🔥 PyTorch Tutorial 1.1: Tensor Basics - From Zero to Hero

Tutorial 1.1: Tensor Basics

This is the first part of my comprehensive PyTorch tutorial series. Full series available on GitHub.

Learning Objectives

• Understand what a Tensor is
• Master tensor creation methods
• Learn basic tensor operations

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1. What is a Tensor?

A Tensor is the fundamental data structure in PyTorch, similar to NumPy's multi-dimensional arrays.

Dimensions	Name	Example
0D	Scalar	A single number 5
1D	Vector	[1, 2, 3]
2D	Matrix	[[1,2], [3,4]]
3D+	Tensor	Image data [batch, channels, height, width]

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2. Tensor Types

PyTorch supports various data types:

Data Type	CPU Tensor	GPU Tensor
32-bit float (default)	torch.FloatTensor	torch.cuda.FloatTensor
64-bit float	torch.DoubleTensor	torch.cuda.DoubleTensor
32-bit integer	torch.IntTensor	torch.cuda.IntTensor
64-bit integer	torch.LongTensor	torch.cuda.LongTensor

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3. Creating Tensors

3.1 From Python Lists

import torch

# Create from list
x = torch.tensor([1, 2, 3])
print(x)  # tensor([1, 2, 3])

# Create 2x3 matrix
matrix = torch.tensor([[1, 2, 3], [4, 5, 6]])
print(matrix)
# tensor([[1, 2, 3],
#         [4, 5, 6]])

3.2 Special Value Tensors

# All zeros
zeros = torch.zeros(2, 3)  # 2 rows, 3 columns

# All ones
ones = torch.ones(2, 3)

# Identity matrix
eye = torch.eye(3)  # 3x3 identity matrix

# Fill with specific value
full = torch.full((2, 3), 7)  # 2x3, filled with 7

3.3 Random Tensors

# Uniform distribution [0, 1)
rand = torch.rand(2, 3)

# Standard normal distribution
randn = torch.randn(2, 3)

# Random integers in range
randint = torch.randint(0, 10, (2, 3))  # Range [0, 10)

3.4 Sequences

# arange: similar to Python range
x = torch.arange(0, 10, 2)  # start, end, step
print(x)  # tensor([0, 2, 4, 6, 8])

# linspace: n evenly spaced points
x = torch.linspace(0, 10, 5)  # start, end, num_points
print(x)  # tensor([ 0.0,  2.5,  5.0,  7.5, 10.0])

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4. Tensor Attributes

x = torch.randn(3, 4)

print(x.shape)      # torch.Size([3, 4])
print(x.dim())      # 2 (number of dimensions)
print(x.numel())    # 12 (total elements)
print(x.dtype)      # torch.float32
print(x.device)     # cpu

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5. Indexing and Slicing

x = torch.tensor([[1, 2, 3],
                  [4, 5, 6],
                  [7, 8, 9]])

# Single element
print(x[0, 0])      # tensor(1)

# Get a row
print(x[0])         # tensor([1, 2, 3])

# Get a column
print(x[:, 0])      # tensor([1, 4, 7])

# Slicing
print(x[0:2, 1:3])  # tensor([[2, 3], [5, 6]])

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6. Shape Operations

6.1 Reshaping

x = torch.arange(12)

# view: reshape (shares memory)
y = x.view(3, 4)

# Use -1 to auto-infer dimension
z = x.view(2, -1)  # becomes (2, 6)

# reshape: more flexible
w = x.reshape(4, 3)

6.2 Adding/Removing Dimensions

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

# Add dimension
y = x.unsqueeze(0)  # shape: [1, 3]
y = x.unsqueeze(1)  # shape: [3, 1]

# Remove dimension (size 1 only)
z = y.squeeze()  # back to [3]

6.3 Transpose

x = torch.tensor([[1, 2, 3],
                  [4, 5, 6]])  # shape: [2, 3]

y = x.t()  # shape: [3, 2]
z = x.transpose(0, 1)  # same as t()

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7. Practice Exercises

Exercise 1: Create a 3x3 zero matrix, a 2x4 random normal matrix, and a vector from 1 to 9.

Exercise 2: Given x = torch.arange(1, 13).view(3, 4), get the second row, last column, and bottom-right 2x2 submatrix.

Exercise 3: Transform a tensor of shape (2, 3, 4) to shape (6, 4).

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Next in Series

• 1.2 Tensor Operations
• 1.3 Data Loading
• 2.1 Neural Network Modules
• ... and 9 more tutorials!

👉 Full tutorial series: GitHub Repo

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