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<!DOCTYPE html>
<html lang="zh-CN">
<head>
<meta charset="UTF-8">
<link rel="apple-touch-icon" sizes="76x76" href="/img/favicon.png">
<link rel="icon" type="image/png" href="/img/favicon.png">
<meta name="viewport"
content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no, shrink-to-fit=no">
<meta http-equiv="x-ua-compatible" content="ie=edge">
<meta http-equiv="Content-Security-Policy" content="upgrade-insecure-requests">
<meta name="theme-color" content="#2f4154">
<meta name="description" content="机器学习，深度学习，强化学习，算法，Linux">
<meta name="author" content="seven">
<meta name="keywords" content="">
<title>SimpleAI</title>
<link rel="stylesheet" href="https://cdn.staticfile.org/twitter-bootstrap/4.4.1/css/bootstrap.min.css" />
<!-- 主题依赖的图标库，不要自行修改 -->
<link rel="stylesheet" href="//at.alicdn.com/t/font_1749284_yg9cfy8wd6.css">
<link rel="stylesheet" href="//at.alicdn.com/t/font_1736178_pjno9b9zyxs.css">
<link rel="stylesheet" href="/css/main.css" />
<!-- 自定义样式保持在最底部 -->
<script src="/js/utils.js" ></script>
<meta name="generator" content="Hexo 4.2.1"><link rel="alternate" href="/feed.xml" title="SimpleAI" type="application/atom+xml">
</head>
<body>
<header style="height: 100vh;">
<nav id="navbar" class="navbar fixed-top navbar-expand-lg navbar-dark scrolling-navbar">
<div class="container">
<a class="navbar-brand"
href="/"> <strong>Simple AI</strong> </a>
<button id="navbar-toggler-btn" class="navbar-toggler" type="button" data-toggle="collapse"
data-target="#navbarSupportedContent"
aria-controls="navbarSupportedContent" aria-expanded="false" aria-label="Toggle navigation">
<div class="animated-icon"><span></span><span></span><span></span></div>
</button>
<!-- Collapsible content -->
<div class="collapse navbar-collapse" id="navbarSupportedContent">
<ul class="navbar-nav ml-auto text-center">
<li class="nav-item">
<a class="nav-link" href="/">
<i class="iconfont icon-home-fill"></i>
首页
</a>
</li>
<li class="nav-item">
<a class="nav-link" href="/archives/">
<i class="iconfont icon-archive-fill"></i>
归档
</a>
</li>
<li class="nav-item">
<a class="nav-link" href="/categories/">
<i class="iconfont icon-category-fill"></i>
分类
</a>
</li>
<li class="nav-item">
<a class="nav-link" href="/tags/">
<i class="iconfont icon-tags-fill"></i>
标签
</a>
</li>
<li class="nav-item">
<a class="nav-link" href="/about/">
<i class="iconfont icon-user-fill"></i>
关于
</a>
</li>
<li class="nav-item dropdown">
<a class="nav-link dropdown-toggle" href="#" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">
<i class="iconfont icon-books"></i>
文档
</a>
<div class="dropdown-menu" aria-labelledby="navbarDropdown">
<a class="dropdown-item" href="http://ai-club.gitee.io/tensorflow-book/" target="_blank" rel="noopener">
TensorFlow 2系列教程
</a>
</div>
</li>
<li class="nav-item" id="search-btn">
<a class="nav-link" data-toggle="modal" data-target="#modalSearch"> <i
class="iconfont icon-search"></i> </a>
</li>
</ul>
</div>
</div>
</nav>
<div class="view intro-2" id="background" parallax=true
style="background: url('/img/default.png') no-repeat center center;
background-size: cover;">
<div class="full-bg-img">
<div class="mask flex-center" style="background-color: rgba(0, 0, 0, 0.3)">
<div class="container text-center white-text fadeInUp">
<span class="h2" id="subtitle">
</span>
</div>
<div class="scroll-down-bar">
<i class="iconfont icon-arrowdown"></i>
</div>
</div>
</div>
</div>
</header>
<main>
<div class="container nopadding-md">
<div class="py-5" id="board"
style=margin-top:0>
<div class="container">
<div class="row">
<div class="col-12 col-md-10 m-auto">
<div class="row mx-auto index-card">
<div class="col-12 col-md-4 m-auto index-img">
<a href="/2020/06/kg/" target="_self">
<img src="https://eveseven.oss-cn-shanghai.aliyuncs.com/20200603150652.png" srcset="/img/loading.gif" alt="知识图谱综述">
</a>
</div>
<div class="col-12 col-md-8 mx-auto index-info">
<a class="index-header" href="/2020/06/kg/">知识图谱综述</a>
<a href="/2020/06/kg/" class="index-excerpt">
<div>知识图谱图谱简述
业务参考公司：
http://www.trs.com.cn/
https://www.plantdata.ai/portal/home
知识抽取
命名实体识别
实体消歧
关系抽取
实体统一
实体统一不仅可以减少实体的种类，也可以降低图谱的稀疏性（Sparsity）。
指代消解
知识图谱设计
通过熟悉业务流程，决定节点和关系
业务原则
一切要</div>
</a>
<div class="index-btm post-metas">
<div class="post-meta mr-3">
<i class="iconfont icon-date"></i> 2020-06-16
</div>
<div class="post-meta mr-3">
<i class="iconfont icon-category"></i>
<a href="/categories/%E7%9F%A5%E8%AF%86%E5%9B%BE%E8%B0%B1/">知识图谱</a>
</div>
<div class="post-meta">
<i class="iconfont icon-tags"></i>
<a href="/tags/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/">深度学习</a>
<a href="/tags/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/">自然语言处理</a>
<a href="/tags/%E7%9F%A5%E8%AF%86%E5%9B%BE%E8%B0%B1/">知识图谱</a>
</div>
</div>
</div>
</div>
<div class="row mx-auto index-card">
<div class="col-12 col-md-4 m-auto index-img">
<a href="/2020/06/DRL-gym/" target="_self">
<img src="https://eveseven.oss-cn-shanghai.aliyuncs.com/20200611102722.png" srcset="/img/loading.gif" alt="强化学习仿真环境搭建">
</a>
</div>
<div class="col-12 col-md-8 mx-auto index-info">
<a class="index-header" href="/2020/06/DRL-gym/">强化学习仿真环境搭建</a>
<a href="/2020/06/DRL-gym/" class="index-excerpt">
<div>环境介绍我们使用的仿真环境为OpenAI 的gym(https://github.com/openai/gym)。
选用gym平台的原因:
首先gym是OpenAI开发的通用强化学习算法测试平台,背后有大神 Pieter Abbeel、Sergey Levine 等人率领的强大团队的支持。
其次,学会了gym的基本应用,可以自己学习使用OpenAI的其他开源强化学习软件,如universe、ro</div>
</a>
<div class="index-btm post-metas">
<div class="post-meta mr-3">
<i class="iconfont icon-date"></i> 2020-06-11
</div>
<div class="post-meta mr-3">
<i class="iconfont icon-category"></i>
<a href="/categories/%E6%B7%B1%E5%BA%A6%E5%BC%BA%E5%8C%96%E5%AD%A6%E4%B9%A0/">深度强化学习</a>
</div>
<div class="post-meta">
<i class="iconfont icon-tags"></i>
<a href="/tags/DRL/">DRL</a>
<a href="/tags/%E5%BC%BA%E5%8C%96%E5%AD%A6%E4%B9%A0/">强化学习</a>
<a href="/tags/gym%EF%BC%8C%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA/">gym，环境搭建</a>
</div>
</div>
</div>
</div>
<div class="row mx-auto index-card">
<div class="col-12 col-md-4 m-auto index-img">
<a href="/2020/06/nlp-ner/" target="_self">
<img src="https://eveseven.oss-cn-shanghai.aliyuncs.com/20200603150652.png" srcset="/img/loading.gif" alt="自然语言处理之命名实体识别">
</a>
</div>
<div class="col-12 col-md-8 mx-auto index-info">
<a class="index-header" href="/2020/06/nlp-ner/">自然语言处理之命名实体识别</a>
<a href="/2020/06/nlp-ner/" class="index-excerpt">
<div>NLP之命名实体识别（NER）实体概念
百度百科：实体(entity)指客观存在、并可相互区别的事物。实体可以是具体的人、事、物，也可以是概念。
【栗子】
文本：我爱北京天安门
实体：北京 天安门
命名实体
命名实体就是以名称为标识的实体。
通俗来讲：我们听到一个名字，就能知道这个东西是哪一个具体的事物，那么这个事物就是命名实体。
从编程语言的角度讲：类的一个实例，就是一个命名实体。
【常见的</div>
</a>
<div class="index-btm post-metas">
<div class="post-meta mr-3">
<i class="iconfont icon-date"></i> 2020-06-03
</div>
<div class="post-meta mr-3">
<i class="iconfont icon-category"></i>
<a href="/categories/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/">自然语言处理</a>
</div>
<div class="post-meta">
<i class="iconfont icon-tags"></i>
<a href="/tags/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/">深度学习</a>
<a href="/tags/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/">自然语言处理</a>
<a href="/tags/%E5%91%BD%E5%90%8D%E5%AE%9E%E4%BD%93%E8%AF%86%E5%88%AB/">命名实体识别</a>
</div>
</div>
</div>
</div>
<div class="row mx-auto index-card">
<div class="col-12 col-md-4 m-auto index-img">
<a href="/2020/06/text_EDA/" target="_self">
<img src="https://eveseven.oss-cn-shanghai.aliyuncs.com/20200603150652.png" srcset="/img/loading.gif" alt="自然语言处理之文本数据增强">
</a>
</div>
<div class="col-12 col-md-8 mx-auto index-info">
<a class="index-header" href="/2020/06/text_EDA/">自然语言处理之文本数据增强</a>
<a href="/2020/06/text_EDA/" class="index-excerpt">
<div>概述数据增强技术已经是图像领域的标配，通过对图像的翻转、旋转、镜像、高斯白噪声等技巧实现数据增强。对图像数据的增强不仅方法多种多样，而且像keras框在做数据预处理的时候已经集成了一些数据增强的方法可以直接调用。
相较于图像数据增强，文本数据增强，现在还是有很多问题的。往更严格的角度看，文本数据增强更像是同义句生成，但又不完全是，它是一个更大范围的概念。很多时候，需要文本数据增强，一个是常常遇到的</div>
</a>
<div class="index-btm post-metas">
<div class="post-meta mr-3">
<i class="iconfont icon-date"></i> 2020-06-03
</div>
<div class="post-meta mr-3">
<i class="iconfont icon-category"></i>
<a href="/categories/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/">自然语言处理</a>
</div>
<div class="post-meta">
<i class="iconfont icon-tags"></i>
<a href="/tags/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/">深度学习</a>
<a href="/tags/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86/">自然语言处理</a>
<a href="/tags/%E6%95%B0%E6%8D%AE%E5%A2%9E%E5%BC%BA/">数据增强</a>
</div>
</div>
</div>
</div>
<div class="row mx-auto index-card">
<div class="col-12 col-md-4 m-auto index-img">
<a href="/2019/06/serving/" target="_self">
<img src="https://eveseven.oss-cn-shanghai.aliyuncs.com/20200603113104.png" srcset="/img/loading.gif" alt="机器学习模型部署-simple-tensorflow-serving">
</a>
</div>
<div class="col-12 col-md-8 mx-auto index-info">
<a class="index-header" href="/2019/06/serving/">机器学习模型部署-simple-tensorflow-serving</a>
<a href="/2019/06/serving/" class="index-excerpt">
<div> simple-tensorflow-serving
TensorFlow Serving是一种灵活，高性能的机器学习模型服务系统，专为生产环境而设计。TensorFlow服务可以轻松部署新算法和实验，同时保持相同的服务器架构和API。TensorFlow Serving提供与TensorFlow模型的开箱即用集成，但可以轻松扩展以提供其他类型的模型和数据。
官方提供的serving只支持Ten</div>
</a>
<div class="index-btm post-metas">
<div class="post-meta mr-3">
<i class="iconfont icon-date"></i> 2019-06-12
</div>
<div class="post-meta mr-3">
<i class="iconfont icon-category"></i>
<a href="/categories/TensorFlow/">TensorFlow</a>
</div>
<div class="post-meta">
<i class="iconfont icon-tags"></i>
<a href="/tags/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/">深度学习</a>
<a href="/tags/tensorflow/">tensorflow</a>
<a href="/tags/serving/">serving</a>
</div>
</div>
</div>
</div>
<div class="row mx-auto index-card">
<div class="col-12 col-md-4 m-auto index-img">
<a href="/2019/04/object-detection-FasterRCNN/" target="_self">
<img src="https://eveseven.oss-cn-shanghai.aliyuncs.com/20200603120014.png" srcset="/img/loading.gif" alt="【RCNN系列】Faster RCNN">
</a>
</div>
<div class="col-12 col-md-8 mx-auto index-info">
<a class="index-header" href="/2019/04/object-detection-FasterRCNN/">【RCNN系列】Faster RCNN</a>
<a href="/2019/04/object-detection-FasterRCNN/" class="index-excerpt">
<div>论文原文链接：Faster R-CNN
摘要 最先进的目标检测网络依靠区域提出算法来假设目标的位置。SPPnet和Fast R-CNN等研究已经减少了这些检测网络的运行时间，使得区域提出计算成为一个瓶颈。在这项工作中，我们引入了一个区域提出网络（RPN），该网络与检测网络共享全图像的卷积特征，从而使近乎零成本的区域提出成为可能。RPN是一个全卷积网络，可以同时在每个位置预测目标边界和目标</div>
</a>
<div class="index-btm post-metas">
<div class="post-meta mr-3">
<i class="iconfont icon-date"></i> 2019-04-10
</div>
<div class="post-meta mr-3">
<i class="iconfont icon-category"></i>
<a href="/categories/%E7%9B%AE%E6%A0%87%E6%A3%80%E6%B5%8B/">目标检测</a>
</div>
<div class="post-meta">
<i class="iconfont icon-tags"></i>
<a href="/tags/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/">深度学习</a>
<a href="/tags/Faster-RCNN/">Faster RCNN</a>
</div>
</div>
</div>
</div>
<div class="row mx-auto index-card">
<div class="col-12 col-md-4 m-auto index-img">
<a href="/2019/04/object-detection-FastRCNN/" target="_self">
<img src="https://eveseven.oss-cn-shanghai.aliyuncs.com/20200603120014.png" srcset="/img/loading.gif" alt="【RCNN系列】Fast RCNN">
</a>
</div>
<div class="col-12 col-md-8 mx-auto index-info">
<a class="index-header" href="/2019/04/object-detection-FastRCNN/">【RCNN系列】Fast RCNN</a>
<a href="/2019/04/object-detection-FastRCNN/" class="index-excerpt">
<div>论文原文链接：Fast R-CNN
RCNN通过卷积神经网络提取图像特征，第一次将目标检测引入了深度学习领域。
SPPNet通过空间金字塔池化，避免了对于同一幅图片多次提取特征的时间花费。
但是无论是RCNN还是SPPNet，其训练都是多阶段的。
首先通过ImageNet预训练网络模型，
然后通过检测数据集微调模型提取每个区域候选的特征，
之后通过SVM分类每个区域候选的种类，
最后通</div>
</a>
<div class="index-btm post-metas">
<div class="post-meta mr-3">
<i class="iconfont icon-date"></i> 2019-04-09
</div>
<div class="post-meta mr-3">
<i class="iconfont icon-category"></i>
<a href="/categories/%E7%9B%AE%E6%A0%87%E6%A3%80%E6%B5%8B/">目标检测</a>
</div>
<div class="post-meta">
<i class="iconfont icon-tags"></i>
<a href="/tags/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/">深度学习</a>
<a href="/tags/Fast-RCNN/">Fast RCNN</a>
</div>
</div>
</div>
</div>
<div class="row mx-auto index-card">
<div class="col-12 col-md-4 m-auto index-img">
<a href="/2019/04/object-detection-SPPNet/" target="_self">
<img src="https://eveseven.oss-cn-shanghai.aliyuncs.com/20200603120014.png" srcset="/img/loading.gif" alt="【RCNN系列】SPPNet">
</a>
</div>
<div class="col-12 col-md-8 mx-auto index-info">
<a class="index-header" href="/2019/04/object-detection-SPPNet/">【RCNN系列】SPPNet</a>
<a href="/2019/04/object-detection-SPPNet/" class="index-excerpt">
<div>论文原文链接：Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
这篇paper，是在R-CNN的基础上提出了空间金字塔变换层(Spatial Pyramid Pooling)，SPPNet大幅度提高了R-CNN的训练速度和测试速度，同时算法的精度也上升了.
摘要沿着上一篇RCNN的思路，</div>
</a>
<div class="index-btm post-metas">
<div class="post-meta mr-3">
<i class="iconfont icon-date"></i> 2019-04-08
</div>
<div class="post-meta mr-3">
<i class="iconfont icon-category"></i>
<a href="/categories/%E7%9B%AE%E6%A0%87%E6%A3%80%E6%B5%8B/">目标检测</a>
</div>
<div class="post-meta">
<i class="iconfont icon-tags"></i>
<a href="/tags/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/">深度学习</a>
<a href="/tags/SPPNet/">SPPNet</a>
</div>
</div>
</div>
</div>
<div class="row mx-auto index-card">
<div class="col-12 col-md-4 m-auto index-img">
<a href="/2019/04/object-detection-RCNN/" target="_self">
<img src="https://eveseven.oss-cn-shanghai.aliyuncs.com/20200603120014.png" srcset="/img/loading.gif" alt="【RCNN系列】R-CNN">
</a>
</div>
<div class="col-12 col-md-8 mx-auto index-info">
<a class="index-header" href="/2019/04/object-detection-RCNN/">【RCNN系列】R-CNN</a>
<a href="/2019/04/object-detection-RCNN/" class="index-excerpt">
<div>论文原文链接：Rich feature hierarchies for accurate object detection and semantic segmentation
RCNN作为第一篇目标检测领域的深度学习文章，大幅提升了目标检测的识别精度，在PASCAL VOC2012数据集上将MAP从35.1%提升至53.7%。使得CNN在目标检测领域成为常态，也使得大家开始探索CNN</div>
</a>
<div class="index-btm post-metas">
<div class="post-meta mr-3">
<i class="iconfont icon-date"></i> 2019-04-07
</div>
<div class="post-meta mr-3">
<i class="iconfont icon-category"></i>
<a href="/categories/%E7%9B%AE%E6%A0%87%E6%A3%80%E6%B5%8B/">目标检测</a>
</div>
<div class="post-meta">
<i class="iconfont icon-tags"></i>
<a href="/tags/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/">深度学习</a>
<a href="/tags/R-CNN/">R-CNN</a>
</div>
</div>
</div>
</div>
<div class="row mx-auto index-card">
<div class="col-12 col-md-4 m-auto index-img">
<a href="/2019/04/pytorch-style/" target="_self">
<img src="https://eveseven.oss-cn-shanghai.aliyuncs.com/20200603112756.jpeg" srcset="/img/loading.gif" alt="pytorch实现风格变换">
</a>
</div>
<div class="col-12 col-md-8 mx-auto index-info">
<a class="index-header" href="/2019/04/pytorch-style/">pytorch实现风格变换</a>
<a href="/2019/04/pytorch-style/" class="index-excerpt">
<div>pytorch实现风格变换#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2019/3/6 19:51
# @Author : Seven
# @File : StyleTransformation.py
# @Software: PyCharm
# function : pytorch实现风格变换
from torch</div>
</a>
<div class="index-btm post-metas">
<div class="post-meta mr-3">
<i class="iconfont icon-date"></i> 2019-04-06
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<div class="post-meta mr-3">
<i class="iconfont icon-category"></i>
<a href="/categories/Pytorch/">Pytorch</a>
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<div class="post-meta">
<i class="iconfont icon-tags"></i>
<a href="/tags/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/">深度学习</a>
<a href="/tags/Pytorch/">Pytorch</a>
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