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Conditional generation countermeasure network CGAN CGAN is one of the earliest GAN innovations that make the generation of target data possible. It can be said to be the most influential one. Next, it introduces the working mode of CGAN and how to implement its small-scale version with MNIST dataset. CGAN principle The generator learns to ge ...

Classic network VGG16 structure According to the size of convolution kernel and the number of convolution layers, VGG can be divided into six configurations: A, a-lrn, B, C, D and E. among them, D and E are commonly used, which are called VGG16 and VGG19 respectively. The following figure shows six structural configurations of VGG:   In ...

Quickly review the soft voting and hard voting in the integration methodThe integration method is to combine the results of two or more separate machine learning algorithms and try to produce more accurate results than any single algorithm.In soft voting, the probability of each category is averaged to produce results. For example, if algorithm ...

Pytoch: fast migration of image style - residual network, fixed style and arbitrary content Copyright: Jingmin Wei, Pattern Recognition and Intelligent System, School of Artificial and Intelligence, Huazhong University of Science and Technology Pytoch tutorial column link This tutorial is not for commercial use. It is only for learning ...

If there are new audiences, if you have any doubts, please read from the beginning of the column "special ai model". Especially for viewers who want to thoroughly understand the origin of the model in deep learning and how to build a custom model format step by step, in addition to the visualization chapter, the first three chapters m ...

Abstract: This paper mainly explains the process of realizing Chinese text classification by CNN, and compares it with Bayesian, decision tree, logistic regression, random forest, KNN, SVM and other classification algorithms. This article is shared from Huawei cloud community< [Python artificial intelligence] 21 Detailed explanation of Wo ...

Introduction to Dropout 1.1 Reasons for Dropout _In the machine learning model, if there are too many parameters of the model and too few training samples, the trained model can easily be fitted. When training the neural network, we often encounter the problem of fitting, which is manifested in the following aspects: the loss function of ...

1. Neural network: build small actual combat and use Sequential Take CIFAR10 model as an example import torch import torchvision from tensorboardX import SummaryWriter from torch import nn from torch.nn import ReLU, Sigmoid, Linear, Conv2d, MaxPool2d, Flatten from torch.utils.data import DataLoader class test_cifar(nn.Module): def _ ...

Novice beginners hope to take notes to record what they have learned. They also hope to help those who are also beginners. They also hope that the big guys can help correct mistakes ~ infringement legislation and deletion. catalogue 1, Principle analysis 2, Code analysis 1. Main part - load_mosaic 2,load_image function 3,random_perspectiv ...

In the neural network optimization algorithm, the most commonly used method is the back propagation algorithm, and its work flow is as follows: As shown in the figure, the back propagation algorithm implements an iterative process. At the beginning of each iteration, a part of training data is selected, which is called a batch. Then, the ...