Introduction to PyTorch: PyTorch Cheatsheet
If you’re starting your deep learning journey and have chosen PyTorch, you’re already on the right track. PyTorch is a powerful open-source deep learning framework developed by Facebook’s AI Research lab, known for its simplicity and flexibility.
In this PyTorch cheatsheet, you’ll find a practical introduction to core concepts that every beginner must know to build neural networks, train models, and manipulate data efficiently.
📌 What is PyTorch?
PyTorch is a machine learning library based on Python and Torch. It allows you to perform tensor computations, build deep neural networks, and even perform dynamic computation graphs (unlike TensorFlow 1.x).
Why PyTorch?
- Pythonic and intuitive
- Dynamic graph computation
- Strong GPU support
- Widely used in research & industry
🧮 PyTorch Cheatsheet (Quick Commands)
Here’s a quick list of commonly used PyTorch commands and operations.
| Operation | Command |
|---|---|
| Import PyTorch | import torch |
| Create Tensor | x = torch.tensor([1.0, 2.0]) |
| Random Tensor | torch.rand(2, 3) |
| Check GPU | torch.cuda.is_available() |
| Send tensor to GPU | x.to('cuda') |
| Reshape Tensor | x.view(2, 1) |
| Basic Math | x + y, x * y |
| Clone Tensor | x.clone() |
| Save model | torch.save(model.state_dict(), 'model.pth') |
| Load model | model.load_state_dict(torch.load('model.pth')) |
🧪 PyTorch Basics
1. Tensors in PyTorch
Tensors are the building blocks of PyTorch. They are like NumPy arrays but with GPU support.
pythonCopy codeimport torch
x = torch.tensor([[1, 2], [3, 4]])
print(x)
2. Creating a Neural Network
import torch.nn as nn
class MyModel(nn.Module):
def __init__(self):
super().__init__()
self.layer = nn.Linear(10, 5)
def forward(self, x):
return self.layer(x)
model = MyModel()
3. Loss Function & Optimizer
pythonCopy codecriterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
4. Training Loop (Simplified)
codefor epoch in range(10):
optimizer.zero_grad()
output = model(torch.rand(1, 10))
loss = criterion(output, torch.rand(1, 5))
loss.backward()
optimizer.step()
🤔 People Also Ask
What is PyTorch used for?
PyTorch is used for building and training neural networks in tasks like image classification, NLP, and reinforcement learning.
Is PyTorch better than TensorFlow?
For beginners and research, PyTorch is often considered more intuitive. TensorFlow is widely used in production.
How to install PyTorch?
Use the command from PyTorch’s official site based on your OS and CUDA support.
Is there a PyTorch cheatsheet?
Yes, and this article is your ultimate beginner-friendly PyTorch cheatsheet.
🏁 Conclusion
This PyTorch cheatsheet is your quick start to deep learning using one of the most powerful libraries available. Whether you’re a beginner or revisiting PyTorch after a break, keep this guide bookmarked for rapid access to essential commands and practices.