一、实验目的
了解分类问题的概念以及基于BP神经网络设计分类器的基本流程。
二、实验平台
MatLab/Simulink仿真平台。
三、实验内容和步骤
1. iris数据集简介
iris数据集的中文名是安德森鸢尾花卉数据集，英文全称是Anderson's Iris data set，是一种多重变量分析的数据集，包含150个样本。每个样本包含了五个元素，其中前四个为特征特征元素，分别为花萼长度、花萼宽度、花瓣长度、花瓣宽度，最后一个为品种信息，即目标属性（也叫target或label。包括山鸢尾Setosa、变色鸢尾Versicolour和维吉尼亚鸢尾Virginica三个品种）。所以iris数据集是一个150行5列的二维表。部分样本数据如下表所示：
2. 基于MatLab的学习器设计
2022-05-16 12:05:05
2.05MB
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人工智能-机器学习-面向蒙授小学生学习的英蒙机器翻译工具软件的设计与实现.pdf
2022-05-10 09:08:04
3.23MB
Abstract
Most image completion methods produce only one result
for each masked input, although there may be many reasonable possibilities. In this paper, we present an approach for pluralistic image completion – the task of generating multiple and diverse plausible solutions for image completion.
A major challenge faced by learning-based approaches is that usually only one ground truth training instance per label. As such, sampling from conditional VAEs still leads
to minimal diversity. To overcome this, we propose a novel and probabilistically principled framework with two parallel paths. One is a reconstructive path that utilizes the only
one given ground truth to get prior distribution of missing parts and rebuild the original image from this distribution. The other is a generative path for which the conditional
prior is coupled to the distribution obtained in the reconstructive path. Both are supported by GANs. We also introduce a new short+long term attention layer that exploits
distant relations among decoder and encoder features, improving appearance consistency. When tested on datasets with buildings (Paris), faces (CelebA-HQ), and natural images (ImageNet), our method not only generated higherquality completion results, but also with multiple and diverse plausible outputs.
2022-05-09 16:57:33
2.93MB
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标注好的打电话数据集，包括图片和标注好的xml文件，里面包含图片和xml文件，可用于yolov4训练、yolov5训练，训练可达到百分90以上。包含图片和使用labelimg软件标注生成的.xml文件，已全部手工标注完成，可以训练完整的识别度极高的.h5模型和.pth模型
2022-05-08 14:09:51
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烟雾数据集，手工精细标注，里面包含图片和xml文件，可用于yolov4训练、yolov5训练，训练可达到百分90以上。包含图片和使用labelimg软件标注生成的.xml文件，已全部手工标注完成，可以训练完整的识别度极高的.h5模型和.pth模型
2022-05-08 09:10:46
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