PyTorch Tutorial

PyTorch is an open-source machine learning library, mainly used for research and development in fields such as computer vision (CV), natural language processing (NLP), and speech recognition.

PyTorch was developed by Facebook's AI research team and is widely used in the machine learning and deep learning community.

PyTorch is known for its flexibility and ease of use, especially suitable for deep learning research and development.

Who is this tutorial for?

As long as you have basic programming knowledge, you can read this tutorial. Learning PyTorch is suitable for people interested in deep learning and machine learning, including data scientists, engineers, researchers, and students.

Before reading this tutorial, you need to know:

Before you start reading this tutorial, the basic knowledge you must have includes Python programming, basic mathematics (linear algebra, probability theory, calculus), basic machine learning concepts, neural network knowledge, and certain English reading ability to consult documentation and materials.

  • Programming basics: Familiar with at least one programming language, especiallyPython, because PyTorch is mainly written in Python.

  • Math basics: Understand basic mathematical knowledge such as linear algebra, probability theory and statistics, calculus, which are the cornerstone of understanding and implementing machine learning algorithms.

  • Machine learning basics: Understand basic concepts of machine learning, such as supervised learning, unsupervised learning, reinforcement learning, and model evaluation metrics (accuracy, recall, F1 score, etc.).

  • Deep learning basics: Be familiar with basic concepts of neural networks, including feedforward neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), long short-term memory networks (LSTM), etc.

  • Computer vision and natural language processing basics: If you plan to apply PyTorch in these fields, understanding relevant background knowledge will be very helpful.

  • Linux/Unix basics: Although not required, understanding the basics of the Linux/Unix operating system can help you use command-line tools and scripts more effectively, especially in data preprocessing and model training.

  • English reading ability: Since many documents, tutorials, and community discussions are conducted in English, having some English reading ability will help you learn and solve problems better.

Examples

Below are some basic tensor operations in PyTorch: how to create random tensors, perform element-wise operations, access specific elements, and compute sums and maximum values.

Examples

import torch

# Set data type and device
dtype = torch.float  # Tensor data type is float
device = torch.device("cpu")  # This computation runs on CPU

# Create and print two random tensors a and b
a = torch.randn(2, 3, device=device, dtype=dtype)  # Create a 2x3 random tensor
b = torch.randn(2, 3, device=device, dtype=dtype)  # Create another 2x3 random tensor

print("Tensor a:")
print(a)

print("Tensor b:")
print(b)

# Multiply element-wise and output the result
print("Element-wise product of a and b:")
print(a * b)

# Output the sum of all elements of tensor a
print("Sum of all elements of tensor a:")
print(a.sum())

# Output the element at row 2, column 3 of tensor a (note indices start from 0)
print("Element at row 2, column 3 of tensor a:")
print(a[1, 2])

# Output the maximum value in tensor a
print("Maximum value in tensor a:")
print(a.max())

Creating tensors:

  • torch.randn(2, 3)Create a 2-row, 3-column tensor filled with random numbers (following a normal distribution).
  • device=deviceanddtype=dtypeThe computation device (CPU or GPU) and data type (floating point) are specified respectively.

Tensor operations:

  • a * b: Element-wise multiplication.
  • a.sum(): Compute the tensorasum of all elements.
  • a[1, 2]: Access the tensoraelement at row 2, column 3 (note indices start from 0).
  • a.max(): Get the tensoramaximum value in.

Output: (values will differ each run)

张量 a:
tensor([[-0.1460, -0.3490,  0.3705],
        [-1.1141,  0.7661,  1.0823]])

张量 b:
tensor([[ 0.6901, -0.9663,  0.3634],
        [-0.6538, -0.3728, -1.1323]])

a 和 b 的逐元素乘积:
tensor([[-0.1007,  0.3372,  0.1346],
        [ 0.7284, -0.2856, -1.2256]])

张量 a 所有元素的总和:
tensor(0.6097)

张量 a 第 2 行第 3 列的元素:
tensor(1.0823)

张量 a 中的最大值:
tensor(1.0823)

Reference Links

PyTorch Official Website:https://pytorch.org/

PyTorch Official Getting Started Tutorial:https://pytorch.org/get-started/locally/

PyTorch Official Documentation:https://pytorch.org/docs/stable/index.html

PyTorch Source Code:https://github.com/pytorch/pytorch

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