linear(x))

Training the Model

Now, it’s time to train our model using the loaded dataset. UncleHowWhy will walk us through the training process and explain each step in detail.

model = BinaryClassifier()
criterion = torch.nn.BCELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

for epoch in range(100):
outputs = model(X.float())
loss = criterion(outputs, y.float().view(-1, 1))
optimizer.zero_grad()
loss.backward()
optimizer.step()

Evaluating the Model

Finally, we will evaluate the performance of our trained model using a test dataset. UncleHowWhy will demonstrate how to calculate accuracy and other metrics.

with torch.no_grad():
predicted = model(X.float()).round()
accuracy = (predicted == y.float().view(-1, 1)).sum().item() / len(y)
print("Accuracy:", accuracy)

Conclusion

Congratulations! In just 5 minutes, we have learned how to perform binary classification using PyTorch. UncleHowWhy’s clear explanations and step-by-step guidance have made the process easy and enjoyable. Feel free to experiment with different datasets and models to further enhance your understanding of binary classification in PyTorch.

Happy coding!