How to use numpy array and matrix multiplication
This time I will show you how to use numpyarray and matrix multiplication, what are the precautions when using numpy array and matrix multiplication, the following is a practical case, let's take a look one time.
1, When it is array, the default d*f is the product of the corresponding elements, multiply is also the product of the corresponding elements, dot (d, f) will be converted into the product of matrices. dot dot multiplication means addition, while multiply only multiplies the corresponding elements and does not add.
2, When it is mat, the default d*f is the product of the matrix, multiply is converted into the product of the corresponding elements, dot (d, f) is the product of the matrix
3. When mixing, generally do not mix.
When mixing, the default is matrix multiplication. , multiply is converted into the product of the corresponding elements, dot (d, f) is the product of the matrix
Summary: The default for array multiplication is dot multiplication, and the default for matrix It is matrix multiplication. When mixed together, the default is matrix multiplication. multiply is converted into the product of the corresponding elements, and dot (d, f) will be converted into the product of the matrix. Note that when multiply does not satisfy the corresponding element, it is performed in the broadcast manner.
I believe you have mastered the method after reading the case in this article. For more exciting information, please pay attention to other related articles on the php Chinese website!
Recommended reading:
How to solve the greatest common divisor in Python
How to merge numpy arrays in Python
The above is the detailed content of How to use numpy array and matrix multiplication. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics

Numpy is an important mathematics library in Python. It provides efficient array operations and scientific calculation functions and is widely used in data analysis, machine learning, deep learning and other fields. When using numpy, we often need to check the version number of numpy to determine the functions supported by the current environment. This article will introduce how to quickly check the numpy version and provide specific code examples. Method 1: Use the __version__ attribute that comes with numpy. The numpy module comes with a __

Teach you step by step to install NumPy in PyCharm and make full use of its powerful functions. Preface: NumPy is one of the basic libraries for scientific computing in Python. It provides high-performance multi-dimensional array objects and various functions required to perform basic operations on arrays. function. It is an important part of most data science and machine learning projects. This article will introduce you to how to install NumPy in PyCharm, and demonstrate its powerful features through specific code examples. Step 1: Install PyCharm First, we

How to upgrade numpy version: Easy-to-follow tutorial, requires concrete code examples Introduction: NumPy is an important Python library used for scientific computing. It provides a powerful multidimensional array object and a series of related functions that can be used to perform efficient numerical operations. As new versions are released, newer features and bug fixes are constantly available to us. This article will describe how to upgrade your installed NumPy library to get the latest features and resolve known issues. Step 1: Check the current NumPy version at the beginning

With the rapid development of fields such as data science, machine learning, and deep learning, Python has become a mainstream language for data analysis and modeling. In Python, NumPy (short for NumericalPython) is a very important library because it provides a set of efficient multi-dimensional array objects and is the basis for many other libraries such as pandas, SciPy and scikit-learn. In the process of using NumPy, you are likely to encounter compatibility issues between different versions, then

Numpy installation guide: One article to solve installation problems, need specific code examples Introduction: Numpy is a powerful scientific computing library in Python. It provides efficient multi-dimensional array objects and tools for operating array data. However, for beginners, installing Numpy may cause some confusion. This article will provide you with a Numpy installation guide to help you quickly solve installation problems. 1. Install the Python environment: Before installing Numpy, you first need to make sure that Py is installed.

The secret of how to quickly uninstall the NumPy library is revealed. Specific code examples are required. NumPy is a powerful Python scientific computing library that is widely used in fields such as data analysis, scientific computing, and machine learning. However, sometimes we may need to uninstall the NumPy library, whether to update the version or for other reasons. This article will introduce some methods to quickly uninstall the NumPy library and provide specific code examples. Method 1: Use pip to uninstall pip is a Python package management tool that can be used to install, upgrade and

Detailed explanation of numpy slicing operation method and practical application guide Introduction: Numpy is one of the most popular scientific computing libraries in Python, providing powerful array operation functions. Among them, slicing operation is one of the commonly used and powerful functions in numpy. This article will introduce the slicing operation method in numpy in detail, and demonstrate the specific use of slicing operation through practical application guide. 1. Introduction to numpy slicing operation method Numpy slicing operation refers to obtaining a subset of an array by specifying an index interval. Its basic form is:

Examples and applications of Tensor and Numpy conversion TensorFlow is a very popular deep learning framework, and Numpy is the core library for Python scientific computing. Since both TensorFlow and Numpy use multi-dimensional arrays to manipulate data, in practical applications, we often need to convert between the two. This article will introduce how to convert between TensorFlow and Numpy through specific code examples, and explain its use in practical applications. head
