Source: Deephub Imba This article is approximately 2100 words long and is suggested to be read in 9 minutes. This article will introduce two methods to apply augmentation to datasets in TensorFlow. For image-related tasks, common data augmentation methods include rotating, blurring, or resizing images. This is because the inherent properties of images make data … Read more
Author: Arjun Sarkar Translator: Chen Zhiyan Proofreader: Ouyang Jin This article is about 1900 words, and is recommended for an 8minute read. This article will teach you how to write custom loss functions using wrapper functions and OOP in Python. Tags: TensorFlow 2, Loss Function Figure 1: Gradient Descent Algorithm (Source: Public Domain, https://commons.wikimedia.org/w/index.php?curid=521422) Neural … Read more
LearnExcelTechnology, Follow WeChat Official Account: excelperfect Introduction: Here I record the issues encountered while installing TensorFlow and their solutions, so that I can refer back to them later and for the reference of friends facing similar problems. Recently, I have been focusing on studying Deep Learning Illustrated. When I reached Chapter 8, I tried to … Read more
Click the above “Mechanical and Electronic Engineering Technology” to follow us The MNIST (Modified National Institute of Standards and Technology) dataset is a classic dataset for handwritten digit recognition, widely used in the fields of machine learning and deep learning. It was created by the National Institute of Standards and Technology (NIST) in the United … Read more
Click the "Xiaobai Learns Vision" above, select to add "Star" or "Top" Heavyweight content delivered promptly Introduction In this article, we will gradually introduce how to easily build your own real-time object detection application using TensorFlow (TF) new Object Detection API and OpenCV in Python 3. Below is the running application: Purpose and Motivation Google … Read more
In previous articles, we have introduced two types of image segmentation loss functions. Today, we will share commonly used multi-class image segmentation loss functions such as multi-class cross-entropy, weighted multi-class cross-entropy, multi-class Dice coefficient, multi-class Focal Loss, etc., and provide code to reproduce the above loss functions in TensorFlow. 1. Cross Entropy The cross-entropy loss … Read more
The global deep learning systems market is expected to reach $93.34 billion by 2028, with a compound annual growth rate of 39.1%. Deep learning, also known as deep neural learning, is a subset of machine learning and an application of artificial intelligence, where machines mimic the working of the human brain and use artificial neural … Read more
Installing TensorFlow on Windows Installing Python First, you need to install Python on your Windows system. It is recommended to use the official Python distribution, which is Anaconda, as it comes with many scientific computing libraries, such as numpy and scipy, which are also used in TensorFlow. You can download and install Anaconda from the … Read more
In the previous article, I introduced how to perform data parallelism with multiple GPUs in TensorFlow. In this article, I will discuss how to debug TensorFlow models. Compared to regular Python code, debugging TensorFlow code can be relatively difficult due to the symbolic nature of TensorFlow. Here, I will introduce some debugging tools included in … Read more
Commonly used image segmentation loss functions include binary cross-entropy, dice coefficient, Tversky, and Focal Loss. Today, I will reproduce the above loss functions in TensorFlow and compare the results. 1. Cross Entropy The cross-entropy loss function compares the predicted class values with the target values on a pixel-by-pixel basis, and then averages the values over … Read more