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# PyTorch Cheatsheet

PyTorch is a popular open-source machine learning library based on the Torch library. It provides easy-to-use APIs for building and training deep neural networks. This cheatsheet provides a quick reference for some of PyTorch's unique features, including code blocks for variables, functions, loops, conditionals, file manipulation, and more. Additionally, it includes a list of resources for further learning.

## Variables

import torch

Create a tensor

x = torch.tensor([1, 2, 3])

Create a matrix

y = torch.tensor([[1, 2], [3, 4]])

Get the shape of a tensor

x.shape

Get the number of dimensions of a tensor

x.ndim

Get the size of a tensor

x.size()

Reshape a tensor

x.reshape(3, 1)

Concatenate two tensors

torch.cat((x, x))

Get the maximum value of a tensor

x.max()


## Functions

import torch.nn.functional as F

Apply softmax function

F.softmax(x, dim=0)

Apply ReLU activation function

F.relu(x)

Apply cross-entropy loss function

F.cross_entropy(y_pred, y_true)


## Loops and Conditionals

For loop

for i in range(10):

print(i)

While loop

while x < 10:

print(x)

x += 1

If statement

if x > 0:

print("x is positive")

elif x == 0:

print("x is zero")

else:

print("x is negative")


## File Manipulation

import torch

Save a tensor to a file

torch.save(x, 'x.pt')

Load a tensor from a file

x = torch.load('x.pt')


## Other Useful Features

import torch

Set the random seed for reproducibility

torch.manual_seed(42)

Move a tensor to the GPU

x.cuda()

Compute gradients

x.requires_grad = True

y = x**2

y.backward()

Define a neural network

import torch.nn as nn

class Net(nn.Module):

def __init__(self):

super(Net, self).__init__()

self.fc1 = nn.Linear(10, 5)

self.fc2 = nn.Linear(5, 1)

def forward(self, x):

x = F.relu(self.fc1(x))

x = self.fc2(x)

return x


## Resources

- [PyTorch documentation](https://pytorch.org/docs/stable/index.html)
- [PyTorch tutorials](https://pytorch.org/tutorials/)
- [Deep Learning with PyTorch book](https://pytorch.org/assets/deep-learning/Deep-Learning-with-PyTorch.pdf)