From c4521257d95f43d2255488f53b02dcb1c572e767 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Fri, 22 Mar 2019 17:22:58 +0800 Subject: [PATCH 001/386] Initial commit --- README.md | 2 ++ 1 file changed, 2 insertions(+) create mode 100644 README.md diff --git a/README.md b/README.md new file mode 100644 index 00000000..9a5af495 --- /dev/null +++ b/README.md @@ -0,0 +1,2 @@ +# 2020- +2020年的算法实习岗位信息表，部分包括内推码 From b25f1cd0ff2deccacb21e4890e6b3d97358efc4a Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Fri, 22 Mar 2019 17:23:46 +0800 Subject: [PATCH 002/386] Update README.md MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 添加内容 --- README.md | 22 ++++++++++++++++++++-- 1 file changed, 20 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 9a5af495..895e6ab3 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,20 @@ -# 2020- -2020年的算法实习岗位信息表，部分包括内推码 +#### 2020算法实习岗位表 +有待完善，持续更新，欢迎各位一起更新。 +|公司 |招聘岗位 |工作地点 |截止时间 |投递方式 |内推方式 |是否投递 |信息来源| +|---------------|--------------------|---------------|---------------------|--------------------|----------------------------|--------------|------------| +|文行知远 |感知算法实习生|北京/广州|未知 |邮箱：hzshuai@gmail.com|简历命名格式：CVer推荐+姓名+电话号码+实习生|无|CVer公众号| +|商汤科技 |深度学习/计算机视觉见习研究员等|北京/上海/深圳/杭州|未知|邮箱：wwlwenli@163.com|邮件主题：CVer推荐+商汤2019春招内推、商汤2020实习内推，简历附件命名：姓名+岗位名称+到岗时间+可见习时长|无|CVer公众号| +|阿里达摩院|计算机视觉算法实习生|北京/杭州/深圳|未知|邮箱：zhoujing.zjh@alibaba-inc.com|简历命名格式：CVer推荐+姓名+电话号码+方向(工程/算法)|无|CVer公众号| +|京东AI Lab|计算机视觉算法实习生|北京朝阳区|未知|邮箱：wangxiaobo8@jd.com|简历命名格式：CVer推荐+姓名+电话号码|无|CVer公众号| +|字节跳动|AI 算法岗|北京|2.25-5.31|投递[官网](https://job.bytedance.com/campus/position)|内推码：WWNUPDJ、NAATGNC，邮箱咨询简历进度：wangyang.wyang@bytedance.com|无|CVer公众号| +|海康威视|AI算法岗|杭州/其他|未知|投递[官网](https://campushr.hikvision.com/)|内推码：P4Y8AQ、587TA3、587LAC、5SYMAJ|无|CVer公众号| +|图森未来|感知算法实习生|北京朝阳区|未知|邮箱：ostnie@foxmail.com|简历命名：CVer推荐+姓名+电话号码+应聘岗位|无|CVer公众号| +|平安科技|计算机视觉实习生|北京/深圳|3.19|[网申](http://campus.pingan.com/tech/position)|内推码：PvqgIB|无|牛客讨论区| +|抖音火山|计算机视觉算法实习生|北京/其他|未知|[网申](https://job.bytedance.com/campus/position)|内推码：QURKAW2|无|牛客讨论区| +|招银网络科技|算法实习生|深圳/杭州/成都/|6.30|[网申](https://mp.weixin.qq.com/s/7l6GZ88yynwb3jwRhx76KA)|内推码：9171XYCD、9211LYCD|无|牛客讨论区| +|奇虎360|算法实习生|北京/其他|3.31|[网申](http://chrcmp.chinahr.com/views/2019-qihu360/index.html)|内推码：暂无|无|牛客讨论区| +|爱奇艺|C++开发工程师|北京|4月中旬|[网申](https://zhaopin.iqiyi.com/job-school.html)|内推码：zNG4KR|无|信息学院qq群| +|蚂蚁金服|算法实习生|杭州/其他|3-5月|邮箱：xiaoxue.sxx@antfin.com|邮件标题：【姓名】+【学校】+【应聘岗位】|无|信息学院qq群| +|华为|算法实习生|成都/其他|3月28|[网申]() +|菜鸟网络|算法实习生|杭州/北京|未知|师兄qq：1193304376|@qq.com|qq联系、qq邮箱|无|信息学院qq群| + From 6956d793a429fa83a178f5d23116af039131ef57 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Fri, 22 Mar 2019 17:25:18 +0800 Subject: [PATCH 003/386] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 895e6ab3..974fef27 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ #### 2020算法实习岗位表 有待完善，持续更新，欢迎各位一起更新。 -|公司 |招聘岗位 |工作地点 |截止时间 |投递方式 |内推方式 |是否投递 |信息来源| -|---------------|--------------------|---------------|---------------------|--------------------|----------------------------|--------------|------------| +|公司 |招聘岗位 |工作地点 |截止时间 |投递方式 |内推方式 |是否投递 |信息来源| +|---- --|------------|-----------|-------------|----------------|----------------|---------|-------| |文行知远 |感知算法实习生|北京/广州|未知 |邮箱：hzshuai@gmail.com|简历命名格式：CVer推荐+姓名+电话号码+实习生|无|CVer公众号| |商汤科技 |深度学习/计算机视觉见习研究员等|北京/上海/深圳/杭州|未知|邮箱：wwlwenli@163.com|邮件主题：CVer推荐+商汤2019春招内推、商汤2020实习内推，简历附件命名：姓名+岗位名称+到岗时间+可见习时长|无|CVer公众号| |阿里达摩院|计算机视觉算法实习生|北京/杭州/深圳|未知|邮箱：zhoujing.zjh@alibaba-inc.com|简历命名格式：CVer推荐+姓名+电话号码+方向(工程/算法)|无|CVer公众号| From 8626f7cb3079c8676dfa17189da039ec34afd763 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Fri, 22 Mar 2019 17:25:32 +0800 Subject: [PATCH 004/386] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 974fef27..fa9a103f 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -#### 2020算法实习岗位表 +## 2020算法实习岗位表 有待完善，持续更新，欢迎各位一起更新。 |公司 |招聘岗位 |工作地点 |截止时间 |投递方式 |内推方式 |是否投递 |信息来源| |---- --|------------|-----------|-------------|----------------|----------------|---------|-------| From d120adabb43507f0cd7e181541b9a560071396e1 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Fri, 22 Mar 2019 17:27:46 +0800 Subject: [PATCH 005/386] Update README.md --- README.md | 1 - 1 file changed, 1 deletion(-) diff --git a/README.md b/README.md index fa9a103f..0c2ae968 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,4 @@ ## 2020算法实习岗位表 -有待完善，持续更新，欢迎各位一起更新。 |公司 |招聘岗位 |工作地点 |截止时间 |投递方式 |内推方式 |是否投递 |信息来源| |---- --|------------|-----------|-------------|----------------|----------------|---------|-------| |文行知远 |感知算法实习生|北京/广州|未知 |邮箱：hzshuai@gmail.com|简历命名格式：CVer推荐+姓名+电话号码+实习生|无|CVer公众号| From e232b31a0b4db5784e5477e29297a6a86a783dce Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Fri, 22 Mar 2019 17:34:12 +0800 Subject: [PATCH 006/386] Update README.md --- README.md | 34 +++++++++++++++++----------------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/README.md b/README.md index 0c2ae968..4b21a8b3 100644 --- a/README.md +++ b/README.md @@ -1,19 +1,19 @@ ## 2020算法实习岗位表 -|公司 |招聘岗位 |工作地点 |截止时间 |投递方式 |内推方式 |是否投递 |信息来源| -|---- --|------------|-----------|-------------|----------------|----------------|---------|-------| -|文行知远 |感知算法实习生|北京/广州|未知 |邮箱：hzshuai@gmail.com|简历命名格式：CVer推荐+姓名+电话号码+实习生|无|CVer公众号| -|商汤科技 |深度学习/计算机视觉见习研究员等|北京/上海/深圳/杭州|未知|邮箱：wwlwenli@163.com|邮件主题：CVer推荐+商汤2019春招内推、商汤2020实习内推，简历附件命名：姓名+岗位名称+到岗时间+可见习时长|无|CVer公众号| -|阿里达摩院|计算机视觉算法实习生|北京/杭州/深圳|未知|邮箱：zhoujing.zjh@alibaba-inc.com|简历命名格式：CVer推荐+姓名+电话号码+方向(工程/算法)|无|CVer公众号| -|京东AI Lab|计算机视觉算法实习生|北京朝阳区|未知|邮箱：wangxiaobo8@jd.com|简历命名格式：CVer推荐+姓名+电话号码|无|CVer公众号| -|字节跳动|AI 算法岗|北京|2.25-5.31|投递[官网](https://job.bytedance.com/campus/position)|内推码：WWNUPDJ、NAATGNC，邮箱咨询简历进度：wangyang.wyang@bytedance.com|无|CVer公众号| -|海康威视|AI算法岗|杭州/其他|未知|投递[官网](https://campushr.hikvision.com/)|内推码：P4Y8AQ、587TA3、587LAC、5SYMAJ|无|CVer公众号| -|图森未来|感知算法实习生|北京朝阳区|未知|邮箱：ostnie@foxmail.com|简历命名：CVer推荐+姓名+电话号码+应聘岗位|无|CVer公众号| -|平安科技|计算机视觉实习生|北京/深圳|3.19|[网申](http://campus.pingan.com/tech/position)|内推码：PvqgIB|无|牛客讨论区| -|抖音火山|计算机视觉算法实习生|北京/其他|未知|[网申](https://job.bytedance.com/campus/position)|内推码：QURKAW2|无|牛客讨论区| -|招银网络科技|算法实习生|深圳/杭州/成都/|6.30|[网申](https://mp.weixin.qq.com/s/7l6GZ88yynwb3jwRhx76KA)|内推码：9171XYCD、9211LYCD|无|牛客讨论区| -|奇虎360|算法实习生|北京/其他|3.31|[网申](http://chrcmp.chinahr.com/views/2019-qihu360/index.html)|内推码：暂无|无|牛客讨论区| -|爱奇艺|C++开发工程师|北京|4月中旬|[网申](https://zhaopin.iqiyi.com/job-school.html)|内推码：zNG4KR|无|信息学院qq群| -|蚂蚁金服|算法实习生|杭州/其他|3-5月|邮箱：xiaoxue.sxx@antfin.com|邮件标题：【姓名】+【学校】+【应聘岗位】|无|信息学院qq群| -|华为|算法实习生|成都/其他|3月28|[网申]() -|菜鸟网络|算法实习生|杭州/北京|未知|师兄qq：1193304376|@qq.com|qq联系、qq邮箱|无|信息学院qq群| +公司 |招聘岗位 |工作地点 |截止时间 |投递方式 |内推方式 |是否投递 |信息来源| +---- --|------------|-----------|-------------|----------------|----------------|---------|-------| +文行知远 |感知算法实习生|北京/广州|未知 |邮箱：hzshuai@gmail.com|简历命名格式：CVer推荐+姓名+电话号码+实习生|无|CVer公众号| +商汤科技 |深度学习/计算机视觉见习研究员等|北京/上海/深圳/杭州|未知|邮箱：wwlwenli@163.com|邮件主题：CVer推荐+商汤2019春招内推、商汤2020实习内推，简历附件命名：姓名+岗位名称+到岗时间+可见习时长|无|CVer公众号| +阿里达摩院|计算机视觉算法实习生|北京/杭州/深圳|未知|邮箱：zhoujing.zjh@alibaba-inc.com|简历命名格式：CVer推荐+姓名+电话号码+方向(工程/算法)|无|CVer公众号| +京东AI Lab|计算机视觉算法实习生|北京朝阳区|未知|邮箱：wangxiaobo8@jd.com|简历命名格式：CVer推荐+姓名+电话号码|无|CVer公众号| +字节跳动|AI 算法岗|北京|2.25-5.31|投递[官网](https://job.bytedance.com/campus/position)|内推码：WWNUPDJ、NAATGNC，邮箱咨询简历进度：wangyang.wyang@bytedance.com|无|CVer公众号| +海康威视|AI算法岗|杭州/其他|未知|投递[官网](https://campushr.hikvision.com/)|内推码：P4Y8AQ、587TA3、587LAC、5SYMAJ|无|CVer公众号| +图森未来|感知算法实习生|北京朝阳区|未知|邮箱：ostnie@foxmail.com|简历命名：CVer推荐+姓名+电话号码+应聘岗位|无|CVer公众号| +平安科技|计算机视觉实习生|北京/深圳|3.19|[网申](http://campus.pingan.com/tech/position)|内推码：PvqgIB|无|牛客讨论区| +抖音火山|计算机视觉算法实习生|北京/其他|未知|[网申](https://job.bytedance.com/campus/position)|内推码：QURKAW2|无|牛客讨论区| +招银网络科技|算法实习生|深圳/杭州/成都/|6.30|[网申](https://mp.weixin.qq.com/s/7l6GZ88yynwb3jwRhx76KA)|内推码：9171XYCD、9211LYCD|无|牛客讨论区| +奇虎360|算法实习生|北京/其他|3.31|[网申](http://chrcmp.chinahr.com/views/2019-qihu360/index.html)|内推码：暂无|无|牛客讨论区| +爱奇艺|C++开发工程师|北京|4月中旬|[网申](https://zhaopin.iqiyi.com/job-school.html)|内推码：zNG4KR|无|信息学院qq群| +蚂蚁金服|算法实习生|杭州/其他|3-5月|邮箱：xiaoxue.sxx@antfin.com|邮件标题：【姓名】+【学校】+【应聘岗位】|无|信息学院qq群| +华为|算法实习生|成都/其他|3月28|[网申]() +菜鸟网络|算法实习生|杭州/北京|未知|师兄qq：1193304376|@qq.com|qq联系、qq邮箱|无|信息学院qq群| From 9065d86dcdd9cc7d28b34d52eda6c31c66ebddd5 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Fri, 22 Mar 2019 17:35:36 +0800 Subject: [PATCH 007/386] Update README.md --- README.md | 34 +++++++++++++++++----------------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/README.md b/README.md index 4b21a8b3..c5dd59b1 100644 --- a/README.md +++ b/README.md @@ -1,19 +1,19 @@ ## 2020算法实习岗位表 -公司 |招聘岗位 |工作地点 |截止时间 |投递方式 |内推方式 |是否投递 |信息来源| ----- --|------------|-----------|-------------|----------------|----------------|---------|-------| -文行知远 |感知算法实习生|北京/广州|未知 |邮箱：hzshuai@gmail.com|简历命名格式：CVer推荐+姓名+电话号码+实习生|无|CVer公众号| -商汤科技 |深度学习/计算机视觉见习研究员等|北京/上海/深圳/杭州|未知|邮箱：wwlwenli@163.com|邮件主题：CVer推荐+商汤2019春招内推、商汤2020实习内推，简历附件命名：姓名+岗位名称+到岗时间+可见习时长|无|CVer公众号| -阿里达摩院|计算机视觉算法实习生|北京/杭州/深圳|未知|邮箱：zhoujing.zjh@alibaba-inc.com|简历命名格式：CVer推荐+姓名+电话号码+方向(工程/算法)|无|CVer公众号| -京东AI Lab|计算机视觉算法实习生|北京朝阳区|未知|邮箱：wangxiaobo8@jd.com|简历命名格式：CVer推荐+姓名+电话号码|无|CVer公众号| -字节跳动|AI 算法岗|北京|2.25-5.31|投递[官网](https://job.bytedance.com/campus/position)|内推码：WWNUPDJ、NAATGNC，邮箱咨询简历进度：wangyang.wyang@bytedance.com|无|CVer公众号| -海康威视|AI算法岗|杭州/其他|未知|投递[官网](https://campushr.hikvision.com/)|内推码：P4Y8AQ、587TA3、587LAC、5SYMAJ|无|CVer公众号| -图森未来|感知算法实习生|北京朝阳区|未知|邮箱：ostnie@foxmail.com|简历命名：CVer推荐+姓名+电话号码+应聘岗位|无|CVer公众号| -平安科技|计算机视觉实习生|北京/深圳|3.19|[网申](http://campus.pingan.com/tech/position)|内推码：PvqgIB|无|牛客讨论区| -抖音火山|计算机视觉算法实习生|北京/其他|未知|[网申](https://job.bytedance.com/campus/position)|内推码：QURKAW2|无|牛客讨论区| -招银网络科技|算法实习生|深圳/杭州/成都/|6.30|[网申](https://mp.weixin.qq.com/s/7l6GZ88yynwb3jwRhx76KA)|内推码：9171XYCD、9211LYCD|无|牛客讨论区| -奇虎360|算法实习生|北京/其他|3.31|[网申](http://chrcmp.chinahr.com/views/2019-qihu360/index.html)|内推码：暂无|无|牛客讨论区| -爱奇艺|C++开发工程师|北京|4月中旬|[网申](https://zhaopin.iqiyi.com/job-school.html)|内推码：zNG4KR|无|信息学院qq群| -蚂蚁金服|算法实习生|杭州/其他|3-5月|邮箱：xiaoxue.sxx@antfin.com|邮件标题：【姓名】+【学校】+【应聘岗位】|无|信息学院qq群| -华为|算法实习生|成都/其他|3月28|[网申]() -菜鸟网络|算法实习生|杭州/北京|未知|师兄qq：1193304376|@qq.com|qq联系、qq邮箱|无|信息学院qq群| +|公司 |招聘岗位 |工作地点 |截止时间 |投递方式 |内推方式 |是否投递 |信息来源| +|---- --|------------|-----------|-------------|----------------|----------------|---------|-------| +|文行知远 |感知算法实习生|北京/广州|未知 |邮箱：hzshuai@gmail.com|简历命名格式：CVer推荐+姓名+电话号码+实习生|无|CVer公众号| +|商汤科技 |深度学习/计算机视觉见习研究员等|北京/上海/深圳/杭州|未知|邮箱：wwlwenli@163.com|邮件主题：CVer推荐+商汤2019春招内推、商汤2020实习内推，简历附件命名：姓名+岗位名称+到岗时间+可见习时长|无|CVer公众号| +|阿里达摩院|计算机视觉算法实习生|北京/杭州/深圳|未知|邮箱：zhoujing.zjh@alibaba-inc.com|简历命名格式：CVer推荐+姓名+电话号码+方向(工程/算法)|无|CVer公众号| +|京东AI Lab|计算机视觉算法实习生|北京朝阳区|未知|邮箱：wangxiaobo8@jd.com|简历命名格式：CVer推荐+姓名+电话号码|无|CVer公众号| +|字节跳动|AI 算法岗|北京|2.25-5.31|投递[官网](https://job.bytedance.com/campus/position)|内推码：WWNUPDJ、NAATGNC，邮箱咨询简历进度：wangyang.wyang@bytedance.com|无|CVer公众号| +|海康威视|AI算法岗|杭州/其他|未知|投递[官网](https://campushr.hikvision.com/)|内推码：P4Y8AQ、587TA3、587LAC、5SYMAJ|无|CVer公众号| +|图森未来|感知算法实习生|北京朝阳区|未知|邮箱：ostnie@foxmail.com|简历命名：CVer推荐+姓名+电话号码+应聘岗位|无|CVer公众号| +|平安科技|计算机视觉实习生|北京/深圳|3.19|[网申](http://campus.pingan.com/tech/position)|内推码：PvqgIB|无|牛客讨论区| +|抖音火山|计算机视觉算法实习生|北京/其他|未知|[网申](https://job.bytedance.com/campus/position)|内推码：QURKAW2|无|牛客讨论区| +|招银网络科技|算法实习生|深圳/杭州/成都/|6.30|[网申](https://mp.weixin.qq.com/s/7l6GZ88yynwb3jwRhx76KA)|内推码：9171XYCD、9211LYCD|无|牛客讨论区| +|奇虎360|算法实习生|北京/其他|3.31|[网申](http://chrcmp.chinahr.com/views/2019-qihu360/index.html)|内推码：暂无|无|牛客讨论区| +|爱奇艺|C++开发工程师|北京|4月中旬|[网申](https://zhaopin.iqiyi.com/job-school.html)|内推码：zNG4KR|无|信息学院qq群| +|蚂蚁金服|算法实习生|杭州/其他|3-5月|邮箱：xiaoxue.sxx@antfin.com|邮件标题：【姓名】+【学校】+【应聘岗位】|无|信息学院qq群| +|华为|算法实习生|成都/其他|3月28|[网申]()|无|无|信息学院qq群| +|菜鸟网络|算法实习生|杭州/北京|未知|师兄qq：1193304376|@qq.com|qq联系、qq邮箱|无|信息学院qq群| From 46761d078071d0da583aaf5ad93f0410d7898295 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Fri, 22 Mar 2019 17:38:57 +0800 Subject: [PATCH 008/386] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index c5dd59b1..b7a8a89c 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,8 @@ ## 2020算法实习岗位表 |公司 |招聘岗位 |工作地点 |截止时间 |投递方式 |内推方式 |是否投递 |信息来源| |---- --|------------|-----------|-------------|----------------|----------------|---------|-------| -|文行知远 |感知算法实习生|北京/广州|未知 |邮箱：hzshuai@gmail.com|简历命名格式：CVer推荐+姓名+电话号码+实习生|无|CVer公众号| -|商汤科技 |深度学习/计算机视觉见习研究员等|北京/上海/深圳/杭州|未知|邮箱：wwlwenli@163.com|邮件主题：CVer推荐+商汤2019春招内推、商汤2020实习内推，简历附件命名：姓名+岗位名称+到岗时间+可见习时长|无|CVer公众号| +|文行知远|感知算法实习生|北京/广州|未知 |邮箱：hzshuai@gmail.com|简历命名格式：CVer推荐+姓名+电话号码+实习生|无|CVer公众号| +|商汤科技|深度学习/计算机视觉见习研究员等|北京/上海/深圳/杭州|未知|邮箱：wwlwenli@163.com|邮件主题：CVer推荐+商汤2019春招内推、商汤2020实习内推，简历附件命名：姓名+岗位名称+到岗时间+可见习时长|无|CVer公众号| |阿里达摩院|计算机视觉算法实习生|北京/杭州/深圳|未知|邮箱：zhoujing.zjh@alibaba-inc.com|简历命名格式：CVer推荐+姓名+电话号码+方向(工程/算法)|无|CVer公众号| |京东AI Lab|计算机视觉算法实习生|北京朝阳区|未知|邮箱：wangxiaobo8@jd.com|简历命名格式：CVer推荐+姓名+电话号码|无|CVer公众号| |字节跳动|AI 算法岗|北京|2.25-5.31|投递[官网](https://job.bytedance.com/campus/position)|内推码：WWNUPDJ、NAATGNC，邮箱咨询简历进度：wangyang.wyang@bytedance.com|无|CVer公众号| From 9d53ef1ebcb6031dd8fa87791c70b3aa6a6f80ce Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Fri, 22 Mar 2019 17:39:27 +0800 Subject: [PATCH 009/386] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index b7a8a89c..0e22d486 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ ## 2020算法实习岗位表 |公司 |招聘岗位 |工作地点 |截止时间 |投递方式 |内推方式 |是否投递 |信息来源| |---- --|------------|-----------|-------------|----------------|----------------|---------|-------| -|文行知远|感知算法实习生|北京/广州|未知 |邮箱：hzshuai@gmail.com|简历命名格式：CVer推荐+姓名+电话号码+实习生|无|CVer公众号| +|文行知远|感知算法实习生|北京/广州|未知|邮箱：hzshuai@gmail.com|简历命名格式：CVer推荐+姓名+电话号码+实习生|无|CVer公众号| |商汤科技|深度学习/计算机视觉见习研究员等|北京/上海/深圳/杭州|未知|邮箱：wwlwenli@163.com|邮件主题：CVer推荐+商汤2019春招内推、商汤2020实习内推，简历附件命名：姓名+岗位名称+到岗时间+可见习时长|无|CVer公众号| |阿里达摩院|计算机视觉算法实习生|北京/杭州/深圳|未知|邮箱：zhoujing.zjh@alibaba-inc.com|简历命名格式：CVer推荐+姓名+电话号码+方向(工程/算法)|无|CVer公众号| |京东AI Lab|计算机视觉算法实习生|北京朝阳区|未知|邮箱：wangxiaobo8@jd.com|简历命名格式：CVer推荐+姓名+电话号码|无|CVer公众号| From 8722f608cfb1adbe35b5325259696567677c6222 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Fri, 22 Mar 2019 17:40:46 +0800 Subject: [PATCH 010/386] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 0e22d486..ab20efdd 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ ## 2020算法实习岗位表 |公司 |招聘岗位 |工作地点 |截止时间 |投递方式 |内推方式 |是否投递 |信息来源| -|---- --|------------|-----------|-------------|----------------|----------------|---------|-------| +|-------|------------|-----------|-------------|----------------|----------------|---------|-------| |文行知远|感知算法实习生|北京/广州|未知|邮箱：hzshuai@gmail.com|简历命名格式：CVer推荐+姓名+电话号码+实习生|无|CVer公众号| |商汤科技|深度学习/计算机视觉见习研究员等|北京/上海/深圳/杭州|未知|邮箱：wwlwenli@163.com|邮件主题：CVer推荐+商汤2019春招内推、商汤2020实习内推，简历附件命名：姓名+岗位名称+到岗时间+可见习时长|无|CVer公众号| |阿里达摩院|计算机视觉算法实习生|北京/杭州/深圳|未知|邮箱：zhoujing.zjh@alibaba-inc.com|简历命名格式：CVer推荐+姓名+电话号码+方向(工程/算法)|无|CVer公众号| From a7e06984a04f30291eca199399ae131d06350c51 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Fri, 22 Mar 2019 17:42:22 +0800 Subject: [PATCH 011/386] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index ab20efdd..52081dc6 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ ## 2020算法实习岗位表 -|公司 |招聘岗位 |工作地点 |截止时间 |投递方式 |内推方式 |是否投递 |信息来源| -|-------|------------|-----------|-------------|----------------|----------------|---------|-------| +|公司 |招聘岗位 |工作地点 |截止时间 |投递方式 |内推方式 |是否投递 |信息来源| +|-------|---------|-----------|-------|------------|------------|---------|-------| |文行知远|感知算法实习生|北京/广州|未知|邮箱：hzshuai@gmail.com|简历命名格式：CVer推荐+姓名+电话号码+实习生|无|CVer公众号| |商汤科技|深度学习/计算机视觉见习研究员等|北京/上海/深圳/杭州|未知|邮箱：wwlwenli@163.com|邮件主题：CVer推荐+商汤2019春招内推、商汤2020实习内推，简历附件命名：姓名+岗位名称+到岗时间+可见习时长|无|CVer公众号| |阿里达摩院|计算机视觉算法实习生|北京/杭州/深圳|未知|邮箱：zhoujing.zjh@alibaba-inc.com|简历命名格式：CVer推荐+姓名+电话号码+方向(工程/算法)|无|CVer公众号| From 9bb1ca4cc9a572dee00d776d5048baa5665e63ff Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Fri, 22 Mar 2019 17:43:14 +0800 Subject: [PATCH 012/386] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 52081dc6..e5e62e62 100644 --- a/README.md +++ b/README.md @@ -15,5 +15,5 @@ |爱奇艺|C++开发工程师|北京|4月中旬|[网申](https://zhaopin.iqiyi.com/job-school.html)|内推码：zNG4KR|无|信息学院qq群| |蚂蚁金服|算法实习生|杭州/其他|3-5月|邮箱：xiaoxue.sxx@antfin.com|邮件标题：【姓名】+【学校】+【应聘岗位】|无|信息学院qq群| |华为|算法实习生|成都/其他|3月28|[网申]()|无|无|信息学院qq群| -|菜鸟网络|算法实习生|杭州/北京|未知|师兄qq：1193304376|@qq.com|qq联系、qq邮箱|无|信息学院qq群| +|菜鸟网络|算法实习生|杭州/北京|未知|qq：1193304376|1193304376@qq.com|qq联系、qq邮箱|无|信息学院qq群| From c90741d981762509b29c29f9c068870b5117b607 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Fri, 22 Mar 2019 17:45:45 +0800 Subject: [PATCH 013/386] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index e5e62e62..0d3687a2 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ ## 2020算法实习岗位表 -|公司 |招聘岗位 |工作地点 |截止时间 |投递方式 |内推方式 |是否投递 |信息来源| -|-------|---------|-----------|-------|------------|------------|---------|-------| +|公司|招聘岗位|工作地点|截止时间|投递方式|内推方式|是否投递|信息来源| +|---|-------|-------|------|-------|-------|------|-------| |文行知远|感知算法实习生|北京/广州|未知|邮箱：hzshuai@gmail.com|简历命名格式：CVer推荐+姓名+电话号码+实习生|无|CVer公众号| |商汤科技|深度学习/计算机视觉见习研究员等|北京/上海/深圳/杭州|未知|邮箱：wwlwenli@163.com|邮件主题：CVer推荐+商汤2019春招内推、商汤2020实习内推，简历附件命名：姓名+岗位名称+到岗时间+可见习时长|无|CVer公众号| |阿里达摩院|计算机视觉算法实习生|北京/杭州/深圳|未知|邮箱：zhoujing.zjh@alibaba-inc.com|简历命名格式：CVer推荐+姓名+电话号码+方向(工程/算法)|无|CVer公众号| From 7760642a836317f9e95bb931784119b8d75e5967 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Fri, 22 Mar 2019 17:51:57 +0800 Subject: [PATCH 014/386] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 0d3687a2..c4f9e2db 100644 --- a/README.md +++ b/README.md @@ -16,4 +16,4 @@ |蚂蚁金服|算法实习生|杭州/其他|3-5月|邮箱：xiaoxue.sxx@antfin.com|邮件标题：【姓名】+【学校】+【应聘岗位】|无|信息学院qq群| |华为|算法实习生|成都/其他|3月28|[网申]()|无|无|信息学院qq群| |菜鸟网络|算法实习生|杭州/北京|未知|qq：1193304376|1193304376@qq.com|qq联系、qq邮箱|无|信息学院qq群| - +|oppo手机|算法实习生|北京|未知|[网申](https://oppo.zhaopin.com/jobs.html)|内推码：15209235173|无|信息学院qq群 From c426a970e4f644265410bc33b87b68f9ebc161f2 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Fri, 22 Mar 2019 17:52:36 +0800 Subject: [PATCH 015/386] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index c4f9e2db..c7eb43ee 100644 --- a/README.md +++ b/README.md @@ -15,5 +15,5 @@ |爱奇艺|C++开发工程师|北京|4月中旬|[网申](https://zhaopin.iqiyi.com/job-school.html)|内推码：zNG4KR|无|信息学院qq群| |蚂蚁金服|算法实习生|杭州/其他|3-5月|邮箱：xiaoxue.sxx@antfin.com|邮件标题：【姓名】+【学校】+【应聘岗位】|无|信息学院qq群| |华为|算法实习生|成都/其他|3月28|[网申]()|无|无|信息学院qq群| -|菜鸟网络|算法实习生|杭州/北京|未知|qq：1193304376|1193304376@qq.com|qq联系、qq邮箱|无|信息学院qq群| +|菜鸟网络|算法实习生|杭州/北京|未知|qq：1193304376|1193304376@qq.com|无|无|信息学院qq群| |oppo手机|算法实习生|北京|未知|[网申](https://oppo.zhaopin.com/jobs.html)|内推码：15209235173|无|信息学院qq群 From ed9a7ae042231cb84f5a445e1fa2f54b032eedfd Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Fri, 22 Mar 2019 20:32:05 +0800 Subject: [PATCH 016/386] Update README.md --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index c7eb43ee..5dfea862 100644 --- a/README.md +++ b/README.md @@ -16,4 +16,5 @@ |蚂蚁金服|算法实习生|杭州/其他|3-5月|邮箱：xiaoxue.sxx@antfin.com|邮件标题：【姓名】+【学校】+【应聘岗位】|无|信息学院qq群| |华为|算法实习生|成都/其他|3月28|[网申]()|无|无|信息学院qq群| |菜鸟网络|算法实习生|杭州/北京|未知|qq：1193304376|1193304376@qq.com|无|无|信息学院qq群| -|oppo手机|算法实习生|北京|未知|[网申](https://oppo.zhaopin.com/jobs.html)|内推码：15209235173|无|信息学院qq群 +|oppo手机|算法实习生|北京|未知|[网申](https://oppo.zhaopin.com/jobs.html)|内推码：15209235173|无|信息学院qq群| + From ba0e7561626d50c91ad6e2111c7a12ceb67ab32b Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Fri, 22 Mar 2019 20:35:43 +0800 Subject: [PATCH 017/386] Update README.md --- README.md | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/README.md b/README.md index 5dfea862..0c179339 100644 --- a/README.md +++ b/README.md @@ -18,3 +18,25 @@ |菜鸟网络|算法实习生|杭州/北京|未知|qq：1193304376|1193304376@qq.com|无|无|信息学院qq群| |oppo手机|算法实习生|北京|未知|[网申](https://oppo.zhaopin.com/jobs.html)|内推码：15209235173|无|信息学院qq群| +### 2019春招算法实习岗位表 +2019年春招近期实习岗位汇总，还有待完善，持续更新，也希望各位一起更新。 +|公司|招聘岗位|工作地点|截止时间|投递方式|内推方式|是否投递|信息来源| +|------|------------|------------|------------|------------|------------|-------------|-----------| +|文行知远|感知算法实习生|北京/广州|未知|邮箱：hzshuai@gmail.com|简历命名格式：CVer推荐+姓名+电话号码+实习生|无|CVer公众号| +|商汤科技|深度学习/计算机视觉见习研究员等|北京/上海/深圳/杭州|未知|邮箱：wwlwenli@163.com|邮件主题：CVer推荐+商汤2019春招内推、商汤2020实习内推，简历附件命名：姓名+岗位名称+到岗时间+可见习时长|无|CVer公众号| +|商汤科技|图像算法工程师(实习)|上海|未知|投递邮箱：Feijingjing1@sensetime.com|邮件主题：学校-姓名-意向岗位-意向城市|无|52CV群| +|阿里达摩院|计算机视觉算法实习生|北京/杭州/深圳|未知|邮箱：zhoujing.zjh@alibaba-inc.com|简历命名格式：CVer推荐+姓名+电话号码+方向(工程/算法)|无|CVer公众号| +|京东AI Lab|计算机视觉算法实习生|北京朝阳区|未知|邮箱：wangxiaobo8@jd.com|简历命名格式：CVer推荐+姓名+电话号码|无|CVer公众号| +|字节跳动|AI 算法岗|北京|2月25-5月31|投递[官网](https://job.bytedance.com/campus/position)|内推码：WWNUPDJ、NAATGNC，邮箱咨询简历进度：wangyang.wyang@bytedance.com|无|CVer公众号| +|海康威视|AI算法岗|杭州/其他|未知|投递[官网](https://campushr.hikvision.com/)|内推码：P4Y8AQ、587TA3、587LAC、5SYMAJ|无|CVer公众号| +|图森未来|感知算法实习生|北京朝阳区|未知|邮箱：ostnie@foxmail.com|简历命名：CVer推荐+姓名+电话号码+应聘岗位|无|CVer公众号| +|平安科技|计算机视觉实习生|北京/深圳|3月19|[网申](http://campus.pingan.com/tech/position)|内推码：PvqgIB|无|牛客讨论区| +|抖音火山|计算机视觉算法实习生|北京/其他|未知|[网申](https://job.bytedance.com/campus/position)|内推码：QURKAW2|无|牛客讨论区| +|招银网络科技|算法实习生|深圳/杭州/成都/|6.30|[网申](https://mp.weixin.qq.com/s/7l6GZ88yynwb3jwRhx76KA)|内推码：9171XYCD、9211LYCD|无|牛客讨论区| +|奇虎360|算法实习生|北京/其他|3月31|[网申](http://chrcmp.chinahr.com/views/2019-qihu360/index.html)|内推码：暂无|无|牛客讨论区| +|爱奇艺|C++开发工程师|北京|4月中旬|[网申](https://zhaopin.iqiyi.com/job-school.html)|内推码：zNG4KR|无|信息学院qq群| +|蚂蚁金服|算法实习生|杭州/其他|3-5月|邮箱：xiaoxue.sxx@antfin.com|邮件标题：【姓名】+【学校】+【应聘岗位】|无|信息学院qq群| +|华为|算法实习生|成都/其他|3月28|[网申]() +|菜鸟网络|算法实习生|杭州/北京|未知|qq：1193304376|1193304376@qq.com|无|无|信息学院qq群| +|oppo手机|算法实习生|北京|未知|[网申](https://oppo.zhaopin.com/jobs. + From ee916df5a890f0ee6845e84b54d6b94191e25ac8 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Fri, 22 Mar 2019 20:36:54 +0800 Subject: [PATCH 018/386] Update README.md --- README.md | 30 +++++++----------------------- 1 file changed, 7 insertions(+), 23 deletions(-) diff --git a/README.md b/README.md index 0c179339..e65b95f5 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,5 @@ -## 2020算法实习岗位表 +### 2019春招算法实习岗位表 +2019年春招近期实习岗位汇总，还有待完善，持续更新，也希望各位一起更新。 |公司|招聘岗位|工作地点|截止时间|投递方式|内推方式|是否投递|信息来源| |---|-------|-------|------|-------|-------|------|-------| |文行知远|感知算法实习生|北京/广州|未知|邮箱：hzshuai@gmail.com|简历命名格式：CVer推荐+姓名+电话号码+实习生|无|CVer公众号| @@ -17,26 +18,9 @@ |华为|算法实习生|成都/其他|3月28|[网申]()|无|无|信息学院qq群| |菜鸟网络|算法实习生|杭州/北京|未知|qq：1193304376|1193304376@qq.com|无|无|信息学院qq群| |oppo手机|算法实习生|北京|未知|[网申](https://oppo.zhaopin.com/jobs.html)|内推码：15209235173|无|信息学院qq群| - -### 2019春招算法实习岗位表 -2019年春招近期实习岗位汇总，还有待完善，持续更新，也希望各位一起更新。 -|公司|招聘岗位|工作地点|截止时间|投递方式|内推方式|是否投递|信息来源| -|------|------------|------------|------------|------------|------------|-------------|-----------| -|文行知远|感知算法实习生|北京/广州|未知|邮箱：hzshuai@gmail.com|简历命名格式：CVer推荐+姓名+电话号码+实习生|无|CVer公众号| -|商汤科技|深度学习/计算机视觉见习研究员等|北京/上海/深圳/杭州|未知|邮箱：wwlwenli@163.com|邮件主题：CVer推荐+商汤2019春招内推、商汤2020实习内推，简历附件命名：姓名+岗位名称+到岗时间+可见习时长|无|CVer公众号| -|商汤科技|图像算法工程师(实习)|上海|未知|投递邮箱：Feijingjing1@sensetime.com|邮件主题：学校-姓名-意向岗位-意向城市|无|52CV群| -|阿里达摩院|计算机视觉算法实习生|北京/杭州/深圳|未知|邮箱：zhoujing.zjh@alibaba-inc.com|简历命名格式：CVer推荐+姓名+电话号码+方向(工程/算法)|无|CVer公众号| -|京东AI Lab|计算机视觉算法实习生|北京朝阳区|未知|邮箱：wangxiaobo8@jd.com|简历命名格式：CVer推荐+姓名+电话号码|无|CVer公众号| -|字节跳动|AI 算法岗|北京|2月25-5月31|投递[官网](https://job.bytedance.com/campus/position)|内推码：WWNUPDJ、NAATGNC，邮箱咨询简历进度：wangyang.wyang@bytedance.com|无|CVer公众号| -|海康威视|AI算法岗|杭州/其他|未知|投递[官网](https://campushr.hikvision.com/)|内推码：P4Y8AQ、587TA3、587LAC、5SYMAJ|无|CVer公众号| -|图森未来|感知算法实习生|北京朝阳区|未知|邮箱：ostnie@foxmail.com|简历命名：CVer推荐+姓名+电话号码+应聘岗位|无|CVer公众号| -|平安科技|计算机视觉实习生|北京/深圳|3月19|[网申](http://campus.pingan.com/tech/position)|内推码：PvqgIB|无|牛客讨论区| -|抖音火山|计算机视觉算法实习生|北京/其他|未知|[网申](https://job.bytedance.com/campus/position)|内推码：QURKAW2|无|牛客讨论区| -|招银网络科技|算法实习生|深圳/杭州/成都/|6.30|[网申](https://mp.weixin.qq.com/s/7l6GZ88yynwb3jwRhx76KA)|内推码：9171XYCD、9211LYCD|无|牛客讨论区| -|奇虎360|算法实习生|北京/其他|3月31|[网申](http://chrcmp.chinahr.com/views/2019-qihu360/index.html)|内推码：暂无|无|牛客讨论区| -|爱奇艺|C++开发工程师|北京|4月中旬|[网申](https://zhaopin.iqiyi.com/job-school.html)|内推码：zNG4KR|无|信息学院qq群| -|蚂蚁金服|算法实习生|杭州/其他|3-5月|邮箱：xiaoxue.sxx@antfin.com|邮件标题：【姓名】+【学校】+【应聘岗位】|无|信息学院qq群| -|华为|算法实习生|成都/其他|3月28|[网申]() -|菜鸟网络|算法实习生|杭州/北京|未知|qq：1193304376|1193304376@qq.com|无|无|信息学院qq群| -|oppo手机|算法实习生|北京|未知|[网申](https://oppo.zhaopin.com/jobs. +|创新工厂人工智能工程院|机器人/自动化实习生|北京|未知|投递邮箱：hr@chuangxin.com|投递格式：姓名+岗位(实习生)+可到岗日期|无|我爱计算机视觉公众号| +|旷世科技detection组|算法实习生|北京|未知|投递邮箱： zhangzhiqiang@megvii.com|暂无内推|无|我爱计算机视觉公众号| +|锐明技术|计算机视觉算法实习生|深圳/重庆|未知|[网申](http://streamax.zhiye.com/Campus)|暂无内推|无|我爱计算机视觉公众号| +|作业帮|计算机视觉方向实习生|北京|未知|投递邮箱：wujunbin@zuoyebang.com|暂无内推|无|我爱计算机视觉公众号| +|爱YTUH莫科技|算法实习生|深圳|未知|投递邮箱：hr@mall-ai.com|暂无内推|无|我爱计算机视觉| From 73379639de434faa8342e8066ed97f3325047305 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Fri, 22 Mar 2019 20:37:17 +0800 Subject: [PATCH 019/386] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index e65b95f5..015b1133 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -### 2019春招算法实习岗位表 +## 2019春招算法实习岗位表 2019年春招近期实习岗位汇总，还有待完善，持续更新，也希望各位一起更新。 |公司|招聘岗位|工作地点|截止时间|投递方式|内推方式|是否投递|信息来源| |---|-------|-------|------|-------|-------|------|-------| From aff4557baf2400e5e8df3e9ce01372153fed2d0e Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Fri, 22 Mar 2019 20:37:44 +0800 Subject: [PATCH 020/386] Update README.md --- README.md | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 015b1133..f2091dc1 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,6 @@ ## 2019春招算法实习岗位表 2019年春招近期实习岗位汇总，还有待完善，持续更新，也希望各位一起更新。 + |公司|招聘岗位|工作地点|截止时间|投递方式|内推方式|是否投递|信息来源| |---|-------|-------|------|-------|-------|------|-------| |文行知远|感知算法实习生|北京/广州|未知|邮箱：hzshuai@gmail.com|简历命名格式：CVer推荐+姓名+电话号码+实习生|无|CVer公众号| @@ -18,9 +19,5 @@ |华为|算法实习生|成都/其他|3月28|[网申]()|无|无|信息学院qq群| |菜鸟网络|算法实习生|杭州/北京|未知|qq：1193304376|1193304376@qq.com|无|无|信息学院qq群| |oppo手机|算法实习生|北京|未知|[网申](https://oppo.zhaopin.com/jobs.html)|内推码：15209235173|无|信息学院qq群| -|创新工厂人工智能工程院|机器人/自动化实习生|北京|未知|投递邮箱：hr@chuangxin.com|投递格式：姓名+岗位(实习生)+可到岗日期|无|我爱计算机视觉公众号| -|旷世科技detection组|算法实习生|北京|未知|投递邮箱： zhangzhiqiang@megvii.com|暂无内推|无|我爱计算机视觉公众号| -|锐明技术|计算机视觉算法实习生|深圳/重庆|未知|[网申](http://streamax.zhiye.com/Campus)|暂无内推|无|我爱计算机视觉公众号| -|作业帮|计算机视觉方向实习生|北京|未知|投递邮箱：wujunbin@zuoyebang.com|暂无内推|无|我爱计算机视觉公众号| -|爱YTUH莫科技|算法实习生|深圳|未知|投递邮箱：hr@mall-ai.com|暂无内推|无|我爱计算机视觉| + From 6c565fc461fdc852391406f92ac902ff21a6032c Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Fri, 22 Mar 2019 20:38:27 +0800 Subject: [PATCH 021/386] Update README.md --- README.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index f2091dc1..76f6f88b 100644 --- a/README.md +++ b/README.md @@ -19,5 +19,9 @@ |华为|算法实习生|成都/其他|3月28|[网申]()|无|无|信息学院qq群| |菜鸟网络|算法实习生|杭州/北京|未知|qq：1193304376|1193304376@qq.com|无|无|信息学院qq群| |oppo手机|算法实习生|北京|未知|[网申](https://oppo.zhaopin.com/jobs.html)|内推码：15209235173|无|信息学院qq群| - +|创新工厂人工智能工程院|机器人/自动化实习生|北京|未知|投递邮箱：hr@chuangxin.com|投递格式：姓名+岗位(实习生)+可到岗日期|无|我爱计算机视觉公众号| +|旷世科技detection组|算法实习生|北京|未知|投递邮箱： zhangzhiqiang@megvii.com|暂无内推|无|我爱计算机视觉公众号| +|锐明技术|计算机视觉算法实习生|深圳/重庆|未知|[网申](http://streamax.zhiye.com/Campus)|暂无内推|无|我爱计算机视觉公众号| +|作业帮|计算机视觉方向实习生|北京|未知|投递邮箱：wujunbin@zuoyebang.com|暂无内推|无|我爱计算机视觉公众号| +|爱YTUH莫科技|算法实习生|深圳|未知|投递邮箱：hr@mall-ai.com|暂无内推|无|我爱计算机视觉| From 5ad5bea92b0785de5ce45a7c152da636f1e6383b Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Fri, 22 Mar 2019 21:09:43 +0800 Subject: [PATCH 022/386] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 76f6f88b..27b2b958 100644 --- a/README.md +++ b/README.md @@ -24,4 +24,4 @@ |锐明技术|计算机视觉算法实习生|深圳/重庆|未知|[网申](http://streamax.zhiye.com/Campus)|暂无内推|无|我爱计算机视觉公众号| |作业帮|计算机视觉方向实习生|北京|未知|投递邮箱：wujunbin@zuoyebang.com|暂无内推|无|我爱计算机视觉公众号| |爱YTUH莫科技|算法实习生|深圳|未知|投递邮箱：hr@mall-ai.com|暂无内推|无|我爱计算机视觉| - +|阿里巴巴业务平台事业部|杭州|3月12-4月23|[网申](https://campus.alibaba.com/index.htm)|暂无内推|无|知乎| From 0ba7eb77c5f236ab645d68509814266004bb47f1 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Fri, 22 Mar 2019 21:23:27 +0800 Subject: [PATCH 023/386] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 27b2b958..19b42a19 100644 --- a/README.md +++ b/README.md @@ -24,4 +24,4 @@ |锐明技术|计算机视觉算法实习生|深圳/重庆|未知|[网申](http://streamax.zhiye.com/Campus)|暂无内推|无|我爱计算机视觉公众号| |作业帮|计算机视觉方向实习生|北京|未知|投递邮箱：wujunbin@zuoyebang.com|暂无内推|无|我爱计算机视觉公众号| |爱YTUH莫科技|算法实习生|深圳|未知|投递邮箱：hr@mall-ai.com|暂无内推|无|我爱计算机视觉| -|阿里巴巴业务平台事业部|杭州|3月12-4月23|[网申](https://campus.alibaba.com/index.htm)|暂无内推|无|知乎| +|阿里巴巴业务平台事业部|算法-机器学习实习生|杭州|3月12-4月23|[网申](https://campus.alibaba.com/index.htm)|暂无内推|无|知乎| From 4638986f60a38abef42a2752b2ee20c2310040e3 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Fri, 22 Mar 2019 23:44:43 +0800 Subject: [PATCH 024/386] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 19b42a19..8d2b49d6 100644 --- a/README.md +++ b/README.md @@ -25,3 +25,4 @@ |作业帮|计算机视觉方向实习生|北京|未知|投递邮箱：wujunbin@zuoyebang.com|暂无内推|无|我爱计算机视觉公众号| |爱YTUH莫科技|算法实习生|深圳|未知|投递邮箱：hr@mall-ai.com|暂无内推|无|我爱计算机视觉| |阿里巴巴业务平台事业部|算法-机器学习实习生|杭州|3月12-4月23|[网申](https://campus.alibaba.com/index.htm)|暂无内推|无|知乎| +|拼多多|算法实习生|上海/其他|3月15-4月30|[网申](https://www.pinduoduo.com/home/campus/)|暂无内推|无|朋友推荐| From 735d4d9e2a763606fe4b343ac02931252e339629 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Sat, 23 Mar 2019 00:03:40 +0800 Subject: [PATCH 025/386] Update README.md --- README.md | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 8d2b49d6..9b5b8ec0 100644 --- a/README.md +++ b/README.md @@ -1,15 +1,15 @@ ## 2019春招算法实习岗位表 2019年春招近期实习岗位汇总，还有待完善，持续更新，也希望各位一起更新。 - +注意：大部分岗位都是面向2020年毕业的学生 |公司|招聘岗位|工作地点|截止时间|投递方式|内推方式|是否投递|信息来源| |---|-------|-------|------|-------|-------|------|-------| -|文行知远|感知算法实习生|北京/广州|未知|邮箱：hzshuai@gmail.com|简历命名格式：CVer推荐+姓名+电话号码+实习生|无|CVer公众号| -|商汤科技|深度学习/计算机视觉见习研究员等|北京/上海/深圳/杭州|未知|邮箱：wwlwenli@163.com|邮件主题：CVer推荐+商汤2019春招内推、商汤2020实习内推，简历附件命名：姓名+岗位名称+到岗时间+可见习时长|无|CVer公众号| -|阿里达摩院|计算机视觉算法实习生|北京/杭州/深圳|未知|邮箱：zhoujing.zjh@alibaba-inc.com|简历命名格式：CVer推荐+姓名+电话号码+方向(工程/算法)|无|CVer公众号| -|京东AI Lab|计算机视觉算法实习生|北京朝阳区|未知|邮箱：wangxiaobo8@jd.com|简历命名格式：CVer推荐+姓名+电话号码|无|CVer公众号| +|文行知远|感知算法实习生|北京/广州|未知|内推邮箱：hzshuai@gmail.com|简历命名格式：CVer推荐+姓名+电话号码+实习生|无|CVer公众号| +|商汤科技|深度学习/计算机视觉见习研究员等|北京/上海/深圳/杭州|未知|内推邮箱：wwlwenli@163.com|邮件主题：CVer推荐+商汤2019春招内推、商汤2020实习内推，简历附件命名：姓名+岗位名称+到岗时间+可见习时长|无|CVer公众号| +|阿里达摩院|计算机视觉算法实习生|北京/杭州/深圳|未知|内推邮箱：zhoujing.zjh@alibaba-inc.com|简历命名格式：CVer推荐+姓名+电话号码+方向(工程/算法)|无|CVer公众号| +|京东AI Lab|计算机视觉算法实习生|北京朝阳区|未知|内推邮箱：wangxiaobo8@jd.com|简历命名格式：CVer推荐+姓名+电话号码|无|CVer公众号| |字节跳动|AI 算法岗|北京|2.25-5.31|投递[官网](https://job.bytedance.com/campus/position)|内推码：WWNUPDJ、NAATGNC，邮箱咨询简历进度：wangyang.wyang@bytedance.com|无|CVer公众号| |海康威视|AI算法岗|杭州/其他|未知|投递[官网](https://campushr.hikvision.com/)|内推码：P4Y8AQ、587TA3、587LAC、5SYMAJ|无|CVer公众号| -|图森未来|感知算法实习生|北京朝阳区|未知|邮箱：ostnie@foxmail.com|简历命名：CVer推荐+姓名+电话号码+应聘岗位|无|CVer公众号| +|图森未来|感知算法实习生|北京朝阳区|未知|内推邮箱：ostnie@foxmail.com|简历命名：CVer推荐+姓名+电话号码+应聘岗位|无|CVer公众号| |平安科技|计算机视觉实习生|北京/深圳|3.19|[网申](http://campus.pingan.com/tech/position)|内推码：PvqgIB|无|牛客讨论区| |抖音火山|计算机视觉算法实习生|北京/其他|未知|[网申](https://job.bytedance.com/campus/position)|内推码：QURKAW2|无|牛客讨论区| |招银网络科技|算法实习生|深圳/杭州/成都/|6.30|[网申](https://mp.weixin.qq.com/s/7l6GZ88yynwb3jwRhx76KA)|内推码：9171XYCD、9211LYCD|无|牛客讨论区| @@ -26,3 +26,7 @@ |爱YTUH莫科技|算法实习生|深圳|未知|投递邮箱：hr@mall-ai.com|暂无内推|无|我爱计算机视觉| |阿里巴巴业务平台事业部|算法-机器学习实习生|杭州|3月12-4月23|[网申](https://campus.alibaba.com/index.htm)|暂无内推|无|知乎| |拼多多|算法实习生|上海/其他|3月15-4月30|[网申](https://www.pinduoduo.com/home/campus/)|暂无内推|无|朋友推荐| +|小马智行|计算机视觉实习生|北京市海淀区|未知|内推邮箱：zhijie@pony.ai|简历命名：姓名+岗位+CVer推荐+实习开始时间+可实习月数|无|CVer公众号| +|上海媒智科技|计算机视觉算法实习生|上海|未知|内推邮箱：chenxi.huang@media-smart.cn|简历格式：姓名+电话号码+全职/实习+CVer推荐|无|CVer公众号| +|旷视科技|算法研究员实习生|北京市海淀区|长期有效|内推邮箱：ur@megvii.com|简历命名：姓名+电话+岗位+CVer推荐(**免笔试**)|无|CVer公众号| +|淘宝-智能制造事业部|杭州|未知|内推邮箱：yuanliang.syl@alibaba-inc.com|简历命名格式：姓名+电话号码+应聘岗位+CVer推荐|无|CVer公众号| From 6eb55dc9bc35eb4082cc7dd99daf1f7f3bfcec82 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Sat, 23 Mar 2019 00:06:19 +0800 Subject: [PATCH 026/386] Update README.md --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 9b5b8ec0..cbd82523 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,7 @@ ## 2019春招算法实习岗位表 2019年春招近期实习岗位汇总，还有待完善，持续更新，也希望各位一起更新。 -注意：大部分岗位都是面向2020年毕业的学生 +注意：大部分岗位都是面向2020年毕业的学生。 + |公司|招聘岗位|工作地点|截止时间|投递方式|内推方式|是否投递|信息来源| |---|-------|-------|------|-------|-------|------|-------| |文行知远|感知算法实习生|北京/广州|未知|内推邮箱：hzshuai@gmail.com|简历命名格式：CVer推荐+姓名+电话号码+实习生|无|CVer公众号| From 78aa0f6e41c0137ce658cd94e87d1b20241ebbe3 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Sat, 23 Mar 2019 00:07:36 +0800 Subject: [PATCH 027/386] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index cbd82523..383fab1c 100644 --- a/README.md +++ b/README.md @@ -30,4 +30,4 @@ |小马智行|计算机视觉实习生|北京市海淀区|未知|内推邮箱：zhijie@pony.ai|简历命名：姓名+岗位+CVer推荐+实习开始时间+可实习月数|无|CVer公众号| |上海媒智科技|计算机视觉算法实习生|上海|未知|内推邮箱：chenxi.huang@media-smart.cn|简历格式：姓名+电话号码+全职/实习+CVer推荐|无|CVer公众号| |旷视科技|算法研究员实习生|北京市海淀区|长期有效|内推邮箱：ur@megvii.com|简历命名：姓名+电话+岗位+CVer推荐(**免笔试**)|无|CVer公众号| -|淘宝-智能制造事业部|杭州|未知|内推邮箱：yuanliang.syl@alibaba-inc.com|简历命名格式：姓名+电话号码+应聘岗位+CVer推荐|无|CVer公众号| +|淘宝-智能制造事业部|图形图像算法实习生杭州|未知|内推邮箱：yuanliang.syl@alibaba-inc.com|简历命名格式：姓名+电话号码+应聘岗位+CVer推荐|无|CVer公众号| From ccf6e613c900205de01c5519337bf126b02014a6 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Sat, 23 Mar 2019 00:19:51 +0800 Subject: [PATCH 028/386] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 383fab1c..12a4631b 100644 --- a/README.md +++ b/README.md @@ -30,4 +30,4 @@ |小马智行|计算机视觉实习生|北京市海淀区|未知|内推邮箱：zhijie@pony.ai|简历命名：姓名+岗位+CVer推荐+实习开始时间+可实习月数|无|CVer公众号| |上海媒智科技|计算机视觉算法实习生|上海|未知|内推邮箱：chenxi.huang@media-smart.cn|简历格式：姓名+电话号码+全职/实习+CVer推荐|无|CVer公众号| |旷视科技|算法研究员实习生|北京市海淀区|长期有效|内推邮箱：ur@megvii.com|简历命名：姓名+电话+岗位+CVer推荐(**免笔试**)|无|CVer公众号| -|淘宝-智能制造事业部|图形图像算法实习生杭州|未知|内推邮箱：yuanliang.syl@alibaba-inc.com|简历命名格式：姓名+电话号码+应聘岗位+CVer推荐|无|CVer公众号| +|淘宝-智能制造事业部|图形图像算法实习生|杭州|未知|内推邮箱：yuanliang.syl@alibaba-inc.com|简历命名格式：姓名+电话号码+应聘岗位+CVer推荐|无|CVer公众号| From f65c3ca2dfda9a31cbfb8d6577a4330670dea2ad Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Mon, 25 Mar 2019 22:50:14 +0800 Subject: [PATCH 029/386] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 12a4631b..e41702a3 100644 --- a/README.md +++ b/README.md @@ -17,7 +17,7 @@ |奇虎360|算法实习生|北京/其他|3.31|[网申](http://chrcmp.chinahr.com/views/2019-qihu360/index.html)|内推码：暂无|无|牛客讨论区| |爱奇艺|C++开发工程师|北京|4月中旬|[网申](https://zhaopin.iqiyi.com/job-school.html)|内推码：zNG4KR|无|信息学院qq群| |蚂蚁金服|算法实习生|杭州/其他|3-5月|邮箱：xiaoxue.sxx@antfin.com|邮件标题：【姓名】+【学校】+【应聘岗位】|无|信息学院qq群| -|华为|算法实习生|成都/其他|3月28|[网申]()|无|无|信息学院qq群| +|华为|算法实习生|成都/其他|3月28|[网申](http://career.huawei.com/reccampportal/campus4_index.html#campus4/pages/joblist/jobList.html?jobFamClsCode=JFC1&type=0)|无|无|信息学院qq群| |菜鸟网络|算法实习生|杭州/北京|未知|qq：1193304376|1193304376@qq.com|无|无|信息学院qq群| |oppo手机|算法实习生|北京|未知|[网申](https://oppo.zhaopin.com/jobs.html)|内推码：15209235173|无|信息学院qq群| |创新工厂人工智能工程院|机器人/自动化实习生|北京|未知|投递邮箱：hr@chuangxin.com|投递格式：姓名+岗位(实习生)+可到岗日期|无|我爱计算机视觉公众号| From d20f97bbebb0afa8445daff29e29001fe87f7802 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Tue, 26 Mar 2019 23:10:05 +0800 Subject: [PATCH 030/386] =?UTF-8?q?Create=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...40\351\235\242\350\257\225\351\242\230.md" | 182 ++++++++++++++++++ 1 file changed, 182 insertions(+) create mode 100644 "\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" new file mode 100644 index 00000000..fb6683b3 --- /dev/null +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -0,0 +1,182 @@ +### CNN权值共享问题 +首先权值共享就是滤波器共享，滤波器的参数是固定的，即是用相同的滤波器去扫一遍图像，提取一次特征特征，得到feature map。在卷积网络中，学好了一个滤波器，就相当于掌握了一种特征，这个滤波器在图像中滑动，进行特征提取，然后所有进行这样操作的区域都会被采集到这种特征，就好比上面的水平线。 + +### CNN结构特点 +局部连接，权值共享，池化操作，多层次结构。 + +局部连接使网络可以提取数据的局部特征；权值共享大大降低了网络的训练难度，一个Filter只提取一个特征，在整个图片（或者语音／文本） 中进行卷积；池化操作与多层次结构一起，实现了数据的降维，将低层次的局部特征组合成为较高层次的特征，从而对整个图片进行表示。如下图所示： + +### 什么样的数据集不适合深度学习 +数据集太小，数据样本不足时，深度学习相对其它机器学习算法，没有明显优势。 +数据集没有局部相关特性，目前深度学习表现比较好的领域主要是图像／语音／自然语言处理等领域，这些领域的一个共性是局部相关性。图像中像素组成物体，语音信号中音位组合成单词，文本数据中单词组合成句子，这些特征元素的组合一旦被打乱，表示的含义同时也被改变。对于没有这样的局部相关性的数据集，不适于使用深度学习算法进行处理。举个例子：预测一个人的健康状况，相关的参数会有年龄、职业、收入、家庭状况等各种元素，将这些元素打乱，并不会影响相关的结果。 +### 什么造成梯度消失问题 +神经网络的训练中，通过改变神经元的权重，使网络的输出值尽可能逼近标签以降低误差值，训练普遍使用BP算法，核心思想是，计算出输出与标签间的损失函数值，然后计算其相对于每个神经元的梯度，进行权值的迭代。 +梯度消失会造成权值更新缓慢，模型训练难度增加。造成梯度消失的一个原因是，许多激活函数将输出值挤压在很小的区间内，在激活函数两端较大范围的定义域内梯度为0，造成学习停止。 +### Overfitting怎么解决 +首先所谓过拟合，指的是一个模型过于复杂之后，它可以很好地“记忆”每一个训练数据中随机噪音的部分而忘记了去“训练”数据中的通用趋势。 +过拟合具体表现在：模型在训练数据上损失函数较小，预测准确率较高；但是在测试数据上损失函数比较大，预测准确率较低。 + +Parameter Norm Penalties(参数范数惩罚)；Dataset Augmentation (数据集增强)；Early Stopping(提前终止)；Parameter Tying and Parameter Sharing (参数绑定与参数共享)；Bagging and Other Ensemble Methods(Bagging 和其他集成方法)；dropout；regularization； batch normalizatin。是解决Overfitting的常用手段。 + +### L1和L2区别 +L1 范数（L1 norm）是指向量中各个元素绝对值之和，也有个美称叫“稀疏规则算子”（Lasso regularization）。 比如 向量 A=[1，-1，3]， 那么 A 的 L1 范数为 |1|+|-1|+|3|。简单总结一下就是： + +L1 范数: 为 x 向量各个元素绝对值之和。 +L2 范数: 为 x 向量各个元素平方和的 1/2 次方，L2 范数又称 Euclidean 范数或 Frobenius 范数 +Lp 范数: 为 x 向量各个元素绝对值 p 次方和的 1/p 次方. +在支持向量机学习过程中，L1 范数实际是一种对于成本函数求解最优的过程，因此，L1 范数正则化通过向成本函数中添加 L1 范数，使得学习得到的结果满足稀疏化，从而方便人类提取特征。 + +L1 范数可以使权值稀疏，方便特征提取。 L2 范数可以防止过拟合，提升模型的泛化能力。 + +### TensorFlow计算图 +Tensorflow 是一个通过计算图的形式来表述计算的编程系统，计算图也叫数据流图，可以把计算图看做是一种有向图，Tensorflow 中的每一个计算都是计算图上的一个节点，而节点之间的边描述了计算之间的依赖关系。 + +### BN（批归一化）的作用 +(1). 可以使用更高的学习率。如果每层的scale不一致，实际上每层需要的学习率是不一样的，同一层不同维度的scale往往也需要不同大小的学习率，通常需要使用最小的那个学习率才能保证损失函数有效下降，Batch Normalization将每层、每维的scale保持一致，那么我们就可以直接使用较高的学习率进行优化。 + +(2). 移除或使用较低的dropout。 dropout是常用的防止overfitting的方法，而导致overfit的位置往往在数据边界处，如果初始化权重就已经落在数据内部，overfit现象就可以得到一定的缓解。论文中最后的模型分别使用10%、5%和0%的dropout训练模型，与之前的40%-50%相比，可以大大提高训练速度。 + +(3). 降低L2权重衰减系数。 还是一样的问题，边界处的局部最优往往有几维的权重（斜率）较大，使用L2衰减可以缓解这一问题，现在用了Batch Normalization，就可以把这个值降低了，论文中降低为原来的5倍。 + +(4). 取消Local Response Normalization层。 由于使用了一种Normalization，再使用LRN就显得没那么必要了。而且LRN实际上也没那么work。 + +(5). Batch Normalization调整了数据的分布，不考虑激活函数，它让每一层的输出归一化到了均值为0方差为1的分布，这保证了梯度的有效性，可以解决反向传播过程中的梯度问题。目前大部分资料都这样解释，比如BN的原始论文认为的缓解了Internal Covariate Shift(ICS)问题。 + +### 什么是梯度消失和爆炸，怎么解决？ +当训练较多层数的模型时，一般会出现梯度消失问题（gradient vanishing problem）和梯度爆炸问题（gradient exploding problem）。注意在反向传播中，当网络模型层数较多时，梯度消失和梯度爆炸是不可避免的。 +深度神经网络中的梯度不稳定性，根本原因在于前面层上的梯度是来自于后面层上梯度的乘积。当存在过多的层次时，就出现了内在本质上的不稳定场景。 +前面的层比后面的层梯度变化更小，故变化更慢，故引起了梯度消失问题。前面层比后面层梯度变化更快，故引起梯度爆炸问题。 + +解决梯度消失和梯度爆炸问题，常用的有以下几个方案： + ++ 预训练模型 + 微调 ++ 梯度剪切 + 正则化 ++ relu、leakrelu、elu等激活函数 ++ BN批归一化 ++ CNN中的残差结构 ++ LSTM结构 +### RNN循环神经网络理解 +循环神经网络（recurrent neural network, RNN）, 主要应用在语音识别、语言模型、机器翻译以及时序分析等问题上。 +在经典应用中，卷积神经网络在不同的空间位置共享参数，循环神经网络是在不同的时间位置共享参数，从而能够使用有限的参数处理任意长度的序列。 +RNN可以看做作是同一神经网络结构在时间序列上被复制多次的结果，这个被复制多次的结构称为循环体，如何设计循环体的网络结构是RNN解决实际问题的关键。 +RNN的输入有两个部分，一部分为上一时刻的状态，另一部分为当前时刻的输入样本。 + +### 训练过程中模型不收敛，是否说明这个模型无效，致模型不收敛的原因有哪些? +不一定。导致模型不收敛的原因有很多种可能，常见的有以下几种： + ++ 没有对数据做归一化。 ++ 没有检查过你的结果。这里的结果包括预处理结果和最终的训练测试结果。 ++ 忘了做数据预处理。 ++ 忘了使用正则化。 ++ Batch Size设的太大。 ++ 学习率设的不对。 ++ 最后一层的激活函数用的不对。 ++ 网络存在坏梯度。比如Relu对负值的梯度为0，反向传播时，0梯度就是不传播。 ++ 参数初始化错误。 ++ 网络太深。隐藏层神经元数量错误。 ++ 更多回答，参考此链接。 + +### 图像处理中平滑和锐化操作是什么？ +平滑处理（smoothing）也称模糊处理（bluring），主要用于消除图像中的噪声部分，平滑处理常用的用途是用来减少图像上的噪点或失真，平滑主要使用图像滤波。在这里，我个人认为可以把图像平滑和图像滤波联系起来，因为图像平滑常用的方法就是图像滤波器。 +在OpenCV3中常用的图像滤波器有以下几种： + ++ 方框滤波——BoxBlur函数 ++ 均值滤波（邻域平均滤波）——Blur函数 ++ 高斯滤波——GaussianBlur函数 ++ 中值滤波——medianBlur函数 ++ 双边滤波——bilateralFilter函数 +图像锐化操作是为了突出显示图像的边界和其他细节，而图像锐化实现的方法是通过各种算子和滤波器实现的——Canny算子、Sobel算子、Laplacian算子以及Scharr滤波器。 + +### VGG使用2个3*3卷积的优势在哪里？ +(1). 减少网络层参数。用两个3*3卷积比用1个5*5卷积拥有更少的参数量，只有后者的2∗3∗35∗5=0.72。但是起到的效果是一样的，两个3*3的卷积层串联相当于一个5*5的卷积层，感受野的大小都是5×5，即1个像素会跟周围5*5的像素产生关联。把下图当成动态图看，很容易看到两个3×3卷积层堆叠（没有空间池化）有5×5的有效感受野。 + +https://www.armcvai.com/wp-content/uploads/2019/03/2个33卷积层.png +(2). 更多的非线性变换。2个3*3卷积层拥有比1个5*5卷积层更多的非线性变换（前者可以使用两次ReLU激活函数，而后者只有一次），使得卷积神经网络对特征的学习能力更强。 + +***paper中给出的相关解释***：三个这样的层具有7×7的有效感受野。那么我们获得了什么？例如通过使用三个3×3卷积层的堆叠来替换单个7×7层。首先，我们结合了三个非线性修正层，而不是单一的，这使得决策函数更具判别性。其次，我们减少参数的数量：假设三层3×3卷积堆叠的输入和输出有C个通道，堆叠卷积层的参数为3(32C2)=27C2个权重；同时，单个7×7卷积层将需要72C2=49C2个参数，即参数多81％。这可以看作是对7×7卷积滤波器进行正则化，迫使它们通过3×3滤波器（在它们之间注入非线性）进行分解。 + +此回答可以参考TensorFlow实战p110，网上很多回答都说的不全。 + +### Relu比Sigmoid效果好在哪里？ +ReLU激活函数公式如下： + +http://latex.codecogs.com/gif.latex?%24%24ReLu%28x%29%3D%5Cleft%5C%7B%5Cbegin%7Bmatrix%7D%20x%20%26%20x%3E0%5C%5C%200%20%26%20x%3C%3D0%20%5Cend%7Bmatrix%7D%5Cright.%24%24 +relu函数方程 +ReLU 的输出要么是 0, 要么是输入本身。虽然方程简单，但实际上效果更好。在网上看了很多版本的解释，有从程序实例分析也有从数学上分析，我找了个相对比较直白的回答，如下： +(1) ReLU函数计算简单，可以减少很多计算量。反向传播求误差梯度时，涉及除法，计算量相对较大，采用ReLU激活函数，可以节省很多计算量； +(2) 避免梯度消失问题。对于深层网络，sigmoid函数反向传播时，很容易就会出现梯度消失问题（在sigmoid接近饱和区时，变换太缓慢，导数趋于0，这种情况会造成信息丢失），从而无法完成深层网络的训练。 +(3) 可以缓解过拟合问题的发生。Relu会使一部分神经元的输出为0，这样就造成了网络的稀疏性，并且减少了参数的相互依存关系，缓解了过拟合问题的发生。 + +#### 参考链接 +ReLU为什么比Sigmoid效果好 + +神经网络中权值共享的理解？ +权值(权重)共享这个词是由LeNet5模型提出来的。以CNN为例，在对一张图偏进行卷积的过程中，使用的是同一个卷积核的参数。 +比如一个3×3×1的卷积核，这个卷积核内9个的参数被整张图共享，而不会因为图像内位置的不同而改变卷积核内的权系数。说的再直白一些，就是用一个卷积核不改变其内权系数的情况下卷积处理整张图片（当然CNN中每一层不会只有一个卷积核的，这样说只是为了方便解释而已）。 + +参考资料 +如何理解CNN中的权值共享 + +对fine-tuning(微调模型的理解)，为什么要修改最后几层神经网络权值？ +使用预训练模型的好处，在于利用训练好的SOTA模型权重去做特征提取，可以节省我们训练模型和调参的时间。 + +至于为什么只微调最后几层神经网络权重，是因为： +(1) CNN中更靠近底部的层（定义模型时先添加到模型中的层）编码的是更加通用的可复用特征，而更靠近顶部的层（最后添加到模型中的层）编码的是更专业业化的特征。微调这些更专业化的特征更加有用，它更代表了新数据集上的有用特征。 +(2) 训练的参数越多，过拟合的风险越大。很多SOTA模型拥有超过千万的参数，在一个不大的数据集上训练这么多参数是有过拟合风险的，除非你的数据集像Imagenet那样大。 + +参考资料 +Python深度学习p127. + +什么是dropout? +dropout可以防止过拟合，dropout简单来说就是：我们在前向传播的时候，让某个神经元的激活值以一定的概率p停止工作，这样可以使模型的泛化性更强，因为它不会依赖某些局部的特征。 + +https://www.armcvai.com/wp-content/uploads/2019/03/dropout.png +dropout具体工作流程 +以 标准神经网络为例，正常的流程是：我们首先把输入数据x通过网络前向传播，然后把误差反向传播一决定如何更新参数让网络进行学习。使用dropout之后，过程变成如下： + +(1) 首先随机（临时）删掉网络中一半的隐藏神经元，输入输出神经元保持不变（图3中虚线为部分临时被删除的神经元）； +(2) 然后把输入x通过修改后的网络进行前向传播计算，然后把得到的损失结果通过修改的网络反向传播。一小批训练样本执行完这个过程后，在没有被删除的神经元上按照随机梯度下降法更新对应的参数（w，b）； +(3) 然后重复这一过程： + +恢复被删掉的神经元（此时被删除的神经元保持原样没有更新w参数，而没有被删除的神经元已经有所更新） +从隐藏层神经元中随机选择一个一半大小的子集临时删除掉（同时备份被删除神经元的参数）。 +对一小批训练样本，先前向传播然后反向传播损失并根据随机梯度下降法更新参数（w，b） （没有被删除的那一部分参数得到更新，删除的神经元参数保持被删除前的结果）。 +dropout在神经网络中的应用 +(1) 在训练模型阶段 + +不可避免的，在训练网络中的每个单元都要添加一道概率流程，标准网络和带有dropout网络的比较图如下所示： + +https://www.armcvai.com/wp-content/uploads/2019/03/添加了dropou的神经网络图.png +(2) 在测试模型阶段 + +预测模型的时候，输入是当前输入，每个神经单元的权重参数要乘以概率p。 + +https://www.armcvai.com/wp-content/uploads/2019/03/dropout_test.png +如何选择dropout 的概率 +input 的dropout概率推荐是0.8， hidden layer 推荐是0.5， 但是也可以在一定的区间上取值。（All dropout nets use p = 0.5 for hidden units and p = 0.8 for input units.） + +参考资料 +Dropout:A Simple Way to Prevent Neural Networks from Overfitting +深度学习中Dropout原理解析 + +HOG算法原理描述 +方向梯度直方图（Histogram of Oriented Gradient, HOG）特征是一种在计算机视觉和图像处理中用来进行物体检测的特征描述子。它通过计算和统计图像局部区域的梯度方向直方图来构成特征。在深度学习取得成功之前，Hog特征结合SVM分类器被广泛应用于图像识别中，在行人检测中获得了较大的成功。 + +HOG特征原理 +HOG的核心思想是所检测的局部物体外形能够被光强梯度或边缘方向的分布所描述。通过将整幅图像分割成小的连接区域（称为cells），每个cell生成一个方向梯度直方图或者cell中pixel的边缘方向，这些直方图的组合可表示出（所检测目标的目标）描述子。为改善准确率，局部直方图可以通过计算图像中一个较大区域(称为block)的光强作为measure被对比标准化，然后用这个值(measure)归一化这个block中的所有cells。这个归一化过程完成了更好的照射/阴影不变性。 +与其他描述子相比，HOG得到的描述子保持了几何和光学转化不变性（除非物体方向改变）。因此HOG描述子尤其适合人的检测。 + +HOG特征提取方法就是将一个image： + +灰度化（将图像看做一个x,y,z（灰度）的三维图像） + 划分成小cells（2*2） +计算每个cell中每个pixel的gradient（即orientation） +统计每个cell的梯度直方图（不同梯度的个数），即可形成每个cell的descriptor。 +HOG特征检测步骤 +https://www.armcvai.com/wp-content/uploads/2019/03/image.png +颜色空间归一化——–>梯度计算————->梯度方向直方图———->重叠块直方图归一化———–>HOG特征 + +Reference +《Batch Normalization Accelerating Deep Network Training by Reducing Internal Covariate Shift》阅读笔记与实现 +深度学习中 Batch Normalization为什么效果好 +详解机器学习中的梯度消失、爆炸原因及其解决方法 From 0129cae07227bb433a376a96c8f03926c739b72c Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Tue, 26 Mar 2019 23:11:42 +0800 Subject: [PATCH 031/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...55\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index fb6683b3..b72cb765 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -1,7 +1,7 @@ -### CNN权值共享问题 +## CNN权值共享问题 首先权值共享就是滤波器共享，滤波器的参数是固定的，即是用相同的滤波器去扫一遍图像，提取一次特征特征，得到feature map。在卷积网络中，学好了一个滤波器，就相当于掌握了一种特征，这个滤波器在图像中滑动，进行特征提取，然后所有进行这样操作的区域都会被采集到这种特征，就好比上面的水平线。 -### CNN结构特点 +## CNN结构特点 局部连接，权值共享，池化操作，多层次结构。 局部连接使网络可以提取数据的局部特征；权值共享大大降低了网络的训练难度，一个Filter只提取一个特征，在整个图片（或者语音／文本） 中进行卷积；池化操作与多层次结构一起，实现了数据的降维，将低层次的局部特征组合成为较高层次的特征，从而对整个图片进行表示。如下图所示： From 7264fec9ce2b62efbb3aa19b07a7e35cefa647d8 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Tue, 26 Mar 2019 23:15:02 +0800 Subject: [PATCH 032/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 语法问题更新 --- ...40\351\235\242\350\257\225\351\242\230.md" | 88 +++++++++---------- 1 file changed, 43 insertions(+), 45 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index b72cb765..b37989cb 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -6,19 +6,19 @@ 局部连接使网络可以提取数据的局部特征；权值共享大大降低了网络的训练难度，一个Filter只提取一个特征，在整个图片（或者语音／文本） 中进行卷积；池化操作与多层次结构一起，实现了数据的降维，将低层次的局部特征组合成为较高层次的特征，从而对整个图片进行表示。如下图所示： -### 什么样的数据集不适合深度学习 +## 什么样的数据集不适合深度学习 数据集太小，数据样本不足时，深度学习相对其它机器学习算法，没有明显优势。 数据集没有局部相关特性，目前深度学习表现比较好的领域主要是图像／语音／自然语言处理等领域，这些领域的一个共性是局部相关性。图像中像素组成物体，语音信号中音位组合成单词，文本数据中单词组合成句子，这些特征元素的组合一旦被打乱，表示的含义同时也被改变。对于没有这样的局部相关性的数据集，不适于使用深度学习算法进行处理。举个例子：预测一个人的健康状况，相关的参数会有年龄、职业、收入、家庭状况等各种元素，将这些元素打乱，并不会影响相关的结果。 -### 什么造成梯度消失问题 +## 什么造成梯度消失问题 神经网络的训练中，通过改变神经元的权重，使网络的输出值尽可能逼近标签以降低误差值，训练普遍使用BP算法，核心思想是，计算出输出与标签间的损失函数值，然后计算其相对于每个神经元的梯度，进行权值的迭代。 梯度消失会造成权值更新缓慢，模型训练难度增加。造成梯度消失的一个原因是，许多激活函数将输出值挤压在很小的区间内，在激活函数两端较大范围的定义域内梯度为0，造成学习停止。 -### Overfitting怎么解决 +## Overfitting怎么解决 首先所谓过拟合，指的是一个模型过于复杂之后，它可以很好地“记忆”每一个训练数据中随机噪音的部分而忘记了去“训练”数据中的通用趋势。 过拟合具体表现在：模型在训练数据上损失函数较小，预测准确率较高；但是在测试数据上损失函数比较大，预测准确率较低。 Parameter Norm Penalties(参数范数惩罚)；Dataset Augmentation (数据集增强)；Early Stopping(提前终止)；Parameter Tying and Parameter Sharing (参数绑定与参数共享)；Bagging and Other Ensemble Methods(Bagging 和其他集成方法)；dropout；regularization； batch normalizatin。是解决Overfitting的常用手段。 -### L1和L2区别 +## L1和L2区别 L1 范数（L1 norm）是指向量中各个元素绝对值之和，也有个美称叫“稀疏规则算子”（Lasso regularization）。 比如 向量 A=[1，-1，3]， 那么 A 的 L1 范数为 |1|+|-1|+|3|。简单总结一下就是： L1 范数: 为 x 向量各个元素绝对值之和。 @@ -28,10 +28,10 @@ Lp 范数: 为 x 向量各个元素绝对值 p 次方和的 1/p 次方. L1 范数可以使权值稀疏，方便特征提取。 L2 范数可以防止过拟合，提升模型的泛化能力。 -### TensorFlow计算图 +## TensorFlow计算图 Tensorflow 是一个通过计算图的形式来表述计算的编程系统，计算图也叫数据流图，可以把计算图看做是一种有向图，Tensorflow 中的每一个计算都是计算图上的一个节点，而节点之间的边描述了计算之间的依赖关系。 -### BN（批归一化）的作用 +## BN（批归一化）的作用 (1). 可以使用更高的学习率。如果每层的scale不一致，实际上每层需要的学习率是不一样的，同一层不同维度的scale往往也需要不同大小的学习率，通常需要使用最小的那个学习率才能保证损失函数有效下降，Batch Normalization将每层、每维的scale保持一致，那么我们就可以直接使用较高的学习率进行优化。 (2). 移除或使用较低的dropout。 dropout是常用的防止overfitting的方法，而导致overfit的位置往往在数据边界处，如果初始化权重就已经落在数据内部，overfit现象就可以得到一定的缓解。论文中最后的模型分别使用10%、5%和0%的dropout训练模型，与之前的40%-50%相比，可以大大提高训练速度。 @@ -42,7 +42,7 @@ Tensorflow 是一个通过计算图的形式来表述计算的编程系统，计 (5). Batch Normalization调整了数据的分布，不考虑激活函数，它让每一层的输出归一化到了均值为0方差为1的分布，这保证了梯度的有效性，可以解决反向传播过程中的梯度问题。目前大部分资料都这样解释，比如BN的原始论文认为的缓解了Internal Covariate Shift(ICS)问题。 -### 什么是梯度消失和爆炸，怎么解决？ +## 什么是梯度消失和爆炸，怎么解决？ 当训练较多层数的模型时，一般会出现梯度消失问题（gradient vanishing problem）和梯度爆炸问题（gradient exploding problem）。注意在反向传播中，当网络模型层数较多时，梯度消失和梯度爆炸是不可避免的。 深度神经网络中的梯度不稳定性，根本原因在于前面层上的梯度是来自于后面层上梯度的乘积。当存在过多的层次时，就出现了内在本质上的不稳定场景。 前面的层比后面的层梯度变化更小，故变化更慢，故引起了梯度消失问题。前面层比后面层梯度变化更快，故引起梯度爆炸问题。 @@ -55,13 +55,13 @@ Tensorflow 是一个通过计算图的形式来表述计算的编程系统，计 + BN批归一化 + CNN中的残差结构 + LSTM结构 -### RNN循环神经网络理解 +## RNN循环神经网络理解 循环神经网络（recurrent neural network, RNN）, 主要应用在语音识别、语言模型、机器翻译以及时序分析等问题上。 在经典应用中，卷积神经网络在不同的空间位置共享参数，循环神经网络是在不同的时间位置共享参数，从而能够使用有限的参数处理任意长度的序列。 RNN可以看做作是同一神经网络结构在时间序列上被复制多次的结果，这个被复制多次的结构称为循环体，如何设计循环体的网络结构是RNN解决实际问题的关键。 RNN的输入有两个部分，一部分为上一时刻的状态，另一部分为当前时刻的输入样本。 -### 训练过程中模型不收敛，是否说明这个模型无效，致模型不收敛的原因有哪些? +## 训练过程中模型不收敛，是否说明这个模型无效，致模型不收敛的原因有哪些? 不一定。导致模型不收敛的原因有很多种可能，常见的有以下几种： + 没有对数据做归一化。 @@ -76,7 +76,7 @@ RNN的输入有两个部分，一部分为上一时刻的状态，另一部分 + 网络太深。隐藏层神经元数量错误。 + 更多回答，参考此链接。 -### 图像处理中平滑和锐化操作是什么？ +## 图像处理中平滑和锐化操作是什么？ 平滑处理（smoothing）也称模糊处理（bluring），主要用于消除图像中的噪声部分，平滑处理常用的用途是用来减少图像上的噪点或失真，平滑主要使用图像滤波。在这里，我个人认为可以把图像平滑和图像滤波联系起来，因为图像平滑常用的方法就是图像滤波器。 在OpenCV3中常用的图像滤波器有以下几种： @@ -87,7 +87,7 @@ RNN的输入有两个部分，一部分为上一时刻的状态，另一部分 + 双边滤波——bilateralFilter函数 图像锐化操作是为了突出显示图像的边界和其他细节，而图像锐化实现的方法是通过各种算子和滤波器实现的——Canny算子、Sobel算子、Laplacian算子以及Scharr滤波器。 -### VGG使用2个3*3卷积的优势在哪里？ +## VGG使用2个3*3卷积的优势在哪里？ (1). 减少网络层参数。用两个3*3卷积比用1个5*5卷积拥有更少的参数量，只有后者的2∗3∗35∗5=0.72。但是起到的效果是一样的，两个3*3的卷积层串联相当于一个5*5的卷积层，感受野的大小都是5×5，即1个像素会跟周围5*5的像素产生关联。把下图当成动态图看，很容易看到两个3×3卷积层堆叠（没有空间池化）有5×5的有效感受野。 https://www.armcvai.com/wp-content/uploads/2019/03/2个33卷积层.png @@ -97,86 +97,84 @@ https://www.armcvai.com/wp-content/uploads/2019/03/2个33卷积层.png 此回答可以参考TensorFlow实战p110，网上很多回答都说的不全。 -### Relu比Sigmoid效果好在哪里？ +## Relu比Sigmoid效果好在哪里？ ReLU激活函数公式如下： http://latex.codecogs.com/gif.latex?%24%24ReLu%28x%29%3D%5Cleft%5C%7B%5Cbegin%7Bmatrix%7D%20x%20%26%20x%3E0%5C%5C%200%20%26%20x%3C%3D0%20%5Cend%7Bmatrix%7D%5Cright.%24%24 relu函数方程 ReLU 的输出要么是 0, 要么是输入本身。虽然方程简单，但实际上效果更好。在网上看了很多版本的解释，有从程序实例分析也有从数学上分析，我找了个相对比较直白的回答，如下： -(1) ReLU函数计算简单，可以减少很多计算量。反向传播求误差梯度时，涉及除法，计算量相对较大，采用ReLU激活函数，可以节省很多计算量； -(2) 避免梯度消失问题。对于深层网络，sigmoid函数反向传播时，很容易就会出现梯度消失问题（在sigmoid接近饱和区时，变换太缓慢，导数趋于0，这种情况会造成信息丢失），从而无法完成深层网络的训练。 -(3) 可以缓解过拟合问题的发生。Relu会使一部分神经元的输出为0，这样就造成了网络的稀疏性，并且减少了参数的相互依存关系，缓解了过拟合问题的发生。 +(1). ReLU函数计算简单，可以减少很多计算量。反向传播求误差梯度时，涉及除法，计算量相对较大，采用ReLU激活函数，可以节省很多计算量； +(2). 避免梯度消失问题。对于深层网络，sigmoid函数反向传播时，很容易就会出现梯度消失问题（在sigmoid接近饱和区时，变换太缓慢，导数趋于0，这种情况会造成信息丢失），从而无法完成深层网络的训练。 +(3). 可以缓解过拟合问题的发生。Relu会使一部分神经元的输出为0，这样就造成了网络的稀疏性，并且减少了参数的相互依存关系，缓解了过拟合问题的发生。 -#### 参考链接 +### 参考链接 ReLU为什么比Sigmoid效果好 -神经网络中权值共享的理解？ +## 神经网络中权值共享的理解？ 权值(权重)共享这个词是由LeNet5模型提出来的。以CNN为例，在对一张图偏进行卷积的过程中，使用的是同一个卷积核的参数。 比如一个3×3×1的卷积核，这个卷积核内9个的参数被整张图共享，而不会因为图像内位置的不同而改变卷积核内的权系数。说的再直白一些，就是用一个卷积核不改变其内权系数的情况下卷积处理整张图片（当然CNN中每一层不会只有一个卷积核的，这样说只是为了方便解释而已）。 -参考资料 +### 参考资料 如何理解CNN中的权值共享 -对fine-tuning(微调模型的理解)，为什么要修改最后几层神经网络权值？ +## 对fine-tuning(微调模型的理解)，为什么要修改最后几层神经网络权值？ 使用预训练模型的好处，在于利用训练好的SOTA模型权重去做特征提取，可以节省我们训练模型和调参的时间。 至于为什么只微调最后几层神经网络权重，是因为： -(1) CNN中更靠近底部的层（定义模型时先添加到模型中的层）编码的是更加通用的可复用特征，而更靠近顶部的层（最后添加到模型中的层）编码的是更专业业化的特征。微调这些更专业化的特征更加有用，它更代表了新数据集上的有用特征。 -(2) 训练的参数越多，过拟合的风险越大。很多SOTA模型拥有超过千万的参数，在一个不大的数据集上训练这么多参数是有过拟合风险的，除非你的数据集像Imagenet那样大。 +(1). CNN中更靠近底部的层（定义模型时先添加到模型中的层）编码的是更加通用的可复用特征，而更靠近顶部的层（最后添加到模型中的层）编码的是更专业业化的特征。微调这些更专业化的特征更加有用，它更代表了新数据集上的有用特征。 +(2). 训练的参数越多，过拟合的风险越大。很多SOTA模型拥有超过千万的参数，在一个不大的数据集上训练这么多参数是有过拟合风险的，除非你的数据集像Imagenet那样大。 -参考资料 +### 参考资料 Python深度学习p127. -什么是dropout? +## 什么是dropout? dropout可以防止过拟合，dropout简单来说就是：我们在前向传播的时候，让某个神经元的激活值以一定的概率p停止工作，这样可以使模型的泛化性更强，因为它不会依赖某些局部的特征。 https://www.armcvai.com/wp-content/uploads/2019/03/dropout.png -dropout具体工作流程 +### dropout具体工作流程 以 标准神经网络为例，正常的流程是：我们首先把输入数据x通过网络前向传播，然后把误差反向传播一决定如何更新参数让网络进行学习。使用dropout之后，过程变成如下： -(1) 首先随机（临时）删掉网络中一半的隐藏神经元，输入输出神经元保持不变（图3中虚线为部分临时被删除的神经元）； -(2) 然后把输入x通过修改后的网络进行前向传播计算，然后把得到的损失结果通过修改的网络反向传播。一小批训练样本执行完这个过程后，在没有被删除的神经元上按照随机梯度下降法更新对应的参数（w，b）； -(3) 然后重复这一过程： - -恢复被删掉的神经元（此时被删除的神经元保持原样没有更新w参数，而没有被删除的神经元已经有所更新） -从隐藏层神经元中随机选择一个一半大小的子集临时删除掉（同时备份被删除神经元的参数）。 -对一小批训练样本，先前向传播然后反向传播损失并根据随机梯度下降法更新参数（w，b） （没有被删除的那一部分参数得到更新，删除的神经元参数保持被删除前的结果）。 -dropout在神经网络中的应用 -(1) 在训练模型阶段 +(1). 首先随机（临时）删掉网络中一半的隐藏神经元，输入输出神经元保持不变（图3中虚线为部分临时被删除的神经元）； +(2). 然后把输入x通过修改后的网络进行前向传播计算，然后把得到的损失结果通过修改的网络反向传播。一小批训练样本执行完这个过程后，在没有被删除的神经元上按照随机梯度下降法更新对应的参数（w，b）； +(3). 然后重复这一过程： ++ 恢复被删掉的神经元（此时被删除的神经元保持原样没有更新w参数，而没有被删除的神经元已经有所更新） ++ 从隐藏层神经元中随机选择一个一半大小的子集临时删除掉（同时备份被删除神经元的参数）。 ++ 对一小批训练样本，先前向传播然后反向传播损失并根据随机梯度下降法更新参数（w，b） （没有被删除的那一部分参数得到更新，删除的神经元参数保持被删除前的结果）。 +### dropout在神经网络中的应用 +(1). 在训练模型阶段 不可避免的，在训练网络中的每个单元都要添加一道概率流程，标准网络和带有dropout网络的比较图如下所示： https://www.armcvai.com/wp-content/uploads/2019/03/添加了dropou的神经网络图.png -(2) 在测试模型阶段 +(2). 在测试模型阶段 预测模型的时候，输入是当前输入，每个神经单元的权重参数要乘以概率p。 https://www.armcvai.com/wp-content/uploads/2019/03/dropout_test.png -如何选择dropout 的概率 +### 如何选择dropout 的概率 input 的dropout概率推荐是0.8， hidden layer 推荐是0.5， 但是也可以在一定的区间上取值。（All dropout nets use p = 0.5 for hidden units and p = 0.8 for input units.） -参考资料 +### 参考资料 Dropout:A Simple Way to Prevent Neural Networks from Overfitting 深度学习中Dropout原理解析 -HOG算法原理描述 +## HOG算法原理描述 方向梯度直方图（Histogram of Oriented Gradient, HOG）特征是一种在计算机视觉和图像处理中用来进行物体检测的特征描述子。它通过计算和统计图像局部区域的梯度方向直方图来构成特征。在深度学习取得成功之前，Hog特征结合SVM分类器被广泛应用于图像识别中，在行人检测中获得了较大的成功。 -HOG特征原理 +### HOG特征原理 HOG的核心思想是所检测的局部物体外形能够被光强梯度或边缘方向的分布所描述。通过将整幅图像分割成小的连接区域（称为cells），每个cell生成一个方向梯度直方图或者cell中pixel的边缘方向，这些直方图的组合可表示出（所检测目标的目标）描述子。为改善准确率，局部直方图可以通过计算图像中一个较大区域(称为block)的光强作为measure被对比标准化，然后用这个值(measure)归一化这个block中的所有cells。这个归一化过程完成了更好的照射/阴影不变性。 与其他描述子相比，HOG得到的描述子保持了几何和光学转化不变性（除非物体方向改变）。因此HOG描述子尤其适合人的检测。 HOG特征提取方法就是将一个image： - -灰度化（将图像看做一个x,y,z（灰度）的三维图像） - 划分成小cells（2*2） -计算每个cell中每个pixel的gradient（即orientation） -统计每个cell的梯度直方图（不同梯度的个数），即可形成每个cell的descriptor。 -HOG特征检测步骤 +1. 灰度化（将图像看做一个x,y,z（灰度）的三维图像） +2. 划分成小cells（2*2） +3. 计算每个cell中每个pixel的gradient（即orientation） +4. 统计每个cell的梯度直方图（不同梯度的个数），即可形成每个cell的descriptor。 +### HOG特征检测步骤 https://www.armcvai.com/wp-content/uploads/2019/03/image.png 颜色空间归一化——–>梯度计算————->梯度方向直方图———->重叠块直方图归一化———–>HOG特征 -Reference +## Reference 《Batch Normalization Accelerating Deep Network Training by Reducing Internal Covariate Shift》阅读笔记与实现 深度学习中 Batch Normalization为什么效果好 详解机器学习中的梯度消失、爆炸原因及其解决方法 From 8b1c71bff0c376ebdf211f969afc70f574dd352a Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Tue, 26 Mar 2019 23:22:12 +0800 Subject: [PATCH 033/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...40\351\235\242\350\257\225\351\242\230.md" | 36 ++++++++++++++----- 1 file changed, 27 insertions(+), 9 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index b37989cb..39ef6233 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -7,11 +7,11 @@ 局部连接使网络可以提取数据的局部特征；权值共享大大降低了网络的训练难度，一个Filter只提取一个特征，在整个图片（或者语音／文本） 中进行卷积；池化操作与多层次结构一起，实现了数据的降维，将低层次的局部特征组合成为较高层次的特征，从而对整个图片进行表示。如下图所示： ## 什么样的数据集不适合深度学习 -数据集太小，数据样本不足时，深度学习相对其它机器学习算法，没有明显优势。 -数据集没有局部相关特性，目前深度学习表现比较好的领域主要是图像／语音／自然语言处理等领域，这些领域的一个共性是局部相关性。图像中像素组成物体，语音信号中音位组合成单词，文本数据中单词组合成句子，这些特征元素的组合一旦被打乱，表示的含义同时也被改变。对于没有这样的局部相关性的数据集，不适于使用深度学习算法进行处理。举个例子：预测一个人的健康状况，相关的参数会有年龄、职业、收入、家庭状况等各种元素，将这些元素打乱，并不会影响相关的结果。 ++ 数据集太小，数据样本不足时，深度学习相对其它机器学习算法，没有明显优势。 ++ 数据集没有局部相关特性，目前深度学习表现比较好的领域主要是图像／语音／自然语言处理等领域，这些领域的一个共性是局部相关性。图像中像素组成物体，语音信号中音位组合成单词，文本数据中单词组合成句子，这些特征元素的组合一旦被打乱，表示的含义同时也被改变。对于没有这样的局部相关性的数据集，不适于使用深度学习算法进行处理。举个例子：预测一个人的健康状况，相关的参数会有年龄、职业、收入、家庭状况等各种元素，将这些元素打乱，并不会影响相关的结果。 ## 什么造成梯度消失问题 -神经网络的训练中，通过改变神经元的权重，使网络的输出值尽可能逼近标签以降低误差值，训练普遍使用BP算法，核心思想是，计算出输出与标签间的损失函数值，然后计算其相对于每个神经元的梯度，进行权值的迭代。 -梯度消失会造成权值更新缓慢，模型训练难度增加。造成梯度消失的一个原因是，许多激活函数将输出值挤压在很小的区间内，在激活函数两端较大范围的定义域内梯度为0，造成学习停止。 ++ 神经网络的训练中，通过改变神经元的权重，使网络的输出值尽可能逼近标签以降低误差值，训练普遍使用BP算法，核心思想是，计算出输出与标签间的损失函数值，然后计算其相对于每个神经元的梯度，进行权值的迭代。 ++ 梯度消失会造成权值更新缓慢，模型训练难度增加。造成梯度消失的一个原因是，许多激活函数将输出值挤压在很小的区间内，在激活函数两端较大范围的定义域内梯度为0，造成学习停止。 ## Overfitting怎么解决 首先所谓过拟合，指的是一个模型过于复杂之后，它可以很好地“记忆”每一个训练数据中随机噪音的部分而忘记了去“训练”数据中的通用趋势。 过拟合具体表现在：模型在训练数据上损失函数较小，预测准确率较高；但是在测试数据上损失函数比较大，预测准确率较低。 @@ -21,9 +21,9 @@ Parameter Norm Penalties(参数范数惩罚)；Dataset Augmentation (数据集 ## L1和L2区别 L1 范数（L1 norm）是指向量中各个元素绝对值之和，也有个美称叫“稀疏规则算子”（Lasso regularization）。 比如 向量 A=[1，-1，3]， 那么 A 的 L1 范数为 |1|+|-1|+|3|。简单总结一下就是： -L1 范数: 为 x 向量各个元素绝对值之和。 -L2 范数: 为 x 向量各个元素平方和的 1/2 次方，L2 范数又称 Euclidean 范数或 Frobenius 范数 -Lp 范数: 为 x 向量各个元素绝对值 p 次方和的 1/p 次方. ++ L1 范数: 为 x 向量各个元素绝对值之和。 ++ L2 范数: 为 x 向量各个元素平方和的 1/2 次方，L2 范数又称 Euclidean 范数或 Frobenius 范数 ++ Lp 范数: 为 x 向量各个元素绝对值 p 次方和的 1/p 次方. 在支持向量机学习过程中，L1 范数实际是一种对于成本函数求解最优的过程，因此，L1 范数正则化通过向成本函数中添加 L1 范数，使得学习得到的结果满足稀疏化，从而方便人类提取特征。 L1 范数可以使权值稀疏，方便特征提取。 L2 范数可以防止过拟合，提升模型的泛化能力。 @@ -57,7 +57,7 @@ Tensorflow 是一个通过计算图的形式来表述计算的编程系统，计 + LSTM结构 ## RNN循环神经网络理解 循环神经网络（recurrent neural network, RNN）, 主要应用在语音识别、语言模型、机器翻译以及时序分析等问题上。 -在经典应用中，卷积神经网络在不同的空间位置共享参数，循环神经网络是在不同的时间位置共享参数，从而能够使用有限的参数处理任意长度的序列。 +***在经典应用中，卷积神经网络在不同的空间位置共享参数，循环神经网络是在不同的时间位置共享参数，从而能够使用有限的参数处理任意长度的序列。*** RNN可以看做作是同一神经网络结构在时间序列上被复制多次的结果，这个被复制多次的结构称为循环体，如何设计循环体的网络结构是RNN解决实际问题的关键。 RNN的输入有两个部分，一部分为上一时刻的状态，另一部分为当前时刻的输入样本。 @@ -173,8 +173,26 @@ HOG特征提取方法就是将一个image： ### HOG特征检测步骤 https://www.armcvai.com/wp-content/uploads/2019/03/image.png 颜色空间归一化——–>梯度计算————->梯度方向直方图———->重叠块直方图归一化———–>HOG特征 - +### 参考资料 +[HOG特征检测－简述](https://blog.csdn.net/liyuqian199695/article/details/53835989) +## 移动端深度学习框架知道哪些，用过哪些？ +知名的有TensorFlow Lite、小米MACE、腾讯的ncnn等，目前都没有用过。 +## 如何提升网络的泛化能力 +和防止模型过拟合的方法类似，另外还有模型融合方法。 +## BN算法，为什么要在后面加加伽马和贝塔，不加可以吗？ +最后的“scale and shift”操作则是为了让因训练所需而“刻意”加入的BN能够有可能还原最初的输入。不加也可以。 +## 激活函数的作用 +**激活函数实现去线性化**。神经元的结构的输出为所有输入的加权和，这导致神经网络是一个线性模型。如果将每一个神经元（也就是神经网络的节点）的输出通过一个非线性函数，那么整个神经网络的模型也就不再是线性的了，这个非线性函数就是激活函数。 +常见的激活函数有：ReLU函数、sigmoid函数、tanh函数。 +ReLU函数：$f(x)=max(x,0)$， +sigmoid函数：$f(x)=\frac{1}{1+e^{-x}}$ +tanh函数：$f(x)=\frac{1+e^{-2x}}{1+e^{-2x}}$ +## 卷积层和池化层有什么区别 +(1). 卷积层有参数，池化层没有参数。 +### 卷积层参数数量计算方法 +假设输入层矩阵维度是96*96*3，第一层卷积层使用尺寸为5*5、深度为16的过滤器（卷积核尺寸为5*5、卷积核数量为16），那么这层卷积层的参数个数为５*5*3*16+16=1216个 ## Reference 《Batch Normalization Accelerating Deep Network Training by Reducing Internal Covariate Shift》阅读笔记与实现 深度学习中 Batch Normalization为什么效果好 详解机器学习中的梯度消失、爆炸原因及其解决方法 + From c8ad9d9f47b27fe71e5d5a1614c0c0b88d34a643 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Tue, 26 Mar 2019 23:30:58 +0800 Subject: [PATCH 034/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 更新语法问题及排版 --- ...40\351\235\242\350\257\225\351\242\230.md" | 39 ++++++++++--------- 1 file changed, 21 insertions(+), 18 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 39ef6233..8497b51b 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -107,15 +107,15 @@ ReLU 的输出要么是 0, 要么是输入本身。虽然方程简单，但实 (2). 避免梯度消失问题。对于深层网络，sigmoid函数反向传播时，很容易就会出现梯度消失问题（在sigmoid接近饱和区时，变换太缓慢，导数趋于0，这种情况会造成信息丢失），从而无法完成深层网络的训练。 (3). 可以缓解过拟合问题的发生。Relu会使一部分神经元的输出为0，这样就造成了网络的稀疏性，并且减少了参数的相互依存关系，缓解了过拟合问题的发生。 -### 参考链接 -ReLU为什么比Sigmoid效果好 +#### 参考链接 +[ReLU为什么比Sigmoid效果好](https://www.twblogs.net/a/5c2dd30fbd9eee35b21c4337/zh-cn) ## 神经网络中权值共享的理解？ 权值(权重)共享这个词是由LeNet5模型提出来的。以CNN为例，在对一张图偏进行卷积的过程中，使用的是同一个卷积核的参数。 比如一个3×3×1的卷积核，这个卷积核内9个的参数被整张图共享，而不会因为图像内位置的不同而改变卷积核内的权系数。说的再直白一些，就是用一个卷积核不改变其内权系数的情况下卷积处理整张图片（当然CNN中每一层不会只有一个卷积核的，这样说只是为了方便解释而已）。 -### 参考资料 -如何理解CNN中的权值共享 +#### 参考资料 +[如何理解CNN中的权值共享](https://blog.csdn.net/chaipp0607/article/details/73650759) ## 对fine-tuning(微调模型的理解)，为什么要修改最后几层神经网络权值？ 使用预训练模型的好处，在于利用训练好的SOTA模型权重去做特征提取，可以节省我们训练模型和调参的时间。 @@ -124,14 +124,14 @@ ReLU为什么比Sigmoid效果好 (1). CNN中更靠近底部的层（定义模型时先添加到模型中的层）编码的是更加通用的可复用特征，而更靠近顶部的层（最后添加到模型中的层）编码的是更专业业化的特征。微调这些更专业化的特征更加有用，它更代表了新数据集上的有用特征。 (2). 训练的参数越多，过拟合的风险越大。很多SOTA模型拥有超过千万的参数，在一个不大的数据集上训练这么多参数是有过拟合风险的，除非你的数据集像Imagenet那样大。 -### 参考资料 +#### 参考资料 Python深度学习p127. ## 什么是dropout? dropout可以防止过拟合，dropout简单来说就是：我们在前向传播的时候，让某个神经元的激活值以一定的概率p停止工作，这样可以使模型的泛化性更强，因为它不会依赖某些局部的特征。 https://www.armcvai.com/wp-content/uploads/2019/03/dropout.png -### dropout具体工作流程 +#### dropout具体工作流程 以 标准神经网络为例，正常的流程是：我们首先把输入数据x通过网络前向传播，然后把误差反向传播一决定如何更新参数让网络进行学习。使用dropout之后，过程变成如下： (1). 首先随机（临时）删掉网络中一半的隐藏神经元，输入输出神经元保持不变（图3中虚线为部分临时被删除的神经元）； @@ -140,7 +140,7 @@ https://www.armcvai.com/wp-content/uploads/2019/03/dropout.png + 恢复被删掉的神经元（此时被删除的神经元保持原样没有更新w参数，而没有被删除的神经元已经有所更新） + 从隐藏层神经元中随机选择一个一半大小的子集临时删除掉（同时备份被删除神经元的参数）。 + 对一小批训练样本，先前向传播然后反向传播损失并根据随机梯度下降法更新参数（w，b） （没有被删除的那一部分参数得到更新，删除的神经元参数保持被删除前的结果）。 -### dropout在神经网络中的应用 +#### dropout在神经网络中的应用 (1). 在训练模型阶段 不可避免的，在训练网络中的每个单元都要添加一道概率流程，标准网络和带有dropout网络的比较图如下所示： @@ -151,17 +151,17 @@ https://www.armcvai.com/wp-content/uploads/2019/03/添加了dropou的神经网 预测模型的时候，输入是当前输入，每个神经单元的权重参数要乘以概率p。 https://www.armcvai.com/wp-content/uploads/2019/03/dropout_test.png -### 如何选择dropout 的概率 +#### 如何选择dropout 的概率 input 的dropout概率推荐是0.8， hidden layer 推荐是0.5， 但是也可以在一定的区间上取值。（All dropout nets use p = 0.5 for hidden units and p = 0.8 for input units.） -### 参考资料 -Dropout:A Simple Way to Prevent Neural Networks from Overfitting -深度学习中Dropout原理解析 +#### 参考资料 +[Dropout:A Simple Way to Prevent Neural Networks from Overfitting] +[深度学习中Dropout原理解析](https://zhuanlan.zhihu.com/p/38200980) ## HOG算法原理描述 方向梯度直方图（Histogram of Oriented Gradient, HOG）特征是一种在计算机视觉和图像处理中用来进行物体检测的特征描述子。它通过计算和统计图像局部区域的梯度方向直方图来构成特征。在深度学习取得成功之前，Hog特征结合SVM分类器被广泛应用于图像识别中，在行人检测中获得了较大的成功。 -### HOG特征原理 +#### HOG特征原理 HOG的核心思想是所检测的局部物体外形能够被光强梯度或边缘方向的分布所描述。通过将整幅图像分割成小的连接区域（称为cells），每个cell生成一个方向梯度直方图或者cell中pixel的边缘方向，这些直方图的组合可表示出（所检测目标的目标）描述子。为改善准确率，局部直方图可以通过计算图像中一个较大区域(称为block)的光强作为measure被对比标准化，然后用这个值(measure)归一化这个block中的所有cells。这个归一化过程完成了更好的照射/阴影不变性。 与其他描述子相比，HOG得到的描述子保持了几何和光学转化不变性（除非物体方向改变）。因此HOG描述子尤其适合人的检测。 @@ -170,10 +170,10 @@ HOG特征提取方法就是将一个image： 2. 划分成小cells（2*2） 3. 计算每个cell中每个pixel的gradient（即orientation） 4. 统计每个cell的梯度直方图（不同梯度的个数），即可形成每个cell的descriptor。 -### HOG特征检测步骤 +#### HOG特征检测步骤 https://www.armcvai.com/wp-content/uploads/2019/03/image.png 颜色空间归一化——–>梯度计算————->梯度方向直方图———->重叠块直方图归一化———–>HOG特征 -### 参考资料 +#### 参考资料 [HOG特征检测－简述](https://blog.csdn.net/liyuqian199695/article/details/53835989) ## 移动端深度学习框架知道哪些，用过哪些？ 知名的有TensorFlow Lite、小米MACE、腾讯的ncnn等，目前都没有用过。 @@ -189,10 +189,13 @@ sigmoid函数：$f(x)=\frac{1}{1+e^{-x}}$ tanh函数：$f(x)=\frac{1+e^{-2x}}{1+e^{-2x}}$ ## 卷积层和池化层有什么区别 (1). 卷积层有参数，池化层没有参数。 -### 卷积层参数数量计算方法 +#### 卷积层参数数量计算方法 假设输入层矩阵维度是96*96*3，第一层卷积层使用尺寸为5*5、深度为16的过滤器（卷积核尺寸为5*5、卷积核数量为16），那么这层卷积层的参数个数为５*5*3*16+16=1216个 ## Reference -《Batch Normalization Accelerating Deep Network Training by Reducing Internal Covariate Shift》阅读笔记与实现 -深度学习中 Batch Normalization为什么效果好 -详解机器学习中的梯度消失、爆炸原因及其解决方法 +[《Batch Normalization Accelerating Deep Network Training by Reducing Internal Covariate Shift》阅读笔记与实现][1] +[深度学习中 Batch Normalization为什么效果好][2] +[详解机器学习中的梯度消失、爆炸原因及其解决方法][3] +[1]:[https://blog.csdn.net/happynear/article/details/44238541]"Markdown" +[2]:[https://blog.csdn.net/happynear/article/details/44238541]"Markdown" +[3]:[https://blog.csdn.net/qq_25737169/article/details/78847691]"Markdown" From d7a21fc13b966eeba8b380a870282cab11fad2da Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Tue, 26 Mar 2019 23:33:42 +0800 Subject: [PATCH 035/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...344\271\240\351\235\242\350\257\225\351\242\230.md" | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 8497b51b..e42d0677 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -192,10 +192,8 @@ tanh函数：$f(x)=\frac{1+e^{-2x}}{1+e^{-2x}}$ #### 卷积层参数数量计算方法 假设输入层矩阵维度是96*96*3，第一层卷积层使用尺寸为5*5、深度为16的过滤器（卷积核尺寸为5*5、卷积核数量为16），那么这层卷积层的参数个数为５*5*3*16+16=1216个 ## Reference -[《Batch Normalization Accelerating Deep Network Training by Reducing Internal Covariate Shift》阅读笔记与实现][1] -[深度学习中 Batch Normalization为什么效果好][2] -[详解机器学习中的梯度消失、爆炸原因及其解决方法][3] -[1]:[https://blog.csdn.net/happynear/article/details/44238541]"Markdown" -[2]:[https://blog.csdn.net/happynear/article/details/44238541]"Markdown" -[3]:[https://blog.csdn.net/qq_25737169/article/details/78847691]"Markdown" +１．[《Batch Normalization Accelerating Deep Network Training by Reducing Internal Covariate Shift》阅读笔记与实现]（https://blog.csdn.net/happynear/article/details/44238541） +２．[深度学习中 Batch Normalization为什么效果好]（https://blog.csdn.net/happynear/article/details/44238541） +３．[详解机器学习中的梯度消失、爆炸原因及其解决方法]（https://blog.csdn.net/qq_25737169/article/details/78847691） + From 9459447ee5ea5998cc2c419b9de30d4abdeeb711 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Tue, 26 Mar 2019 23:33:54 +0800 Subject: [PATCH 036/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit From 93a9013199265724fc3e393a1d149a4f59161691 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Tue, 26 Mar 2019 23:34:32 +0800 Subject: [PATCH 037/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index e42d0677..85215122 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -192,8 +192,8 @@ tanh函数：$f(x)=\frac{1+e^{-2x}}{1+e^{-2x}}$ #### 卷积层参数数量计算方法 假设输入层矩阵维度是96*96*3，第一层卷积层使用尺寸为5*5、深度为16的过滤器（卷积核尺寸为5*5、卷积核数量为16），那么这层卷积层的参数个数为５*5*3*16+16=1216个 ## Reference -１．[《Batch Normalization Accelerating Deep Network Training by Reducing Internal Covariate Shift》阅读笔记与实现]（https://blog.csdn.net/happynear/article/details/44238541） -２．[深度学习中 Batch Normalization为什么效果好]（https://blog.csdn.net/happynear/article/details/44238541） -３．[详解机器学习中的梯度消失、爆炸原因及其解决方法]（https://blog.csdn.net/qq_25737169/article/details/78847691） +[《Batch Normalization Accelerating Deep Network Training by Reducing Internal Covariate Shift》阅读笔记与实现]（https://blog.csdn.net/happynear/article/details/44238541） +[深度学习中 Batch Normalization为什么效果好]（https://blog.csdn.net/happynear/article/details/44238541） +[详解机器学习中的梯度消失、爆炸原因及其解决方法]（https://blog.csdn.net/qq_25737169/article/details/78847691） From ba8db8672bad431fae53f559f885b27b460a2dc8 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Tue, 26 Mar 2019 23:36:08 +0800 Subject: [PATCH 038/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 85215122..a6f0cced 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -192,8 +192,8 @@ tanh函数：$f(x)=\frac{1+e^{-2x}}{1+e^{-2x}}$ #### 卷积层参数数量计算方法 假设输入层矩阵维度是96*96*3，第一层卷积层使用尺寸为5*5、深度为16的过滤器（卷积核尺寸为5*5、卷积核数量为16），那么这层卷积层的参数个数为５*5*3*16+16=1216个 ## Reference -[《Batch Normalization Accelerating Deep Network Training by Reducing Internal Covariate Shift》阅读笔记与实现]（https://blog.csdn.net/happynear/article/details/44238541） -[深度学习中 Batch Normalization为什么效果好]（https://blog.csdn.net/happynear/article/details/44238541） -[详解机器学习中的梯度消失、爆炸原因及其解决方法]（https://blog.csdn.net/qq_25737169/article/details/78847691） +[《Batch Normalization Accelerating Deep Network Training by Reducing Internal Covariate Shift》阅读笔记与实现](https://blog.csdn.net/happynear/article/details/44238541) +[深度学习中 Batch Normalization为什么效果好](https://blog.csdn.net/happynear/article/details/44238541) +[详解机器学习中的梯度消失、爆炸原因及其解决方法](https://blog.csdn.net/qq_25737169/article/details/78847691) From 66bc123dd1d0d8a64202a1b48d777dd3f63bcb5e Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Tue, 26 Mar 2019 23:37:12 +0800 Subject: [PATCH 039/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 更新多个超链接排版问题 --- ...344\271\240\351\235\242\350\257\225\351\242\230.md" | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index a6f0cced..2a141ac7 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -155,8 +155,8 @@ https://www.armcvai.com/wp-content/uploads/2019/03/dropout_test.png input 的dropout概率推荐是0.8， hidden layer 推荐是0.5， 但是也可以在一定的区间上取值。（All dropout nets use p = 0.5 for hidden units and p = 0.8 for input units.） #### 参考资料 -[Dropout:A Simple Way to Prevent Neural Networks from Overfitting] -[深度学习中Dropout原理解析](https://zhuanlan.zhihu.com/p/38200980) +1. [Dropout:A Simple Way to Prevent Neural Networks from Overfitting] +2. [深度学习中Dropout原理解析](https://zhuanlan.zhihu.com/p/38200980) ## HOG算法原理描述 方向梯度直方图（Histogram of Oriented Gradient, HOG）特征是一种在计算机视觉和图像处理中用来进行物体检测的特征描述子。它通过计算和统计图像局部区域的梯度方向直方图来构成特征。在深度学习取得成功之前，Hog特征结合SVM分类器被广泛应用于图像识别中，在行人检测中获得了较大的成功。 @@ -192,8 +192,8 @@ tanh函数：$f(x)=\frac{1+e^{-2x}}{1+e^{-2x}}$ #### 卷积层参数数量计算方法 假设输入层矩阵维度是96*96*3，第一层卷积层使用尺寸为5*5、深度为16的过滤器（卷积核尺寸为5*5、卷积核数量为16），那么这层卷积层的参数个数为５*5*3*16+16=1216个 ## Reference -[《Batch Normalization Accelerating Deep Network Training by Reducing Internal Covariate Shift》阅读笔记与实现](https://blog.csdn.net/happynear/article/details/44238541) -[深度学习中 Batch Normalization为什么效果好](https://blog.csdn.net/happynear/article/details/44238541) -[详解机器学习中的梯度消失、爆炸原因及其解决方法](https://blog.csdn.net/qq_25737169/article/details/78847691) +1. [《Batch Normalization Accelerating Deep Network Training by Reducing Internal Covariate Shift》阅读笔记与实现](https://blog.csdn.net/happynear/article/details/44238541) +2. [深度学习中 Batch Normalization为什么效果好](https://blog.csdn.net/happynear/article/details/44238541) +3. [详解机器学习中的梯度消失、爆炸原因及其解决方法](https://blog.csdn.net/qq_25737169/article/details/78847691) From 33f979bd70a9ab1f663f0e44cd2f3fccceb1bcc8 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Tue, 26 Mar 2019 23:45:39 +0800 Subject: [PATCH 040/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 2a141ac7..6c2cce8b 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -90,7 +90,7 @@ RNN的输入有两个部分，一部分为上一时刻的状态，另一部分 ## VGG使用2个3*3卷积的优势在哪里？ (1). 减少网络层参数。用两个3*3卷积比用1个5*5卷积拥有更少的参数量，只有后者的2∗3∗35∗5=0.72。但是起到的效果是一样的，两个3*3的卷积层串联相当于一个5*5的卷积层，感受野的大小都是5×5，即1个像素会跟周围5*5的像素产生关联。把下图当成动态图看，很容易看到两个3×3卷积层堆叠（没有空间池化）有5×5的有效感受野。 -https://www.armcvai.com/wp-content/uploads/2019/03/2个33卷积层.png +！[2个３*卷积层]:https://www.armcvai.com/wp-content/uploads/2019/03/2个33卷积层.png (2). 更多的非线性变换。2个3*3卷积层拥有比1个5*5卷积层更多的非线性变换（前者可以使用两次ReLU激活函数，而后者只有一次），使得卷积神经网络对特征的学习能力更强。 ***paper中给出的相关解释***：三个这样的层具有7×7的有效感受野。那么我们获得了什么？例如通过使用三个3×3卷积层的堆叠来替换单个7×7层。首先，我们结合了三个非线性修正层，而不是单一的，这使得决策函数更具判别性。其次，我们减少参数的数量：假设三层3×3卷积堆叠的输入和输出有C个通道，堆叠卷积层的参数为3(32C2)=27C2个权重；同时，单个7×7卷积层将需要72C2=49C2个参数，即参数多81％。这可以看作是对7×7卷积滤波器进行正则化，迫使它们通过3×3滤波器（在它们之间注入非线性）进行分解。 @@ -100,7 +100,7 @@ https://www.armcvai.com/wp-content/uploads/2019/03/2个33卷积层.png ## Relu比Sigmoid效果好在哪里？ ReLU激活函数公式如下： -http://latex.codecogs.com/gif.latex?%24%24ReLu%28x%29%3D%5Cleft%5C%7B%5Cbegin%7Bmatrix%7D%20x%20%26%20x%3E0%5C%5C%200%20%26%20x%3C%3D0%20%5Cend%7Bmatrix%7D%5Cright.%24%24 +![relu激活函数]http://latex.codecogs.com/gif.latex?%24%24ReLu%28x%29%3D%5Cleft%5C%7B%5Cbegin%7Bmatrix%7D%20x%20%26%20x%3E0%5C%5C%200%20%26%20x%3C%3D0%20%5Cend%7Bmatrix%7D%5Cright.%24%24 relu函数方程 ReLU 的输出要么是 0, 要么是输入本身。虽然方程简单，但实际上效果更好。在网上看了很多版本的解释，有从程序实例分析也有从数学上分析，我找了个相对比较直白的回答，如下： (1). ReLU函数计算简单，可以减少很多计算量。反向传播求误差梯度时，涉及除法，计算量相对较大，采用ReLU激活函数，可以节省很多计算量； @@ -130,7 +130,7 @@ Python深度学习p127. ## 什么是dropout? dropout可以防止过拟合，dropout简单来说就是：我们在前向传播的时候，让某个神经元的激活值以一定的概率p停止工作，这样可以使模型的泛化性更强，因为它不会依赖某些局部的特征。 -https://www.armcvai.com/wp-content/uploads/2019/03/dropout.png +![dropou直观展示]https://www.armcvai.com/wp-content/uploads/2019/03/dropout.png #### dropout具体工作流程 以 标准神经网络为例，正常的流程是：我们首先把输入数据x通过网络前向传播，然后把误差反向传播一决定如何更新参数让网络进行学习。使用dropout之后，过程变成如下： From a9123647bd2b6bd79e956d1dcc9f67ef4a0332c2 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Tue, 26 Mar 2019 23:49:14 +0800 Subject: [PATCH 041/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...4\271\240\351\235\242\350\257\225\351\242\230.md" | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 6c2cce8b..bb17e596 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -90,7 +90,7 @@ RNN的输入有两个部分，一部分为上一时刻的状态，另一部分 ## VGG使用2个3*3卷积的优势在哪里？ (1). 减少网络层参数。用两个3*3卷积比用1个5*5卷积拥有更少的参数量，只有后者的2∗3∗35∗5=0.72。但是起到的效果是一样的，两个3*3的卷积层串联相当于一个5*5的卷积层，感受野的大小都是5×5，即1个像素会跟周围5*5的像素产生关联。把下图当成动态图看，很容易看到两个3×3卷积层堆叠（没有空间池化）有5×5的有效感受野。 -！[2个３*卷积层]:https://www.armcvai.com/wp-content/uploads/2019/03/2个33卷积层.png +！[2个３*卷积层](https://www.armcvai.com/wp-content/uploads/2019/03/2个33卷积层.png) (2). 更多的非线性变换。2个3*3卷积层拥有比1个5*5卷积层更多的非线性变换（前者可以使用两次ReLU激活函数，而后者只有一次），使得卷积神经网络对特征的学习能力更强。 ***paper中给出的相关解释***：三个这样的层具有7×7的有效感受野。那么我们获得了什么？例如通过使用三个3×3卷积层的堆叠来替换单个7×7层。首先，我们结合了三个非线性修正层，而不是单一的，这使得决策函数更具判别性。其次，我们减少参数的数量：假设三层3×3卷积堆叠的输入和输出有C个通道，堆叠卷积层的参数为3(32C2)=27C2个权重；同时，单个7×7卷积层将需要72C2=49C2个参数，即参数多81％。这可以看作是对7×7卷积滤波器进行正则化，迫使它们通过3×3滤波器（在它们之间注入非线性）进行分解。 @@ -100,7 +100,7 @@ RNN的输入有两个部分，一部分为上一时刻的状态，另一部分 ## Relu比Sigmoid效果好在哪里？ ReLU激活函数公式如下： -![relu激活函数]http://latex.codecogs.com/gif.latex?%24%24ReLu%28x%29%3D%5Cleft%5C%7B%5Cbegin%7Bmatrix%7D%20x%20%26%20x%3E0%5C%5C%200%20%26%20x%3C%3D0%20%5Cend%7Bmatrix%7D%5Cright.%24%24 +![relu激活函数](http://latex.codecogs.com/gif.latex?%24%24ReLu%28x%29%3D%5Cleft%5C%7B%5Cbegin%7Bmatrix%7D%20x%20%26%20x%3E0%5C%5C%200%20%26%20x%3C%3D0%20%5Cend%7Bmatrix%7D%5Cright.%24%24) relu函数方程 ReLU 的输出要么是 0, 要么是输入本身。虽然方程简单，但实际上效果更好。在网上看了很多版本的解释，有从程序实例分析也有从数学上分析，我找了个相对比较直白的回答，如下： (1). ReLU函数计算简单，可以减少很多计算量。反向传播求误差梯度时，涉及除法，计算量相对较大，采用ReLU激活函数，可以节省很多计算量； @@ -130,7 +130,7 @@ Python深度学习p127. ## 什么是dropout? dropout可以防止过拟合，dropout简单来说就是：我们在前向传播的时候，让某个神经元的激活值以一定的概率p停止工作，这样可以使模型的泛化性更强，因为它不会依赖某些局部的特征。 -![dropou直观展示]https://www.armcvai.com/wp-content/uploads/2019/03/dropout.png +![dropou直观展示](https://www.armcvai.com/wp-content/uploads/2019/03/dropout.png) #### dropout具体工作流程 以 标准神经网络为例，正常的流程是：我们首先把输入数据x通过网络前向传播，然后把误差反向传播一决定如何更新参数让网络进行学习。使用dropout之后，过程变成如下： @@ -145,12 +145,12 @@ dropout可以防止过拟合，dropout简单来说就是：我们在前向传播 不可避免的，在训练网络中的每个单元都要添加一道概率流程，标准网络和带有dropout网络的比较图如下所示： -https://www.armcvai.com/wp-content/uploads/2019/03/添加了dropou的神经网络图.png +![dropout在训练阶段](https://www.armcvai.com/wp-content/uploads/2019/03/添加了dropou的神经网络图.png) (2). 在测试模型阶段 预测模型的时候，输入是当前输入，每个神经单元的权重参数要乘以概率p。 -https://www.armcvai.com/wp-content/uploads/2019/03/dropout_test.png +![dropout在测试模型时](https://www.armcvai.com/wp-content/uploads/2019/03/dropout_test.png) #### 如何选择dropout 的概率 input 的dropout概率推荐是0.8， hidden layer 推荐是0.5， 但是也可以在一定的区间上取值。（All dropout nets use p = 0.5 for hidden units and p = 0.8 for input units.） @@ -171,7 +171,7 @@ HOG特征提取方法就是将一个image： 3. 计算每个cell中每个pixel的gradient（即orientation） 4. 统计每个cell的梯度直方图（不同梯度的个数），即可形成每个cell的descriptor。 #### HOG特征检测步骤 -https://www.armcvai.com/wp-content/uploads/2019/03/image.png +![HOG特征检测步骤](https://www.armcvai.com/wp-content/uploads/2019/03/image.png) 颜色空间归一化——–>梯度计算————->梯度方向直方图———->重叠块直方图归一化———–>HOG特征 #### 参考资料 [HOG特征检测－简述](https://blog.csdn.net/liyuqian199695/article/details/53835989) From bdbedf947471b47e358518c804194aea9c19b6a8 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Tue, 26 Mar 2019 23:50:36 +0800 Subject: [PATCH 042/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index bb17e596..a17dae7c 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -172,6 +172,7 @@ HOG特征提取方法就是将一个image： 4. 统计每个cell的梯度直方图（不同梯度的个数），即可形成每个cell的descriptor。 #### HOG特征检测步骤 ![HOG特征检测步骤](https://www.armcvai.com/wp-content/uploads/2019/03/image.png) + 颜色空间归一化——–>梯度计算————->梯度方向直方图———->重叠块直方图归一化———–>HOG特征 #### 参考资料 [HOG特征检测－简述](https://blog.csdn.net/liyuqian199695/article/details/53835989) @@ -184,9 +185,9 @@ HOG特征提取方法就是将一个image： ## 激活函数的作用 **激活函数实现去线性化**。神经元的结构的输出为所有输入的加权和，这导致神经网络是一个线性模型。如果将每一个神经元（也就是神经网络的节点）的输出通过一个非线性函数，那么整个神经网络的模型也就不再是线性的了，这个非线性函数就是激活函数。 常见的激活函数有：ReLU函数、sigmoid函数、tanh函数。 -ReLU函数：$f(x)=max(x,0)$， -sigmoid函数：$f(x)=\frac{1}{1+e^{-x}}$ -tanh函数：$f(x)=\frac{1+e^{-2x}}{1+e^{-2x}}$ ++ ReLU函数：$f(x)=max(x,0)$， ++ sigmoid函数：$f(x)=\frac{1}{1+e^{-x}}$ ++ tanh函数：$f(x)=\frac{1+e^{-2x}}{1+e^{-2x}}$ ## 卷积层和池化层有什么区别 (1). 卷积层有参数，池化层没有参数。 #### 卷积层参数数量计算方法 From aa0a9a33cfe08e65ae9b977e9807bcf7ab5108c2 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Tue, 26 Mar 2019 23:52:52 +0800 Subject: [PATCH 043/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index a17dae7c..72d7cc49 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -185,9 +185,10 @@ HOG特征提取方法就是将一个image： ## 激活函数的作用 **激活函数实现去线性化**。神经元的结构的输出为所有输入的加权和，这导致神经网络是一个线性模型。如果将每一个神经元（也就是神经网络的节点）的输出通过一个非线性函数，那么整个神经网络的模型也就不再是线性的了，这个非线性函数就是激活函数。 常见的激活函数有：ReLU函数、sigmoid函数、tanh函数。 -+ ReLU函数：$f(x)=max(x,0)$， -+ sigmoid函数：$f(x)=\frac{1}{1+e^{-x}}$ -+ tanh函数：$f(x)=\frac{1+e^{-2x}}{1+e^{-2x}}$ ++ ReLU函数：http://chart.googleapis.com/chart?cht=tx&chl=\Large f(x)=max(x,0) ++ sigmoid函数：http://chart.googleapis.com/chart?cht=tx&chl=\Large f(x)=\frac{1}{1+e^{-x}} ++ tanh函数：http://chart.googleapis.com/chart?cht=tx&chl=\Large f(x)=\frac{1+e^{-2x}}{1+e^{-2x}} +http://chart.googleapis.com/chart?cht=tx&chl=\Large x=\frac{-b\pm\sqrt{b^2-4ac}}{2a}) ## 卷积层和池化层有什么区别 (1). 卷积层有参数，池化层没有参数。 #### 卷积层参数数量计算方法 From 8ca1f21ae1f6dd5909e4af9bdb8ce84fe261b1da Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Tue, 26 Mar 2019 23:54:52 +0800 Subject: [PATCH 044/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...46\344\271\240\351\235\242\350\257\225\351\242\230.md" | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 72d7cc49..941c84b9 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -185,10 +185,10 @@ HOG特征提取方法就是将一个image： ## 激活函数的作用 **激活函数实现去线性化**。神经元的结构的输出为所有输入的加权和，这导致神经网络是一个线性模型。如果将每一个神经元（也就是神经网络的节点）的输出通过一个非线性函数，那么整个神经网络的模型也就不再是线性的了，这个非线性函数就是激活函数。 常见的激活函数有：ReLU函数、sigmoid函数、tanh函数。 -+ ReLU函数：http://chart.googleapis.com/chart?cht=tx&chl=\Large f(x)=max(x,0) -+ sigmoid函数：http://chart.googleapis.com/chart?cht=tx&chl=\Large f(x)=\frac{1}{1+e^{-x}} -+ tanh函数：http://chart.googleapis.com/chart?cht=tx&chl=\Large f(x)=\frac{1+e^{-2x}}{1+e^{-2x}} -http://chart.googleapis.com/chart?cht=tx&chl=\Large x=\frac{-b\pm\sqrt{b^2-4ac}}{2a}) ++ ReLU函数：(http://chart.googleapis.com/chart?cht=tx&chl=\Large f(x)=max(x,0)) ++ sigmoid函数：(http://chart.googleapis.com/chart?cht=tx&chl=\Large f(x)=\frac{1}{1+e^{-x}}) ++ tanh函数：(http://chart.googleapis.com/chart?cht=tx&chl=\Large f(x)=\frac{1+e^{-2x}}{1+e^{-2x}}) +(http://chart.googleapis.com/chart?cht=tx&chl=\Large x=\frac{-b\pm\sqrt{b^2-4ac}}{2a}) ## 卷积层和池化层有什么区别 (1). 卷积层有参数，池化层没有参数。 #### 卷积层参数数量计算方法 From 580a1ffc88a5357ed03c3fbdb8010810d7d67b4d Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Tue, 26 Mar 2019 23:56:51 +0800 Subject: [PATCH 045/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...5\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 5 +++++ 1 file changed, 5 insertions(+) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 941c84b9..7ba5bb80 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -91,6 +91,7 @@ RNN的输入有两个部分，一部分为上一时刻的状态，另一部分 (1). 减少网络层参数。用两个3*3卷积比用1个5*5卷积拥有更少的参数量，只有后者的2∗3∗35∗5=0.72。但是起到的效果是一样的，两个3*3的卷积层串联相当于一个5*5的卷积层，感受野的大小都是5×5，即1个像素会跟周围5*5的像素产生关联。把下图当成动态图看，很容易看到两个3×3卷积层堆叠（没有空间池化）有5×5的有效感受野。 ！[2个３*卷积层](https://www.armcvai.com/wp-content/uploads/2019/03/2个33卷积层.png) + (2). 更多的非线性变换。2个3*3卷积层拥有比1个5*5卷积层更多的非线性变换（前者可以使用两次ReLU激活函数，而后者只有一次），使得卷积神经网络对特征的学习能力更强。 ***paper中给出的相关解释***：三个这样的层具有7×7的有效感受野。那么我们获得了什么？例如通过使用三个3×3卷积层的堆叠来替换单个7×7层。首先，我们结合了三个非线性修正层，而不是单一的，这使得决策函数更具判别性。其次，我们减少参数的数量：假设三层3×3卷积堆叠的输入和输出有C个通道，堆叠卷积层的参数为3(32C2)=27C2个权重；同时，单个7×7卷积层将需要72C2=49C2个参数，即参数多81％。这可以看作是对7×7卷积滤波器进行正则化，迫使它们通过3×3滤波器（在它们之间注入非线性）进行分解。 @@ -101,6 +102,7 @@ RNN的输入有两个部分，一部分为上一时刻的状态，另一部分 ReLU激活函数公式如下： ![relu激活函数](http://latex.codecogs.com/gif.latex?%24%24ReLu%28x%29%3D%5Cleft%5C%7B%5Cbegin%7Bmatrix%7D%20x%20%26%20x%3E0%5C%5C%200%20%26%20x%3C%3D0%20%5Cend%7Bmatrix%7D%5Cright.%24%24) + relu函数方程 ReLU 的输出要么是 0, 要么是输入本身。虽然方程简单，但实际上效果更好。在网上看了很多版本的解释，有从程序实例分析也有从数学上分析，我找了个相对比较直白的回答，如下： (1). ReLU函数计算简单，可以减少很多计算量。反向传播求误差梯度时，涉及除法，计算量相对较大，采用ReLU激活函数，可以节省很多计算量； @@ -131,6 +133,7 @@ Python深度学习p127. dropout可以防止过拟合，dropout简单来说就是：我们在前向传播的时候，让某个神经元的激活值以一定的概率p停止工作，这样可以使模型的泛化性更强，因为它不会依赖某些局部的特征。 ![dropou直观展示](https://www.armcvai.com/wp-content/uploads/2019/03/dropout.png) + #### dropout具体工作流程 以 标准神经网络为例，正常的流程是：我们首先把输入数据x通过网络前向传播，然后把误差反向传播一决定如何更新参数让网络进行学习。使用dropout之后，过程变成如下： @@ -146,11 +149,13 @@ dropout可以防止过拟合，dropout简单来说就是：我们在前向传播 不可避免的，在训练网络中的每个单元都要添加一道概率流程，标准网络和带有dropout网络的比较图如下所示： ![dropout在训练阶段](https://www.armcvai.com/wp-content/uploads/2019/03/添加了dropou的神经网络图.png) + (2). 在测试模型阶段 预测模型的时候，输入是当前输入，每个神经单元的权重参数要乘以概率p。 ![dropout在测试模型时](https://www.armcvai.com/wp-content/uploads/2019/03/dropout_test.png) + #### 如何选择dropout 的概率 input 的dropout概率推荐是0.8， hidden layer 推荐是0.5， 但是也可以在一定的区间上取值。（All dropout nets use p = 0.5 for hidden units and p = 0.8 for input units.） From 10985fa9af3b6f4d38bffc2769525c6fb2c783ba Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Wed, 27 Mar 2019 00:03:12 +0800 Subject: [PATCH 046/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 2 ++ 1 file changed, 2 insertions(+) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 7ba5bb80..fdaef83b 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -194,6 +194,8 @@ HOG特征提取方法就是将一个image： + sigmoid函数：(http://chart.googleapis.com/chart?cht=tx&chl=\Large f(x)=\frac{1}{1+e^{-x}}) + tanh函数：(http://chart.googleapis.com/chart?cht=tx&chl=\Large f(x)=\frac{1+e^{-2x}}{1+e^{-2x}}) (http://chart.googleapis.com/chart?cht=tx&chl=\Large x=\frac{-b\pm\sqrt{b^2-4ac}}{2a}) + + ## 卷积层和池化层有什么区别 (1). 卷积层有参数，池化层没有参数。 #### 卷积层参数数量计算方法 From 692bc4fb28ddd4be7770211b7fa78a6a24527b23 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Wed, 27 Mar 2019 00:04:17 +0800 Subject: [PATCH 047/386] Create README.md --- images/README.md | 2 ++ 1 file changed, 2 insertions(+) create mode 100644 images/README.md diff --git a/images/README.md b/images/README.md new file mode 100644 index 00000000..91ef911d --- /dev/null +++ b/images/README.md @@ -0,0 +1,2 @@ +## 目录说明 +此目录存放深度学习面试题.md文件所用到的图片文件。 From ddda8b49c3ddac7f93d1bb02574480ebc7ef75e0 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Wed, 27 Mar 2019 00:06:00 +0800 Subject: [PATCH 048/386] Add files via upload --- ...*3\345\215\267\347\247\257\345\261\202.png" | 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zs-Wq;4p}oeNzNRu66g>QX~=GGeit{ST9?34%Q zE6!=;(v?}nE)$@F^h?0SmdPxzG&C0J)H;#IVBDyB6mPK64XSj{XrRzhlB5$mU-YOG zXTivg`!F68mjtn7kSlTl>~<~BuzBWkVq&#!MT6G*-qEnCBtJ2Jqj$t@rfC^0g{CN^ zPJBxp*THW%u0y|X{9BJ(p`Ba<(OKQ1s`!X4w>fUD2ue0D<^` za3+z!`#lAd(mCb9dOyh{2}s9_=p9}o{bvtHfs&UGonsR#(xD$ytp;U5cnY@i&Oe*C(G3es)+j+saTjGk}>0>71S};QndWk?Xnw|z7H?UneKN&7vf0A8kx@uUm~|U6PPBa zg-W5ptkaCslMM_v*1))!M5j=y1box-mV*Gm_?Swui o{@?rl4hEt5V*k94_Ve*T_)LzI;ep5r^6qU&iz|p#d^Pm{U*+_JeE Date: Wed, 27 Mar 2019 00:06:42 +0800 Subject: [PATCH 049/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...344\271\240\351\235\242\350\257\225\351\242\230.md" | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index fdaef83b..314648d6 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -190,11 +190,11 @@ HOG特征提取方法就是将一个image： ## 激活函数的作用 **激活函数实现去线性化**。神经元的结构的输出为所有输入的加权和，这导致神经网络是一个线性模型。如果将每一个神经元（也就是神经网络的节点）的输出通过一个非线性函数，那么整个神经网络的模型也就不再是线性的了，这个非线性函数就是激活函数。 常见的激活函数有：ReLU函数、sigmoid函数、tanh函数。 -+ ReLU函数：(http://chart.googleapis.com/chart?cht=tx&chl=\Large f(x)=max(x,0)) -+ sigmoid函数：(http://chart.googleapis.com/chart?cht=tx&chl=\Large f(x)=\frac{1}{1+e^{-x}}) -+ tanh函数：(http://chart.googleapis.com/chart?cht=tx&chl=\Large f(x)=\frac{1+e^{-2x}}{1+e^{-2x}}) -(http://chart.googleapis.com/chart?cht=tx&chl=\Large x=\frac{-b\pm\sqrt{b^2-4ac}}{2a}) - ++ ReLU函数：(http://chart.googleapis.com/chart?cht=tx&chl=\Large $f(x)=max(x,0))$ ++ sigmoid函数：(http://chart.googleapis.com/chart?cht=tx&chl=\Large $f(x)=\frac{1}{1+e^{-x}})$ ++ tanh函数：(http://chart.googleapis.com/chart?cht=tx&chl=\Large $f(x)=\frac{1+e^{-2x}}{1+e^{-2x}})$ + + ## 卷积层和池化层有什么区别 (1). 卷积层有参数，池化层没有参数。 From e4db76e9581e97ae286567abb5df2756dd3ed2d8 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Wed, 27 Mar 2019 00:07:32 +0800 Subject: [PATCH 050/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...46\344\271\240\351\235\242\350\257\225\351\242\230.md" | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 314648d6..20166596 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -190,9 +190,11 @@ HOG特征提取方法就是将一个image： ## 激活函数的作用 **激活函数实现去线性化**。神经元的结构的输出为所有输入的加权和，这导致神经网络是一个线性模型。如果将每一个神经元（也就是神经网络的节点）的输出通过一个非线性函数，那么整个神经网络的模型也就不再是线性的了，这个非线性函数就是激活函数。 常见的激活函数有：ReLU函数、sigmoid函数、tanh函数。 -+ ReLU函数：(http://chart.googleapis.com/chart?cht=tx&chl=\Large $f(x)=max(x,0))$ -+ sigmoid函数：(http://chart.googleapis.com/chart?cht=tx&chl=\Large $f(x)=\frac{1}{1+e^{-x}})$ -+ tanh函数：(http://chart.googleapis.com/chart?cht=tx&chl=\Large $f(x)=\frac{1+e^{-2x}}{1+e^{-2x}})$ ++ ReLU函数：(http://chart.googleapis.com/chart?cht=tx&chl=\Large $f(x)=max(x,0)$) ++ sigmoid函数：(http://chart.googleapis.com/chart?cht=tx&chl=\Large $f(x)=\frac{1}{1+e^{-x}}$) ++ tanh函数： + +(http://chart.googleapis.com/chart?cht=tx&chl=\Large $f(x)=\frac{1+e^{-2x}}{1+e^{-2x}}$) From 15f6d906c026bde7b47aa4cb289f86cbc1b0b754 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Wed, 27 Mar 2019 00:09:17 +0800 Subject: [PATCH 051/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...344\271\240\351\235\242\350\257\225\351\242\230.md" | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 20166596..5ce686f1 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -190,13 +190,11 @@ HOG特征提取方法就是将一个image： ## 激活函数的作用 **激活函数实现去线性化**。神经元的结构的输出为所有输入的加权和，这导致神经网络是一个线性模型。如果将每一个神经元（也就是神经网络的节点）的输出通过一个非线性函数，那么整个神经网络的模型也就不再是线性的了，这个非线性函数就是激活函数。 常见的激活函数有：ReLU函数、sigmoid函数、tanh函数。 -+ ReLU函数：(http://chart.googleapis.com/chart?cht=tx&chl=\Large $f(x)=max(x,0)$) -+ sigmoid函数：(http://chart.googleapis.com/chart?cht=tx&chl=\Large $f(x)=\frac{1}{1+e^{-x}}$) -+ tanh函数： - -(http://chart.googleapis.com/chart?cht=tx&chl=\Large $f(x)=\frac{1+e^{-2x}}{1+e^{-2x}}$) - ++ ReLU函数：http://chart.googleapis.com/chart?cht=tx&chl=\Large $f(x)=max(x,0)$) ++ sigmoid函数：http://chart.googleapis.com/chart?cht=tx&chl=\Large $f(x)=\frac{1}{1+e^{-x}}$ ++ tanh函数：http://chart.googleapis.com/chart?cht=tx&chl=\Large $f(x)=\frac{1+e^{-2x}}{1+e^{-2x}}$ +http://www.forkosh.com/mathtex.cgi?\Large x=\frac{-b\pm\sqrt{b^2-4ac}}{2a} ## 卷积层和池化层有什么区别 (1). 卷积层有参数，池化层没有参数。 From da8fb8b9a6eda6b5ee4a51980ffff1976e38ef4b Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Wed, 27 Mar 2019 00:10:00 +0800 Subject: [PATCH 052/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...5\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 1 + 1 file changed, 1 insertion(+) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 5ce686f1..bb6c2048 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -1,3 +1,4 @@ + ## CNN权值共享问题 首先权值共享就是滤波器共享，滤波器的参数是固定的，即是用相同的滤波器去扫一遍图像，提取一次特征特征，得到feature map。在卷积网络中，学好了一个滤波器，就相当于掌握了一种特征，这个滤波器在图像中滑动，进行特征提取，然后所有进行这样操作的区域都会被采集到这种特征，就好比上面的水平线。 From b160817fdb0e6f05e6bef299627ecb9f3014e9a1 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Wed, 27 Mar 2019 00:10:54 +0800 Subject: [PATCH 053/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...6\344\271\240\351\235\242\350\257\225\351\242\230.md" | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index bb6c2048..1782445a 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -1,4 +1,3 @@ - ## CNN权值共享问题 首先权值共享就是滤波器共享，滤波器的参数是固定的，即是用相同的滤波器去扫一遍图像，提取一次特征特征，得到feature map。在卷积网络中，学好了一个滤波器，就相当于掌握了一种特征，这个滤波器在图像中滑动，进行特征提取，然后所有进行这样操作的区域都会被采集到这种特征，就好比上面的水平线。 @@ -191,11 +190,9 @@ HOG特征提取方法就是将一个image： ## 激活函数的作用 **激活函数实现去线性化**。神经元的结构的输出为所有输入的加权和，这导致神经网络是一个线性模型。如果将每一个神经元（也就是神经网络的节点）的输出通过一个非线性函数，那么整个神经网络的模型也就不再是线性的了，这个非线性函数就是激活函数。 常见的激活函数有：ReLU函数、sigmoid函数、tanh函数。 -+ ReLU函数：http://chart.googleapis.com/chart?cht=tx&chl=\Large $f(x)=max(x,0)$) -+ sigmoid函数：http://chart.googleapis.com/chart?cht=tx&chl=\Large $f(x)=\frac{1}{1+e^{-x}}$ -+ tanh函数：http://chart.googleapis.com/chart?cht=tx&chl=\Large $f(x)=\frac{1+e^{-2x}}{1+e^{-2x}}$ - -http://www.forkosh.com/mathtex.cgi?\Large x=\frac{-b\pm\sqrt{b^2-4ac}}{2a} ++ ReLU函数：$f(x)=max(x,0)$ ++ sigmoid函数：$f(x)=\frac{1}{1+e^{-x}}$ ++ tanh函数：$f(x)=\frac{1+e^{-2x}}{1+e^{-2x}}$ ## 卷积层和池化层有什么区别 (1). 卷积层有参数，池化层没有参数。 From 990563a7005c70a2529e09d6dc0f1c939cea7e4d Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Wed, 27 Mar 2019 00:12:01 +0800 Subject: [PATCH 054/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 1782445a..9eb57e7e 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -90,7 +90,7 @@ RNN的输入有两个部分，一部分为上一时刻的状态，另一部分 ## VGG使用2个3*3卷积的优势在哪里？ (1). 减少网络层参数。用两个3*3卷积比用1个5*5卷积拥有更少的参数量，只有后者的2∗3∗35∗5=0.72。但是起到的效果是一样的，两个3*3的卷积层串联相当于一个5*5的卷积层，感受野的大小都是5×5，即1个像素会跟周围5*5的像素产生关联。把下图当成动态图看，很容易看到两个3×3卷积层堆叠（没有空间池化）有5×5的有效感受野。 -！[2个３*卷积层](https://www.armcvai.com/wp-content/uploads/2019/03/2个33卷积层.png) +！[2个３*卷积层](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/2%E4%B8%AA3*3%E5%8D%B7%E7%A7%AF%E5%B1%82.png) (2). 更多的非线性变换。2个3*3卷积层拥有比1个5*5卷积层更多的非线性变换（前者可以使用两次ReLU激活函数，而后者只有一次），使得卷积神经网络对特征的学习能力更强。 From bd56177133eae9f04785b0e7fd4450ff418f41e4 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Wed, 27 Mar 2019 00:13:44 +0800 Subject: [PATCH 055/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 9eb57e7e..50ed21a6 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -90,7 +90,7 @@ RNN的输入有两个部分，一部分为上一时刻的状态，另一部分 ## VGG使用2个3*3卷积的优势在哪里？ (1). 减少网络层参数。用两个3*3卷积比用1个5*5卷积拥有更少的参数量，只有后者的2∗3∗35∗5=0.72。但是起到的效果是一样的，两个3*3的卷积层串联相当于一个5*5的卷积层，感受野的大小都是5×5，即1个像素会跟周围5*5的像素产生关联。把下图当成动态图看，很容易看到两个3×3卷积层堆叠（没有空间池化）有5×5的有效感受野。 -！[2个３*卷积层](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/2%E4%B8%AA3*3%E5%8D%B7%E7%A7%AF%E5%B1%82.png) +![2个３*3卷积层](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/2%E4%B8%AA3*3%E5%8D%B7%E7%A7%AF%E5%B1%82.png) (2). 更多的非线性变换。2个3*3卷积层拥有比1个5*5卷积层更多的非线性变换（前者可以使用两次ReLU激活函数，而后者只有一次），使得卷积神经网络对特征的学习能力更强。 From 6287f48da563dd56609e9f3f90d3a4326e091fd4 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Wed, 27 Mar 2019 00:17:18 +0800 Subject: [PATCH 056/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 50ed21a6..7c67a10b 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -195,7 +195,8 @@ HOG特征提取方法就是将一个image： + tanh函数：$f(x)=\frac{1+e^{-2x}}{1+e^{-2x}}$ ## 卷积层和池化层有什么区别 -(1). 卷积层有参数，池化层没有参数。 +(1). 卷积层有参数，池化层没有参数； +(2). 经过卷积层节点矩阵深度会改变，池化层不会改变节点矩阵的深度，但是它可以缩小节点矩阵的大小。 #### 卷积层参数数量计算方法 假设输入层矩阵维度是96*96*3，第一层卷积层使用尺寸为5*5、深度为16的过滤器（卷积核尺寸为5*5、卷积核数量为16），那么这层卷积层的参数个数为５*5*3*16+16=1216个 ## Reference From 78f59cc4eb04c55f71819e541de4c91faeeecd02 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Wed, 27 Mar 2019 00:19:00 +0800 Subject: [PATCH 057/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...55\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 7c67a10b..62af9a88 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -195,8 +195,8 @@ HOG特征提取方法就是将一个image： + tanh函数：$f(x)=\frac{1+e^{-2x}}{1+e^{-2x}}$ ## 卷积层和池化层有什么区别 -(1). 卷积层有参数，池化层没有参数； -(2). 经过卷积层节点矩阵深度会改变，池化层不会改变节点矩阵的深度，但是它可以缩小节点矩阵的大小。 +(1). 卷积层有参数，池化层没有参数 +(2). 经过卷积层节点矩阵深度会改变，池化层不会改变节点矩阵的深度，但是它可以缩小节点矩阵的大小 #### 卷积层参数数量计算方法 假设输入层矩阵维度是96*96*3，第一层卷积层使用尺寸为5*5、深度为16的过滤器（卷积核尺寸为5*5、卷积核数量为16），那么这层卷积层的参数个数为５*5*3*16+16=1216个 ## Reference From 742abd9aaa7eed8ab922dd44f2edf6549da7c53c Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Wed, 27 Mar 2019 00:22:32 +0800 Subject: [PATCH 058/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...55\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 62af9a88..ca682348 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -4,7 +4,9 @@ ## CNN结构特点 局部连接，权值共享，池化操作，多层次结构。 -局部连接使网络可以提取数据的局部特征；权值共享大大降低了网络的训练难度，一个Filter只提取一个特征，在整个图片（或者语音／文本） 中进行卷积；池化操作与多层次结构一起，实现了数据的降维，将低层次的局部特征组合成为较高层次的特征，从而对整个图片进行表示。如下图所示： ++ 局部连接使网络可以提取数据的局部特征； ++ 权值共享大大降低了网络的训练难度，一个Filter只提取一个特征，在整个图片（或者语音／文本） 中进行卷积； ++ 池化操作与多层次结构一起，实现了数据的降维，将低层次的局部特征组合成为较高层次的特征，从而对整个图片进行表示。如下图所示： ## 什么样的数据集不适合深度学习 + 数据集太小，数据样本不足时，深度学习相对其它机器学习算法，没有明显优势。 From 8ef211fc2d20ad04bbd356e78c8f5f5d17c9bf57 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Wed, 27 Mar 2019 00:25:38 +0800 Subject: [PATCH 059/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 加粗部分内容 --- ...40\351\235\242\350\257\225\351\242\230.md" | 30 +++++++++---------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index ca682348..0a03c5e4 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -4,9 +4,9 @@ ## CNN结构特点 局部连接，权值共享，池化操作，多层次结构。 -+ 局部连接使网络可以提取数据的局部特征； -+ 权值共享大大降低了网络的训练难度，一个Filter只提取一个特征，在整个图片（或者语音／文本） 中进行卷积； -+ 池化操作与多层次结构一起，实现了数据的降维，将低层次的局部特征组合成为较高层次的特征，从而对整个图片进行表示。如下图所示： ++ 局部连接使网络可以提取数据的局部特征 ++ 权值共享大大降低了网络的训练难度，一个Filter只提取一个特征，在整个图片（或者语音／文本） 中进行卷积 ++ 池化操作与多层次结构一起，实现了数据的降维，将低层次的局部特征组合成为较高层次的特征，从而对整个图片进行表示。 ## 什么样的数据集不适合深度学习 + 数据集太小，数据样本不足时，深度学习相对其它机器学习算法，没有明显优势。 @@ -16,7 +16,7 @@ + 梯度消失会造成权值更新缓慢，模型训练难度增加。造成梯度消失的一个原因是，许多激活函数将输出值挤压在很小的区间内，在激活函数两端较大范围的定义域内梯度为0，造成学习停止。 ## Overfitting怎么解决 首先所谓过拟合，指的是一个模型过于复杂之后，它可以很好地“记忆”每一个训练数据中随机噪音的部分而忘记了去“训练”数据中的通用趋势。 -过拟合具体表现在：模型在训练数据上损失函数较小，预测准确率较高；但是在测试数据上损失函数比较大，预测准确率较低。 +**过拟合具体表现在：模型在训练数据上损失函数较小，预测准确率较高；但是在测试数据上损失函数比较大，预测准确率较低。** Parameter Norm Penalties(参数范数惩罚)；Dataset Augmentation (数据集增强)；Early Stopping(提前终止)；Parameter Tying and Parameter Sharing (参数绑定与参数共享)；Bagging and Other Ensemble Methods(Bagging 和其他集成方法)；dropout；regularization； batch normalizatin。是解决Overfitting的常用手段。 @@ -34,7 +34,7 @@ L1 范数可以使权值稀疏，方便特征提取。 L2 范数可以防止过 Tensorflow 是一个通过计算图的形式来表述计算的编程系统，计算图也叫数据流图，可以把计算图看做是一种有向图，Tensorflow 中的每一个计算都是计算图上的一个节点，而节点之间的边描述了计算之间的依赖关系。 ## BN（批归一化）的作用 -(1). 可以使用更高的学习率。如果每层的scale不一致，实际上每层需要的学习率是不一样的，同一层不同维度的scale往往也需要不同大小的学习率，通常需要使用最小的那个学习率才能保证损失函数有效下降，Batch Normalization将每层、每维的scale保持一致，那么我们就可以直接使用较高的学习率进行优化。 +(1). **可以使用更高的学习率。**如果每层的scale不一致，实际上每层需要的学习率是不一样的，同一层不同维度的scale往往也需要不同大小的学习率，通常需要使用最小的那个学习率才能保证损失函数有效下降，Batch Normalization将每层、每维的scale保持一致，那么我们就可以直接使用较高的学习率进行优化。 (2). 移除或使用较低的dropout。 dropout是常用的防止overfitting的方法，而导致overfit的位置往往在数据边界处，如果初始化权重就已经落在数据内部，overfit现象就可以得到一定的缓解。论文中最后的模型分别使用10%、5%和0%的dropout训练模型，与之前的40%-50%相比，可以大大提高训练速度。 @@ -42,12 +42,12 @@ Tensorflow 是一个通过计算图的形式来表述计算的编程系统，计 (4). 取消Local Response Normalization层。 由于使用了一种Normalization，再使用LRN就显得没那么必要了。而且LRN实际上也没那么work。 -(5). Batch Normalization调整了数据的分布，不考虑激活函数，它让每一层的输出归一化到了均值为0方差为1的分布，这保证了梯度的有效性，可以解决反向传播过程中的梯度问题。目前大部分资料都这样解释，比如BN的原始论文认为的缓解了Internal Covariate Shift(ICS)问题。 +(5). **Batch Normalization调整了数据的分布，不考虑激活函数，它让每一层的输出归一化到了均值为0方差为1的分布**，这保证了梯度的有效性，可以解决反向传播过程中的梯度问题。目前大部分资料都这样解释，比如BN的原始论文认为的缓解了Internal Covariate Shift(ICS)问题。 ## 什么是梯度消失和爆炸，怎么解决？ 当训练较多层数的模型时，一般会出现梯度消失问题（gradient vanishing problem）和梯度爆炸问题（gradient exploding problem）。注意在反向传播中，当网络模型层数较多时，梯度消失和梯度爆炸是不可避免的。 -深度神经网络中的梯度不稳定性，根本原因在于前面层上的梯度是来自于后面层上梯度的乘积。当存在过多的层次时，就出现了内在本质上的不稳定场景。 -前面的层比后面的层梯度变化更小，故变化更慢，故引起了梯度消失问题。前面层比后面层梯度变化更快，故引起梯度爆炸问题。 + +**深度神经网络中的梯度不稳定性，根本原因在于前面层上的梯度是来自于后面层上梯度的乘积。**当存在过多的层次时，就出现了内在本质上的不稳定场景。前面的层比后面的层梯度变化更小，故变化更慢，故引起了梯度消失问题。前面层比后面层梯度变化更快，故引起梯度爆炸问题。 解决梯度消失和梯度爆炸问题，常用的有以下几个方案： @@ -79,7 +79,7 @@ RNN的输入有两个部分，一部分为上一时刻的状态，另一部分 + 更多回答，参考此链接。 ## 图像处理中平滑和锐化操作是什么？ -平滑处理（smoothing）也称模糊处理（bluring），主要用于消除图像中的噪声部分，平滑处理常用的用途是用来减少图像上的噪点或失真，平滑主要使用图像滤波。在这里，我个人认为可以把图像平滑和图像滤波联系起来，因为图像平滑常用的方法就是图像滤波器。 +**平滑处理（smoothing）也称模糊处理（bluring）**，主要用于消除图像中的噪声部分，平滑处理常用的用途是用来减少图像上的噪点或失真，平滑主要使用图像滤波。在这里，我个人认为可以把图像平滑和图像滤波联系起来，因为图像平滑常用的方法就是图像滤波器。 在OpenCV3中常用的图像滤波器有以下几种： + 方框滤波——BoxBlur函数 @@ -90,11 +90,11 @@ RNN的输入有两个部分，一部分为上一时刻的状态，另一部分 图像锐化操作是为了突出显示图像的边界和其他细节，而图像锐化实现的方法是通过各种算子和滤波器实现的——Canny算子、Sobel算子、Laplacian算子以及Scharr滤波器。 ## VGG使用2个3*3卷积的优势在哪里？ -(1). 减少网络层参数。用两个3*3卷积比用1个5*5卷积拥有更少的参数量，只有后者的2∗3∗35∗5=0.72。但是起到的效果是一样的，两个3*3的卷积层串联相当于一个5*5的卷积层，感受野的大小都是5×5，即1个像素会跟周围5*5的像素产生关联。把下图当成动态图看，很容易看到两个3×3卷积层堆叠（没有空间池化）有5×5的有效感受野。 +(1). **减少网络层参数**。用两个3*3卷积比用1个5*5卷积拥有更少的参数量，只有后者的2∗3∗35∗5=0.72。但是起到的效果是一样的，两个3*3的卷积层串联相当于一个5*5的卷积层，感受野的大小都是5×5，即1个像素会跟周围5*5的像素产生关联。把下图当成动态图看，很容易看到两个3×3卷积层堆叠（没有空间池化）有5×5的有效感受野。 ![2个３*3卷积层](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/2%E4%B8%AA3*3%E5%8D%B7%E7%A7%AF%E5%B1%82.png) -(2). 更多的非线性变换。2个3*3卷积层拥有比1个5*5卷积层更多的非线性变换（前者可以使用两次ReLU激活函数，而后者只有一次），使得卷积神经网络对特征的学习能力更强。 +(2). **更多的非线性变换**。2个3*3卷积层拥有比1个5*5卷积层更多的非线性变换（前者可以使用两次ReLU激活函数，而后者只有一次），使得卷积神经网络对特征的学习能力更强。 ***paper中给出的相关解释***：三个这样的层具有7×7的有效感受野。那么我们获得了什么？例如通过使用三个3×3卷积层的堆叠来替换单个7×7层。首先，我们结合了三个非线性修正层，而不是单一的，这使得决策函数更具判别性。其次，我们减少参数的数量：假设三层3×3卷积堆叠的输入和输出有C个通道，堆叠卷积层的参数为3(32C2)=27C2个权重；同时，单个7×7卷积层将需要72C2=49C2个参数，即参数多81％。这可以看作是对7×7卷积滤波器进行正则化，迫使它们通过3×3滤波器（在它们之间注入非线性）进行分解。 @@ -108,7 +108,7 @@ ReLU激活函数公式如下： relu函数方程 ReLU 的输出要么是 0, 要么是输入本身。虽然方程简单，但实际上效果更好。在网上看了很多版本的解释，有从程序实例分析也有从数学上分析，我找了个相对比较直白的回答，如下： (1). ReLU函数计算简单，可以减少很多计算量。反向传播求误差梯度时，涉及除法，计算量相对较大，采用ReLU激活函数，可以节省很多计算量； -(2). 避免梯度消失问题。对于深层网络，sigmoid函数反向传播时，很容易就会出现梯度消失问题（在sigmoid接近饱和区时，变换太缓慢，导数趋于0，这种情况会造成信息丢失），从而无法完成深层网络的训练。 +(2). **避免梯度消失问题**。对于深层网络，sigmoid函数反向传播时，很容易就会出现梯度消失问题（在sigmoid接近饱和区时，变换太缓慢，导数趋于0，这种情况会造成信息丢失），从而无法完成深层网络的训练。 (3). 可以缓解过拟合问题的发生。Relu会使一部分神经元的输出为0，这样就造成了网络的稀疏性，并且减少了参数的相互依存关系，缓解了过拟合问题的发生。 #### 参考链接 @@ -132,7 +132,7 @@ ReLU 的输出要么是 0, 要么是输入本身。虽然方程简单，但实 Python深度学习p127. ## 什么是dropout? -dropout可以防止过拟合，dropout简单来说就是：我们在前向传播的时候，让某个神经元的激活值以一定的概率p停止工作，这样可以使模型的泛化性更强，因为它不会依赖某些局部的特征。 +dropout可以防止过拟合，dropout简单来说就是：我们在前向传播的时候，**让某个神经元的激活值以一定的概率p停止工作**，这样可以使模型的泛化性更强，因为它不会依赖某些局部的特征。 ![dropou直观展示](https://www.armcvai.com/wp-content/uploads/2019/03/dropout.png) @@ -166,7 +166,7 @@ input 的dropout概率推荐是0.8， hidden layer 推荐是0.5， 但是也可 2. [深度学习中Dropout原理解析](https://zhuanlan.zhihu.com/p/38200980) ## HOG算法原理描述 -方向梯度直方图（Histogram of Oriented Gradient, HOG）特征是一种在计算机视觉和图像处理中用来进行物体检测的特征描述子。它通过计算和统计图像局部区域的梯度方向直方图来构成特征。在深度学习取得成功之前，Hog特征结合SVM分类器被广泛应用于图像识别中，在行人检测中获得了较大的成功。 +**方向梯度直方图（Histogram of Oriented Gradient, HOG）特征**是一种在计算机视觉和图像处理中用来进行物体检测的特征描述子。它通过计算和统计图像局部区域的梯度方向直方图来构成特征。在深度学习取得成功之前，Hog特征结合SVM分类器被广泛应用于图像识别中，在行人检测中获得了较大的成功。 #### HOG特征原理 HOG的核心思想是所检测的局部物体外形能够被光强梯度或边缘方向的分布所描述。通过将整幅图像分割成小的连接区域（称为cells），每个cell生成一个方向梯度直方图或者cell中pixel的边缘方向，这些直方图的组合可表示出（所检测目标的目标）描述子。为改善准确率，局部直方图可以通过计算图像中一个较大区域(称为block)的光强作为measure被对比标准化，然后用这个值(measure)归一化这个block中的所有cells。这个归一化过程完成了更好的照射/阴影不变性。 @@ -198,7 +198,7 @@ HOG特征提取方法就是将一个image： ## 卷积层和池化层有什么区别 (1). 卷积层有参数，池化层没有参数 -(2). 经过卷积层节点矩阵深度会改变，池化层不会改变节点矩阵的深度，但是它可以缩小节点矩阵的大小 +(2). **经过卷积层节点矩阵深度会改变**，池化层不会改变节点矩阵的深度，但是它可以缩小节点矩阵的大小 #### 卷积层参数数量计算方法 假设输入层矩阵维度是96*96*3，第一层卷积层使用尺寸为5*5、深度为16的过滤器（卷积核尺寸为5*5、卷积核数量为16），那么这层卷积层的参数个数为５*5*3*16+16=1216个 ## Reference From 587b675d17d7806d6a5d1f9d45bf46cb5187f256 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Wed, 27 Mar 2019 00:27:04 +0800 Subject: [PATCH 060/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...46\344\271\240\351\235\242\350\257\225\351\242\230.md" | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 0a03c5e4..31f85dfa 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -1,5 +1,5 @@ ## CNN权值共享问题 -首先权值共享就是滤波器共享，滤波器的参数是固定的，即是用相同的滤波器去扫一遍图像，提取一次特征特征，得到feature map。在卷积网络中，学好了一个滤波器，就相当于掌握了一种特征，这个滤波器在图像中滑动，进行特征提取，然后所有进行这样操作的区域都会被采集到这种特征，就好比上面的水平线。 +首先**权值共享就是滤波器共享**，滤波器的参数是固定的，即是用相同的滤波器去扫一遍图像，提取一次特征特征，得到feature map。在卷积网络中，学好了一个滤波器，就相当于掌握了一种特征，这个滤波器在图像中滑动，进行特征提取，然后所有进行这样操作的区域都会被采集到这种特征，就好比上面的水平线。 ## CNN结构特点 局部连接，权值共享，池化操作，多层次结构。 @@ -16,7 +16,7 @@ + 梯度消失会造成权值更新缓慢，模型训练难度增加。造成梯度消失的一个原因是，许多激活函数将输出值挤压在很小的区间内，在激活函数两端较大范围的定义域内梯度为0，造成学习停止。 ## Overfitting怎么解决 首先所谓过拟合，指的是一个模型过于复杂之后，它可以很好地“记忆”每一个训练数据中随机噪音的部分而忘记了去“训练”数据中的通用趋势。 -**过拟合具体表现在：模型在训练数据上损失函数较小，预测准确率较高；但是在测试数据上损失函数比较大，预测准确率较低。** +**过拟合具体表现在：模型在训练数据上损失函数较小，预测准确率较高；但是在测试数据上损失函数比较大，预测准确率较低**。 Parameter Norm Penalties(参数范数惩罚)；Dataset Augmentation (数据集增强)；Early Stopping(提前终止)；Parameter Tying and Parameter Sharing (参数绑定与参数共享)；Bagging and Other Ensemble Methods(Bagging 和其他集成方法)；dropout；regularization； batch normalizatin。是解决Overfitting的常用手段。 @@ -34,7 +34,7 @@ L1 范数可以使权值稀疏，方便特征提取。 L2 范数可以防止过 Tensorflow 是一个通过计算图的形式来表述计算的编程系统，计算图也叫数据流图，可以把计算图看做是一种有向图，Tensorflow 中的每一个计算都是计算图上的一个节点，而节点之间的边描述了计算之间的依赖关系。 ## BN（批归一化）的作用 -(1). **可以使用更高的学习率。**如果每层的scale不一致，实际上每层需要的学习率是不一样的，同一层不同维度的scale往往也需要不同大小的学习率，通常需要使用最小的那个学习率才能保证损失函数有效下降，Batch Normalization将每层、每维的scale保持一致，那么我们就可以直接使用较高的学习率进行优化。 +(1). **可以使用更高的学习率**。如果每层的scale不一致，实际上每层需要的学习率是不一样的，同一层不同维度的scale往往也需要不同大小的学习率，通常需要使用最小的那个学习率才能保证损失函数有效下降，Batch Normalization将每层、每维的scale保持一致，那么我们就可以直接使用较高的学习率进行优化。 (2). 移除或使用较低的dropout。 dropout是常用的防止overfitting的方法，而导致overfit的位置往往在数据边界处，如果初始化权重就已经落在数据内部，overfit现象就可以得到一定的缓解。论文中最后的模型分别使用10%、5%和0%的dropout训练模型，与之前的40%-50%相比，可以大大提高训练速度。 @@ -47,7 +47,7 @@ Tensorflow 是一个通过计算图的形式来表述计算的编程系统，计 ## 什么是梯度消失和爆炸，怎么解决？ 当训练较多层数的模型时，一般会出现梯度消失问题（gradient vanishing problem）和梯度爆炸问题（gradient exploding problem）。注意在反向传播中，当网络模型层数较多时，梯度消失和梯度爆炸是不可避免的。 -**深度神经网络中的梯度不稳定性，根本原因在于前面层上的梯度是来自于后面层上梯度的乘积。**当存在过多的层次时，就出现了内在本质上的不稳定场景。前面的层比后面的层梯度变化更小，故变化更慢，故引起了梯度消失问题。前面层比后面层梯度变化更快，故引起梯度爆炸问题。 +**深度神经网络中的梯度不稳定性，根本原因在于前面层上的梯度是来自于后面层上梯度的乘积**。当存在过多的层次时，就出现了内在本质上的不稳定场景。前面的层比后面的层梯度变化更小，故变化更慢，故引起了梯度消失问题。前面层比后面层梯度变化更快，故引起梯度爆炸问题。 解决梯度消失和梯度爆炸问题，常用的有以下几个方案： From 632e4e4c298e10ed96c42bac278d79618764f06b Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Wed, 27 Mar 2019 00:28:16 +0800 Subject: [PATCH 061/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...55\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 31f85dfa..6608c788 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -197,8 +197,8 @@ HOG特征提取方法就是将一个image： + tanh函数：$f(x)=\frac{1+e^{-2x}}{1+e^{-2x}}$ ## 卷积层和池化层有什么区别 -(1). 卷积层有参数，池化层没有参数 -(2). **经过卷积层节点矩阵深度会改变**，池化层不会改变节点矩阵的深度，但是它可以缩小节点矩阵的大小 +1. 卷积层有参数，池化层没有参数 +2. **经过卷积层节点矩阵深度会改变**，池化层不会改变节点矩阵的深度，但是它可以缩小节点矩阵的大小 #### 卷积层参数数量计算方法 假设输入层矩阵维度是96*96*3，第一层卷积层使用尺寸为5*5、深度为16的过滤器（卷积核尺寸为5*5、卷积核数量为16），那么这层卷积层的参数个数为５*5*3*16+16=1216个 ## Reference From b68bbc3eb0abe5f4f8c97bed326722cbd10b5b29 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Wed, 27 Mar 2019 00:31:16 +0800 Subject: [PATCH 062/386] Update README.md MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 更新readme.md，添加深度学习面试题标题 --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index e41702a3..ef101532 100644 --- a/README.md +++ b/README.md @@ -31,3 +31,5 @@ |上海媒智科技|计算机视觉算法实习生|上海|未知|内推邮箱：chenxi.huang@media-smart.cn|简历格式：姓名+电话号码+全职/实习+CVer推荐|无|CVer公众号| |旷视科技|算法研究员实习生|北京市海淀区|长期有效|内推邮箱：ur@megvii.com|简历命名：姓名+电话+岗位+CVer推荐(**免笔试**)|无|CVer公众号| |淘宝-智能制造事业部|图形图像算法实习生|杭州|未知|内推邮箱：yuanliang.syl@alibaba-inc.com|简历命名格式：姓名+电话号码+应聘岗位+CVer推荐|无|CVer公众号| + +## [深度学习面试题](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E9%9D%A2%E8%AF%95%E9%A2%98.md) From ee93c76c973d8413181a0fd514409f3328cdcdd7 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Wed, 27 Mar 2019 00:53:40 +0800 Subject: [PATCH 063/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...6\344\271\240\351\235\242\350\257\225\351\242\230.md" | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 6608c788..0966edaa 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -1,3 +1,4 @@ +# 深度学习 ## CNN权值共享问题 首先**权值共享就是滤波器共享**，滤波器的参数是固定的，即是用相同的滤波器去扫一遍图像，提取一次特征特征，得到feature map。在卷积网络中，学好了一个滤波器，就相当于掌握了一种特征，这个滤波器在图像中滑动，进行特征提取，然后所有进行这样操作的区域都会被采集到这种特征，就好比上面的水平线。 @@ -207,3 +208,11 @@ HOG特征提取方法就是将一个image： 3. [详解机器学习中的梯度消失、爆炸原因及其解决方法](https://blog.csdn.net/qq_25737169/article/details/78847691) +# 机器学习 +## Focal Loss 介绍一下 + +## 数据不平衡怎么办? + +## AUC的理解 + +## AUC的计算公式 From d9c673a7b3232126f86a9da9102dc1a17aba59e6 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Wed, 27 Mar 2019 00:54:39 +0800 Subject: [PATCH 064/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...55\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 4 ++++ 1 file changed, 4 insertions(+) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 0966edaa..71eb1db1 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -202,6 +202,10 @@ HOG特征提取方法就是将一个image： 2. **经过卷积层节点矩阵深度会改变**，池化层不会改变节点矩阵的深度，但是它可以缩小节点矩阵的大小 #### 卷积层参数数量计算方法 假设输入层矩阵维度是96*96*3，第一层卷积层使用尺寸为5*5、深度为16的过滤器（卷积核尺寸为5*5、卷积核数量为16），那么这层卷积层的参数个数为５*5*3*16+16=1216个 +## 神经网络为什么用交叉熵损失函数 +判断一个输出向量和期望的向量有多接近，交叉熵（cross entroy）是常用的评判方法之一。交叉熵刻画了两个概率分布之间的距离，是分类问题中使用比较广泛的一种损失函数。 +给定两个概率分布p和q，通过q来表示p的交叉熵公式为： +H(p,q)=−∑p(x)logq(x) ## Reference 1. [《Batch Normalization Accelerating Deep Network Training by Reducing Internal Covariate Shift》阅读笔记与实现](https://blog.csdn.net/happynear/article/details/44238541) 2. [深度学习中 Batch Normalization为什么效果好](https://blog.csdn.net/happynear/article/details/44238541) From 414f873b368e39be496cd4c461f0808b18571d19 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Wed, 27 Mar 2019 00:55:39 +0800 Subject: [PATCH 065/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 71eb1db1..c257ca48 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -196,7 +196,6 @@ HOG特征提取方法就是将一个image： + ReLU函数：$f(x)=max(x,0)$ + sigmoid函数：$f(x)=\frac{1}{1+e^{-x}}$ + tanh函数：$f(x)=\frac{1+e^{-2x}}{1+e^{-2x}}$ - ## 卷积层和池化层有什么区别 1. 卷积层有参数，池化层没有参数 2. **经过卷积层节点矩阵深度会改变**，池化层不会改变节点矩阵的深度，但是它可以缩小节点矩阵的大小 @@ -204,6 +203,7 @@ HOG特征提取方法就是将一个image： 假设输入层矩阵维度是96*96*3，第一层卷积层使用尺寸为5*5、深度为16的过滤器（卷积核尺寸为5*5、卷积核数量为16），那么这层卷积层的参数个数为５*5*3*16+16=1216个 ## 神经网络为什么用交叉熵损失函数 判断一个输出向量和期望的向量有多接近，交叉熵（cross entroy）是常用的评判方法之一。交叉熵刻画了两个概率分布之间的距离，是分类问题中使用比较广泛的一种损失函数。 + 给定两个概率分布p和q，通过q来表示p的交叉熵公式为： H(p,q)=−∑p(x)logq(x) ## Reference From b81959b9d011d22a9efff8378880dc558ae07a90 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Wed, 27 Mar 2019 00:56:18 +0800 Subject: [PATCH 066/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...5\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 1 - 1 file changed, 1 deletion(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index c257ca48..b332ceb2 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -203,7 +203,6 @@ HOG特征提取方法就是将一个image： 假设输入层矩阵维度是96*96*3，第一层卷积层使用尺寸为5*5、深度为16的过滤器（卷积核尺寸为5*5、卷积核数量为16），那么这层卷积层的参数个数为５*5*3*16+16=1216个 ## 神经网络为什么用交叉熵损失函数 判断一个输出向量和期望的向量有多接近，交叉熵（cross entroy）是常用的评判方法之一。交叉熵刻画了两个概率分布之间的距离，是分类问题中使用比较广泛的一种损失函数。 - 给定两个概率分布p和q，通过q来表示p的交叉熵公式为： H(p,q)=−∑p(x)logq(x) ## Reference From 0492b5bf49cb7a5903e3565f6723a5982bac76ea Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Thu, 28 Mar 2019 23:35:33 +0800 Subject: [PATCH 067/386] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index ef101532..f494dd6d 100644 --- a/README.md +++ b/README.md @@ -31,5 +31,5 @@ |上海媒智科技|计算机视觉算法实习生|上海|未知|内推邮箱：chenxi.huang@media-smart.cn|简历格式：姓名+电话号码+全职/实习+CVer推荐|无|CVer公众号| |旷视科技|算法研究员实习生|北京市海淀区|长期有效|内推邮箱：ur@megvii.com|简历命名：姓名+电话+岗位+CVer推荐(**免笔试**)|无|CVer公众号| |淘宝-智能制造事业部|图形图像算法实习生|杭州|未知|内推邮箱：yuanliang.syl@alibaba-inc.com|简历命名格式：姓名+电话号码+应聘岗位+CVer推荐|无|CVer公众号| - +|360安全集团|机器学习算法实习生|北京|未知|[网申](http://chrcmp.chinahr.com/pages/360safe/jobs)|内推码:nAJvUTY2(免简历筛选)|无|qq群| ## [深度学习面试题](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E9%9D%A2%E8%AF%95%E9%A2%98.md) From eb419fe61446973f8d27d6065295e1988e804962 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Sat, 30 Mar 2019 21:47:50 +0800 Subject: [PATCH 068/386] Update README.md --- README.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index f494dd6d..1fdd20a2 100644 --- a/README.md +++ b/README.md @@ -24,7 +24,7 @@ |旷世科技detection组|算法实习生|北京|未知|投递邮箱： zhangzhiqiang@megvii.com|暂无内推|无|我爱计算机视觉公众号| |锐明技术|计算机视觉算法实习生|深圳/重庆|未知|[网申](http://streamax.zhiye.com/Campus)|暂无内推|无|我爱计算机视觉公众号| |作业帮|计算机视觉方向实习生|北京|未知|投递邮箱：wujunbin@zuoyebang.com|暂无内推|无|我爱计算机视觉公众号| -|爱YTUH莫科技|算法实习生|深圳|未知|投递邮箱：hr@mall-ai.com|暂无内推|无|我爱计算机视觉| +|爱莫科技|算法实习生|深圳|未知|投递邮箱：hr@mall-ai.com|暂无内推|无|我爱计算机视觉| |阿里巴巴业务平台事业部|算法-机器学习实习生|杭州|3月12-4月23|[网申](https://campus.alibaba.com/index.htm)|暂无内推|无|知乎| |拼多多|算法实习生|上海/其他|3月15-4月30|[网申](https://www.pinduoduo.com/home/campus/)|暂无内推|无|朋友推荐| |小马智行|计算机视觉实习生|北京市海淀区|未知|内推邮箱：zhijie@pony.ai|简历命名：姓名+岗位+CVer推荐+实习开始时间+可实习月数|无|CVer公众号| @@ -32,4 +32,8 @@ |旷视科技|算法研究员实习生|北京市海淀区|长期有效|内推邮箱：ur@megvii.com|简历命名：姓名+电话+岗位+CVer推荐(**免笔试**)|无|CVer公众号| |淘宝-智能制造事业部|图形图像算法实习生|杭州|未知|内推邮箱：yuanliang.syl@alibaba-inc.com|简历命名格式：姓名+电话号码+应聘岗位+CVer推荐|无|CVer公众号| |360安全集团|机器学习算法实习生|北京|未知|[网申](http://chrcmp.chinahr.com/pages/360safe/jobs)|内推码:nAJvUTY2(免简历筛选)|无|qq群| +|图森未来|感知算法实习生等|北京|未知|内推邮箱:yifyang29@gmail.com|简历命名格式：姓名+电话号码+应聘岗位|无|我爱计算机视觉公众号| +|最右|视频图像算法工程师实习|北京|未知|内推邮箱:sunhaiyong2014@xiaochuankeji.cn|简历命名格式：CVer推荐+姓名+电话+社招/校招/实习|CVer公众号| +|爱奇艺智能平台部-视频分析组视频理解算法实习生|北京中关村|未知|内推邮箱:zhangyuntao@qiyi.com|简历命名格式：CVer推荐+学校+研究方向+实习开始时间+毕业时间|CVer公众号| + ## [深度学习面试题](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E9%9D%A2%E8%AF%95%E9%A2%98.md) From 640731138f7d0b9ebc53cf4e8d06718790ad7999 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Sat, 30 Mar 2019 21:48:20 +0800 Subject: [PATCH 069/386] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 1fdd20a2..28c76e7a 100644 --- a/README.md +++ b/README.md @@ -34,6 +34,6 @@ |360安全集团|机器学习算法实习生|北京|未知|[网申](http://chrcmp.chinahr.com/pages/360safe/jobs)|内推码:nAJvUTY2(免简历筛选)|无|qq群| |图森未来|感知算法实习生等|北京|未知|内推邮箱:yifyang29@gmail.com|简历命名格式：姓名+电话号码+应聘岗位|无|我爱计算机视觉公众号| |最右|视频图像算法工程师实习|北京|未知|内推邮箱:sunhaiyong2014@xiaochuankeji.cn|简历命名格式：CVer推荐+姓名+电话+社招/校招/实习|CVer公众号| -|爱奇艺智能平台部-视频分析组视频理解算法实习生|北京中关村|未知|内推邮箱:zhangyuntao@qiyi.com|简历命名格式：CVer推荐+学校+研究方向+实习开始时间+毕业时间|CVer公众号| +|爱奇艺智能平台部-视频分析组|视频理解算法实习生|北京中关村|未知|内推邮箱:zhangyuntao@qiyi.com|简历命名格式：CVer推荐+学校+研究方向+实习开始时间+毕业时间|CVer公众号| ## [深度学习面试题](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E9%9D%A2%E8%AF%95%E9%A2%98.md) From 5e30ad59883879c8cb5b54c5187cc7643b3a06c3 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Tue, 2 Apr 2019 22:14:10 +0800 Subject: [PATCH 070/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...55\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index b332ceb2..40b17de0 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -209,7 +209,9 @@ H(p,q)=−∑p(x)logq(x) 1. [《Batch Normalization Accelerating Deep Network Training by Reducing Internal Covariate Shift》阅读笔记与实现](https://blog.csdn.net/happynear/article/details/44238541) 2. [深度学习中 Batch Normalization为什么效果好](https://blog.csdn.net/happynear/article/details/44238541) 3. [详解机器学习中的梯度消失、爆炸原因及其解决方法](https://blog.csdn.net/qq_25737169/article/details/78847691) - +## 1*1卷积的主要作用有以下几点： ++ **降维（ dimension reductionality ）**。比如，一张500 * 500且厚度depth为100 的图片在20个filter上做1*1的卷积，那么结果的大小为500*500*20。 ++ **加入非线性**。卷积层之后经过激励层，1*1的卷积在前一层的学习表示上添加了非线性激励（ non-linear activation ），提升网络的表达能力； # 机器学习 ## Focal Loss 介绍一下 From 4a78c241b7ef7c6b73c71d943166425115cc7306 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Mon, 22 Apr 2019 19:17:52 +0800 Subject: [PATCH 071/386] Add files via upload --- ...256\236\344\271\240\351\235\242\347\273\217.md" | 14 ++++++++++++++ 1 file changed, 14 insertions(+) create mode 100644 "\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" new file mode 100644 index 00000000..b31c8baf --- /dev/null +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" @@ -0,0 +1,14 @@ +## 阿里算法工程师(计算机视觉方向) +### 一面(1个小时10分钟)--->简历面 +1. 自我介绍，差不多10分钟。 +2. 简历项目和比赛介绍，中间有问一些项目和比赛细节，问了一些延伸和开放性问题： + + Adam和SGD优化器哪个更好，好在哪里，哪个使模型更加容易发散? + + FPN作用 + + 讲下yolov3的架构，和two-stage的mask-rcnn有什么区别 +3. 代码测试，求n个数里面前k个最大的数。 +我最开始说用快排，面试说还有其他方法吗，我一紧张说了个时间复杂度更大的方法，面试官提醒我可以考虑树排序，但是我没学过，回答不上来，最后面试官说你本科没学过数据结构，那就先算了。 +4. 问了几个机器学习算法，KNN和SVM的细节。 +这里答的不好，太久没用传统机器学习算法，很多东西都忘了，中间一个简单的几何中常见距离计算方式(欧式距离)，我忘了居然答余弦距离。 +5. 问了我有什么想问的。 +#### 一面总结： +面试官人比较友好，自己项目细节一定要熟悉，简历上的东西最好清楚掌握，数据结构和常用算法一定要掌握，这是我的第一个面试经历，不管接下来的面试能否通过，都还是值得纪念和**自省**的。 \ No newline at end of file From 44ba0f68020c1b3239d735aed75ed75e50a0af78 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Sun, 28 Apr 2019 16:10:54 +0800 Subject: [PATCH 072/386] =?UTF-8?q?Update=20=E8=AE=A1=E7=AE=97=E6=9C=BA?= =?UTF-8?q?=E8=A7=86=E8=A7=89=E5=B2=97=E5=AE=9E=E4=B9=A0=E9=9D=A2=E7=BB=8F?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...36\344\271\240\351\235\242\347\273\217.md" | 20 ++++++++++++++++++- 1 file changed, 19 insertions(+), 1 deletion(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" index b31c8baf..3a964e13 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" @@ -11,4 +11,22 @@ 这里答的不好，太久没用传统机器学习算法，很多东西都忘了，中间一个简单的几何中常见距离计算方式(欧式距离)，我忘了居然答余弦距离。 5. 问了我有什么想问的。 #### 一面总结： -面试官人比较友好，自己项目细节一定要熟悉，简历上的东西最好清楚掌握，数据结构和常用算法一定要掌握，这是我的第一个面试经历，不管接下来的面试能否通过，都还是值得纪念和**自省**的。 \ No newline at end of file +面试官人比较友好，自己项目细节一定要熟悉，简历上的东西最好清楚掌握，数据结构和常用算法一定要掌握，这是我的第一个面试经历，不管接下来的面试能否通过，都还是值得纪念和**自省**的。 +## 格林深瞳算法实习生 +###　一面（29分钟）-->基础面/项目面/终面 +1. 自我介绍，差不多３分钟 + + 自我介绍要简介些，我这里自我介绍有点太详细了 +2. 钢筋检测项目介绍和目标检测框架细节 + + 大致介绍自己的工作和项目细节 + + 问了faster rcnn、Mask rcnn的**细节**，faster rcnn的rpn结构介绍下，rpn的loss是什么，master rcnn和faster rcnn有什么区别和改进 + + retinanet的结构和创新点，讲一下ssd和retinanet的区别 +3. 鲸鱼识别项目介绍和图像分类网络细节介绍 + + 大致介绍下鲸鱼识别项目 + + resnet网络的创新，为什么能解决梯度消失问题，残差模块详细介绍下，为什么能解决网络层数加深带来的梯度消失和网络退化问题。 +4. 你有什么想问的 + + 问了去了之后我能做什么 + + 什么时候能出面试结果 + + 面试官给我提出建议：加强论文阅读和基础原理细节掌握、加强原理的表述和表达能力 + ### 面试总结 + 格林深瞳实习生面试只有一面，所以项目和基础都在这一面都问了。感觉自己这次面试没有表现好，不知道是昨晚没睡好的原因，还是自己本身水平和表达能力不够好，有些自己掌握原理的还是没有表达清楚，很多项目细节没有阐述。不过这次面试官问的很多问题，真的给了我很多启发，自己项目虽然做的多，但是在很多理论和基础原理上细节功夫下的不够。 + 其实自己也知道，自己在基础理论和原理方面掌握得不够深，但是由于缺乏时间，我还是没做到自己的目标，希望借这次面试反映出的自己理论缺失点，来提醒和激励自己一定要把基础理论和原理彻底掌握！！！ From e132a6835b17b96c02ff88dc00dc309b07b25b23 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Sun, 28 Apr 2019 16:11:23 +0800 Subject: [PATCH 073/386] =?UTF-8?q?Update=20=E8=AE=A1=E7=AE=97=E6=9C=BA?= =?UTF-8?q?=E8=A7=86=E8=A7=89=E5=B2=97=E5=AE=9E=E4=B9=A0=E9=9D=A2=E7=BB=8F?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...62\227\345\256\236\344\271\240\351\235\242\347\273\217.md" | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" index 3a964e13..6333872e 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" @@ -28,5 +28,5 @@ + 什么时候能出面试结果 + 面试官给我提出建议：加强论文阅读和基础原理细节掌握、加强原理的表述和表达能力 ### 面试总结 - 格林深瞳实习生面试只有一面，所以项目和基础都在这一面都问了。感觉自己这次面试没有表现好，不知道是昨晚没睡好的原因，还是自己本身水平和表达能力不够好，有些自己掌握原理的还是没有表达清楚，很多项目细节没有阐述。不过这次面试官问的很多问题，真的给了我很多启发，自己项目虽然做的多，但是在很多理论和基础原理上细节功夫下的不够。 - 其实自己也知道，自己在基础理论和原理方面掌握得不够深，但是由于缺乏时间，我还是没做到自己的目标，希望借这次面试反映出的自己理论缺失点，来提醒和激励自己一定要把基础理论和原理彻底掌握！！！ +１．格林深瞳实习生面试只有一面，所以项目和基础都在这一面都问了。感觉自己这次面试没有表现好，不知道是昨晚没睡好的原因，还是自己本身水平和表达能力不够好，有些自己掌握原理的还是没有表达清楚，很多项目细节没有阐述。不过这次面试官问的很多问题，真的给了我很多启发，自己项目虽然做的多，但是在很多理论和基础原理上细节功夫下的不够。 +２．其实自己也知道，自己在基础理论和原理方面掌握得不够深，但是由于缺乏时间，我还是没做到自己的目标，希望借这次面试反映出的自己理论缺失点，来提醒和激励自己一定要把基础理论和原理彻底掌握！！！ From 92571ca53af5e654f34560693266e6c4abd27388 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Sun, 28 Apr 2019 16:17:01 +0800 Subject: [PATCH 074/386] =?UTF-8?q?Update=20=E8=AE=A1=E7=AE=97=E6=9C=BA?= =?UTF-8?q?=E8=A7=86=E8=A7=89=E5=B2=97=E5=AE=9E=E4=B9=A0=E9=9D=A2=E7=BB=8F?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...2\227\345\256\236\344\271\240\351\235\242\347\273\217.md" | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" index 6333872e..064ed9dd 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" @@ -13,7 +13,7 @@ #### 一面总结： 面试官人比较友好，自己项目细节一定要熟悉，简历上的东西最好清楚掌握，数据结构和常用算法一定要掌握，这是我的第一个面试经历，不管接下来的面试能否通过，都还是值得纪念和**自省**的。 ## 格林深瞳算法实习生 -###　一面（29分钟）-->基础面/项目面/终面 +### 一面（29分钟）-->基础面/项目面/终面 1. 自我介绍，差不多３分钟 + 自我介绍要简介些，我这里自我介绍有点太详细了 2. 钢筋检测项目介绍和目标检测框架细节 @@ -28,5 +28,6 @@ + 什么时候能出面试结果 + 面试官给我提出建议：加强论文阅读和基础原理细节掌握、加强原理的表述和表达能力 ### 面试总结 -１．格林深瞳实习生面试只有一面，所以项目和基础都在这一面都问了。感觉自己这次面试没有表现好，不知道是昨晚没睡好的原因，还是自己本身水平和表达能力不够好，有些自己掌握原理的还是没有表达清楚，很多项目细节没有阐述。不过这次面试官问的很多问题，真的给了我很多启发，自己项目虽然做的多，但是在很多理论和基础原理上细节功夫下的不够。 +１．格林深瞳实习生面试只有一面，所以项目和基础都在这一面都问了。这次面试官问的很多问题，给了我很多启发，自己项目虽然做的多，但是在很多理论和基础原理上细节功夫下的不够，论文看的不够多！！！ ２．其实自己也知道，自己在基础理论和原理方面掌握得不够深，但是由于缺乏时间，我还是没做到自己的目标，希望借这次面试反映出的自己理论缺失点，来提醒和激励自己一定要把基础理论和原理彻底掌握！！！ +3. 经过阿里的面试，自己回去把更多的项目细节掌握了，这次格林深瞳面试之后要把基础理论和原理掌握，从图像分类网络:**resnet**等，到目标检测和图像分割网络：**faster rcnn**、**mask rcnn**、**ssd**、**yolov3**等彻底掌握基础原理和细节，多看相关论文和博客。 From a1ad91c3bba16f1dd57695a59c922a8e24928a10 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Sun, 28 Apr 2019 16:17:42 +0800 Subject: [PATCH 075/386] =?UTF-8?q?Update=20=E8=AE=A1=E7=AE=97=E6=9C=BA?= =?UTF-8?q?=E8=A7=86=E8=A7=89=E5=B2=97=E5=AE=9E=E4=B9=A0=E9=9D=A2=E7=BB=8F?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" index 064ed9dd..4ff144f1 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" @@ -30,4 +30,4 @@ ### 面试总结 １．格林深瞳实习生面试只有一面，所以项目和基础都在这一面都问了。这次面试官问的很多问题，给了我很多启发，自己项目虽然做的多，但是在很多理论和基础原理上细节功夫下的不够，论文看的不够多！！！ ２．其实自己也知道，自己在基础理论和原理方面掌握得不够深，但是由于缺乏时间，我还是没做到自己的目标，希望借这次面试反映出的自己理论缺失点，来提醒和激励自己一定要把基础理论和原理彻底掌握！！！ -3. 经过阿里的面试，自己回去把更多的项目细节掌握了，这次格林深瞳面试之后要把基础理论和原理掌握，从图像分类网络:**resnet**等，到目标检测和图像分割网络：**faster rcnn**、**mask rcnn**、**ssd**、**yolov3**等彻底掌握基础原理和细节，多看相关论文和博客。 +3. 经过阿里的面试，自己回去把更多的项目细节掌握了，这次格林深瞳面试之后一定要把基础理论和原理掌握，从图像分类网络:**resnet**等，到目标检测和图像分割网络：**faster rcnn**、**mask rcnn**、**ssd**、**yolov3**等彻底掌握基础原理和细节，多看相关论文和博客。 From 7384165b84392380f1f63073f0380af6eb75263d Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Sun, 28 Apr 2019 16:21:23 +0800 Subject: [PATCH 076/386] =?UTF-8?q?Update=20=E8=AE=A1=E7=AE=97=E6=9C=BA?= =?UTF-8?q?=E8=A7=86=E8=A7=89=E5=B2=97=E5=AE=9E=E4=B9=A0=E9=9D=A2=E7=BB=8F?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...62\227\345\256\236\344\271\240\351\235\242\347\273\217.md" | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" index 4ff144f1..5d5a987c 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" @@ -28,6 +28,6 @@ + 什么时候能出面试结果 + 面试官给我提出建议：加强论文阅读和基础原理细节掌握、加强原理的表述和表达能力 ### 面试总结 -１．格林深瞳实习生面试只有一面，所以项目和基础都在这一面都问了。这次面试官问的很多问题，给了我很多启发，自己项目虽然做的多，但是在很多理论和基础原理上细节功夫下的不够，论文看的不够多！！！ -２．其实自己也知道，自己在基础理论和原理方面掌握得不够深，但是由于缺乏时间，我还是没做到自己的目标，希望借这次面试反映出的自己理论缺失点，来提醒和激励自己一定要把基础理论和原理彻底掌握！！！ +１．格林深瞳实习生面试只有一面，所以项目和基础都在这一面都问了。这次面试官问的很多问题，给了我很多启发，自己项目虽然做的多，但是在很多理论和基础原理上细节功夫下的不够，论文看的不够多。 +2. 其实自己也知道，自己在基础理论和原理方面掌握得不够深，但是由于缺乏时间，我还是没做到自己的目标，希望借这次面试反映出的自己理论缺失点，来提醒和激励自己一定要把基础理论和原理彻底掌握。 3. 经过阿里的面试，自己回去把更多的项目细节掌握了，这次格林深瞳面试之后一定要把基础理论和原理掌握，从图像分类网络:**resnet**等，到目标检测和图像分割网络：**faster rcnn**、**mask rcnn**、**ssd**、**yolov3**等彻底掌握基础原理和细节，多看相关论文和博客。 From 38f23e6257842388bf96a5e0f08c379edd98158b Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Sun, 28 Apr 2019 16:21:39 +0800 Subject: [PATCH 077/386] =?UTF-8?q?Update=20=E8=AE=A1=E7=AE=97=E6=9C=BA?= =?UTF-8?q?=E8=A7=86=E8=A7=89=E5=B2=97=E5=AE=9E=E4=B9=A0=E9=9D=A2=E7=BB=8F?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" | 2 ++ 1 file changed, 2 insertions(+) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" index 5d5a987c..e0e4be35 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" @@ -29,5 +29,7 @@ + 面试官给我提出建议：加强论文阅读和基础原理细节掌握、加强原理的表述和表达能力 ### 面试总结 １．格林深瞳实习生面试只有一面，所以项目和基础都在这一面都问了。这次面试官问的很多问题，给了我很多启发，自己项目虽然做的多，但是在很多理论和基础原理上细节功夫下的不够，论文看的不够多。 + 2. 其实自己也知道，自己在基础理论和原理方面掌握得不够深，但是由于缺乏时间，我还是没做到自己的目标，希望借这次面试反映出的自己理论缺失点，来提醒和激励自己一定要把基础理论和原理彻底掌握。 + 3. 经过阿里的面试，自己回去把更多的项目细节掌握了，这次格林深瞳面试之后一定要把基础理论和原理掌握，从图像分类网络:**resnet**等，到目标检测和图像分割网络：**faster rcnn**、**mask rcnn**、**ssd**、**yolov3**等彻底掌握基础原理和细节，多看相关论文和博客。 From c00a0693dd219436d524d7cc22b8d5a7d358d0ef Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Sun, 28 Apr 2019 16:22:02 +0800 Subject: [PATCH 078/386] =?UTF-8?q?Update=20=E8=AE=A1=E7=AE=97=E6=9C=BA?= =?UTF-8?q?=E8=A7=86=E8=A7=89=E5=B2=97=E5=AE=9E=E4=B9=A0=E9=9D=A2=E7=BB=8F?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 更新格林深瞳面经和排版信息 From 2f69558c84d5adeb0bd3b6f4a3a4657f04ebb95f Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Sun, 28 Apr 2019 16:22:37 +0800 Subject: [PATCH 079/386] =?UTF-8?q?Update=20=E8=AE=A1=E7=AE=97=E6=9C=BA?= =?UTF-8?q?=E8=A7=86=E8=A7=89=E5=B2=97=E5=AE=9E=E4=B9=A0=E9=9D=A2=E7=BB=8F?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...62\227\345\256\236\344\271\240\351\235\242\347\273\217.md" | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" index e0e4be35..b7a08f12 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" @@ -30,6 +30,6 @@ ### 面试总结 １．格林深瞳实习生面试只有一面，所以项目和基础都在这一面都问了。这次面试官问的很多问题，给了我很多启发，自己项目虽然做的多，但是在很多理论和基础原理上细节功夫下的不够，论文看的不够多。 -2. 其实自己也知道，自己在基础理论和原理方面掌握得不够深，但是由于缺乏时间，我还是没做到自己的目标，希望借这次面试反映出的自己理论缺失点，来提醒和激励自己一定要把基础理论和原理彻底掌握。 +２．其实自己也知道，自己在基础理论和原理方面掌握得不够深，但是由于缺乏时间，我还是没做到自己的目标，希望借这次面试反映出的自己理论缺失点，来提醒和激励自己一定要把基础理论和原理彻底掌握。 -3. 经过阿里的面试，自己回去把更多的项目细节掌握了，这次格林深瞳面试之后一定要把基础理论和原理掌握，从图像分类网络:**resnet**等，到目标检测和图像分割网络：**faster rcnn**、**mask rcnn**、**ssd**、**yolov3**等彻底掌握基础原理和细节，多看相关论文和博客。 +３．经过阿里的面试，自己回去把更多的项目细节掌握了，这次格林深瞳面试之后一定要把基础理论和原理掌握，从图像分类网络:**resnet**等，到目标检测和图像分割网络：**faster rcnn**、**mask rcnn**、**ssd**、**yolov3**等彻底掌握基础原理和细节，多看相关论文和博客。 From 0ada87c01d9bfd60f9af4535659a31966e71c55e Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Sun, 28 Apr 2019 19:22:15 +0800 Subject: [PATCH 080/386] =?UTF-8?q?Update=20=E8=AE=A1=E7=AE=97=E6=9C=BA?= =?UTF-8?q?=E8=A7=86=E8=A7=89=E5=B2=97=E5=AE=9E=E4=B9=A0=E9=9D=A2=E7=BB=8F?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" index b7a08f12..7d107b40 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" @@ -13,7 +13,7 @@ #### 一面总结： 面试官人比较友好，自己项目细节一定要熟悉，简历上的东西最好清楚掌握，数据结构和常用算法一定要掌握，这是我的第一个面试经历，不管接下来的面试能否通过，都还是值得纪念和**自省**的。 ## 格林深瞳算法实习生 -### 一面（29分钟）-->基础面/项目面/终面 +### 一面（29分钟）-->基础面/项目面/终面(4月28日晚更新，已挂) 1. 自我介绍，差不多３分钟 + 自我介绍要简介些，我这里自我介绍有点太详细了 2. 钢筋检测项目介绍和目标检测框架细节 From cc53503ed63459a472ccb31c94e92aef064ec47a Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Sun, 28 Apr 2019 21:15:21 +0800 Subject: [PATCH 081/386] =?UTF-8?q?Update=20=E8=AE=A1=E7=AE=97=E6=9C=BA?= =?UTF-8?q?=E8=A7=86=E8=A7=89=E5=B2=97=E5=AE=9E=E4=B9=A0=E9=9D=A2=E7=BB=8F?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" index 7d107b40..466462e5 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" @@ -12,7 +12,7 @@ 5. 问了我有什么想问的。 #### 一面总结： 面试官人比较友好，自己项目细节一定要熟悉，简历上的东西最好清楚掌握，数据结构和常用算法一定要掌握，这是我的第一个面试经历，不管接下来的面试能否通过，都还是值得纪念和**自省**的。 -## 格林深瞳算法实习生 +## 格灵深瞳算法实习生 ### 一面（29分钟）-->基础面/项目面/终面(4月28日晚更新，已挂) 1. 自我介绍，差不多３分钟 + 自我介绍要简介些，我这里自我介绍有点太详细了 From 1bacd6f01611e02fc8bc4ddcaed3ffa7139084f5 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Sat, 4 May 2019 16:40:50 +0800 Subject: [PATCH 082/386] Add files via upload MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 图解算法学习笔记资料 --- ...46\344\271\240\347\254\224\350\256\260.md" | 444 ++++++++++++++++++ 1 file changed, 444 insertions(+) create mode 100644 "\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" diff --git "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" new file mode 100644 index 00000000..6bf33cae --- /dev/null +++ "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" @@ -0,0 +1,444 @@ +# 第一章，算法简介 +## 1.2，二分法查找元素 +一般而言，对于包含n个元素的列表查找某个元素，使用二分法最多需要$log_{2}n$步(**时间复杂度为$log_{2}n$**)，简单查找最多需要n步。大O表示法指出了算法最糟糕情况下的运行时间 +# 第二章，选择排序 +## 2.1，内存工作原理 +在计算机中，存储多项数据时，有两种基本方式－数组和链表。但它们并非适用于所有情形。 +### 2.2.1，链表 +链表中的元素可存储在内存的任何地方。 +链表的每个元素都存储了下一个元素的地址，从而使一系列随机的内存地址串在一起。链表结构直观显示如下图所示： +![61769620.png](数据结构与算法学习笔记_files/61769620.png) +链表的优势在插入元素方面，那数组的优势又是什么呢？ +### 2.2.2，数组 +需要随机地读取元素时，数组的效率很高，因为可迅速找到数组的任何元素。**在链表中，元素并非靠在一起的**，你无法迅速计算出第五个元素的内存 + +地址，而必须先访问第一个元素以获取第二个元素的地址，再访问第二个元素以获取第三个元素 + +的地址，以此类推，直到访问第五个元素。 +### 2.2.3，术语 +数组的元素带编号，编号从**0**而不是１开始，几乎所有的编程语言都从０开始对数组元素进行编号，比如C/C++的数组结构和Python的列表结构。 +元素的位置称为索引。下面是常见数组和链表操作的运行时间. +## 2.3，选择排序 +**选择排序时间复杂度$O(n^{2})$** +```python +def findSmallest(arr): + smallest = arr[0] # 存储最小的值 + smallest_index = 0 # 存储最小元素的索引 + for i in range(1, len(arr)): + if arr[i] < smallest: + smallest_index = i + smallest = arr[i] + return smallest +# 选择排序法对数组进行排序 +def selectionSort(arr): + newArr = [] + for i in range(len(arr)): + smallest = findSmallest(arr) + arr.remove(smallest) + newArr.append(smallest) + return newArr +# 实例应用 +print(selectionSort([5, 3, 6, 100])) # [3, 5, 6, 100] +``` +## 2.4，小结 ++ 计算机内存犹如一大堆抽屉。 + + ++ 需要存储多个元素时，可使用数组或链表。 + + ++ 数组的元素都在一起。 + + ++ 链表的元素是分开的，其中每个元素都存储了下一个元素的地址。 + + ++ 数组的读取速度很快。 + + ++ 链表的插入和删除速度很快。 + + ++ 在同一个数组中，所有元素的类型都必须相同（都为int、 double等）。 +# 第三章，递归 +学习如何将问题分成基线条件和递归条件，学习如何使用递归算法，递归算法直观上更好理解，步骤简单。 +## 3.2，基线条件和递归条件 +编写递归函数时，必须告诉它何时停止，因此，每个递归函数有两个部分：**基线条件(base case)和递归条件(recursive case)**。递归条件指的是函数调用自己，而基线条件则 + +指的是函数不再调用自己，从而避免形成无限循环。 +## 3.3，栈 +栈的定义：栈是一种只能从表的一端存取数据且遵循 "先进后出" 原则的线性存储结构。 +调用栈(call stack) +### 3.3.1，调用栈 +计算机在内部使用被称为调用栈的栈。**调用另一个函数时，当前函数暂停 + +并处于未完成状态**。该函数的所有变量的值都还在内存中。栈顶的方框指出了当前执行 + +到了什么地方。 +### 3.3.2，递归调用栈 +栈在递归中扮演着重要角色。使用栈虽然很方便，但是也要付出代价：存储详尽的信息可能占用大量的内存。每个函数调 + +用都要占用一定的内存，如果栈很高，就意味着计算机存储了大量函数调用的信息。在这种情况 下，你有两种选择。 ++ 重新编写代码 ++ 使用尾递归 +## 3.4，小结 ++ 递归值的是调用自己的函数 ++ 每个递归函数都有两个条件：基线条件和递归条件 ++ 栈有两种操作：压如和弹出 ++ 所有函数调用都进入调用栈 ++ 调用栈可能很长，这将占用大量内存 +# 第四章，快速排序 +快速排序使用分而治之的策略，分而治之是我们学习的第一种通用的问题解决办法。 +**分而治之**(divide and conquer，D&C)-一种著名的递归式问题解决办法。 +## 4.1，分而治之 +D&C算法是递归的。使用D&C解决问题的过程包括两个步骤： + ++ 找出基线条件，这种条件必须尽可能简单。 + + + ++ 不断将问题分解（或者说缩小规模），直到符合基线条件。 +D&C并非可直接用于解决问题的算法，而是一种解决问题的思路。 +## 4.2 快速排序 +C语言标准库中的函数qsort实现的就是快速排序。快速排序也是用了D&C思想。 +对数组进行快速排序，步骤如下： +1. 随机选择一个基准值； +2. 将数组分成两个子数组：小于基准值的元素和大于基准值额元素； +3. 对这两个子数组进行排序。 + +**在平均情况下，快速排序时间复杂度$O(nlogn)$** +快速排序代码如下： +```python +def quicksort(array): + if len(array) < 2: + # 基线条件：为空或只包含一个元素的数组是“有序”的 + return array + else: + # 递归条件 + pivot = array[0] + less = [x for x in array[1:] if x <= pivot] + greater = [x for x in array[1:] if x > pivot] + return quicksort(less) + [pivot] + quicksort(greater) +print(quicksort([4, 90, 0, 2, 17, 79, 12])) # [0, 2, 4, 12, 17, 79, 90] +``` +## 4.3 再谈大Ｏ表示法 +快速排序的独特之处在于，其速度取决于选择的基准值。在讨论快速排序的运行时间前，我 + +们再来看看最常见的大O运行时间。 +![82090059.png](数据结构与算法学习笔记_files/82090059.png) +选择排序，其运行时间为O(n2)，速度非常慢。 + + + +还有一种名为合并排序（ merge sort） 的排序算法，其运行时间为O(n log n)，比选择排序快得多！快速排序的情况比较棘手，在最糟情况下，其运行时间为O(n2)。与选择排序一样慢！但这是最糟情况。**在平均情况下，快速排序的运行时间为O(n log n)。** +由对数的换底公式，loga n和 logb n只有一个常数因子不同，这个因子在大O记法中被丢弃。因此记作O(log n)，**而不论对数的底是多少，是对数时间算法的标准记法**。 +## 4.4，小结 ++ D&C将问题逐步分解。使用D&C处理列表时，基线条件很可能是空数组或只包含一个元 + +素的数组。 + + ++ 实现快速排序时，请随机地选择用作基准值的元素。**快速排序的平均运行时间为O(n log n)**。 + + ++ 大O表示法中的常量有时候事关重大，这就是快速排序比合并排序快的原因所在。 + + ++ 比较简单查找和二分查找时，常量几乎无关紧要，因为列表很长时， O(log n)的速度比O(n) + +快得多。 + +# 第五章，散列表 +数组和链表结构可以用以查找，栈不行。 +散列表也叫哈希表(Hash table)，散列表有些类似Python中的字典dict结构。 +散列表可用以： ++ 模拟映射关系； ++ 防止重复； ++ 缓冲/记住数据，以免服务器再通过处理来生成它们。 +## 5.3，冲突 +给两个键分配的位置相同，这被称为冲突（collision）。处理冲突最简单的办法就是：如果两个键映射到了同一个位置，就在这个位置存储一个链表。 +## 5.4，性能 +散列表，数组，链表的查找、插入、删除元素的时间复杂度，如下表所示： +![60409970.png](数据结构与算法学习笔记_files/60409970.png) +在平均情况下，散列表的查找（获取给定索引处的值）速度与数组一样快，而插入和删除速 + +度与链表一样快，因此它兼具两者的优点！但在最糟情况下，散列表的各种操作的速度都很慢。 + +因此，在使用散列表时，避开最糟情况至关重要。为此，需要避免冲突。而要避免冲突，需要有： ++ 较低的填装因子； ++ 良好的散列函数。 +## 5.5，小结 +散列表是一种功能强大的数据结构，其操作速度快，还能让你以不同的方式建立数据模型。 + + + +你可能很快会发现自己经常在使用它。 + + ++ 你可以结合散列函数和数组来创建散列表。 + + ++ 冲突很糟糕，你应使用可以最大限度减少冲突的散列函数。 + + ++ 散列表的查找、插入和删除速度都非常快。 + + ++ 散列表适合用于模拟映射关系。 + + ++ 一旦填装因子超过0.7，就该调整散列表的长度。 + + ++ 散列表可用于缓存数据（例如，在Web服务器上）。 + + ++ 散列表非常适合用于防止重复。 + +# 第六章，广度优先搜索 +图算法：广度优先搜索（breadth-first search, **BFS**）算法 +广度优先搜索让你能够找出两样东西之间的最短距离，不过最短距离的含义有很多！使用广度优先搜索可以： + + ++ 编写国际跳棋AI，计算最少走多少步就可获胜； + + ++ 编写拼写检查器，计算最少编辑多少个地方就可将错拼的单词改成正确的单词，如将 + +READED改为READER需要编辑一个地方； + + ++ 根据你的人际关系网络找到关系最近的医生。 + +解决最短路径问题的算法被称为**广度有限算法**，一般步骤为： +1. 使用图来建立问题模型。 +2. 使用广度优先搜索解决问题。 +## 6.1，图是什么 +图由节点（node）和边（edge）组成。 +## 6.2，广度优先搜索 +在广度优先搜索的执行过程中，搜索范围从起点开始逐渐向外延伸，即先检查一度关系，再检查二度关系。 +## 6.3，队列 +栈：后进先出（Last In First Out，LIFO）的数据结构 +队列：先进先出（First In First Out，FIFO）的数据结构，支持入队和出对操作。 +## 6.4，代码实现图结构 +图中每个节点都与相邻节点相连，**散列表**结构可以表示这种关系。 +图分为有向图（directed graph）和无向图（undirected graph），有向图关系是单向的，无向图没有箭头，直接相连的节点互为邻居。对从自己出发有指向他人的箭头，则有邻居。 +## 6.4.1 运行时间 +如果你在你的整个人际关系网中搜索芒果销售商，就意味着你将沿每条边前行（记住，边是从一个人到另一个人的箭头或连接），因此运行时间至少为O(边数)。你还使用了一个队列，其中包含要检查的每个人。将一个人添加到队列需要的时间是固定的，即为O(1)，因此对每个人都这样做需要的总时间为O(人数)。所以，广度优先搜索的运行时间为O(人数 + 边数)，这通常写作O(V + E)，其中V为顶点（ vertice）数， E为边数。 +# 第七章，迪克斯特拉算法 +## 7.1，使用迪克斯特拉算法 +迪克斯特拉算法能够找出加权图中前往X的最短路径。对于寻找耗时最少的路径，迪克斯特拉算法包含４个步骤： +(1) 找出“最便宜”的节点，即可在最短时间内到达的节点。 + + + +(2) 更新该节点的邻居的开销，其含义将稍后介绍。 + + + +(3) 重复这个过程，直到对图中的每个节点都这样做了。 + + + +(4) 计算最终路径。 +每个节点都有开销。开销指的是从起点前往该节点需要多长时间。 +## 7.2，术语 ++ 带权重的图称为加权图（ weighted graph），不带权重的图称为非加权图（ unweighted graph）。 ++ 要计算非加权图中的最短路径，可使用广度优先搜索。要计算加权图中的最短路径，可使用狄克斯特拉算法。 ++ 在无向图中，每条边都是一个环。狄克斯特拉算法只适用于有向无环图（ directed acyclic + +graph， DAG）。 + +图可能有环，所谓环，是指由一个节点出发，走一圈后可以又回到原节点，如下图所示： +![77626056.png](数据结构与算法学习笔记_files/77626056.png) +## 7.3，负权边 +因此， **不能将狄克斯特拉算法用于包含负权边的图**。在包含负权边的图中，要找出最短路径，可使用另一种算法——贝尔曼福德算法（ Bellman-Ford + +algorithm）。 +## 7.4，编程实现狄克斯特拉算法 +以下图为例，编程实现耗时最短的路径。 +![79746550.png](数据结构与算法学习笔记_files/79746550.png) +代码如下： +```python +# 为了实现带权图，可使用散列表，散列表用Python字典实现 +graph = {} +# 存储起始节点邻居和前往邻居的开销 +graph['start'] = {} +graph["start"]["a"] = 6 +graph["start"]["b"] = 2 +print(graph["start"].keys()) +# 添加其他节点及其邻居 +graph["a"] = {} +graph["a"]["fin"] = 1 + +graph["b"] = {} +graph["b"]["a"] = 3 +graph["b"]["fin"] = 5 + +# 终点没有任何邻居 +graph['fin'] = {} + +# 创建存储每个节点开销的开销表 +infinity = float("inf") +costs = {} +costs["a"] = 6 +costs["b"] = 2 +costs["fin"] = infinity + +# 创建存储父节点的散列表 +parents = {} +parents["a"] = "start" +parents["b"] = "start" +parents["fin"] = None +# 创建一个数组，用于记录处理过的节点 +processed = [] + +# 找出开销最低的节点 +def find_lowest_cost_node(costs): + lowest_cost = float("inf") + lowest_cost_node = None + for node in costs: + cost = costs[node] + if cost < lowest_cost and node not in processed: + lowest_cost = cost + lowest_cost_node = node + return lowest_cost_node + +# 在未处理的节点中找出开销最小的节点 +node = find_lowest_cost_node(costs) +while node is not None: + cost = costs[node] + neighbors = graph[node] + # 遍历当前节点的邻居 + for n in neighbors.keys(): + new_cost = cost + neighbors[n] + # 如果当前节点前往该邻居更近，就更新该邻居的开销, 同时将该邻居的父节点设置为当前节点 + if costs[n] > new_cost: + costs[n] = new_cost + parents[n] = node + # 将当前节点标记为处理过 + processed.append(node) + # 找出接下来要处理的节点，并循环 + node = find_lowest_cost_node(costs) + +print("Cost from the start to each node:") +print(costs) +``` +## 7.5，小结 ++ 广度优先搜索用于在非加权图中查找最短路径。 + + ++ 狄克斯特拉算法用于在加权图中查找最短路径。 + + ++ 仅当权重为正时狄克斯特拉算法才管用。 + + ++ 如果图中包含负权边，请使用贝尔曼福德算法。 +# 第八章，贪婪(贪心)算法 +贪婪算法思想很简单：每步都采取最优的做法，专业术语说，就是每步都选择局部最优解，最终得到的就是全局最优解。 +## 8.1，教室调度问题 +根据给定课表，尽可能将更多的课程安排在某间教室。 +解决办法：贪婪算法可找到最优解。 +## 8.2，背包问题 +背包重量有限，根据策略使得装入背包的物品价值最高。 +在这里， 贪婪策略显然不能获得最优解，但非常接近。在有些情况下，完美是优秀的敌人。有时候，你只需找到一个能够大致解决问题的算法，此时贪婪算法正好可派上用场，因为它们实现起来很容易，得到的结果又与正确结果相当接近。 +## 8.3，集合覆盖问题 +每个广播台都覆盖特定区域的州，找出覆盖全美50个州的最小广播集合。 +贪婪算法解决这个问题，当广播台数量过多，算法所耗费的时间将激增。 +## 8.3.1，近似算法 +贪婪算法可化解危机！使用下面的贪婪算法可得到非常接近的解。 + + + +(1) 选出这样一个广播台，即它**覆盖了最多的未覆盖州**。即便这个广播台覆盖了一些已覆盖的州，也没有关系。 + + + +(2) 重复第一步，直到覆盖了所有的州。 + + + +这是一种近似算法（ approximation algorithm） 。在获得精确解需要的时间太长时，可使用近似算法。判断近似算法优劣的标准如下： + + ++ 速度有多快； + + ++ 得到的近似解与最优解的接近程度。 +代码实例： +```python +""" +准备工作 +""" +# 创建一个列表，包含要覆盖的州 +states_needed = set(["mt", "wa", "or", "id", "nv", "ut", "ca", "az"]) +# 广播台清单 +stations = {} +stations["kone"] = set(["id", "nv", "ut"]) +stations["ktwo"] = set(["wa", "id", "mt"]) +stations["kthree"] = set(["or", "nv", "ca"]) +stations["kfour"] = set(["nv", "ut"]) +stations["kfive"] = set(["ca", "az"]) +# 定义一个集合存储最终选择的广播台 +final_stations = set() +""" +计算答案 +""" +best_station = None +while states_needed: + best_station = None + states_covered = set() + for station, states in stations.items(): + covered = states_needed & states + if len(covered) > len(states_covered): + best_station = station + states_covered = covered + states_needed -= states_covered + final_stations.add(best_station) +print(final_stations) +``` +程序输出如下： +> {'kone', 'ktwo', 'kthree', 'kfive'} + +贪心算法的实质是每次选出当前的最优解，不管整体，是基于一定假设下的最优解。 +## 8.4，NP完全问题 +旅行商问题和集合覆盖问题有一些共同之处：**你需要计算所有的解，并从中选出最小/最短的那个**。这两个问题都属于NP完全问题。NP完全问题的简单定义是，以难解著称的问题，如**旅行商问题和集合覆盖**问题。很多非常聪明的人都认为，根本不可能编写出可快速解决这些问题的算法。 +## 8.4.1，如何识别NP完全问题 +NP完全问题无处不在！如果能够判断出要解决的问题属于NP完全问题就好了，这样就不用 去寻找完美的解决方案，而是使用近似算法即可。但要判断问题是不是NP完全问题很难，易于解决的问题和NP完全问题的差别通常很小。 +但如果要找出经由指定几个点的的最短路径，就是旅行商问题——NP完全问题。简言之，没办法判断问题是不是NP完全问题，但还是有一些蛛丝马迹可循的。 + + ++ 元素较少时算法的运行速度非常快，但随着元素数量的增加，速度会变得非常慢。 + + ++ 涉及“所有组合”的问题通常是NP完全问题。 + + ++ 不能将问题分成小问题，必须考虑各种可能的情况。这可能是NP完全问题。 + + ++ 如果问题涉及序列（如旅行商问题中的城市序列）且难以解决，它可能就是NP完全问题。 + + ++ 如果问题涉及集合（如广播台集合）且难以解决，它可能就是NP完全问题。 + + ++ 如果问题可转换为集合覆盖问题或旅行商问题，那它肯定是NP完全问题。 +# 8.5，小结 ++ 贪婪算法寻找局部最优解，企图以这种方式获得全局最优解。 + + ++ 对于NP完全问题，还没有找到快速解决方案。 + + ++ 面临NP完全问题时，最佳的做法是使用近似算法。 + + ++ 贪婪算法易于实现、运行速度快，是不错的近似算法。 +# 第九章，动态规划 +学习动态规划，这是一种解决棘手问题的方法，**它将问题分成小问题**，并先着手解决这些小问题。 +工作原理：动态规划先解决子问题，再逐步解决大问题。 +# 第十章，K最近邻算法 + +# 第十一章　接下来如何做 \ No newline at end of file From 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zAONWcVutmMjDoQ+!Qqjx8DuBvNiZP7j1gfTaissxZa_^s+S&^?`dMtaVw!1|TvNgO zBVp8>>#{0vt2ljFt=GZ4PdOCCqvH0p8?Nb{)%WxBBaRelEG*Q=V{E6VQQ!$e;NY;= zsbYf~dQs?Xh;L=wZ|N1#M$LdpGBzDB_{P~0hx*YKsiKGJUs;H=lEP5+AP-iHGAp|$o#)|pQeJJ+C6F_vf_7?E9F@~$_O730B zO5LnDIr`h*OZPe3RqF9{;}V0;y+(055rhg2F!H~z~VTH zscQl@LAnW;JIpKlydSD*pOqEf&8}mPD}??NtBXV}`f(Bz{D&ZoCY?uWf1OfIUi|4p z?kUw}B?W64vj{^WbP&FbK(JF#b|W<{5Pb1n_Z|UYUKko1H)Is7|5>ii$L_|XpV2}= zW#BlUBAGJ+NE_8}at=eX1U&tDy#CN3)o`_xe*MUroq;`bkFv5teLQzbE!*n_GcDFbcW;g}AN&j|4tf+izNmMAO&2pD17?)JFD;2yp^qdM}~KH%#+AKmWRS^KZYk>T#%RCNd5=^crR?k}>8SCg}W$tPHv@^HL1 zh!@LQRW3(dr;mM8y~)4rZa`}YDMe*PmBM3Eumr%-;vcF48mLhCISB9tTY>4cW1G>< z_Eom~to*C@IH^kc@(oSZ0#=?-`{ziBsCBF0rOJIz^-J<^4+={nj37{y5B&=bguUkvwA3eD8=4URr%)} zz+p57@t<2w9(_8Yym+1Jy49!ycw=_?9krVNtNM8>Pkrj2>PnS~O%4%ndJw0Yyw&&Q h^!Li53sFCXLW>6Z2a@@h;3<79`r0O11)BTM{SQ~7>~a7A literal 0 HcmV?d00001 From c67d1a40b19cab859e4c2eb21c27df24c789ae6a Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Sat, 4 May 2019 16:57:36 +0800 Subject: [PATCH 084/386] =?UTF-8?q?Update=20=E6=95=B0=E6=8D=AE=E7=BB=93?= =?UTF-8?q?=E6=9E=84=E4=B8=8E=E7=AE=97=E6=B3=95=E5=AD=A6=E4=B9=A0=E7=AC=94?= =?UTF-8?q?=E8=AE=B0.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...46\344\271\240\347\254\224\350\256\260.md" | 240 +++++------------- 1 file changed, 61 insertions(+), 179 deletions(-) diff --git "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" index 6bf33cae..95bebc04 100644 --- "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" +++ "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" @@ -7,14 +7,11 @@ ### 2.2.1，链表 链表中的元素可存储在内存的任何地方。 链表的每个元素都存储了下一个元素的地址，从而使一系列随机的内存地址串在一起。链表结构直观显示如下图所示： -![61769620.png](数据结构与算法学习笔记_files/61769620.png) +![2.1 链表示意图] +(https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/2.1%E9%93%BE%E8%A1%A8.png) 链表的优势在插入元素方面，那数组的优势又是什么呢？ ### 2.2.2，数组 -需要随机地读取元素时，数组的效率很高，因为可迅速找到数组的任何元素。**在链表中，元素并非靠在一起的**，你无法迅速计算出第五个元素的内存 - -地址，而必须先访问第一个元素以获取第二个元素的地址，再访问第二个元素以获取第三个元素 - -的地址，以此类推，直到访问第五个元素。 +需要随机地读取元素时，数组的效率很高，因为可迅速找到数组的任何元素。**在链表中，元素并非靠在一起的**，你无法迅速计算出第五个元素的内存 地址，而必须先访问第一个元素以获取第二个元素的地址，再访问第二个元素以获取第三个元素 的地址，以此类推，直到访问第五个元素。 ### 2.2.3，术语 数组的元素带编号，编号从**0**而不是１开始，几乎所有的编程语言都从０开始对数组元素进行编号，比如C/C++的数组结构和Python的列表结构。 元素的位置称为索引。下面是常见数组和链表操作的运行时间. @@ -41,44 +38,24 @@ def selectionSort(arr): print(selectionSort([5, 3, 6, 100])) # [3, 5, 6, 100] ``` ## 2.4，小结 -+ 计算机内存犹如一大堆抽屉。 - - -+ 需要存储多个元素时，可使用数组或链表。 - - -+ 数组的元素都在一起。 - - -+ 链表的元素是分开的，其中每个元素都存储了下一个元素的地址。 - - -+ 数组的读取速度很快。 - - -+ 链表的插入和删除速度很快。 - - ++ 计算机内存犹如一大堆抽屉。 ++ 需要存储多个元素时，可使用数组或链表。 ++ 数组的元素都在一起。 ++ 链表的元素是分开的，其中每个元素都存储了下一个元素的地址。 ++ 数组的读取速度很快。 ++ 链表的插入和删除速度很快。 + 在同一个数组中，所有元素的类型都必须相同（都为int、 double等）。 # 第三章，递归 学习如何将问题分成基线条件和递归条件，学习如何使用递归算法，递归算法直观上更好理解，步骤简单。 ## 3.2，基线条件和递归条件 -编写递归函数时，必须告诉它何时停止，因此，每个递归函数有两个部分：**基线条件(base case)和递归条件(recursive case)**。递归条件指的是函数调用自己，而基线条件则 - -指的是函数不再调用自己，从而避免形成无限循环。 +编写递归函数时，必须告诉它何时停止，因此，每个递归函数有两个部分：**基线条件(base case)和递归条件(recursive case)**。递归条件指的是函数调用自己，而基线条件则 指的是函数不再调用自己，从而避免形成无限循环。 ## 3.3，栈 栈的定义：栈是一种只能从表的一端存取数据且遵循 "先进后出" 原则的线性存储结构。 调用栈(call stack) ### 3.3.1，调用栈 -计算机在内部使用被称为调用栈的栈。**调用另一个函数时，当前函数暂停 - -并处于未完成状态**。该函数的所有变量的值都还在内存中。栈顶的方框指出了当前执行 - -到了什么地方。 +计算机在内部使用被称为调用栈的栈。**调用另一个函数时，当前函数暂停 并处于未完成状态**。该函数的所有变量的值都还在内存中。栈顶的方框指出了当前执行 到了什么地方。 ### 3.3.2，递归调用栈 -栈在递归中扮演着重要角色。使用栈虽然很方便，但是也要付出代价：存储详尽的信息可能占用大量的内存。每个函数调 - -用都要占用一定的内存，如果栈很高，就意味着计算机存储了大量函数调用的信息。在这种情况 下，你有两种选择。 +栈在递归中扮演着重要角色。使用栈虽然很方便，但是也要付出代价：存储详尽的信息可能占用大量的内存。每个函数调 用都要占用一定的内存，如果栈很高，就意味着计算机存储了大量函数调用的信息。在这种情况 下，你有两种选择。 + 重新编写代码 + 使用尾递归 ## 3.4，小结 @@ -92,11 +69,7 @@ print(selectionSort([5, 3, 6, 100])) # [3, 5, 6, 100] **分而治之**(divide and conquer，D&C)-一种著名的递归式问题解决办法。 ## 4.1，分而治之 D&C算法是递归的。使用D&C解决问题的过程包括两个步骤： - -+ 找出基线条件，这种条件必须尽可能简单。 - - - ++ 找出基线条件，这种条件必须尽可能简单。 + 不断将问题分解（或者说缩小规模），直到符合基线条件。 D&C并非可直接用于解决问题的算法，而是一种解决问题的思路。 ## 4.2 快速排序 @@ -122,31 +95,18 @@ def quicksort(array): print(quicksort([4, 90, 0, 2, 17, 79, 12])) # [0, 2, 4, 12, 17, 79, 90] ``` ## 4.3 再谈大Ｏ表示法 -快速排序的独特之处在于，其速度取决于选择的基准值。在讨论快速排序的运行时间前，我 - -们再来看看最常见的大O运行时间。 -![82090059.png](数据结构与算法学习笔记_files/82090059.png) -选择排序，其运行时间为O(n2)，速度非常慢。 - - - +快速排序的独特之处在于，其速度取决于选择的基准值。在讨论快速排序的运行时间前，我 们再来看看最常见的大O运行时间。 +![4.3 再谈大O表示法] +(https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/4.3%E5%86%8D%E8%B0%88%E5%A4%A7O%E8%A1%A8%E7%A4%BA%E6%B3%95.png) +选择排序，其运行时间为O(n2)，速度非常慢。 还有一种名为合并排序（ merge sort） 的排序算法，其运行时间为O(n log n)，比选择排序快得多！快速排序的情况比较棘手，在最糟情况下，其运行时间为O(n2)。与选择排序一样慢！但这是最糟情况。**在平均情况下，快速排序的运行时间为O(n log n)。** 由对数的换底公式，loga n和 logb n只有一个常数因子不同，这个因子在大O记法中被丢弃。因此记作O(log n)，**而不论对数的底是多少，是对数时间算法的标准记法**。 -## 4.4，小结 -+ D&C将问题逐步分解。使用D&C处理列表时，基线条件很可能是空数组或只包含一个元 - -素的数组。 - -+ 实现快速排序时，请随机地选择用作基准值的元素。**快速排序的平均运行时间为O(n log n)**。 - - -+ 大O表示法中的常量有时候事关重大，这就是快速排序比合并排序快的原因所在。 - - -+ 比较简单查找和二分查找时，常量几乎无关紧要，因为列表很长时， O(log n)的速度比O(n) - -快得多。 +## 4.4，小结 ++ D&C将问题逐步分解。使用D&C处理列表时，基线条件很可能是空数组或只包含一个元 素的数组。 ++ 实现快速排序时，请随机地选择用作基准值的元素。**快速排序的平均运行时间为O(n log n)**。 ++ 大O表示法中的常量有时候事关重大，这就是快速排序比合并排序快的原因所在。 ++ 比较简单查找和二分查找时，常量几乎无关紧要，因为列表很长时， O(log n)的速度比O(n) 快得多。 # 第五章，散列表 数组和链表结构可以用以查找，栈不行。 @@ -159,55 +119,27 @@ print(quicksort([4, 90, 0, 2, 17, 79, 12])) # [0, 2, 4, 12, 17, 79, 90] 给两个键分配的位置相同，这被称为冲突（collision）。处理冲突最简单的办法就是：如果两个键映射到了同一个位置，就在这个位置存储一个链表。 ## 5.4，性能 散列表，数组，链表的查找、插入、删除元素的时间复杂度，如下表所示： -![60409970.png](数据结构与算法学习笔记_files/60409970.png) -在平均情况下，散列表的查找（获取给定索引处的值）速度与数组一样快，而插入和删除速 - -度与链表一样快，因此它兼具两者的优点！但在最糟情况下，散列表的各种操作的速度都很慢。 - -因此，在使用散列表时，避开最糟情况至关重要。为此，需要避免冲突。而要避免冲突，需要有： +![散列表查找、插入删除元素操作时间复杂度分析] +(https://github.com/HarleysZhang/201数据结构与算法学习笔记_files/60409970.png9_algorithm_intern_information/blob/master/images/5.4%E6%80%A7%E8%83%BD.png) +在平均情况下，散列表的查找（获取给定索引处的值）速度与数组一样快，而插入和删除速 度与链表一样快，因此它兼具两者的优点！但在最糟情况下，散列表的各种操作的速度都很慢。 因此，在使用散列表时，避开最糟情况至关重要。为此，需要避免冲突。而要避免冲突，需要有： + 较低的填装因子； + 良好的散列函数。 ## 5.5，小结 -散列表是一种功能强大的数据结构，其操作速度快，还能让你以不同的方式建立数据模型。 - - - -你可能很快会发现自己经常在使用它。 - - -+ 你可以结合散列函数和数组来创建散列表。 - - -+ 冲突很糟糕，你应使用可以最大限度减少冲突的散列函数。 - - -+ 散列表的查找、插入和删除速度都非常快。 - - -+ 散列表适合用于模拟映射关系。 - - -+ 一旦填装因子超过0.7，就该调整散列表的长度。 - - -+ 散列表可用于缓存数据（例如，在Web服务器上）。 - - +散列表是一种功能强大的数据结构，其操作速度快，还能让你以不同的方式建立数据模型。 +你可能很快会发现自己经常在使用它。 ++ 你可以结合散列函数和数组来创建散列表。 ++ 冲突很糟糕，你应使用可以最大限度减少冲突的散列函数。 ++ 散列表的查找、插入和删除速度都非常快。 ++ 散列表适合用于模拟映射关系。 ++ 一旦填装因子超过0.7，就该调整散列表的长度。 ++ 散列表可用于缓存数据（例如，在Web服务器上）。 + 散列表非常适合用于防止重复。 # 第六章，广度优先搜索 图算法：广度优先搜索（breadth-first search, **BFS**）算法 -广度优先搜索让你能够找出两样东西之间的最短距离，不过最短距离的含义有很多！使用广度优先搜索可以： - - -+ 编写国际跳棋AI，计算最少走多少步就可获胜； - - -+ 编写拼写检查器，计算最少编辑多少个地方就可将错拼的单词改成正确的单词，如将 - -READED改为READER需要编辑一个地方； - - +广度优先搜索让你能够找出两样东西之间的最短距离，不过最短距离的含义有很多！使用广度优先搜索可以： ++ 编写国际跳棋AI，计算最少走多少步就可获胜； ++ 编写拼写检查器，计算最少编辑多少个地方就可将错拼的单词改成正确的单词，如将 READED改为READER需要编辑一个地方； + 根据你的人际关系网络找到关系最近的医生。 解决最短路径问题的算法被称为**广度有限算法**，一般步骤为： @@ -228,36 +160,23 @@ READED改为READER需要编辑一个地方； # 第七章，迪克斯特拉算法 ## 7.1，使用迪克斯特拉算法 迪克斯特拉算法能够找出加权图中前往X的最短路径。对于寻找耗时最少的路径，迪克斯特拉算法包含４个步骤： -(1) 找出“最便宜”的节点，即可在最短时间内到达的节点。 - - - -(2) 更新该节点的邻居的开销，其含义将稍后介绍。 - - - -(3) 重复这个过程，直到对图中的每个节点都这样做了。 - - - +(1) 找出“最便宜”的节点，即可在最短时间内到达的节点。 +(2) 更新该节点的邻居的开销，其含义将稍后介绍。 +(3) 重复这个过程，直到对图中的每个节点都这样做了。 (4) 计算最终路径。 每个节点都有开销。开销指的是从起点前往该节点需要多长时间。 ## 7.2，术语 + 带权重的图称为加权图（ weighted graph），不带权重的图称为非加权图（ unweighted graph）。 + 要计算非加权图中的最短路径，可使用广度优先搜索。要计算加权图中的最短路径，可使用狄克斯特拉算法。 -+ 在无向图中，每条边都是一个环。狄克斯特拉算法只适用于有向无环图（ directed acyclic - -graph， DAG）。 ++ 在无向图中，每条边都是一个环。狄克斯特拉算法只适用于有向无环图（ directed acyclic graph， DAG）。 图可能有环，所谓环，是指由一个节点出发，走一圈后可以又回到原节点，如下图所示： -![77626056.png](数据结构与算法学习笔记_files/77626056.png) +![环示意图] https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/7.2%E7%8E%AF%E7%A4%BA%E6%84%8F%E5%9B%BE.png() ## 7.3，负权边 -因此， **不能将狄克斯特拉算法用于包含负权边的图**。在包含负权边的图中，要找出最短路径，可使用另一种算法——贝尔曼福德算法（ Bellman-Ford - -algorithm）。 +因此， **不能将狄克斯特拉算法用于包含负权边的图**。在包含负权边的图中，要找出最短路径，可使用另一种算法——贝尔曼福德算法（ Bellman-Ford algorithm）。 ## 7.4，编程实现狄克斯特拉算法 以下图为例，编程实现耗时最短的路径。 -![79746550.png](数据结构与算法学习笔记_files/79746550.png) +![图算法问题] (https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/7.4%E5%9B%BE%E7%AE%97%E6%B3%95%E9%97%AE%E9%A2%98.png) 代码如下： ```python # 为了实现带权图，可使用散列表，散列表用Python字典实现 @@ -325,15 +244,9 @@ print("Cost from the start to each node:") print(costs) ``` ## 7.5，小结 -+ 广度优先搜索用于在非加权图中查找最短路径。 - - -+ 狄克斯特拉算法用于在加权图中查找最短路径。 - - -+ 仅当权重为正时狄克斯特拉算法才管用。 - - ++ 广度优先搜索用于在非加权图中查找最短路径。 ++ 狄克斯特拉算法用于在加权图中查找最短路径。 ++ 仅当权重为正时狄克斯特拉算法才管用。 + 如果图中包含负权边，请使用贝尔曼福德算法。 # 第八章，贪婪(贪心)算法 贪婪算法思想很简单：每步都采取最优的做法，专业术语说，就是每步都选择局部最优解，最终得到的就是全局最优解。 @@ -347,24 +260,11 @@ print(costs) 每个广播台都覆盖特定区域的州，找出覆盖全美50个州的最小广播集合。 贪婪算法解决这个问题，当广播台数量过多，算法所耗费的时间将激增。 ## 8.3.1，近似算法 -贪婪算法可化解危机！使用下面的贪婪算法可得到非常接近的解。 - - - -(1) 选出这样一个广播台，即它**覆盖了最多的未覆盖州**。即便这个广播台覆盖了一些已覆盖的州，也没有关系。 - - - -(2) 重复第一步，直到覆盖了所有的州。 - - - -这是一种近似算法（ approximation algorithm） 。在获得精确解需要的时间太长时，可使用近似算法。判断近似算法优劣的标准如下： - - -+ 速度有多快； - - +贪婪算法可化解危机！使用下面的贪婪算法可得到非常接近的解。 +(1) 选出这样一个广播台，即它**覆盖了最多的未覆盖州**。即便这个广播台覆盖了一些已覆盖的州，也没有关系。 +(2) 重复第一步，直到覆盖了所有的州。 +这是一种近似算法（ approximation algorithm） 。在获得精确解需要的时间太长时，可使用近似算法。判断近似算法优劣的标准如下： ++ 速度有多快； + 得到的近似解与最优解的接近程度。 代码实例： ```python @@ -406,39 +306,21 @@ print(final_stations) 旅行商问题和集合覆盖问题有一些共同之处：**你需要计算所有的解，并从中选出最小/最短的那个**。这两个问题都属于NP完全问题。NP完全问题的简单定义是，以难解著称的问题，如**旅行商问题和集合覆盖**问题。很多非常聪明的人都认为，根本不可能编写出可快速解决这些问题的算法。 ## 8.4.1，如何识别NP完全问题 NP完全问题无处不在！如果能够判断出要解决的问题属于NP完全问题就好了，这样就不用 去寻找完美的解决方案，而是使用近似算法即可。但要判断问题是不是NP完全问题很难，易于解决的问题和NP完全问题的差别通常很小。 -但如果要找出经由指定几个点的的最短路径，就是旅行商问题——NP完全问题。简言之，没办法判断问题是不是NP完全问题，但还是有一些蛛丝马迹可循的。 - - -+ 元素较少时算法的运行速度非常快，但随着元素数量的增加，速度会变得非常慢。 - - -+ 涉及“所有组合”的问题通常是NP完全问题。 - - -+ 不能将问题分成小问题，必须考虑各种可能的情况。这可能是NP完全问题。 - - -+ 如果问题涉及序列（如旅行商问题中的城市序列）且难以解决，它可能就是NP完全问题。 - - -+ 如果问题涉及集合（如广播台集合）且难以解决，它可能就是NP完全问题。 - - +但如果要找出经由指定几个点的的最短路径，就是旅行商问题——NP完全问题。简言之，没办法判断问题是不是NP完全问题，但还是有一些蛛丝马迹可循的。 ++ 元素较少时算法的运行速度非常快，但随着元素数量的增加，速度会变得非常慢。 ++ 涉及“所有组合”的问题通常是NP完全问题。 ++ 不能将问题分成小问题，必须考虑各种可能的情况。这可能是NP完全问题。 ++ 如果问题涉及序列（如旅行商问题中的城市序列）且难以解决，它可能就是NP完全问题。 ++ 如果问题涉及集合（如广播台集合）且难以解决，它可能就是NP完全问题。 + 如果问题可转换为集合覆盖问题或旅行商问题，那它肯定是NP完全问题。 # 8.5，小结 -+ 贪婪算法寻找局部最优解，企图以这种方式获得全局最优解。 - - -+ 对于NP完全问题，还没有找到快速解决方案。 - - -+ 面临NP完全问题时，最佳的做法是使用近似算法。 - - ++ 贪婪算法寻找局部最优解，企图以这种方式获得全局最优解。 ++ 对于NP完全问题，还没有找到快速解决方案。 ++ 面临NP完全问题时，最佳的做法是使用近似算法。 + 贪婪算法易于实现、运行速度快，是不错的近似算法。 # 第九章，动态规划 学习动态规划，这是一种解决棘手问题的方法，**它将问题分成小问题**，并先着手解决这些小问题。 工作原理：动态规划先解决子问题，再逐步解决大问题。 # 第十章，K最近邻算法 -# 第十一章　接下来如何做 \ No newline at end of file +# 第十一章　接下来如何做 From 70e0c3ff19ac5d65074de1850a95587c63da9840 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Sat, 4 May 2019 17:01:48 +0800 Subject: [PATCH 085/386] =?UTF-8?q?Update=20=E6=95=B0=E6=8D=AE=E7=BB=93?= =?UTF-8?q?=E6=9E=84=E4=B8=8E=E7=AE=97=E6=B3=95=E5=AD=A6=E4=B9=A0=E7=AC=94?= =?UTF-8?q?=E8=AE=B0.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 更新图片显示语法错误 --- ...\255\246\344\271\240\347\254\224\350\256\260.md" | 13 +++++-------- 1 file changed, 5 insertions(+), 8 deletions(-) diff --git "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" index 95bebc04..67e01838 100644 --- "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" +++ "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" @@ -7,8 +7,7 @@ ### 2.2.1，链表 链表中的元素可存储在内存的任何地方。 链表的每个元素都存储了下一个元素的地址，从而使一系列随机的内存地址串在一起。链表结构直观显示如下图所示： -![2.1 链表示意图] -(https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/2.1%E9%93%BE%E8%A1%A8.png) +![2.1链表示意图](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/2.1%E9%93%BE%E8%A1%A8.png) 链表的优势在插入元素方面，那数组的优势又是什么呢？ ### 2.2.2，数组 需要随机地读取元素时，数组的效率很高，因为可迅速找到数组的任何元素。**在链表中，元素并非靠在一起的**，你无法迅速计算出第五个元素的内存 地址，而必须先访问第一个元素以获取第二个元素的地址，再访问第二个元素以获取第三个元素 的地址，以此类推，直到访问第五个元素。 @@ -96,8 +95,7 @@ print(quicksort([4, 90, 0, 2, 17, 79, 12])) # [0, 2, 4, 12, 17, 79, 90] ``` ## 4.3 再谈大Ｏ表示法 快速排序的独特之处在于，其速度取决于选择的基准值。在讨论快速排序的运行时间前，我 们再来看看最常见的大O运行时间。 -![4.3 再谈大O表示法] -(https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/4.3%E5%86%8D%E8%B0%88%E5%A4%A7O%E8%A1%A8%E7%A4%BA%E6%B3%95.png) +![4.3 再谈大O表示法](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/4.3%E5%86%8D%E8%B0%88%E5%A4%A7O%E8%A1%A8%E7%A4%BA%E6%B3%95.png) 选择排序，其运行时间为O(n2)，速度非常慢。 还有一种名为合并排序（ merge sort） 的排序算法，其运行时间为O(n log n)，比选择排序快得多！快速排序的情况比较棘手，在最糟情况下，其运行时间为O(n2)。与选择排序一样慢！但这是最糟情况。**在平均情况下，快速排序的运行时间为O(n log n)。** 由对数的换底公式，loga n和 logb n只有一个常数因子不同，这个因子在大O记法中被丢弃。因此记作O(log n)，**而不论对数的底是多少，是对数时间算法的标准记法**。 @@ -119,8 +117,7 @@ print(quicksort([4, 90, 0, 2, 17, 79, 12])) # [0, 2, 4, 12, 17, 79, 90] 给两个键分配的位置相同，这被称为冲突（collision）。处理冲突最简单的办法就是：如果两个键映射到了同一个位置，就在这个位置存储一个链表。 ## 5.4，性能 散列表，数组，链表的查找、插入、删除元素的时间复杂度，如下表所示： -![散列表查找、插入删除元素操作时间复杂度分析] -(https://github.com/HarleysZhang/201数据结构与算法学习笔记_files/60409970.png9_algorithm_intern_information/blob/master/images/5.4%E6%80%A7%E8%83%BD.png) +![散列表查找、插入删除元素操作时间复杂度分析](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/5.4%E6%80%A7%E8%83%BD.png) 在平均情况下，散列表的查找（获取给定索引处的值）速度与数组一样快，而插入和删除速 度与链表一样快，因此它兼具两者的优点！但在最糟情况下，散列表的各种操作的速度都很慢。 因此，在使用散列表时，避开最糟情况至关重要。为此，需要避免冲突。而要避免冲突，需要有： + 较低的填装因子； + 良好的散列函数。 @@ -171,12 +168,12 @@ print(quicksort([4, 90, 0, 2, 17, 79, 12])) # [0, 2, 4, 12, 17, 79, 90] + 在无向图中，每条边都是一个环。狄克斯特拉算法只适用于有向无环图（ directed acyclic graph， DAG）。 图可能有环，所谓环，是指由一个节点出发，走一圈后可以又回到原节点，如下图所示： -![环示意图] https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/7.2%E7%8E%AF%E7%A4%BA%E6%84%8F%E5%9B%BE.png() +![环示意图](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/7.2%E7%8E%AF%E7%A4%BA%E6%84%8F%E5%9B%BE.png) ## 7.3，负权边 因此， **不能将狄克斯特拉算法用于包含负权边的图**。在包含负权边的图中，要找出最短路径，可使用另一种算法——贝尔曼福德算法（ Bellman-Ford algorithm）。 ## 7.4，编程实现狄克斯特拉算法 以下图为例，编程实现耗时最短的路径。 -![图算法问题] (https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/7.4%E5%9B%BE%E7%AE%97%E6%B3%95%E9%97%AE%E9%A2%98.png) +![图算法问题](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/7.4%E5%9B%BE%E7%AE%97%E6%B3%95%E9%97%AE%E9%A2%98.png) 代码如下： ```python # 为了实现带权图，可使用散列表，散列表用Python字典实现 From 0bc1e6d434cfe9bc6d29413c3bd67f3db405bfb9 Mon Sep 17 00:00:00 2001 From: Zhang <37138671+HarleysZhang@users.noreply.github.com> Date: Sat, 4 May 2019 17:03:06 +0800 Subject: [PATCH 086/386] =?UTF-8?q?Update=20=E6=95=B0=E6=8D=AE=E7=BB=93?= =?UTF-8?q?=E6=9E=84=E4=B8=8E=E7=AE=97=E6=B3=95=E5=AD=A6=E4=B9=A0=E7=AC=94?= =?UTF-8?q?=E8=AE=B0.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...3\225\345\255\246\344\271\240\347\254\224\350\256\260.md" | 5 +++++ 1 file changed, 5 insertions(+) diff --git "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" index 67e01838..90cfbdb5 100644 --- "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" +++ "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" @@ -8,6 +8,7 @@ 链表中的元素可存储在内存的任何地方。 链表的每个元素都存储了下一个元素的地址，从而使一系列随机的内存地址串在一起。链表结构直观显示如下图所示： ![2.1链表示意图](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/2.1%E9%93%BE%E8%A1%A8.png) + 链表的优势在插入元素方面，那数组的优势又是什么呢？ ### 2.2.2，数组 需要随机地读取元素时，数组的效率很高，因为可迅速找到数组的任何元素。**在链表中，元素并非靠在一起的**，你无法迅速计算出第五个元素的内存 地址，而必须先访问第一个元素以获取第二个元素的地址，再访问第二个元素以获取第三个元素 的地址，以此类推，直到访问第五个元素。 @@ -96,6 +97,7 @@ print(quicksort([4, 90, 0, 2, 17, 79, 12])) # [0, 2, 4, 12, 17, 79, 90] ## 4.3 再谈大Ｏ表示法 快速排序的独特之处在于，其速度取决于选择的基准值。在讨论快速排序的运行时间前，我 们再来看看最常见的大O运行时间。 ![4.3 再谈大O表示法](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/4.3%E5%86%8D%E8%B0%88%E5%A4%A7O%E8%A1%A8%E7%A4%BA%E6%B3%95.png) + 选择排序，其运行时间为O(n2)，速度非常慢。 还有一种名为合并排序（ merge sort） 的排序算法，其运行时间为O(n log n)，比选择排序快得多！快速排序的情况比较棘手，在最糟情况下，其运行时间为O(n2)。与选择排序一样慢！但这是最糟情况。**在平均情况下，快速排序的运行时间为O(n log n)。** 由对数的换底公式，loga n和 logb n只有一个常数因子不同，这个因子在大O记法中被丢弃。因此记作O(log n)，**而不论对数的底是多少，是对数时间算法的标准记法**。 @@ -118,6 +120,7 @@ print(quicksort([4, 90, 0, 2, 17, 79, 12])) # [0, 2, 4, 12, 17, 79, 90] ## 5.4，性能 散列表，数组，链表的查找、插入、删除元素的时间复杂度，如下表所示： ![散列表查找、插入删除元素操作时间复杂度分析](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/5.4%E6%80%A7%E8%83%BD.png) + 在平均情况下，散列表的查找（获取给定索引处的值）速度与数组一样快，而插入和删除速 度与链表一样快，因此它兼具两者的优点！但在最糟情况下，散列表的各种操作的速度都很慢。 因此，在使用散列表时，避开最糟情况至关重要。为此，需要避免冲突。而要避免冲突，需要有： + 较低的填装因子； + 良好的散列函数。 @@ -169,11 +172,13 @@ print(quicksort([4, 90, 0, 2, 17, 79, 12])) # [0, 2, 4, 12, 17, 79, 90] 图可能有环，所谓环，是指由一个节点出发，走一圈后可以又回到原节点，如下图所示： ![环示意图](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/7.2%E7%8E%AF%E7%A4%BA%E6%84%8F%E5%9B%BE.png) + ## 7.3，负权边 因此， **不能将狄克斯特拉算法用于包含负权边的图**。在包含负权边的图中，要找出最短路径，可使用另一种算法——贝尔曼福德算法（ Bellman-Ford algorithm）。 ## 7.4，编程实现狄克斯特拉算法 以下图为例，编程实现耗时最短的路径。 ![图算法问题](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/7.4%E5%9B%BE%E7%AE%97%E6%B3%95%E9%97%AE%E9%A2%98.png) + 代码如下： ```python # 为了实现带权图，可使用散列表，散列表用Python字典实现 From 0d80194fdde5e84bbc2f611755dc4ec81c11013f Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 8 May 2019 16:57:35 +0800 Subject: [PATCH 087/386] =?UTF-8?q?Update=20=E8=AE=A1=E7=AE=97=E6=9C=BA?= =?UTF-8?q?=E8=A7=86=E8=A7=89=E5=B2=97=E5=AE=9E=E4=B9=A0=E9=9D=A2=E7=BB=8F?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 更新南京地平线机器人算法岗面经 --- ...36\344\271\240\351\235\242\347\273\217.md" | 28 +++++++++++++++++++ 1 file changed, 28 insertions(+) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" index 466462e5..519795ff 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" @@ -33,3 +33,31 @@ ２．其实自己也知道，自己在基础理论和原理方面掌握得不够深，但是由于缺乏时间，我还是没做到自己的目标，希望借这次面试反映出的自己理论缺失点，来提醒和激励自己一定要把基础理论和原理彻底掌握。 ３．经过阿里的面试，自己回去把更多的项目细节掌握了，这次格林深瞳面试之后一定要把基础理论和原理掌握，从图像分类网络:**resnet**等，到目标检测和图像分割网络：**faster rcnn**、**mask rcnn**、**ssd**、**yolov3**等彻底掌握基础原理和细节，多看相关论文和博客。 +## 南京地平线机器人　智能驾驶算法实习生 +## 笔试(１个小时)--> ５道编程题 +1. 给定两个字符串a和b（长度超过100w），在字符类型上b是a的子集，求b在a字符类型上的补集; +2. 给定正整数N, 返回小于等于N且至少有一位重复数字的正整数的个数; +3. 电话号码组合。下图是一个手机按键的样例，每一个数字包含一些字母。比如字母“A”可以通过按一次“2”得到，字母“B”可以通过按两次“2”得到，以此类推。当给定一个数字字符串，我们也可以得到相应的映射，比如“22”, 代表字母组成的可能性是[“AA”, “B”]。要求：输入为一个数字字符串，例如”2321241499844211”。输出为可能代表的所有字母组合。 +4. 给定两矩形的左上角和右下角坐标，求两矩形的重叠区域面积（overlap），若不重叠，返回0。（其实就是计算IOU）。 +5. 实现softmax，包括init，forward，backward。 +如果把笔试题写出来侵权，一定要联系我删除笔试题哈。 +### 一面（48分钟） +1. 自我介绍 + + 面地平线的这次自我介绍，比之前的面试算是有了一些改变，不再流水线式的介绍学习经历和项目经历，而是突出性格和技术栈重点。 +2. 图像基础操作题,对图像做45度旋转，如何使图像完整不缺失，缺失和超出的部分如何处理？ +3. 项目细节 + + 离线过采样和在线过采样哪个更快？ + + 如何针对已有的网络做改进，提升速度？ + + 如何解决类别不平衡问题？ + + 训练网络的指标，除了基本的的acc,loss，roc、auc有了解吗？ + 4. 算法细节 + + ROI Pooling和ROI Align的区别及演进 + + + 5. Python和计算机常考基础 + + 装饰器怎么用 + + 深拷贝和浅拷贝的区别 + + 多线程和多进程的区别 + 6. Linux和git命令操作基础 + + linux查找、查看文件的3个常用命令：which、find、wheresis。（这里应该是查找命令，当时也没听清楚，连就说了cat查看文件、which、find） + + 统计文件夹下的文件个数:ls -l | grep "^_" | wc -l（这个操作，我之前用过很多次，但是没说的很清楚，不过意思应该表达清楚了） + 7. git的一个操作（具体问题真的忘了） From 57204e3297eb80ea2529b7948658aa193199b221 Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 8 May 2019 17:01:38 +0800 Subject: [PATCH 088/386] =?UTF-8?q?Update=20=E8=AE=A1=E7=AE=97=E6=9C=BA?= =?UTF-8?q?=E8=A7=86=E8=A7=89=E5=B2=97=E5=AE=9E=E4=B9=A0=E9=9D=A2=E7=BB=8F?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...2\227\345\256\236\344\271\240\351\235\242\347\273\217.md" | 5 +++++ 1 file changed, 5 insertions(+) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" index 519795ff..881d29f2 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" @@ -61,3 +61,8 @@ + linux查找、查看文件的3个常用命令：which、find、wheresis。（这里应该是查找命令，当时也没听清楚，连就说了cat查看文件、which、find） + 统计文件夹下的文件个数:ls -l | grep "^_" | wc -l（这个操作，我之前用过很多次，但是没说的很清楚，不过意思应该表达清楚了） 7. git的一个操作（具体问题真的忘了） + ### 一面总结 + + Python一些基础还是要搞清楚，向迭代器、深拷贝、浅拷贝，我之前都看过面经和用法，都还是忘了，真是不应该。 + + 地平线机器人面试真的问的很广，但大部分都是基础常见问题，偏工程向。 + + 自己要加强Python基础的一些技术盲点。 + + 以后面试表达要有针对性，不要拓展太多。 From d64afce50e2ed2c6b132cd00aa8c29884e98dd10 Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 8 May 2019 17:03:26 +0800 Subject: [PATCH 089/386] =?UTF-8?q?Update=20=E8=AE=A1=E7=AE=97=E6=9C=BA?= =?UTF-8?q?=E8=A7=86=E8=A7=89=E5=B2=97=E5=AE=9E=E4=B9=A0=E9=9D=A2=E7=BB=8F?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...227\345\256\236\344\271\240\351\235\242\347\273\217.md" | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" index 881d29f2..48f24b1f 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" @@ -40,7 +40,8 @@ 3. 电话号码组合。下图是一个手机按键的样例，每一个数字包含一些字母。比如字母“A”可以通过按一次“2”得到，字母“B”可以通过按两次“2”得到，以此类推。当给定一个数字字符串，我们也可以得到相应的映射，比如“22”, 代表字母组成的可能性是[“AA”, “B”]。要求：输入为一个数字字符串，例如”2321241499844211”。输出为可能代表的所有字母组合。 4. 给定两矩形的左上角和右下角坐标，求两矩形的重叠区域面积（overlap），若不重叠，返回0。（其实就是计算IOU）。 5. 实现softmax，包括init，forward，backward。 -如果把笔试题写出来侵权，一定要联系我删除笔试题哈。 + +**如果把笔试题写出来侵权，一定要联系我删除笔试题哈**。 ### 一面（48分钟） 1. 自我介绍 + 面地平线的这次自我介绍，比之前的面试算是有了一些改变，不再流水线式的介绍学习经历和项目经历，而是突出性格和技术栈重点。 @@ -49,7 +50,7 @@ + 离线过采样和在线过采样哪个更快？ + 如何针对已有的网络做改进，提升速度？ + 如何解决类别不平衡问题？ - + 训练网络的指标，除了基本的的acc,loss，roc、auc有了解吗？ + + 训练网络的指标，除了基本的的acc,loss，**roc、auc**有了解吗？ 4. 算法细节 + ROI Pooling和ROI Align的区别及演进 + @@ -64,5 +65,5 @@ ### 一面总结 + Python一些基础还是要搞清楚，向迭代器、深拷贝、浅拷贝，我之前都看过面经和用法，都还是忘了，真是不应该。 + 地平线机器人面试真的问的很广，但大部分都是基础常见问题，偏工程向。 - + 自己要加强Python基础的一些技术盲点。 + + 自己要加强Python基础的一些**技术盲点**。 + 以后面试表达要有针对性，不要拓展太多。 From caba33fa90f77aa9f33ba55be859f60e42fec94b Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 8 May 2019 17:05:06 +0800 Subject: [PATCH 090/386] =?UTF-8?q?Update=20=E8=AE=A1=E7=AE=97=E6=9C=BA?= =?UTF-8?q?=E8=A7=86=E8=A7=89=E5=B2=97=E5=AE=9E=E4=B9=A0=E9=9D=A2=E7=BB=8F?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" index 48f24b1f..3dd761ad 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" @@ -64,6 +64,6 @@ 7. git的一个操作（具体问题真的忘了） ### 一面总结 + Python一些基础还是要搞清楚，向迭代器、深拷贝、浅拷贝，我之前都看过面经和用法，都还是忘了，真是不应该。 - + 地平线机器人面试真的问的很广，但大部分都是基础常见问题，偏工程向。 + + 地平线机器人面试真的问的很广，偏工程向，碰到不会的也不要太紧张，之后一定要去补课。 + 自己要加强Python基础的一些**技术盲点**。 + 以后面试表达要有针对性，不要拓展太多。 From bfed75520dcf04c39c1a416f3b98e04bd72af7b8 Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 8 May 2019 21:42:55 +0800 Subject: [PATCH 091/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 更新Python/C/C++/计算机基础问题 --- ...40\351\235\242\350\257\225\351\242\230.md" | 26 +++++++++++++++++++ 1 file changed, 26 insertions(+) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 40b17de0..9c1e79a3 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -212,6 +212,31 @@ H(p,q)=−∑p(x)logq(x) ## 1*1卷积的主要作用有以下几点： + **降维（ dimension reductionality ）**。比如，一张500 * 500且厚度depth为100 的图片在20个filter上做1*1的卷积，那么结果的大小为500*500*20。 + **加入非线性**。卷积层之后经过激励层，1*1的卷积在前一层的学习表示上添加了非线性激励（ non-linear activation ），提升网络的表达能力； +# Python/C/C++/计算机基础 +## C++指针和引用的区别 ++ 不存在空引用, 引用必须链接到一块合法的内存地址； ++ 一旦引用被初始化为一个对象，就不能指向另一个对象。指针可以在任何时候指向任何一个对象； ++ 引用必须在创建时被初始化。指针可以在任何时间初始化。 +## Python装饰器解释 +装饰器本质上是一个 Python 函数或类，**它可以让其他函数或类在不需要做任何代码修改的前提下增加额外功能**，装饰器的返回值也是一个函数/类对象。它经常用于有切面需求的场景，比如：插入日志、性能测试、事务处理、缓存、权限校验等场景，装饰器是解决这类问题的绝佳设计。有了装饰器，我们就可以**抽离出大量与函数功能本身无关的雷同代码到装饰器中并继续重用**。概括的讲，**装饰器的作用就是为已经存在的对象添加额外的功能**。 +## 多进程与多线程区别 ++ 线程是进程的一部分，一个进程至少有一个线程； ++ 对于操作系统来说，一个任务就是一个进程，进程内的“子任务”称为线程； ++ 多线程和多进程最大的不同在于，多进程中，同一个变量，各自有一份拷贝存在于每个进程中，互不影响，而**多线程中，所有变量都由所有线程共享**，所以，任何一个变量都可以被任何一个线程修改，因此，线程之间共享数据最大的危险在于多个线程同时改一个变量，把内容给改乱了。 +## map与reduce函数用法解释下 +1. map函数接收两个参数，一个是函数，一个是Iterable，map将传入的函数依次作用到序列的**每个元素**，并将结果作为新的Iterator返回，简单示例代码如下： +```Python3 +# 示例１ +def square(x): + return x ** 2 +r = map(square, [1, 2, 3, 4, 5, 6, 7]) +squareed_list = list(r) +print(squareed_list) # [1, 4, 9, 16, 25, 36, 49] +# 示例２ +list(map(str, [1, 2, 3, 4, 5, 6, 7, 8, 9]))　＃　['1', '2', '3', '4', '5', '6', '7', '8', '9'] +``` +**注意map函数返回的是一个Iterator（惰性序列），要通过list函数转化为常用列表结构**。map()作为高阶函数，事实上它是把运算规则抽象了。 + # 机器学习 ## Focal Loss 介绍一下 @@ -221,3 +246,4 @@ H(p,q)=−∑p(x)logq(x) ## AUC的理解 ## AUC的计算公式 + From 862d1129474ddfaebfd855e3f74820c348318d7b Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 8 May 2019 21:46:57 +0800 Subject: [PATCH 092/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 9c1e79a3..34d18ed1 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -236,7 +236,8 @@ print(squareed_list) # [1, 4, 9, 16, 25, 36, 49] list(map(str, [1, 2, 3, 4, 5, 6, 7, 8, 9]))　＃　['1', '2', '3', '4', '5', '6', '7', '8', '9'] ``` **注意map函数返回的是一个Iterator（惰性序列），要通过list函数转化为常用列表结构**。map()作为高阶函数，事实上它是把运算规则抽象了。 - +2. reduce()函数也接受两个参数，一个是函数，一个是序列，与map不同的是**reduce把结果继续和序列的下一个元素做累积计算**。 +'''reduce(f, [x1, x2, x3, x4]) = f(f(f(x1, x2), x3), x4)''' # 机器学习 ## Focal Loss 介绍一下 From ce151b11b766ae5cb7731ed4ec98d034b8889d7c Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 8 May 2019 22:15:53 +0800 Subject: [PATCH 093/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...40\351\235\242\350\257\225\351\242\230.md" | 37 ++++++++++++++++--- 1 file changed, 32 insertions(+), 5 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 34d18ed1..82219dab 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -205,13 +205,16 @@ HOG特征提取方法就是将一个image： 判断一个输出向量和期望的向量有多接近，交叉熵（cross entroy）是常用的评判方法之一。交叉熵刻画了两个概率分布之间的距离，是分类问题中使用比较广泛的一种损失函数。 给定两个概率分布p和q，通过q来表示p的交叉熵公式为： H(p,q)=−∑p(x)logq(x) +## softmax公式写一下： +softmax(y)_{i} = \frac{e^{yi}}{\sum_{j=1}^{n}e^{yj}} +[softmax公式]() +## 1*1卷积的主要作用有以下几点： ++ **降维（ dimension reductionality ）**。比如，一张500 * 500且厚度depth为100 的图片在20个filter上做1*1的卷积，那么结果的大小为500*500*20。 ++ **加入非线性**。卷积层之后经过激励层，1*1的卷积在前一层的学习表示上添加了非线性激励（ non-linear activation ），提升网络的表达能力； ## Reference 1. [《Batch Normalization Accelerating Deep Network Training by Reducing Internal Covariate Shift》阅读笔记与实现](https://blog.csdn.net/happynear/article/details/44238541) 2. [深度学习中 Batch Normalization为什么效果好](https://blog.csdn.net/happynear/article/details/44238541) 3. [详解机器学习中的梯度消失、爆炸原因及其解决方法](https://blog.csdn.net/qq_25737169/article/details/78847691) -## 1*1卷积的主要作用有以下几点： -+ **降维（ dimension reductionality ）**。比如，一张500 * 500且厚度depth为100 的图片在20个filter上做1*1的卷积，那么结果的大小为500*500*20。 -+ **加入非线性**。卷积层之后经过激励层，1*1的卷积在前一层的学习表示上添加了非线性激励（ non-linear activation ），提升网络的表达能力； # Python/C/C++/计算机基础 ## C++指针和引用的区别 + 不存在空引用, 引用必须链接到一块合法的内存地址； @@ -232,12 +235,36 @@ def square(x): r = map(square, [1, 2, 3, 4, 5, 6, 7]) squareed_list = list(r) print(squareed_list) # [1, 4, 9, 16, 25, 36, 49] +# 使用lambda匿名函数简化为一行代码 +list(map(lambda x: x * x, [1, 2, 3, 4, 5, 6, 7, 8, 9])) # 示例２ list(map(str, [1, 2, 3, 4, 5, 6, 7, 8, 9]))　＃　['1', '2', '3', '4', '5', '6', '7', '8', '9'] ``` **注意map函数返回的是一个Iterator（惰性序列），要通过list函数转化为常用列表结构**。map()作为高阶函数，事实上它是把运算规则抽象了。 -2. reduce()函数也接受两个参数，一个是函数，一个是序列，与map不同的是**reduce把结果继续和序列的下一个元素做累积计算**。 -'''reduce(f, [x1, x2, x3, x4]) = f(f(f(x1, x2), x3), x4)''' +2. reduce()函数也接受两个参数，一个是函数（**两个参数**），一个是序列，与map不同的是**reduce把结果继续和序列的下一个元素做累积计算**,效果如下： +`reduce(f, [x1, x2, x3, x4]) = f(f(f(x1, x2), x3), x4)` +示例代码如下： +```Python3 +from functools import reduce +CHAR_TO_INT = { + '0': 0, + '1': 1, + '2': 2, + '3': 3, + '4': 4, + '5': 5, + '6': 6, + '7': 7, + '8': 8, + '9': 9 +} +def str2int(str): + ints = map(lambda x:CHAR_TO_INT[x], str) # str对象是Iterable对象 + return reduce(lambda x,y:10*x + y, ints) +print(str2int('0')) +print(str2int('12300')) +print(str2int('0012345')) # 0012345 +``` # 机器学习 ## Focal Loss 介绍一下 From d6c46e58fb66be9da4a0d866051fe6950fd11c93 Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 8 May 2019 22:16:48 +0800 Subject: [PATCH 094/386] Add files via upload --- "images/softmax\345\205\254\345\274\217.png" | Bin 0 -> 2949 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 "images/softmax\345\205\254\345\274\217.png" diff --git "a/images/softmax\345\205\254\345\274\217.png" "b/images/softmax\345\205\254\345\274\217.png" new file mode 100644 index 0000000000000000000000000000000000000000..94b1b0ab4150be61ec7b464792b992a4d0040d2e GIT binary patch literal 2949 zcmZ{mc{o(>`^U%H5MoTIP=t>(B&LsLgb`VatYt}-u``&ljk0AK*~>l{qKxbfF(`Xu zvKPiSRQ4oW)@*&x@AvQb`kw1L=eeHi+|Rk6b1$#w9cOH$%guS769fWr>+5No0^<+h z)&{c!&xD}hRbXKC)6zEw16K&x=@~FT=dWYwZ|39TALM{_2Dy6spq*v?P*`VYZ$CF5 z|8>6(Zm`6{#Ft0RKPxfbjp@!xx-nIxE)xZHcg(Y9uKXt^Du{RM%B)R8|JFA-( zKW+|mNfR0T)wk9}ljmY#MdDKb`rCjaFHMQDNrKPZ;xynD#Y59Csc~JAM&g7_jb{rk z=k|+>rUcm}l71AzPj6NiuBb9U)ud3}I8BWsH{|DMAtgK}P`)_rag3U%u6jX`+ERcX zGR{m#mDn{j1PKfZ0#<;&50~5%5)};uUY;<}OyDA-_A8SgP03%sKKggyXlF(6_U+s9 z3JS~?7U-o9JXXfDBV;o9X|9qFGYG2F{%31xqH$rtd8+vd#imH)9I&qua&2u?dpi=O zDVNn5b6Rk`ac3t$Tb%Fp@UnMYki;jdWJrQVw?Q!^Un86c#LLRm?F2bIJhZg4qm@6X zwrp-}RDoT)h6)R7nfqPOTxEF{t{pjQ?;TTv?d%+6$k#lNp>LamYm$ooxI=K zVT2H0s2=iWMAeFH%<=H@cImJ5Brq%U_h8W)7^7!+8E#N)n)O;$qFI2?1P>b2jQCb42`oeR&tKja9G8%HlohdbHLaX+w4T z`ug6%Q0fJ`g~l79^3lFsV{&V=v*IKYX}scvwWxXsSjR*aM*m`IV}lrFk$`eKpwX2{ z5WYtp6oHY%MfaS=pOf3$+c?!p*JMP0$fNRSMbChwz+kXDTfpvbFY})q{fPoH%Eiqsc0Ng* zX!Odul36*sn$Wud0x=kjhM;v(Ts|d(I)~BSGlS}Y?5lPX1g~u@^ky{$W*SJ|y>|~- z;TRr%5irK>{yOJb%YegmOx{u`v8$_SMuTc@zhgi^Mz$~ z)LxMKw#XOvLu>?MeKp?y_V)=n8 zqy*nc5)u+}!(E$Y{V|fJnd!_D!PJ&bBv#f+)QuVs!&xFKAFZk=N$6wiS|TrvSc(19 zJMUCToabVX>T?{0Bv8FZ=>}v=xN@=|`qpaQ;@%QF4TC4S*VjzW8QGt7ZO7K*Ziwt9 zC=e3)cw!061UXmqKZ8yRRov+k%X&nHfxHl0Lf^yEMIBV3pwfM%7kf)7Zz=eJ(viGi z{+#mS`3ral2?$5jQt;S8(wfeGR&qlF5SO6!uhfnX=EIfsbqc*SslElb$sDKMu^ASl z)n0GVk~uwXJJo<;X+s)!`}-JDzWh``WDxFT0X>_YfM(CCzm+g$e?xhl%?xX{St1VFc)l`$q^ ze_Zn1%QBgjqatZXd;7M4z`n6+yX)%eYX??9uANX60Dpl6{r!d;YCh%f8yn*$Cai3% z3XScbOhP%?qP4{C8X1WTan)e*%gTUmMH!gMWxW_&!YmDl0O-oSf}fDmF_BkO^E%Ai zS{h_buHwte_BUo&L~lNx$2~HEK5X)j6Ulu)mXw?<+N58bRl@PJDH-qyFnW2}J-yc% z@bb~|`n{6D+q$}&$A^3MQ{|W4GeguYmtxZm>} zT{M~)m>EF#*Ot(P($XtjXU?E}e9i!cmYtogX=i6g{E#FilbzNYi^m(wWIVqsW&4k_ zvZ1-L#aW8?bWL%%st$l&nG8-`K5e4bRb(am{vi;|H|8=Gd2`C6jkOan0Re%}!^13A znb%cRmiM0oC%K3=84jZ_zCRlx6DX2HQXyeI^d%tND=JBbD6(ZId_>=|>kKQ>t*nLQ z4x`89w7rD&*4M{&I?&o?+^I@8rxW!IQb){7c2AQnE2=u_?3K#N<)^&}da3hop1GJ) zYz$<;kxXqC-b#?TXhSl8T(qpl*M56T@fUB|E~3cUm7%5PJ)3)KJS`^1id2)s;ym^W z5z}Ef10alFopxP7Wi~%@%2bmWu@rn+h)Yyu8f6%4Wrj_aI5*=vE-jECvd*#?HIOqf z8=N27O1O&;X7*0gw{BPB^l0)LEm?Li932AKvn%i6#AH@#{s2qtcO;R{7suwkt?&LR z>z?Bh-8oYg%$7r%m~^aLB=tpJ8XrQ@s7jm$`}}@tlSbu3vI+z)Mcz6Tioe_fed-0zy`Zw*p4d`d++w)HkL{<#@_S)Vh)t9Ow;SqBUnO(1lq`>$RSg+Ry z8vu7m-b8bm1)&DN+A>-@Wd+F*zPUDyFqb(ClamXyfj&5oe#R{fi^gp&aupEc_MYv@xQfo`rXXZ^PcDn5pn`(3n(vNz!Y6qds+*m8+Kx>vn>gXY0u359L6BTi zGjv}t3!44|+*CC;S?Bp&qI6+;&zh)JQYHhOYvUX3pZoCR-iXoM^*EI$yIu?m5s*Nt zTGG&tm9gp+XIpE6maFyqot+(g&$L@gdFP^Hfdrgg(kM?)B2qU+RD`kPxp3eH=S1@I zR4&oE$yfTd@}z<04GB5YTRv-H!3SpbLU=ctjTwzw+in)0MCP{u5&6 zY3dma)cSiVKwd%O>`^F|#)mwB*zoH7eS5{SGPm}|olCqMQ;#12T1OkG>}z%{YNj9W zj|BiQ=HlXdTLZ8~oEL2W#ow>y8CXH3ghbmAGL+F_#g`>J=m0 zWsX1#yESDkYORKIn;I9b0)f`bbk0Krt$iB93NI-SyfZ3Plto{*#$cMkSERoLPTu50 znn3IK_ zOx}Q?>K*bwRr5016(F#G*wg=4lZ_3mGY)!4j*d=Xi_x+FLu>+a^7YIAHr&z>l(sf3 T)R8s;bTmj`$4I+M%Rcfy?XHXq literal 0 HcmV?d00001 From d78d3459fac4cea6a89f611ce0f4534b41143621 Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 8 May 2019 22:21:30 +0800 Subject: [PATCH 095/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 82219dab..528b2170 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -207,7 +207,7 @@ HOG特征提取方法就是将一个image： H(p,q)=−∑p(x)logq(x) ## softmax公式写一下： softmax(y)_{i} = \frac{e^{yi}}{\sum_{j=1}^{n}e^{yj}} -[softmax公式]() +![softmax公式](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/softmax%E5%85%AC%E5%BC%8F.png) ## 1*1卷积的主要作用有以下几点： + **降维（ dimension reductionality ）**。比如，一张500 * 500且厚度depth为100 的图片在20个filter上做1*1的卷积，那么结果的大小为500*500*20。 + **加入非线性**。卷积层之后经过激励层，1*1的卷积在前一层的学习表示上添加了非线性激励（ non-linear activation ），提升网络的表达能力； @@ -241,6 +241,7 @@ list(map(lambda x: x * x, [1, 2, 3, 4, 5, 6, 7, 8, 9])) list(map(str, [1, 2, 3, 4, 5, 6, 7, 8, 9]))　＃　['1', '2', '3', '4', '5', '6', '7', '8', '9'] ``` **注意map函数返回的是一个Iterator（惰性序列），要通过list函数转化为常用列表结构**。map()作为高阶函数，事实上它是把运算规则抽象了。 + 2. reduce()函数也接受两个参数，一个是函数（**两个参数**），一个是序列，与map不同的是**reduce把结果继续和序列的下一个元素做累积计算**,效果如下： `reduce(f, [x1, x2, x3, x4]) = f(f(f(x1, x2), x3), x4)` 示例代码如下： From 475a0cc4cab905a35826ba0a95ebb62cd1cc00ab Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 8 May 2019 23:03:35 +0800 Subject: [PATCH 096/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 更新数据增强方式和方法 --- ...40\351\235\242\350\257\225\351\242\230.md" | 76 ++++++++++++++++++- 1 file changed, 73 insertions(+), 3 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 528b2170..bf797dd3 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -80,12 +80,12 @@ RNN的输入有两个部分，一部分为上一时刻的状态，另一部分 + 更多回答，参考此链接。 ## 图像处理中平滑和锐化操作是什么？ -**平滑处理（smoothing）也称模糊处理（bluring）**，主要用于消除图像中的噪声部分，平滑处理常用的用途是用来减少图像上的噪点或失真，平滑主要使用图像滤波。在这里，我个人认为可以把图像平滑和图像滤波联系起来，因为图像平滑常用的方法就是图像滤波器。 +**平滑处理（smoothing）也称模糊处理（bluring）**，主要用于**消除图像中的噪声部分**，平滑处理常用的用途是用来减少图像上的噪点或失真，平滑主要使用图像滤波。在这里，我个人认为可以把图像平滑和图像滤波联系起来，因为图像平滑常用的方法就是图像滤波器。 在OpenCV3中常用的图像滤波器有以下几种： + 方框滤波——BoxBlur函数 + 均值滤波（邻域平均滤波）——Blur函数 -+ 高斯滤波——GaussianBlur函数 ++ 高斯滤波——GaussianBlur函数（高斯低通滤波是模糊，高斯高通滤波是锐化） + 中值滤波——medianBlur函数 + 双边滤波——bilateralFilter函数 图像锐化操作是为了突出显示图像的边界和其他细节，而图像锐化实现的方法是通过各种算子和滤波器实现的——Canny算子、Sobel算子、Laplacian算子以及Scharr滤波器。 @@ -199,7 +199,7 @@ HOG特征提取方法就是将一个image： ## 卷积层和池化层有什么区别 1. 卷积层有参数，池化层没有参数 2. **经过卷积层节点矩阵深度会改变**，池化层不会改变节点矩阵的深度，但是它可以缩小节点矩阵的大小 -#### 卷积层参数数量计算方法 +## 卷积层参数数量计算方法 假设输入层矩阵维度是96*96*3，第一层卷积层使用尺寸为5*5、深度为16的过滤器（卷积核尺寸为5*5、卷积核数量为16），那么这层卷积层的参数个数为５*5*3*16+16=1216个 ## 神经网络为什么用交叉熵损失函数 判断一个输出向量和期望的向量有多接近，交叉熵（cross entroy）是常用的评判方法之一。交叉熵刻画了两个概率分布之间的距离，是分类问题中使用比较广泛的一种损失函数。 @@ -211,6 +211,76 @@ softmax(y)_{i} = \frac{e^{yi}}{\sum_{j=1}^{n}e^{yj}} ## 1*1卷积的主要作用有以下几点： + **降维（ dimension reductionality ）**。比如，一张500 * 500且厚度depth为100 的图片在20个filter上做1*1的卷积，那么结果的大小为500*500*20。 + **加入非线性**。卷积层之后经过激励层，1*1的卷积在前一层的学习表示上添加了非线性激励（ non-linear activation ），提升网络的表达能力； +## 目标检测基本概念 +#### 准确率、召回率、F1 +混淆矩阵： ++ True Positive(真正例, TP)：将正类预测为正类数. ++ True Negative(真负例, TN)：将负类预测为负类数. ++ False Positive(假正例, FP)：将负类预测为正类数 → 误报 (Type I error). ++ False Negative(假负例子, FN)：将正类预测为负类数 → 漏报 (Type II error). + +查准率（准确率）P = TP/(TP+FP) +查全率（召回率）R = TP/(TP+FN) +**准确率描述了模型有多准**，即在预测为正例的结果中，有多少是真正例；**召回率则描述了模型有多全**，即在为真的样本中，有多少被我们的模型预测为正例。 +以查准率P为纵轴、查全率R为横轴作图，就得到了查准率－查全率曲线，简称**”P-R“**曲线，显示改该曲线的图称为”P-R“图。 +查准率、查全率性能的性能度量，除了”平衡点“（BEP），更为常用的是**F1度量**： +**$$F1 = \frac{2*P*R}{P+R} = \frac{2*TP}{样例总数+TP-TN}$$** + +F1度量的一般形式：$F_{\beta}$，能让我们表达出对查准率/查全率的偏见，公式如下： +$$F_{\beta} = \frac{1+\beta ^{2}*P*R}{(\beta ^{2}*P)+R}$$ +$\beta >1$对查全率有更大影响，$\beta < 1$对查准率有更大影响。 + +不同的计算机视觉问题，对两类错误有不同的偏好，常常在某一类错误不多于一定阈值的情况下，努力减少另一类错误。在目标检测中，**mAP**（mean Average Precision）作为一个统一的指标将这两种错误兼顾考虑。 +#### map指标解释 +具体来说就是，在目标检测中，对于每张图片检测模型会输出多个预测框（远超真实框的个数），我们使用IoU(Intersection Over Union，交并比)来标记预测框是否预测准确。标记完成后，随着预测框的增多，查全率R总会上升，**在不同查全率R水平下对准确率P做平均，即得到AP**，最后再对所有类别按其所占比例做平均，即得到mAP指标。 +#### 交并比IOU +交并比（Intersection-over-Union，IoU），目标检测中使用的一个概念，是产生的候选框（candidate bound）与原标记框（ground truth bound）的交叠率，即它们的交集与并集的比值。最理想情况是完全重叠，即比值为1。 +计算公式如下： + +代码实现如下： +```python +# candidateBound = [x1, y1, x2, y2] +def calculateIoU(candidateBound, groundTruthBound): + cx1 = candidateBound[0] + cy1 = candidateBound[1] + cx2 = candidateBound[2] + cy2 = candidateBound[3] + gx1 = groundTruthBound[0] + gy1 = groundTruthBound[1] + gx2 = groundTruthBound[2] + gy2 = groundTruthBound[3] + + carea = (cx2 - cx1) * (cy2 - cy1) #C的面积 + garea = (gx2 - gx1) * (gy2 - gy1) #G的面积 + x1 = max(cx1, gx1) + y1 = min(cy1, gy1) # 原点为(0, 0)，所以这里是min不是max + x2 = min(cx2, gx2) + y2 = max(cy2, gy2) + w = max(0, (x2 - x1)) + h = max(0, (y2 - y1)) + area = w * h #C∩G的面积 + + iou = area / (carea + garea - area) + return iou + +``` +## 数据增强方法，离线数据增强和在线数据增强有什么区别？ +常用数据增强方法： ++ 翻转：Fliplr,Flipud。不同于旋转180度，这是类似镜面的翻折，跟人在镜子中的映射类似，常用水平、上下镜面翻转。 ++ 旋转：rotate。顺时针/逆时针旋转，最好旋转90-180度，否则会出现边缘缺失或者超出问题，如旋转45度。 ++ 缩放：zoom。图像可以被放大或缩小，imgaug库可用Scal函数实现。 ++ 裁剪：crop。一般叫随机裁剪，操作步骤是：随机从图像中选择一部分，然后降这部分图像裁剪出来，然后调整为原图像的大小。 ++ 平移：translation。平移是将图像沿着x或者y方向（或者两个方向）移动。我们在平移的时候需对背景进行假设，比如说假设为黑色等等，因为平移的时候有一部分图像是空的，由于图片中的物体可能出现在任意的位置，所以说平移增强方法十分有用。 ++ 放射变换：Affine。包含：平移(Translation)、旋转(Rotation)、放缩(zoom)、错切(shear)。 ++ 添加噪声：过拟合通常发生在神经网络学习高频特征的时候，为消除高频特征的过拟合，可以随机加入噪声数据来消除这些高频特征。imgaug库使用GaussianBlur函数。 ++ 亮度、对比度增强：这是图像色彩进行增强的操作 ++ 锐化：Sharpen。imgaug库使用Sharpen函数。 +数据增强分两类，一类是离线增强，一类是在线增强： +1. 离线增强 ： 直接对数据集进行处理，数据的数目会变成增强因子 x 原数据集的数目 ，这种方法常常用于数据集很小的时候 +2. 在线增强 ： 这种增强的方法用于，获得 batch 数据之后，然后对这个 batch 的数据进行增强，如旋转、平移、翻折等相应的变化，由于有些数据集不能接受线性级别的增长，这种方法长用于大的数据集，很多机器学习框架已经支持了这种数据增强方式，并且可以使用GPU优化计算。 + +## Reference +1.[深度学习中的数据增强](https://blog.csdn.net/zhelong3205/article/details/81810743 ) ## Reference 1. [《Batch Normalization Accelerating Deep Network Training by Reducing Internal Covariate Shift》阅读笔记与实现](https://blog.csdn.net/happynear/article/details/44238541) 2. [深度学习中 Batch Normalization为什么效果好](https://blog.csdn.net/happynear/article/details/44238541) From 1a53d475fc7d218ac68cb33a4923cdf78458c934 Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 8 May 2019 23:26:57 +0800 Subject: [PATCH 097/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 更新图像锐化操作 --- ...40\351\235\242\350\257\225\351\242\230.md" | 27 +++++++++++++++---- 1 file changed, 22 insertions(+), 5 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index bf797dd3..cd57477b 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -29,7 +29,7 @@ L1 范数（L1 norm）是指向量中各个元素绝对值之和，也有个美 + Lp 范数: 为 x 向量各个元素绝对值 p 次方和的 1/p 次方. 在支持向量机学习过程中，L1 范数实际是一种对于成本函数求解最优的过程，因此，L1 范数正则化通过向成本函数中添加 L1 范数，使得学习得到的结果满足稀疏化，从而方便人类提取特征。 -L1 范数可以使权值稀疏，方便特征提取。 L2 范数可以防止过拟合，提升模型的泛化能力。 +L1 范数可以使权值参数稀疏，方便特征提取。 L2 范数可以防止过拟合，提升模型的泛化能力。 ## TensorFlow计算图 Tensorflow 是一个通过计算图的形式来表述计算的编程系统，计算图也叫数据流图，可以把计算图看做是一种有向图，Tensorflow 中的每一个计算都是计算图上的一个节点，而节点之间的边描述了计算之间的依赖关系。 @@ -275,17 +275,21 @@ def calculateIoU(candidateBound, groundTruthBound): + 添加噪声：过拟合通常发生在神经网络学习高频特征的时候，为消除高频特征的过拟合，可以随机加入噪声数据来消除这些高频特征。imgaug库使用GaussianBlur函数。 + 亮度、对比度增强：这是图像色彩进行增强的操作 + 锐化：Sharpen。imgaug库使用Sharpen函数。 + 数据增强分两类，一类是离线增强，一类是在线增强： 1. 离线增强 ： 直接对数据集进行处理，数据的数目会变成增强因子 x 原数据集的数目 ，这种方法常常用于数据集很小的时候 -2. 在线增强 ： 这种增强的方法用于，获得 batch 数据之后，然后对这个 batch 的数据进行增强，如旋转、平移、翻折等相应的变化，由于有些数据集不能接受线性级别的增长，这种方法长用于大的数据集，很多机器学习框架已经支持了这种数据增强方式，并且可以使用GPU优化计算。 - +2. 在线增强 ： 这种增强的方法用于，获得 batch 数据之后，然后对这个batch的数据进行增强，如旋转、平移、翻折等相应的变化，由于有些数据集不能接受线性级别的增长，这种方法长用于大的数据集，很多机器学习框架已经支持了这种数据增强方式，并且可以使用GPU优化计算。 +## ROI Pooling替换为ROI Align，及各自原理 +faster rcnn将roi pooling替换为roi align效果有所提升 +#### ROI Pooling原理 +#### ROI Align原理 ## Reference 1.[深度学习中的数据增强](https://blog.csdn.net/zhelong3205/article/details/81810743 ) ## Reference 1. [《Batch Normalization Accelerating Deep Network Training by Reducing Internal Covariate Shift》阅读笔记与实现](https://blog.csdn.net/happynear/article/details/44238541) 2. [深度学习中 Batch Normalization为什么效果好](https://blog.csdn.net/happynear/article/details/44238541) 3. [详解机器学习中的梯度消失、爆炸原因及其解决方法](https://blog.csdn.net/qq_25737169/article/details/78847691) -# Python/C/C++/计算机基础 +# Python/C/C++/计算机基础/图像处理基础 ## C++指针和引用的区别 + 不存在空引用, 引用必须链接到一块合法的内存地址； + 一旦引用被初始化为一个对象，就不能指向另一个对象。指针可以在任何时候指向任何一个对象； @@ -336,7 +340,20 @@ print(str2int('0')) print(str2int('12300')) print(str2int('0012345')) # 0012345 ``` - +## 图像锐化方法 +**锐化主要影响图像中的低频分量，不影响图像中的高频分量**像锐化的主要目的有两个： +1. 增强图像边缘，使模糊的图像变得更加清晰，颜色变得鲜明突出，图像的质量有所改善，产生更适合人眼观察和识别的图像； +2. 过锐化处理后，目标物体的边缘鲜明，以便于提取目标的边缘、对图像进行分割、目标区域识别、区域形状提取等，进一步的图像理解与分析奠定基础。 + +图像锐化一般有两种方法： +1. 微分法 +2. 高通滤波法 + +一般来说，图像的能量主要集中在其低频部分，噪声所在的频段主要在高频段，同时图像边缘信息也主要集中在其高频部分。这将导致原始图像在平滑处理之后，图像边缘和图像轮廓模糊的情况出现。为了减少这类不利效果的影响，就需要利用图像锐化技术，使图像的边缘变得清晰。图像锐化处理的目的是为了使图像的边缘、轮廓线以及图像的细节变得清晰，经过平滑的图像变得模糊的根本原因是因为图像受到了平均或积分运算，因此可以对其进行逆运算(如微分运算)就可以使图像变得清晰。微分运算是求信号的变化率，由傅立叶变换的微分性质可知，微分运算具有较强高频分量作用。从频率域来考虑，图像模糊的实质是因为其高频分量被衰减，因此可以用高通滤波器来使图像清晰。但要注意能够进行锐化处理的图像必须有较高的性噪比，否则锐化后图像性噪比反而更低，从而使得噪声增加的比信号还要多，因此一般是先去除或减轻噪声后再进行锐化处理。 +### Reference +[图像增强－图像锐化](https://www.cnblogs.com/BYTEMAN/archive/2012/07/21/2603021.html) +## numpy手撕代码 +人脸识别的场景下，输入105*12的feature map，用这个在1000512的特征库当中用欧氏距离去匹配105*12的feature map，用这个在1000*512的特征库当中用欧氏距离去匹配10*1000的特征，得到这个output # 机器学习 ## Focal Loss 介绍一下 From 0604ab93381e7cb66a1e33152cc7e94e7fee901b Mon Sep 17 00:00:00 2001 From: Zhang Date: Thu, 9 May 2019 15:04:48 +0800 Subject: [PATCH 098/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 更新深拷贝与浅拷贝区别 --- ...40\351\235\242\350\257\225\351\242\230.md" | 29 +++++++++++++++++++ 1 file changed, 29 insertions(+) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index cd57477b..b5eeac66 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -340,6 +340,35 @@ print(str2int('0')) print(str2int('12300')) print(str2int('0012345')) # 0012345 ``` +## Python深拷贝、浅拷贝区别 +1. 直接赋值：其实就是对象的引用（别名）。 +2. 浅拷贝(copy)：拷贝父对象，不会拷贝对象的内部的子对象。 +3. 深拷贝(deepcopy)： copy 模块的 deepcopy 方法，完全拷贝了父对象及其子对象，两者是完全独立的。 +看一个示例程序，就能明白浅拷贝与深拷贝的区别了： +```Python3 +#!/usr/bin/Python3 +# -*-coding:utf-8 -*- + +import copy +a = [1, 2, 3, ['a', 'b', 'c']] + +b = a # 赋值，传对象的引用 +c = copy.copy(a) # 浅拷贝 +d = copy.deepcopy(a) # 深拷贝 + +a.append(4) +a[3].append('d') + +print('a = ', a) +print('b = ', b) +print('c = ', c) +print('d = ', d) # [1, 2, 3, ['a', 'b', 'c']] +``` +程序输出如下： +> a = [1, 2, 3, ['a', 'b', 'c', 'd'], 4] +b = [1, 2, 3, ['a', 'b', 'c', 'd'], 4] +c = [1, 2, 3, ['a', 'b', 'c', 'd']] +d = [1, 2, 3, ['a', 'b', 'c']] ## 图像锐化方法 **锐化主要影响图像中的低频分量，不影响图像中的高频分量**像锐化的主要目的有两个： 1. 增强图像边缘，使模糊的图像变得更加清晰，颜色变得鲜明突出，图像的质量有所改善，产生更适合人眼观察和识别的图像； From f63bb26651c8833daf7a8e8dfe69e3c2c29e4d32 Mon Sep 17 00:00:00 2001 From: Zhang Date: Thu, 9 May 2019 16:15:24 +0800 Subject: [PATCH 099/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index b5eeac66..daaa4497 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -342,8 +342,8 @@ print(str2int('0012345')) # 0012345 ``` ## Python深拷贝、浅拷贝区别 1. 直接赋值：其实就是对象的引用（别名）。 -2. 浅拷贝(copy)：拷贝父对象，不会拷贝对象的内部的子对象。 -3. 深拷贝(deepcopy)： copy 模块的 deepcopy 方法，完全拷贝了父对象及其子对象，两者是完全独立的。 +2. 浅拷贝(copy)：拷贝父对象，不会拷贝对象的内部的子对象。**copy浅拷贝，没有拷贝子对象，所以原始数据改变，子对象会改变。** +3. 深拷贝(deepcopy)： copy 模块的 deepcopy 方法，完全拷贝了父对象及其子对象，两者是完全独立的。**深拷贝，包含对象里面的自对象的拷贝，所以原始对象的改变不会造成深拷贝里任何子元素的改变**。 看一个示例程序，就能明白浅拷贝与深拷贝的区别了： ```Python3 #!/usr/bin/Python3 @@ -382,7 +382,7 @@ d = [1, 2, 3, ['a', 'b', 'c']] ### Reference [图像增强－图像锐化](https://www.cnblogs.com/BYTEMAN/archive/2012/07/21/2603021.html) ## numpy手撕代码 -人脸识别的场景下，输入105*12的feature map，用这个在1000512的特征库当中用欧氏距离去匹配105*12的feature map，用这个在1000*512的特征库当中用欧氏距离去匹配10*1000的特征，得到这个output +人脸识别的场景下，输入105*12的feature map，用这个在1000512的特征库当中用欧氏距离去匹配105*12的feature map，用这个在1000*512的特征库当中用欧氏距离去匹配10*1000的特征，得到这个output。 # 机器学习 ## Focal Loss 介绍一下 From 11648a526c7f15145237ae6beb58258d7a657491 Mon Sep 17 00:00:00 2001 From: Zhang Date: Mon, 13 May 2019 17:06:20 +0800 Subject: [PATCH 100/386] =?UTF-8?q?Update=20=E8=AE=A1=E7=AE=97=E6=9C=BA?= =?UTF-8?q?=E8=A7=86=E8=A7=89=E5=B2=97=E5=AE=9E=E4=B9=A0=E9=9D=A2=E7=BB=8F?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 更新地平线机器人二面和“合心科技”糟糕的面试过程 --- ...36\344\271\240\351\235\242\347\273\217.md" | 45 +++++++++++++++++++ 1 file changed, 45 insertions(+) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" index 3dd761ad..99aa9726 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" @@ -67,3 +67,48 @@ + 地平线机器人面试真的问的很广，偏工程向，碰到不会的也不要太紧张，之后一定要去补课。 + 自己要加强Python基础的一些**技术盲点**。 + 以后面试表达要有针对性，不要拓展太多。 + + +### 二面（70分钟） +1. 项目介绍 + + 项目细节，和由项目延伸的原理问题 +2. 目标检测框架原理问题 + + RPN结构讲下，RPN的loss有哪些，分类loss是二分类还是多分类 + + ROI Pooling是在RPN前面还是后面，讲下原理 + + ROI Polling和ROI Align的区别 + + Mask RCNN基本结构讲下 +3. CNN的SOTA模型原理 + + ResNet结构讲下，它解决了什么问题 + + InceptionV3结构讲下 +4. C/C++/Python基础 + + Python装饰器解释下，基本要求是什么（参数为函数，返回为函数，本质是嵌套函数） + + C的结构体和C++类的区别(C结构体不能定义函数) + + __init__函数有什么用 + + Python怎么继承父类的__init__函数（super操作） +5. Linux系统基础操作 + + 一些基本命令 + + 管道命令解释下 + + 统计文件夹下的文件个数:ls -l | grep "^_" | wc -l +6. git相关操作 + + git熟不熟悉，平常怎么用 + + 除了commit、pull等基本命令，还用过哪些 + 7. 嵌入式Linux系统 + + tensorflow安装是源码安装还是pip/conda安装，交叉编译用过吗 + + cmake语法了解吗 + 8. 有什么想问我的 + + 对我此次面试评价如何，我有什么需要改进和学习的地方（在学习一定要加强系统学习专业基础，在公司很难有完整时间系统学习知识） + + 部门主要是做什么的，我去了之后做什么方向 + ### 二面总结 + 此次面试时间比较长，总的来说，这次面试自己还是有点进展，面试一定要保持心态放松和良好，表达要流利、清楚，针对面试官指定的问题，尽量不要拓展太多（超出问题本身），技术上一定要系统学习自己的方向。 + +## 合心科技（一家不尊重面试者、极其不友好的公司）(不到10分钟) +### 一面（不到10分钟） +1. 基本介绍（不确定他有在听吗） +2. 项目介绍（这个过程，面试语气度非常不友好，我也不确定他有在听我讲项目没，反正我讲完了，他也没问我什么问题，评价了下我做的东西太简单、太low了（大意是这个，**检测的项目就是套框架、没有自己实现框架**），目标检测的项目虽然是用了mask rcnn的框架，但是我自己也做了其他的工作，测试的程序是自己写的。还有，这个过程体验极其糟糕，我深深地感受到了面试官不尊重人、看不起人的语气和态度） +3. 你有什么想问我的吗（到这里面试官有些不耐烦了，估计就是照着流程问下，我问了这个岗位主要做什么方向的产品，被直接怼，你都不看招聘要求吗，我说招聘信息没写清楚，面试官不耐烦的讲了下是做教育产品，面试结束） + +### 面试总结 +1. 我承认自己技术水平不够，还需要不断学习，但是这不能成为这家公司面试官不尊重、看不起起我的理由，既然我通过了你们的简历筛选，就说明我的简历和技术水平得到了你们的部分承认，但是面试过程，不仅是在浪费双方时间，我更直接地感受到了“合心科技”这家公司深深的恶意和不尊重人。 +2. 说实话，我实习面了有９家公司了，阿里、地平线机器人、格灵深瞳、小鹏汽车都面过，面试官都是很友好的，但是这家"合心科技"公司的面试官真的态度超级糟糕，全程一副不尊重人、看不起人的态度，面试迟到、全程一副高高在上的态度、那种看不起人、不尊重面试者的语气，对不起，我真的实在是受不了。 +3. 最后，对于合心科技，这家创业公司，公司规模（50-150）人，我在这里写出面试过程，希望以后找实习的同学尽量避免这家公司的坑，不要让糟糕的面试体验影响了大家找工作的心情和态度。 +４. 写下这个总结的过程，我是尽量平复了自己的内心，尽量希望自己糟糕的心情不要影响了我的文字表达能力。这个面经我也保持了客观的态度，以上内容没有任何虚假。 From 8406229d0d6fbea4dfe649b3ec38ebb7c258f0f2 Mon Sep 17 00:00:00 2001 From: Zhang Date: Mon, 13 May 2019 17:09:47 +0800 Subject: [PATCH 101/386] Add files via upload --- "images/hr\344\272\244\346\265\201.jpg" | Bin 0 -> 199872 bytes ...54\345\217\270\346\246\202\345\206\265.jpg" | Bin 0 -> 602060 bytes ...25\350\200\205\344\275\223\351\252\214.jpg" | Bin 0 -> 457933 bytes 3 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 "images/hr\344\272\244\346\265\201.jpg" create mode 100644 "images/\345\205\254\345\217\270\346\246\202\345\206\265.jpg" create mode 100644 "images/\345\205\266\344\273\226\351\235\242\350\257\225\350\200\205\344\275\223\351\252\214.jpg" diff 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zg@3DJe7%9aV)^$zQla$!$X?jlxj9s*VZQn!B=#u`R3QSCK;boBll#cNovLYC-bb7K zyyw3B3e6`_{^gY}e=Vjq9}w#!=z9vw;NxA+_u^>Us_k4`eqjyz@2JNF%;-rsC(Kr- zv=q6m)FyybPlY~(+_D|<1q|EH=-!n~qH{FwkmP7q9{raJgM4!zE%Jj!>KKNFk%{CqCH znby?~9}x(6Qb~LUtKGJlpKq2h$)LmiAV#sZA$NcDs|l>1P-{(uIX5)59_D3BCp4&%Qc>;zZd;mv4`CwMHqq zrj(LfrSvA{~63ca!SgnKZ(BkH4wT&aKb`@3#hr`zZKMe{1CW!}_n z+%40S@Q;vrD?h~eGl%?JAH4i2oY@1$zx@t@dJOlk0ud!oV*a^Y Date: Mon, 13 May 2019 17:15:00 +0800 Subject: [PATCH 103/386] =?UTF-8?q?Update=20=E8=AE=A1=E7=AE=97=E6=9C=BA?= =?UTF-8?q?=E8=A7=86=E8=A7=89=E5=B2=97=E5=AE=9E=E4=B9=A0=E9=9D=A2=E7=BB=8F?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...62\227\345\256\236\344\271\240\351\235\242\347\273\217.md" | 4 ++++ 1 file changed, 4 insertions(+) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" index 99aa9726..b0619728 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" @@ -112,3 +112,7 @@ 2. 说实话，我实习面了有９家公司了，阿里、地平线机器人、格灵深瞳、小鹏汽车都面过，面试官都是很友好的，但是这家"合心科技"公司的面试官真的态度超级糟糕，全程一副不尊重人、看不起人的态度，面试迟到、全程一副高高在上的态度、那种看不起人、不尊重面试者的语气，对不起，我真的实在是受不了。 3. 最后，对于合心科技，这家创业公司，公司规模（50-150）人，我在这里写出面试过程，希望以后找实习的同学尽量避免这家公司的坑，不要让糟糕的面试体验影响了大家找工作的心情和态度。 ４. 写下这个总结的过程，我是尽量平复了自己的内心，尽量希望自己糟糕的心情不要影响了我的文字表达能力。这个面经我也保持了客观的态度，以上内容没有任何虚假。 +### 这家公司的概况和其他面试者评价 +![公司在拉勾网上的概况信息](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E5%85%AC%E5%8F%B8%E6%A6%82%E5%86%B5.jpg) +![其他面试者评价](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E5%85%B6%E4%BB%96%E9%9D%A2%E8%AF%95%E8%80%85%E4%BD%93%E9%AA%8C.jpg) +![其他面试者评价2](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E5%85%B6%E4%BB%96%E9%9D%A2%E8%AF%95%E8%80%85%E4%BD%93%E9%AA%8C2.png) From 520c89375b502171cf940e8ff76982c89c6e7458 Mon Sep 17 00:00:00 2001 From: Zhang Date: Mon, 13 May 2019 17:17:38 +0800 Subject: [PATCH 104/386] Add files via upload --- ...4\345\217\270\346\246\202\345\206\2651.png" | Bin 0 -> 217120 bytes ...5\350\200\205\344\275\223\351\252\2141.png" | Bin 0 -> 138142 bytes 2 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 "images/\345\205\254\345\217\270\346\246\202\345\206\2651.png" create mode 100644 "images/\345\205\266\344\273\226\351\235\242\350\257\225\350\200\205\344\275\223\351\252\2141.png" diff --git "a/images/\345\205\254\345\217\270\346\246\202\345\206\2651.png" 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z!6$_O%bdTl?kjseA*2G%*GScb(ezry9OiFrdT~F4?!;}ssu1VVd$# z{?%YWU|m~##EqY{3#NOO`JY@AE<&&O-uZX3;Ii0b9s9r?~fYf z^YA=3K5ueW0m-(b5ylHia<0G0hEx&se_3@X>j?EDDmQto9HajT(mZJv8ekz155%#z@m+p&ZgREv6$g+Q*uD{fXA=d_OZBb zyJ#1FkVJii_vRvOFo)ulnEWH6uTjt4htm{~m zQQy4a!7%@_;qt$m-O9lZVKA5|g7EF{{eISwDTYkj$i`8ZwB;FH=GHev-BOhN_P;wmRz|99MPevrmJQ!h3M@XBS;)iioiqu~(qAKMvfbN~PV literal 0 HcmV?d00001 From c2aa7a955ad5e8529d0e795f653eb455c4f3ce0c Mon Sep 17 00:00:00 2001 From: Zhang Date: Mon, 13 May 2019 17:18:23 +0800 Subject: [PATCH 105/386] =?UTF-8?q?Update=20=E8=AE=A1=E7=AE=97=E6=9C=BA?= =?UTF-8?q?=E8=A7=86=E8=A7=89=E5=B2=97=E5=AE=9E=E4=B9=A0=E9=9D=A2=E7=BB=8F?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...62\227\345\256\236\344\271\240\351\235\242\347\273\217.md" | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" index b0619728..44923d6c 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" @@ -113,6 +113,6 @@ 3. 最后，对于合心科技，这家创业公司，公司规模（50-150）人，我在这里写出面试过程，希望以后找实习的同学尽量避免这家公司的坑，不要让糟糕的面试体验影响了大家找工作的心情和态度。 ４. 写下这个总结的过程，我是尽量平复了自己的内心，尽量希望自己糟糕的心情不要影响了我的文字表达能力。这个面经我也保持了客观的态度，以上内容没有任何虚假。 ### 这家公司的概况和其他面试者评价 -![公司在拉勾网上的概况信息](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E5%85%AC%E5%8F%B8%E6%A6%82%E5%86%B5.jpg) -![其他面试者评价](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E5%85%B6%E4%BB%96%E9%9D%A2%E8%AF%95%E8%80%85%E4%BD%93%E9%AA%8C.jpg) +![公司在拉勾网上的概况信息](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E5%85%AC%E5%8F%B8%E6%A6%82%E5%86%B51.png) +![其他面试者评价1](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E5%85%B6%E4%BB%96%E9%9D%A2%E8%AF%95%E8%80%85%E4%BD%93%E9%AA%8C1.png) ![其他面试者评价2](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E5%85%B6%E4%BB%96%E9%9D%A2%E8%AF%95%E8%80%85%E4%BD%93%E9%AA%8C2.png) From f3a38f448c39108dd34ffe32cfd17746ec0535c9 Mon Sep 17 00:00:00 2001 From: Zhang Date: Mon, 13 May 2019 17:24:25 +0800 Subject: [PATCH 106/386] =?UTF-8?q?Update=20=E8=AE=A1=E7=AE=97=E6=9C=BA?= =?UTF-8?q?=E8=A7=86=E8=A7=89=E5=B2=97=E5=AE=9E=E4=B9=A0=E9=9D=A2=E7=BB=8F?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...62\227\345\256\236\344\271\240\351\235\242\347\273\217.md" | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" index 44923d6c..0d7d9d18 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" @@ -77,6 +77,7 @@ + ROI Pooling是在RPN前面还是后面，讲下原理 + ROI Polling和ROI Align的区别 + Mask RCNN基本结构讲下 + + 1*1卷积作用（降维－改变特征通道数，加入非线性） 3. CNN的SOTA模型原理 + ResNet结构讲下，它解决了什么问题 + InceptionV3结构讲下 @@ -85,6 +86,7 @@ + C的结构体和C++类的区别(C结构体不能定义函数) + __init__函数有什么用 + Python怎么继承父类的__init__函数（super操作） + + 面向对象编程和面向过程编程区别 5. Linux系统基础操作 + 一些基本命令 + 管道命令解释下 @@ -99,7 +101,7 @@ + 对我此次面试评价如何，我有什么需要改进和学习的地方（在学习一定要加强系统学习专业基础，在公司很难有完整时间系统学习知识） + 部门主要是做什么的，我去了之后做什么方向 ### 二面总结 - 此次面试时间比较长，总的来说，这次面试自己还是有点进展，面试一定要保持心态放松和良好，表达要流利、清楚，针对面试官指定的问题，尽量不要拓展太多（超出问题本身），技术上一定要系统学习自己的方向。 + 此次面试时间比较长，总的来说，这次面试自己还是有点进展，面试一定要保持**心态放松和良好，表达要流利、清楚**，针对面试官指定的问题，尽量不要拓展太多（超出问题本身），技术上一定要**系统学习自己的研究方向**。 ## 合心科技（一家不尊重面试者、极其不友好的公司）(不到10分钟) ### 一面（不到10分钟） From f1c6b1aae1587648c70b6ef6c91bf23b0c7bd799 Mon Sep 17 00:00:00 2001 From: Zhang Date: Mon, 13 May 2019 17:26:22 +0800 Subject: [PATCH 107/386] =?UTF-8?q?Update=20=E8=AE=A1=E7=AE=97=E6=9C=BA?= =?UTF-8?q?=E8=A7=86=E8=A7=89=E5=B2=97=E5=AE=9E=E4=B9=A0=E9=9D=A2=E7=BB=8F?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...62\227\345\256\236\344\271\240\351\235\242\347\273\217.md" | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" index 0d7d9d18..4d1b4e27 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" @@ -106,13 +106,13 @@ ## 合心科技（一家不尊重面试者、极其不友好的公司）(不到10分钟) ### 一面（不到10分钟） 1. 基本介绍（不确定他有在听吗） -2. 项目介绍（这个过程，面试语气度非常不友好，我也不确定他有在听我讲项目没，反正我讲完了，他也没问我什么问题，评价了下我做的东西太简单、太low了（大意是这个，**检测的项目就是套框架、没有自己实现框架**），目标检测的项目虽然是用了mask rcnn的框架，但是我自己也做了其他的工作，测试的程序是自己写的。还有，这个过程体验极其糟糕，我深深地感受到了面试官不尊重人、看不起人的语气和态度） +2. 项目介绍（这个过程，面试语气度非常不友好，我也不确定他有在听我讲项目没，反正我讲完了，他也没问我什么问题，评价了下我做的东西太简单、太low了（大意是这个），**说我检测的项目就是套框架、没有自己实现框架**，目标检测的项目虽然是用了mask rcnn的框架，但是我自己也做了很多其他的工作，比如测试的程序是自己写的等。这个过程体验真的极其糟糕，我深深地感受到了面试官不尊重人、看不起人的语气和态度） 3. 你有什么想问我的吗（到这里面试官有些不耐烦了，估计就是照着流程问下，我问了这个岗位主要做什么方向的产品，被直接怼，你都不看招聘要求吗，我说招聘信息没写清楚，面试官不耐烦的讲了下是做教育产品，面试结束） ### 面试总结 1. 我承认自己技术水平不够，还需要不断学习，但是这不能成为这家公司面试官不尊重、看不起起我的理由，既然我通过了你们的简历筛选，就说明我的简历和技术水平得到了你们的部分承认，但是面试过程，不仅是在浪费双方时间，我更直接地感受到了“合心科技”这家公司深深的恶意和不尊重人。 2. 说实话，我实习面了有９家公司了，阿里、地平线机器人、格灵深瞳、小鹏汽车都面过，面试官都是很友好的，但是这家"合心科技"公司的面试官真的态度超级糟糕，全程一副不尊重人、看不起人的态度，面试迟到、全程一副高高在上的态度、那种看不起人、不尊重面试者的语气，对不起，我真的实在是受不了。 -3. 最后，对于合心科技，这家创业公司，公司规模（50-150）人，我在这里写出面试过程，希望以后找实习的同学尽量避免这家公司的坑，不要让糟糕的面试体验影响了大家找工作的心情和态度。 +3. 最后，对于合心科技，这家创业公司，公司规模（50-150）人，我在这里写出面试过程，**希望以后找实习的同学尽量避免这家公司（合心科技）的坑，不要让糟糕的面试体验影响了大家找工作的心情和态度**。 ４. 写下这个总结的过程，我是尽量平复了自己的内心，尽量希望自己糟糕的心情不要影响了我的文字表达能力。这个面经我也保持了客观的态度，以上内容没有任何虚假。 ### 这家公司的概况和其他面试者评价 ![公司在拉勾网上的概况信息](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E5%85%AC%E5%8F%B8%E6%A6%82%E5%86%B51.png) From 1a69c5a241b1a40901b5867a13ecf1b058d896b0 Mon Sep 17 00:00:00 2001 From: Zhang Date: Mon, 13 May 2019 17:27:25 +0800 Subject: [PATCH 108/386] =?UTF-8?q?Update=20=E8=AE=A1=E7=AE=97=E6=9C=BA?= =?UTF-8?q?=E8=A7=86=E8=A7=89=E5=B2=97=E5=AE=9E=E4=B9=A0=E9=9D=A2=E7=BB=8F?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...27\345\256\236\344\271\240\351\235\242\347\273\217.md" | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" index 4d1b4e27..9aede296 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" @@ -71,15 +71,15 @@ ### 二面（70分钟） 1. 项目介绍 - + 项目细节，和由项目延伸的原理问题 + + **项目细节，和由项目延伸的原理问题** 2. 目标检测框架原理问题 + RPN结构讲下，RPN的loss有哪些，分类loss是二分类还是多分类 + ROI Pooling是在RPN前面还是后面，讲下原理 + ROI Polling和ROI Align的区别 - + Mask RCNN基本结构讲下 + + **Mask RCNN基本结构**讲下 + 1*1卷积作用（降维－改变特征通道数，加入非线性） 3. CNN的SOTA模型原理 - + ResNet结构讲下，它解决了什么问题 + + **ResNet结构讲下，它解决了什么问题** + InceptionV3结构讲下 4. C/C++/Python基础 + Python装饰器解释下，基本要求是什么（参数为函数，返回为函数，本质是嵌套函数） @@ -89,7 +89,7 @@ + 面向对象编程和面向过程编程区别 5. Linux系统基础操作 + 一些基本命令 - + 管道命令解释下 + + **管道命令**解释下 + 统计文件夹下的文件个数:ls -l | grep "^_" | wc -l 6. git相关操作 + git熟不熟悉，平常怎么用 From 66d9427dfd66f658c6db0b9b462e46f5073ca847 Mon Sep 17 00:00:00 2001 From: Zhang Date: Mon, 13 May 2019 17:35:02 +0800 Subject: [PATCH 109/386] =?UTF-8?q?Update=20=E8=AE=A1=E7=AE=97=E6=9C=BA?= =?UTF-8?q?=E8=A7=86=E8=A7=89=E5=B2=97=E5=AE=9E=E4=B9=A0=E9=9D=A2=E7=BB=8F?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...36\344\271\240\351\235\242\347\273\217.md" | 22 ++++++++++++++----- 1 file changed, 17 insertions(+), 5 deletions(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" index 9aede296..63942b09 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" @@ -68,8 +68,7 @@ + 自己要加强Python基础的一些**技术盲点**。 + 以后面试表达要有针对性，不要拓展太多。 - -### 二面（70分钟） + ### 二面（70分钟） 1. 项目介绍 + **项目细节，和由项目延伸的原理问题** 2. 目标检测框架原理问题 @@ -100,10 +99,23 @@ 8. 有什么想问我的 + 对我此次面试评价如何，我有什么需要改进和学习的地方（在学习一定要加强系统学习专业基础，在公司很难有完整时间系统学习知识） + 部门主要是做什么的，我去了之后做什么方向 - ### 二面总结 +### 二面总结 此次面试时间比较长，总的来说，这次面试自己还是有点进展，面试一定要保持**心态放松和良好，表达要流利、清楚**，针对面试官指定的问题，尽量不要拓展太多（超出问题本身），技术上一定要**系统学习自己的研究方向**。 - -## 合心科技（一家不尊重面试者、极其不友好的公司）(不到10分钟) +## 小鹏汽车－图像算法实习生 +### 一面（30分钟） +1. 自我介绍 +2. 项目介绍 + + 项目细节询问 + + 数据增强用了哪些，为什么用 +3. 拓展问题 + + 图像分割结果，如果边缘信息本来是直线的，但是分割出来效果线确是弯的，怎么解决（有点记不清了） +4. 你有什么想问我的 +### 二面（27分钟） +1. 项目介绍 +2. 你平时看过哪些论文，最新的论文看过哪些 +3. 平时几点钟回去, 代码量多少，平时用C、C++还是Python编程 +４．你有什么想问的 +## 合心科技算法实习生（一家不尊重面试者、面试体验极其糟糕的公司）(不到10分钟) ### 一面（不到10分钟） 1. 基本介绍（不确定他有在听吗） 2. 项目介绍（这个过程，面试语气度非常不友好，我也不确定他有在听我讲项目没，反正我讲完了，他也没问我什么问题，评价了下我做的东西太简单、太low了（大意是这个），**说我检测的项目就是套框架、没有自己实现框架**，目标检测的项目虽然是用了mask rcnn的框架，但是我自己也做了很多其他的工作，比如测试的程序是自己写的等。这个过程体验真的极其糟糕，我深深地感受到了面试官不尊重人、看不起人的语气和态度） From ea2a72ec56fd2b7f761d3176fe050d0446097bb5 Mon Sep 17 00:00:00 2001 From: Zhang Date: Mon, 13 May 2019 17:35:33 +0800 Subject: [PATCH 110/386] =?UTF-8?q?Update=20=E8=AE=A1=E7=AE=97=E6=9C=BA?= =?UTF-8?q?=E8=A7=86=E8=A7=89=E5=B2=97=E5=AE=9E=E4=B9=A0=E9=9D=A2=E7=BB=8F?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" index 63942b09..512eb529 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" @@ -114,7 +114,8 @@ 1. 项目介绍 2. 你平时看过哪些论文，最新的论文看过哪些 3. 平时几点钟回去, 代码量多少，平时用C、C++还是Python编程 -４．你有什么想问的 +4．你有什么想问的 + ## 合心科技算法实习生（一家不尊重面试者、面试体验极其糟糕的公司）(不到10分钟) ### 一面（不到10分钟） 1. 基本介绍（不确定他有在听吗） From 547b65e5f90b088dcf8517200ca0b76f244a5848 Mon Sep 17 00:00:00 2001 From: Zhang Date: Mon, 13 May 2019 18:16:33 +0800 Subject: [PATCH 111/386] =?UTF-8?q?Update=20=E8=AE=A1=E7=AE=97=E6=9C=BA?= =?UTF-8?q?=E8=A7=86=E8=A7=89=E5=B2=97=E5=AE=9E=E4=B9=A0=E9=9D=A2=E7=BB=8F?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" index 512eb529..d891a401 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" @@ -119,7 +119,7 @@ ## 合心科技算法实习生（一家不尊重面试者、面试体验极其糟糕的公司）(不到10分钟) ### 一面（不到10分钟） 1. 基本介绍（不确定他有在听吗） -2. 项目介绍（这个过程，面试语气度非常不友好，我也不确定他有在听我讲项目没，反正我讲完了，他也没问我什么问题，评价了下我做的东西太简单、太low了（大意是这个），**说我检测的项目就是套框架、没有自己实现框架**，目标检测的项目虽然是用了mask rcnn的框架，但是我自己也做了很多其他的工作，比如测试的程序是自己写的等。这个过程体验真的极其糟糕，我深深地感受到了面试官不尊重人、看不起人的语气和态度） +2. 项目介绍（这个过程，面试语气度非常不友好，我也不确定他有在听我讲项目没，反正我讲完了，他也没问我什么问题，评价了下我做的东西太简单、太low了（大意是这个），**说我检测的项目就是套框架、没有自己实现框架**，目标检测的项目虽然是用了mask rcnn的框架，但是我自己也做了很多其他的工作，比如测试的程序、数据过采样、数据标签生成、训练策略调整等是自己写的。这个过程体验真的极其糟糕，我深深地感受到了面试官不尊重人、看不起人的语气和态度） 3. 你有什么想问我的吗（到这里面试官有些不耐烦了，估计就是照着流程问下，我问了这个岗位主要做什么方向的产品，被直接怼，你都不看招聘要求吗，我说招聘信息没写清楚，面试官不耐烦的讲了下是做教育产品，面试结束） ### 面试总结 From 6f37ad2c750ff64dc0e59da727862d8d93342a7b Mon Sep 17 00:00:00 2001 From: Zhang Date: Mon, 13 May 2019 18:20:09 +0800 Subject: [PATCH 112/386] =?UTF-8?q?Update=20=E8=AE=A1=E7=AE=97=E6=9C=BA?= =?UTF-8?q?=E8=A7=86=E8=A7=89=E5=B2=97=E5=AE=9E=E4=B9=A0=E9=9D=A2=E7=BB=8F?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" index d891a401..07da96fb 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" @@ -124,7 +124,7 @@ ### 面试总结 1. 我承认自己技术水平不够，还需要不断学习，但是这不能成为这家公司面试官不尊重、看不起起我的理由，既然我通过了你们的简历筛选，就说明我的简历和技术水平得到了你们的部分承认，但是面试过程，不仅是在浪费双方时间，我更直接地感受到了“合心科技”这家公司深深的恶意和不尊重人。 -2. 说实话，我实习面了有９家公司了，阿里、地平线机器人、格灵深瞳、小鹏汽车都面过，面试官都是很友好的，但是这家"合心科技"公司的面试官真的态度超级糟糕，全程一副不尊重人、看不起人的态度，面试迟到、全程一副高高在上的态度、那种看不起人、不尊重面试者的语气，对不起，我真的实在是受不了。 +2. 说实话，我**实习面了有９家公司了**，阿里、地平线机器人、格灵深瞳、小鹏汽车都面过，面试官都是很友好的，但是这家"合心科技"公司的面试官真的态度超级糟糕，全程一副不尊重人、看不起人的态度，面试迟到、全程一副高高在上的态度、那种看不起人、不尊重面试者的语气，对不起，我真的实在是受不了。 3. 最后，对于合心科技，这家创业公司，公司规模（50-150）人，我在这里写出面试过程，**希望以后找实习的同学尽量避免这家公司（合心科技）的坑，不要让糟糕的面试体验影响了大家找工作的心情和态度**。 ４. 写下这个总结的过程，我是尽量平复了自己的内心，尽量希望自己糟糕的心情不要影响了我的文字表达能力。这个面经我也保持了客观的态度，以上内容没有任何虚假。 ### 这家公司的概况和其他面试者评价 From 81654fba605c6f1e72e1c37701c7351428677cdd Mon Sep 17 00:00:00 2001 From: Zhang Date: Mon, 13 May 2019 23:57:34 +0800 Subject: [PATCH 113/386] Add files via upload --- ...42\350\257\225\344\275\223\351\252\2143.png" | Bin 0 -> 57112 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 "images/\345\205\266\344\273\226\351\235\242\350\257\225\344\275\223\351\252\2143.png" diff --git "a/images/\345\205\266\344\273\226\351\235\242\350\257\225\344\275\223\351\252\2143.png" "b/images/\345\205\266\344\273\226\351\235\242\350\257\225\344\275\223\351\252\2143.png" new file mode 100644 index 0000000000000000000000000000000000000000..80f0277589e63f50fed66a1e248bb13011cc9b2d GIT binary patch literal 57112 zcmdpdWmg5%O`m?KqmaD3Rg{z0LvpI~Vy@Q?kR~J)fb8~waD+kvz*e*dB7%~_c zaS=7o%;QxbHQj}*^OrLoDZKa==|EITd_rXO2t6!4h|_l>YIl>t2(VcuCz~}d*k{j< z?~J|PV%bCE=~~-Ar}!XUW=}iOYXK|>R;12qO0&>NY-#0o%ItF|=Id@w75}=;6-15Lj%b^ z1pP;P^uoy`|M!*z9Ls;3gUA?Y$iqMVw+o!H$S0BLe|w-relYo;b`dRQTheR7R7LQ^ 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z^60sZTvx}ZRjI52_|m+<$E5A3%?Qn&<)57h$l$B1B{$AZOnePxHk=uUAM?tT1v1R5 zy65IZrzVyIZ6692EKQG-F5BB;U>!!Ryf%tUN`_5|RwTR!=Dw{fBQOe(y7KRlC5e3N zw>%P@Rd@RW9V#}cdnbyWz558``y@{Nu?!NfBNPHejtVwLws??+%0*Pm|K>*FR;~0)Ff71jKhR?7-|MCd}q3FQ>3J^$yy_XIE{Fit}+m%K4KJs5# zkXqcUPh7_|YsehK6V*(=iIG3lV)xbc>?#_DC^EiQ2jaz(4TR5pxuvg|DA@fK=D%t= zOQU|SXi_Uwe-z1j6Q3$kPoJkxKzzLbD|UW=*fMW-E^iBkgCpbob%uGVR<-Cel5*!W z0bU>TW-!`7tt>TX{#%kMrN2A|Tbtwgk$meHqAx-3)nAB!KZH~bME z?;O4t4gTBacGLrgluYooXxu-woHsd2(C`lVrEXHW^hX+@?34fYfT}MgDN& zzH{N|d>QwlM_G}ne8wE>?^KP6{AYCNT+_ANvqJhI@2Jw~%MnZAI%deGGWOKw`{&Yv z2ckc=2IA+g+r3uGdwgJ9Fm#Y=#tt~Q(rHm>za3oQfSU#yCQ~5#D}|g9hA=YR$Pv$Z zAECVu%@|yk*WZV`Q-4)O>NFwG7&F1oPi1U>Gdo<=jaxuED3CY57cw=@1sV!^@kNL3 zO1yfQ3>7PAW%ty5r$C4?K~5DfED%7!W|4*Zell46(`}1X~GXB*hsg?hT5UPbgg=&{15+9}G zpa5I^C?=27xx3_ea6;D{w{&xNEnUiSC@$F!RN{rZ-a7w<*5}AAKDi^5C&Ul)eM)lP(>`wJa~e)j$YU#;&bT0^SVW&I-J zr1K$PxkB$<+UTO-A*W5t{8pd^tY1w`7ZBP1tLZx2L_91khRfFZmDy5h9P3?~r?b*; z6C}rLRPN75;Ei-iU?Yvb#LwU8L(u5MsC`2&S+QC0>UogdUi@}!s)pA(85Lw#R*~%& zl49iN_Af*6uGIdnp*35FUpgi}=TqWM1ZyAMzd~l14SlC9K6v4HdY$Hn3W$jeNuab~ zoHld+z-}h)|AD~qzQ374|6jl;^LOr_0gE1B`(HJH<^vr6!&BThL|y;h(_)bPkMQ1S`n`)Y$S{%~SdKYDoNrB$TLB}@bU4`5ggNB{r; literal 0 HcmV?d00001 From c8ddc42c5df6f37f9fb00a72a5de81951edafe62 Mon Sep 17 00:00:00 2001 From: Zhang Date: Mon, 13 May 2019 23:58:25 +0800 Subject: [PATCH 114/386] =?UTF-8?q?Update=20=E8=AE=A1=E7=AE=97=E6=9C=BA?= =?UTF-8?q?=E8=A7=86=E8=A7=89=E5=B2=97=E5=AE=9E=E4=B9=A0=E9=9D=A2=E7=BB=8F?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" index 07da96fb..658980b7 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" @@ -130,4 +130,4 @@ ### 这家公司的概况和其他面试者评价 ![公司在拉勾网上的概况信息](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E5%85%AC%E5%8F%B8%E6%A6%82%E5%86%B51.png) ![其他面试者评价1](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E5%85%B6%E4%BB%96%E9%9D%A2%E8%AF%95%E8%80%85%E4%BD%93%E9%AA%8C1.png) -![其他面试者评价2](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E5%85%B6%E4%BB%96%E9%9D%A2%E8%AF%95%E8%80%85%E4%BD%93%E9%AA%8C2.png) +![其他面试者评价2](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/其他面试体验3.png) From 5531977aa1cc2a2768e1260257cabb8e2d59cbe4 Mon Sep 17 00:00:00 2001 From: Zhang Date: Tue, 14 May 2019 00:01:50 +0800 Subject: [PATCH 115/386] =?UTF-8?q?Update=20=E8=AE=A1=E7=AE=97=E6=9C=BA?= =?UTF-8?q?=E8=A7=86=E8=A7=89=E5=B2=97=E5=AE=9E=E4=B9=A0=E9=9D=A2=E7=BB=8F?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" index 658980b7..f3328264 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" @@ -115,7 +115,8 @@ 2. 你平时看过哪些论文，最新的论文看过哪些 3. 平时几点钟回去, 代码量多少，平时用C、C++还是Python编程 4．你有什么想问的 - +### 面试总结 +二面的面试官提到我最新的论文看的不多，其实**最新的论文是一定要看的**，紧跟行业发展，了解技术的最新发展动向，而且也有助于拓展自己的思维，学习下别人的idea。 ## 合心科技算法实习生（一家不尊重面试者、面试体验极其糟糕的公司）(不到10分钟) ### 一面（不到10分钟） 1. 基本介绍（不确定他有在听吗） From bed2b640088d9ea8d38a4e2f4241bde790aaadb9 Mon Sep 17 00:00:00 2001 From: Zhang Date: Tue, 14 May 2019 00:14:43 +0800 Subject: [PATCH 116/386] =?UTF-8?q?Update=20=E8=AE=A1=E7=AE=97=E6=9C=BA?= =?UTF-8?q?=E8=A7=86=E8=A7=89=E5=B2=97=E5=AE=9E=E4=B9=A0=E9=9D=A2=E7=BB=8F?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...27\345\256\236\344\271\240\351\235\242\347\273\217.md" | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" index f3328264..083a42c1 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" @@ -53,7 +53,7 @@ + 训练网络的指标，除了基本的的acc,loss，**roc、auc**有了解吗？ 4. 算法细节 + ROI Pooling和ROI Align的区别及演进 - + + + 离线图像增强与在线图像增强有什么区别 5. Python和计算机常考基础 + 装饰器怎么用 + 深拷贝和浅拷贝的区别 @@ -71,12 +71,14 @@ ### 二面（70分钟） 1. 项目介绍 + **项目细节，和由项目延伸的原理问题** + + 细粒度图像分类了解吗 2. 目标检测框架原理问题 + RPN结构讲下，RPN的loss有哪些，分类loss是二分类还是多分类 - + ROI Pooling是在RPN前面还是后面，讲下原理 + + ROI Pooling是在RPN前面还是后面，讲下原理，有什么作用 + ROI Polling和ROI Align的区别 + **Mask RCNN基本结构**讲下 + 1*1卷积作用（降维－改变特征通道数，加入非线性） + + Faster RCNN的loss有哪些，分别讲下 3. CNN的SOTA模型原理 + **ResNet结构讲下，它解决了什么问题** + InceptionV3结构讲下 @@ -85,7 +87,7 @@ + C的结构体和C++类的区别(C结构体不能定义函数) + __init__函数有什么用 + Python怎么继承父类的__init__函数（super操作） - + 面向对象编程和面向过程编程区别 + + **面向对象编程和面向过程编程区别** 5. Linux系统基础操作 + 一些基本命令 + **管道命令**解释下 From 6edae1398da68d62895f2216fc001bacf6591b11 Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 15 May 2019 16:26:40 +0800 Subject: [PATCH 117/386] =?UTF-8?q?Create=20=E6=9A=91=E6=9C=9F=E8=AE=A1?= =?UTF-8?q?=E7=AE=97=E6=9C=BA=E8=A7=86=E8=A7=89=E5=AE=9E=E4=B9=A0=E5=BA=94?= =?UTF-8?q?=E8=81=98=E6=80=BB=E7=BB=93.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...345\272\224\350\201\230\346\200\273\347\273\223.md" | 10 ++++++++++ 1 file changed, 10 insertions(+) create mode 100644 "\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" diff --git "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" new file mode 100644 index 00000000..f9d3fe05 --- /dev/null +++ "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" @@ -0,0 +1,10 @@ +## 找实习感想 +从4月１日开始找实习，到5月13日收到南京地平线机器人口头offer和北京小鹏汽车技术面通过通知，这40来天的时间真的过的不容易，每天早上醒来都被“找实习”这三个字 +压得沉甸甸的，一有空闲时间就看牛客网面经和投简历，前前后后差不多有60家公司，大公司、小公司、独角兽公司只要招深度学习/计算机视觉/图像算法相关岗位，我都投， +投递的渠道最开始是官网投递，后来发现太慢了，招银、平安科技这两家公司填写的简历信息太多太耗时了，后来就转boss直聘、拉勾网、牛客网和内推邮箱渠道，这样就省事 +很多，一个平台填好简历，可以投递不同公司。 +## 找实习建议 +1. **心态第一，坚持为胜，找实习是一个持久战**。对于技术实力一般的人来说，真的要保持良好的心态，不抛弃、不放弃，这中间也许你会经历很多失败，但是真的只要坚持下来，我不保证你能拿到很好公司的 +offer，但是你自己本省一定能从面试中学到很多东西，尤其是很多大公司的面试官会给你一些很恳切的建议，可以让你受益匪浅。 +2. **实习要趁早**。建议有条件的研一或者大三就去实习，对于暑期实习来说，简历投递一定要趁早，最好2月份就开始，我很晚才投简历，很可能就会错过内推时间和岗位 +hc没有的情况，我4月份开始投简历已经算是很晚了，所以也直接导致我投了海康威视之后，一直是简历复选中，很有可能就是hc已经没了。 From 8386b6a13eb1f253ccf761f3ea4bb397e045bb13 Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 15 May 2019 17:08:55 +0800 Subject: [PATCH 118/386] =?UTF-8?q?Update=20=E6=9A=91=E6=9C=9F=E8=AE=A1?= =?UTF-8?q?=E7=AE=97=E6=9C=BA=E8=A7=86=E8=A7=89=E5=AE=9E=E4=B9=A0=E5=BA=94?= =?UTF-8?q?=E8=81=98=E6=80=BB=E7=BB=93.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 更新找实习总结 --- ...24\350\201\230\346\200\273\347\273\223.md" | 30 ++++++++++++++----- 1 file changed, 22 insertions(+), 8 deletions(-) diff --git "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" index f9d3fe05..2d2ea3bf 100644 --- "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" +++ "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" @@ -1,10 +1,24 @@ ## 找实习感想 -从4月１日开始找实习，到5月13日收到南京地平线机器人口头offer和北京小鹏汽车技术面通过通知，这40来天的时间真的过的不容易，每天早上醒来都被“找实习”这三个字 -压得沉甸甸的，一有空闲时间就看牛客网面经和投简历，前前后后差不多有60家公司，大公司、小公司、独角兽公司只要招深度学习/计算机视觉/图像算法相关岗位，我都投， -投递的渠道最开始是官网投递，后来发现太慢了，招银、平安科技这两家公司填写的简历信息太多太耗时了，后来就转boss直聘、拉勾网、牛客网和内推邮箱渠道，这样就省事 -很多，一个平台填好简历，可以投递不同公司。 +从4月１日开始找实习，到5月13日收到南京地平线机器人口头offer和北京小鹏汽车技术面通过通知，这40来天的时间真的过的不容易，每天早上醒来都被“找实习”这三个字压得沉甸甸的，一有空闲时间就看牛客网面经和投简历，前前后后差不多有60家公司，大公司、小公司、独角兽公司只要招深度学习/计算机视觉/图像算法相关岗位，我都投，投递的渠道最开始是官网投递，后来发现太慢了，招银、平安科技这两家公司填写的简历信息太多太耗时了，后来就转boss直聘、拉勾网、牛客网和内推邮箱渠道，这样就省事很多，一个平台填好简历，可以投递不同公司。 ## 找实习建议 -1. **心态第一，坚持为胜，找实习是一个持久战**。对于技术实力一般的人来说，真的要保持良好的心态，不抛弃、不放弃，这中间也许你会经历很多失败，但是真的只要坚持下来，我不保证你能拿到很好公司的 -offer，但是你自己本省一定能从面试中学到很多东西，尤其是很多大公司的面试官会给你一些很恳切的建议，可以让你受益匪浅。 -2. **实习要趁早**。建议有条件的研一或者大三就去实习，对于暑期实习来说，简历投递一定要趁早，最好2月份就开始，我很晚才投简历，很可能就会错过内推时间和岗位 -hc没有的情况，我4月份开始投简历已经算是很晚了，所以也直接导致我投了海康威视之后，一直是简历复选中，很有可能就是hc已经没了。 +1. **心态第一，坚持为胜，找实习是一个持久战**。对于技术实力一般的人来说，真的要保持良好的心态，不抛弃、不放弃，这中间也许你会经历很多失败，但是真的只要坚持下来，我不保证你能拿到很好公司的offer，但是你自己本省一定能从面试中学到很多东西，尤其是很多大公司的面试官会给你一些很恳切的建议，可以让你受益匪浅。 +2. **实习要趁早**。建议有条件的研一或者大三就去实习，对于暑期实习来说，简历投递一定要趁早，最好2月份就开始，我很晚才投简历，很可能就会错过内推时间和岗位hc没有的情况，我4月份开始投简历已经算是很晚了，所以也直接导致我投了海康威视之后，一直是简历复选中，很有可能就是hc已经没了。 +3. **数据结构与算法题必须刷**。虽然这是老生常谈的建议，但是我们必须记住很多大公司一定会有笔试题，就算是内推免笔试的，面试过程中也很有可能会出数据结构与算法题。这里我建议去**leetcode**或者牛客网上刷**剑指offer**，一般把简单和中等难度题刷会就可以，笔试题一般都不会很难，除非是谷歌、MSRA那种公司可能会对笔试题有更高的要求。刷题的话，第一次刷不会可以去看参考解题思路和答案，**看完后要自己写出代码**。 +4. **常见面试题要掌握**。其实关于计算机基础和计算机视觉算法原理的面试题，可以提前准备下，有些题频繁的问到，可以提前准备下，比如：1*1卷积作用，链式求导公式，多线程与多进程区别，tcp/udp通信原理等。这些常见面试题，都可以在牛客网找的到，当然**不同岗位面试题不一样，甚至每个人的面试问题都有很大不一样**，不要因为别人面经上的题不会，你就有些失去信心，但是**对于同一个岗位的频繁出现的面试问题还是必须掌握**。机器学习、算法工程师面试考点汇总，参考[这里](https://www.nowcoder.com/discuss/165930) +## 面试过程建议 +1. 不要紧张，表达要清楚流利，要记住，绝大部分面试官都是很友好和亲切的，尤其是大公司的面试官真的超级温和，**这里为阿里和地平线机器人面试官点赞**！对于那种不尊重人和看不起人的面试官，我个人觉得没必要去他们公司了，一般这种面试官会出现在小公司，一个面试官连对面试者基本的尊重都做不到，我难以想象这家公司的文化是什么样子。 +2. 要对自己有信心，但是**千万不能撒谎和装逼**，一般面试过程中不会的问题，面试官也会跳过或者安慰你没事的，不用太紧张，碰到１、2个不会的问题也属正常。 +## 计算机视觉岗找实习心得 +首先，我的水平真的算是很一般的，真正的大佬都是很轻松的拿到数个满意offer。我自己是本科是双非大学自动化专业，研究生是中等211大学控制工程专业，本科主要搞嵌入式方向，研究生才转为计算机视觉方向和深度学习方向，这里也给后来者一个建议，如果不是真心热爱、喜欢你所从事的计算机视觉方向，只是为了钱的话那就真的没必要了，CV岗竞争真的很激烈，nlp和开发岗好很多，而且现在开发岗工资真的很高啊！ +最开始找实习的时候，我没有刷过数据结构和算法题，导致我华为(程序写出来了，但是输入输出格式没注意)、百度、腾讯笔试统统挂了，那段时间真的超级难过，后悔没有提前刷题。4月20号之后，我开始在boss直聘上投简历，这里陆陆续续收到2家小公司面试通知和offer，也算是给了我些信心。这段时间一边把之前项目细节搞清楚，一边开始复习栈、队列、链表、二叉树和经典数据结构算法原理，并在[leetcode](https://leetcode-cn.com/)上刷题，因为时间关系刷的不多，然后就是把Faster RCNN、FPN、Mask RCNN、retinanet、ResNet、VGG、InceptionV3等网络结构原理和细节部分彻底搞清楚，并去看了部分检测框架源码，然后就是深度学习算法的一些基本原理：链式求导过程、BP反向传播、SGD优化器原理、激活函数公式及原理、常见图像处理算法等，这里涉及一些公式，还就是C/C++/Python编程基础，和计算机基础等。每经过一次面试，我都会自我总结，这使得我后期对面试也开始有了些自己的经验和心得。 +对于CV实习岗，面试涉及到的知识可能有以下部分： +1. 目标检测算法原理和网络结构细节: + + 经典的two-stage算法: Faster RCNN、FPN、Mask RCNN等 + + one-stage算法：SSD、yolo、retinanet等 +2. CNN的SOTA网络原理和细节: ResNet、VGG、InceptionV3、DenseNet等. +3. 深度学习算法公式理解：链式求导过程、BP反向传播、SGD优化器原理、激活函数公式及原理、常见图像处理算法等. +4. C/C++/Java/Python编程基础 +5. 常用工程工具使用基础 + + cmake、git语法等和操作 + + linux系统开发，常见命令使用和shell语法 +6. 项目或者paper**细节**，根据项目细节延伸问如何做提升和改变 From 13facc8f8ed8289b59d7307f1e60d2eaa6715182 Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 15 May 2019 17:10:31 +0800 Subject: [PATCH 119/386] =?UTF-8?q?Update=20=E6=9A=91=E6=9C=9F=E8=AE=A1?= =?UTF-8?q?=E7=AE=97=E6=9C=BA=E8=A7=86=E8=A7=89=E5=AE=9E=E4=B9=A0=E5=BA=94?= =?UTF-8?q?=E8=81=98=E6=80=BB=E7=BB=93.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\240\345\272\224\350\201\230\346\200\273\347\273\223.md" | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" index 2d2ea3bf..058576e3 100644 --- "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" +++ "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" @@ -9,9 +9,11 @@ 1. 不要紧张，表达要清楚流利，要记住，绝大部分面试官都是很友好和亲切的，尤其是大公司的面试官真的超级温和，**这里为阿里和地平线机器人面试官点赞**！对于那种不尊重人和看不起人的面试官，我个人觉得没必要去他们公司了，一般这种面试官会出现在小公司，一个面试官连对面试者基本的尊重都做不到，我难以想象这家公司的文化是什么样子。 2. 要对自己有信心，但是**千万不能撒谎和装逼**，一般面试过程中不会的问题，面试官也会跳过或者安慰你没事的，不用太紧张，碰到１、2个不会的问题也属正常。 ## 计算机视觉岗找实习心得 -首先，我的水平真的算是很一般的，真正的大佬都是很轻松的拿到数个满意offer。我自己是本科是双非大学自动化专业，研究生是中等211大学控制工程专业，本科主要搞嵌入式方向，研究生才转为计算机视觉方向和深度学习方向，这里也给后来者一个建议，如果不是真心热爱、喜欢你所从事的计算机视觉方向，只是为了钱的话那就真的没必要了，CV岗竞争真的很激烈，nlp和开发岗好很多，而且现在开发岗工资真的很高啊！ -最开始找实习的时候，我没有刷过数据结构和算法题，导致我华为(程序写出来了，但是输入输出格式没注意)、百度、腾讯笔试统统挂了，那段时间真的超级难过，后悔没有提前刷题。4月20号之后，我开始在boss直聘上投简历，这里陆陆续续收到2家小公司面试通知和offer，也算是给了我些信心。这段时间一边把之前项目细节搞清楚，一边开始复习栈、队列、链表、二叉树和经典数据结构算法原理，并在[leetcode](https://leetcode-cn.com/)上刷题，因为时间关系刷的不多，然后就是把Faster RCNN、FPN、Mask RCNN、retinanet、ResNet、VGG、InceptionV3等网络结构原理和细节部分彻底搞清楚，并去看了部分检测框架源码，然后就是深度学习算法的一些基本原理：链式求导过程、BP反向传播、SGD优化器原理、激活函数公式及原理、常见图像处理算法等，这里涉及一些公式，还就是C/C++/Python编程基础，和计算机基础等。每经过一次面试，我都会自我总结，这使得我后期对面试也开始有了些自己的经验和心得。 +1. 首先，我的水平真的算是很一般的，真正的大佬都是很轻松的拿到数个满意offer。我自己是本科是双非大学自动化专业，研究生是中等211大学控制工程专业，本科主要搞嵌入式方向，研究生才转为计算机视觉方向和深度学习方向，这里也给后来者一个建议，如果不是真心热爱、喜欢你所从事的计算机视觉方向，只是为了钱的话那就真的没必要了，CV岗竞争真的很激烈，nlp和开发岗好很多，而且现在开发岗工资真的很高啊！ +2. 最开始找实习的时候，我没有刷过数据结构和算法题，导致我华为(程序写出来了，但是输入输出格式没注意)、百度、腾讯笔试统统挂了，那段时间真的超级难过，后悔没有提前刷题。4月20号之后，我开始在boss直聘上投简历，这里陆陆续续收到2家小公司面试通知和offer，也算是给了我些信心。 +3. 最后，在这段时间一边把之前项目细节搞清楚，一边开始复习栈、队列、链表、二叉树和经典数据结构算法原理，并在[leetcode](https://leetcode-cn.com/)上刷题，因为时间关系刷的不多，然后就是把Faster RCNN、FPN、Mask RCNN、retinanet、ResNet、VGG、InceptionV3等网络结构原理和细节部分彻底搞清楚，并去看了部分检测框架源码，然后就是深度学习算法的一些基本原理：链式求导过程、BP反向传播、SGD优化器原理、激活函数公式及原理、常见图像处理算法等，这里涉及一些公式，还就是C/C++/Python编程基础，和计算机基础等。每经过一次面试，我都会自我总结，这使得我后期对面试也开始有了些自己的经验和心得。 对于CV实习岗，面试涉及到的知识可能有以下部分： +## 计算机视觉面试问题分类总结 1. 目标检测算法原理和网络结构细节: + 经典的two-stage算法: Faster RCNN、FPN、Mask RCNN等 + one-stage算法：SSD、yolo、retinanet等 From 2c10f033477820d30e2f403d9f29205b0eb2e170 Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 15 May 2019 19:30:11 +0800 Subject: [PATCH 120/386] =?UTF-8?q?Update=20=E6=9A=91=E6=9C=9F=E8=AE=A1?= =?UTF-8?q?=E7=AE=97=E6=9C=BA=E8=A7=86=E8=A7=89=E5=AE=9E=E4=B9=A0=E5=BA=94?= =?UTF-8?q?=E8=81=98=E6=80=BB=E7=BB=93.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...40\345\272\224\350\201\230\346\200\273\347\273\223.md" | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" index 058576e3..bd3e1d3d 100644 --- "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" +++ "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" @@ -15,12 +15,12 @@ 对于CV实习岗，面试涉及到的知识可能有以下部分： ## 计算机视觉面试问题分类总结 1. 目标检测算法原理和网络结构细节: - + 经典的two-stage算法: Faster RCNN、FPN、Mask RCNN等 - + one-stage算法：SSD、yolo、retinanet等 + + 经典的two-stage算法: Faster RCNN、FPN、Mask RCNN等 + + one-stage算法：SSD、yolo、retinanet等 2. CNN的SOTA网络原理和细节: ResNet、VGG、InceptionV3、DenseNet等. 3. 深度学习算法公式理解：链式求导过程、BP反向传播、SGD优化器原理、激活函数公式及原理、常见图像处理算法等. 4. C/C++/Java/Python编程基础 5. 常用工程工具使用基础 - + cmake、git语法等和操作 - + linux系统开发，常见命令使用和shell语法 + + cmake、git语法等和操作 + + linux系统开发，常见命令使用和shell语法 6. 项目或者paper**细节**，根据项目细节延伸问如何做提升和改变 From a7db1d7ef940c812eb49606ce584c41b2474cb55 Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 15 May 2019 19:41:20 +0800 Subject: [PATCH 121/386] =?UTF-8?q?Update=20=E6=9A=91=E6=9C=9F=E8=AE=A1?= =?UTF-8?q?=E7=AE=97=E6=9C=BA=E8=A7=86=E8=A7=89=E5=AE=9E=E4=B9=A0=E5=BA=94?= =?UTF-8?q?=E8=81=98=E6=80=BB=E7=BB=93.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...72\224\350\201\230\346\200\273\347\273\223.md" | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" index bd3e1d3d..693c6f86 100644 --- "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" +++ "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" @@ -11,7 +11,7 @@ ## 计算机视觉岗找实习心得 1. 首先，我的水平真的算是很一般的，真正的大佬都是很轻松的拿到数个满意offer。我自己是本科是双非大学自动化专业，研究生是中等211大学控制工程专业，本科主要搞嵌入式方向，研究生才转为计算机视觉方向和深度学习方向，这里也给后来者一个建议，如果不是真心热爱、喜欢你所从事的计算机视觉方向，只是为了钱的话那就真的没必要了，CV岗竞争真的很激烈，nlp和开发岗好很多，而且现在开发岗工资真的很高啊！ 2. 最开始找实习的时候，我没有刷过数据结构和算法题，导致我华为(程序写出来了，但是输入输出格式没注意)、百度、腾讯笔试统统挂了，那段时间真的超级难过，后悔没有提前刷题。4月20号之后，我开始在boss直聘上投简历，这里陆陆续续收到2家小公司面试通知和offer，也算是给了我些信心。 -3. 最后，在这段时间一边把之前项目细节搞清楚，一边开始复习栈、队列、链表、二叉树和经典数据结构算法原理，并在[leetcode](https://leetcode-cn.com/)上刷题，因为时间关系刷的不多，然后就是把Faster RCNN、FPN、Mask RCNN、retinanet、ResNet、VGG、InceptionV3等网络结构原理和细节部分彻底搞清楚，并去看了部分检测框架源码，然后就是深度学习算法的一些基本原理：链式求导过程、BP反向传播、SGD优化器原理、激活函数公式及原理、常见图像处理算法等，这里涉及一些公式，还就是C/C++/Python编程基础，和计算机基础等。每经过一次面试，我都会自我总结，这使得我后期对面试也开始有了些自己的经验和心得。 +3. 最后，在这段时间一边把之前项目细节搞清楚，一边开始复习**栈、队列、链表、二叉树和经典数据结构算法原理**，并在[leetcode](https://leetcode-cn.com/)上刷题，因为时间关系刷的不多，然后就是把Faster RCNN、FPN、Mask RCNN、retinanet、ResNet、VGG、InceptionV3等网络结构原理和细节部分彻底搞清楚，并去看了部分检测框架源码，然后就是深度学习算法的一些基本原理：链式求导过程、BP反向传播、SGD优化器原理、激活函数公式及原理、常见图像处理算法等，这里涉及一些公式，还就是C/C++/Python编程基础，和计算机基础等。每经过一次面试，我都会自我总结，这使得我后期对面试也开始有了些自己的经验和心得。 对于CV实习岗，面试涉及到的知识可能有以下部分： ## 计算机视觉面试问题分类总结 1. 目标检测算法原理和网络结构细节: @@ -20,7 +20,16 @@ 2. CNN的SOTA网络原理和细节: ResNet、VGG、InceptionV3、DenseNet等. 3. 深度学习算法公式理解：链式求导过程、BP反向传播、SGD优化器原理、激活函数公式及原理、常见图像处理算法等. 4. C/C++/Java/Python编程基础 -5. 常用工程工具使用基础 + + C++构造函数与析构函数意义、指针和引用区别、new，malloc区别、抽象类和接口的区别等 + + Python浅拷贝、深拷贝区别、装饰器使用、super()用法、高阶函数：map/reduce/filter/sorted用法、try...except使用等 + + 如何用C++/Java/Python写多进程和多线程代码 +5. 计算机基础:计算机网络、操作系统、数据库 + + TCP/IP算法, IP寻址, socket通信流程 + + 大端小端存储，如何将小端存储模式转为大端存储模式 + + OSI七层模型解释 + + 数据库基本操作，及sql语句 + + 多进程与多线程区别 +6. 常用工程工具使用基础 + cmake、git语法等和操作 + linux系统开发，常见命令使用和shell语法 -6. 项目或者paper**细节**，根据项目细节延伸问如何做提升和改变 +7. 项目或者paper**细节**，根据项目细节延伸问如何做提升和改变 From 2d22d3c7b9af34d82611696ff9fb730a9b69a80f Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 15 May 2019 19:51:22 +0800 Subject: [PATCH 122/386] =?UTF-8?q?Update=20=E6=9A=91=E6=9C=9F=E8=AE=A1?= =?UTF-8?q?=E7=AE=97=E6=9C=BA=E8=A7=86=E8=A7=89=E5=AE=9E=E4=B9=A0=E5=BA=94?= =?UTF-8?q?=E8=81=98=E6=80=BB=E7=BB=93.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 更新已面试的公司和进展 --- ...24\350\201\230\346\200\273\347\273\223.md" | 22 ++++++++++++++++--- 1 file changed, 19 insertions(+), 3 deletions(-) diff --git "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" index 693c6f86..95673fbc 100644 --- "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" +++ "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" @@ -20,9 +20,9 @@ 2. CNN的SOTA网络原理和细节: ResNet、VGG、InceptionV3、DenseNet等. 3. 深度学习算法公式理解：链式求导过程、BP反向传播、SGD优化器原理、激活函数公式及原理、常见图像处理算法等. 4. C/C++/Java/Python编程基础 - + C++构造函数与析构函数意义、指针和引用区别、new，malloc区别、抽象类和接口的区别等 - + Python浅拷贝、深拷贝区别、装饰器使用、super()用法、高阶函数：map/reduce/filter/sorted用法、try...except使用等 - + 如何用C++/Java/Python写多进程和多线程代码 + + **C++构造函数与析构函数意义**、指针和引用区别、new，malloc区别、抽象类和接口的区别等 + + Python浅拷贝和深拷贝区别、装饰器使用、super()用法、高阶函数：**map/reduce/filter/sorted用法**、try...except使用等 + + 如何用C++/Java/Python**写多进程和多线程代码** 5. 计算机基础:计算机网络、操作系统、数据库 + TCP/IP算法, IP寻址, socket通信流程 + 大端小端存储，如何将小端存储模式转为大端存储模式 @@ -33,3 +33,19 @@ + cmake、git语法等和操作 + linux系统开发，常见命令使用和shell语法 7. 项目或者paper**细节**，根据项目细节延伸问如何做提升和改变 +## 已面试的公司和进展 +|投递公司|进展| +|-------|----| +|北京阿里|一面挂| +|北京格林深瞳|一面挂| +|北京百度|笔试挂| +|深圳腾讯|笔试挂| +|川渝华为|笔试挂| +|北京小鹏汽车|二面过| +|南京地平线机器人|口头offer| +|深圳平安科技|简历过，等待面试| +|深圳pony.ai|简历过，等待笔试| +|深圳中科类脑|简历过，婉拒面试| +|杭州新再灵|技术面过，hr面挂| +|康尚生物医疗|offer，婉拒| +|北京矩视智能|口头offer，婉拒| From e8d0ee5824f078b164c601f2421974033b30a5d1 Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 15 May 2019 19:57:24 +0800 Subject: [PATCH 123/386] =?UTF-8?q?Update=20=E6=9A=91=E6=9C=9F=E8=AE=A1?= =?UTF-8?q?=E7=AE=97=E6=9C=BA=E8=A7=86=E8=A7=89=E5=AE=9E=E4=B9=A0=E5=BA=94?= =?UTF-8?q?=E8=81=98=E6=80=BB=E7=BB=93.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...24\350\201\230\346\200\273\347\273\223.md" | 31 ++++++++++--------- 1 file changed, 16 insertions(+), 15 deletions(-) diff --git "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" index 95673fbc..7519ecec 100644 --- "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" +++ "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" @@ -34,18 +34,19 @@ + linux系统开发，常见命令使用和shell语法 7. 项目或者paper**细节**，根据项目细节延伸问如何做提升和改变 ## 已面试的公司和进展 -|投递公司|进展| -|-------|----| -|北京阿里|一面挂| -|北京格林深瞳|一面挂| -|北京百度|笔试挂| -|深圳腾讯|笔试挂| -|川渝华为|笔试挂| -|北京小鹏汽车|二面过| -|南京地平线机器人|口头offer| -|深圳平安科技|简历过，等待面试| -|深圳pony.ai|简历过，等待笔试| -|深圳中科类脑|简历过，婉拒面试| -|杭州新再灵|技术面过，hr面挂| -|康尚生物医疗|offer，婉拒| -|北京矩视智能|口头offer，婉拒| +|投递公司|投递渠道|进展| +|-------|----|------| +|北京阿里|朋友内推|一面挂| +|北京格林深瞳|boss直聘投递|一面挂| +|北京百度|网申|笔试挂| +|深圳腾讯|朋友内推|笔试挂| +|川渝华为|官网投递|笔试挂| +|北京小鹏汽车|boss直聘投递|二面过| +|南京地平线机器人|boss直聘投递|口头offer| +|深圳平安科技|boss直聘投递|简历过，等待面试| +|深圳pony.ai|boss直聘投递|简历过，等待笔试| +|深圳中科类脑|boss直聘投递|简历过，婉拒面试| +|杭州新再灵|拉钩网|技术面过，hr面挂| +|康尚生物医疗|boss直聘投递|offer，婉拒| +|北京矩视智能|boss直聘投递|口头offer，婉拒| +|上海拼多多|朋友内推|简历过，没参加笔试| From 6b423ff32c2058a67910457bc4dfa423ef31cd06 Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 15 May 2019 20:11:20 +0800 Subject: [PATCH 124/386] =?UTF-8?q?Update=20=E6=9A=91=E6=9C=9F=E8=AE=A1?= =?UTF-8?q?=E7=AE=97=E6=9C=BA=E8=A7=86=E8=A7=89=E5=AE=9E=E4=B9=A0=E5=BA=94?= =?UTF-8?q?=E8=81=98=E6=80=BB=E7=BB=93.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...240\345\272\224\350\201\230\346\200\273\347\273\223.md" | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" index 7519ecec..a3995f8d 100644 --- "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" +++ "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" @@ -1,5 +1,8 @@ ## 找实习感想 -从4月１日开始找实习，到5月13日收到南京地平线机器人口头offer和北京小鹏汽车技术面通过通知，这40来天的时间真的过的不容易，每天早上醒来都被“找实习”这三个字压得沉甸甸的，一有空闲时间就看牛客网面经和投简历，前前后后差不多有60家公司，大公司、小公司、独角兽公司只要招深度学习/计算机视觉/图像算法相关岗位，我都投，投递的渠道最开始是官网投递，后来发现太慢了，招银、平安科技这两家公司填写的简历信息太多太耗时了，后来就转boss直聘、拉勾网、牛客网和内推邮箱渠道，这样就省事很多，一个平台填好简历，可以投递不同公司。 +1. 从4月１日开始找实习，到5月13日收到南京地平线机器人口头offer和北京小鹏汽车技术面通过通知，这40来天的时间真的过的不容易，每天早上醒来都被“**找实习**”这三个字压得沉甸甸的，一有空闲时间就看牛客网面经和投简历，前前后后差不多有60家公司，大公司、小公司、独角兽公司只要招深度学习/计算机视觉/图像算法相关岗位，我都投，投递的渠道最开始是官网投递，后来发现太慢了，招银、平安科技这两家公司填写的简历信息太多太耗时了，后来就转boss直聘、拉勾网、牛客网和内推邮箱渠道，这样就省事很多，一个平台填好简历，可以投递不同公司。 +2. 这段时间自己也学到了很多东西，弥补了些自身技术盲点和加深了对一些基本原理和理论的理解，更重要的是认识到了**与大佬之间的差距**，对于搞技术的人来说，一定要保持终身学习的态度和激情，也要保持心态良好和身体健康。 +3. 找算法实习岗对大多数人来说是心智和体力的持久战，据我所知，大多数人都花了１个月以上的时间，所以要做好长期的打算，如果**一时失利也别灰心**，出去放松一天，放松下自己的心情，**保持良好的精神和身体状况很重要**。 +4. 实习平台很重要，**一定要尽量投大公司、知名企业或者行业独角兽**，不仅是面试正规和面试官温和亲切的态度，更重要的是自己本身面试之后经过反省总结，也能学到很多东西，小公司的面试大多都不是很正规。好的面试官，不管你技术实力是否满足他们要求，面试官一般都会很温和，让你放轻松，给你足够的尊重，有些面试官甚至会给你提些很有帮助和恳切的建议。这里，给我留下良好印象的是阿里和地平线机器人的面试官，面试官人真的超级友好和亲切。 ## 找实习建议 1. **心态第一，坚持为胜，找实习是一个持久战**。对于技术实力一般的人来说，真的要保持良好的心态，不抛弃、不放弃，这中间也许你会经历很多失败，但是真的只要坚持下来，我不保证你能拿到很好公司的offer，但是你自己本省一定能从面试中学到很多东西，尤其是很多大公司的面试官会给你一些很恳切的建议，可以让你受益匪浅。 2. **实习要趁早**。建议有条件的研一或者大三就去实习，对于暑期实习来说，简历投递一定要趁早，最好2月份就开始，我很晚才投简历，很可能就会错过内推时间和岗位hc没有的情况，我4月份开始投简历已经算是很晚了，所以也直接导致我投了海康威视之后，一直是简历复选中，很有可能就是hc已经没了。 @@ -50,3 +53,5 @@ |康尚生物医疗|boss直聘投递|offer，婉拒| |北京矩视智能|boss直聘投递|口头offer，婉拒| |上海拼多多|朋友内推|简历过，没参加笔试| +## 我的面经 +文章地址在[这里](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89%E5%B2%97%E5%AE%9E%E4%B9%A0%E9%9D%A2%E7%BB%8F.md) From 2ea526ead8f322870843d314bf955fbdf7bb0b53 Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 15 May 2019 20:12:47 +0800 Subject: [PATCH 125/386] =?UTF-8?q?Update=20=E6=9A=91=E6=9C=9F=E8=AE=A1?= =?UTF-8?q?=E7=AE=97=E6=9C=BA=E8=A7=86=E8=A7=89=E5=AE=9E=E4=B9=A0=E5=BA=94?= =?UTF-8?q?=E8=81=98=E6=80=BB=E7=BB=93.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...71\240\345\272\224\350\201\230\346\200\273\347\273\223.md" | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" index a3995f8d..1e7c60f2 100644 --- "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" +++ "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" @@ -1,5 +1,5 @@ ## 找实习感想 -1. 从4月１日开始找实习，到5月13日收到南京地平线机器人口头offer和北京小鹏汽车技术面通过通知，这40来天的时间真的过的不容易，每天早上醒来都被“**找实习**”这三个字压得沉甸甸的，一有空闲时间就看牛客网面经和投简历，前前后后差不多有60家公司，大公司、小公司、独角兽公司只要招深度学习/计算机视觉/图像算法相关岗位，我都投，投递的渠道最开始是官网投递，后来发现太慢了，招银、平安科技这两家公司填写的简历信息太多太耗时了，后来就转boss直聘、拉勾网、牛客网和内推邮箱渠道，这样就省事很多，一个平台填好简历，可以投递不同公司。 +1. 从4月１日开始找实习，到5月13日收到南京地平线机器人口头offer和北京小鹏汽车技术面通过通知，这40来天的时间真的过的不容易，每天早上醒来都被“**找实习**”这三个字压得沉甸甸的，一有空闲时间就看牛客网面经和投简历，前前后后差不多有60家公司，大公司、小公司、独角兽公司，只要招深度学习/计算机视觉/图像算法相关岗位，我都投，投递的渠道最开始是官网投递，后来发现太慢了，招银、平安科技这两家公司填写的简历信息太多太耗时了，后来就转boss直聘、拉勾网、牛客网和内推邮箱渠道，这样就省事很多，**一个平台填好简历，可以投递不同公司**。 2. 这段时间自己也学到了很多东西，弥补了些自身技术盲点和加深了对一些基本原理和理论的理解，更重要的是认识到了**与大佬之间的差距**，对于搞技术的人来说，一定要保持终身学习的态度和激情，也要保持心态良好和身体健康。 3. 找算法实习岗对大多数人来说是心智和体力的持久战，据我所知，大多数人都花了１个月以上的时间，所以要做好长期的打算，如果**一时失利也别灰心**，出去放松一天，放松下自己的心情，**保持良好的精神和身体状况很重要**。 4. 实习平台很重要，**一定要尽量投大公司、知名企业或者行业独角兽**，不仅是面试正规和面试官温和亲切的态度，更重要的是自己本身面试之后经过反省总结，也能学到很多东西，小公司的面试大多都不是很正规。好的面试官，不管你技术实力是否满足他们要求，面试官一般都会很温和，让你放轻松，给你足够的尊重，有些面试官甚至会给你提些很有帮助和恳切的建议。这里，给我留下良好印象的是阿里和地平线机器人的面试官，面试官人真的超级友好和亲切。 @@ -13,7 +13,7 @@ 2. 要对自己有信心，但是**千万不能撒谎和装逼**，一般面试过程中不会的问题，面试官也会跳过或者安慰你没事的，不用太紧张，碰到１、2个不会的问题也属正常。 ## 计算机视觉岗找实习心得 1. 首先，我的水平真的算是很一般的，真正的大佬都是很轻松的拿到数个满意offer。我自己是本科是双非大学自动化专业，研究生是中等211大学控制工程专业，本科主要搞嵌入式方向，研究生才转为计算机视觉方向和深度学习方向，这里也给后来者一个建议，如果不是真心热爱、喜欢你所从事的计算机视觉方向，只是为了钱的话那就真的没必要了，CV岗竞争真的很激烈，nlp和开发岗好很多，而且现在开发岗工资真的很高啊！ -2. 最开始找实习的时候，我没有刷过数据结构和算法题，导致我华为(程序写出来了，但是输入输出格式没注意)、百度、腾讯笔试统统挂了，那段时间真的超级难过，后悔没有提前刷题。4月20号之后，我开始在boss直聘上投简历，这里陆陆续续收到2家小公司面试通知和offer，也算是给了我些信心。 +2. 其次，最开始找实习的时候，我没有刷过数据结构和算法题，导致我华为(程序写出来了，但是输入输出格式没注意)、百度、腾讯笔试统统挂了，那段时间真的超级难过，后悔没有提前刷题。4月20号之后，我开始在boss直聘上投简历，这里陆陆续续收到2家小公司面试通知和offer，也算是给了我些信心。 3. 最后，在这段时间一边把之前项目细节搞清楚，一边开始复习**栈、队列、链表、二叉树和经典数据结构算法原理**，并在[leetcode](https://leetcode-cn.com/)上刷题，因为时间关系刷的不多，然后就是把Faster RCNN、FPN、Mask RCNN、retinanet、ResNet、VGG、InceptionV3等网络结构原理和细节部分彻底搞清楚，并去看了部分检测框架源码，然后就是深度学习算法的一些基本原理：链式求导过程、BP反向传播、SGD优化器原理、激活函数公式及原理、常见图像处理算法等，这里涉及一些公式，还就是C/C++/Python编程基础，和计算机基础等。每经过一次面试，我都会自我总结，这使得我后期对面试也开始有了些自己的经验和心得。 对于CV实习岗，面试涉及到的知识可能有以下部分： ## 计算机视觉面试问题分类总结 From de6cf92c2a14f6e0801949c7c68232b18ed59d76 Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 15 May 2019 20:23:18 +0800 Subject: [PATCH 126/386] =?UTF-8?q?Update=20=E6=9A=91=E6=9C=9F=E8=AE=A1?= =?UTF-8?q?=E7=AE=97=E6=9C=BA=E8=A7=86=E8=A7=89=E5=AE=9E=E4=B9=A0=E5=BA=94?= =?UTF-8?q?=E8=81=98=E6=80=BB=E7=BB=93.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" index 1e7c60f2..55375b1e 100644 --- "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" +++ "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" @@ -37,7 +37,7 @@ + linux系统开发，常见命令使用和shell语法 7. 项目或者paper**细节**，根据项目细节延伸问如何做提升和改变 ## 已面试的公司和进展 -|投递公司|投递渠道|进展| +|投递公司|简历投递渠道|进展| |-------|----|------| |北京阿里|朋友内推|一面挂| |北京格林深瞳|boss直聘投递|一面挂| @@ -53,5 +53,6 @@ |康尚生物医疗|boss直聘投递|offer，婉拒| |北京矩视智能|boss直聘投递|口头offer，婉拒| |上海拼多多|朋友内推|简历过，没参加笔试| +|北京合心科技|拉勾网投递|**技术面，面试官极其不尊重人，双方放弃**| ## 我的面经 文章地址在[这里](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89%E5%B2%97%E5%AE%9E%E4%B9%A0%E9%9D%A2%E7%BB%8F.md) From 5cd95faa971701c59079ab29d2cff67cd3e3b5e1 Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 15 May 2019 20:45:13 +0800 Subject: [PATCH 127/386] =?UTF-8?q?Update=20=E6=9A=91=E6=9C=9F=E8=AE=A1?= =?UTF-8?q?=E7=AE=97=E6=9C=BA=E8=A7=86=E8=A7=89=E5=AE=9E=E4=B9=A0=E5=BA=94?= =?UTF-8?q?=E8=81=98=E6=80=BB=E7=BB=93.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...4\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" | 1 + 1 file changed, 1 insertion(+) diff --git "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" index 55375b1e..20713f4f 100644 --- "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" +++ "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" @@ -8,6 +8,7 @@ 2. **实习要趁早**。建议有条件的研一或者大三就去实习，对于暑期实习来说，简历投递一定要趁早，最好2月份就开始，我很晚才投简历，很可能就会错过内推时间和岗位hc没有的情况，我4月份开始投简历已经算是很晚了，所以也直接导致我投了海康威视之后，一直是简历复选中，很有可能就是hc已经没了。 3. **数据结构与算法题必须刷**。虽然这是老生常谈的建议，但是我们必须记住很多大公司一定会有笔试题，就算是内推免笔试的，面试过程中也很有可能会出数据结构与算法题。这里我建议去**leetcode**或者牛客网上刷**剑指offer**，一般把简单和中等难度题刷会就可以，笔试题一般都不会很难，除非是谷歌、MSRA那种公司可能会对笔试题有更高的要求。刷题的话，第一次刷不会可以去看参考解题思路和答案，**看完后要自己写出代码**。 4. **常见面试题要掌握**。其实关于计算机基础和计算机视觉算法原理的面试题，可以提前准备下，有些题频繁的问到，可以提前准备下，比如：1*1卷积作用，链式求导公式，多线程与多进程区别，tcp/udp通信原理等。这些常见面试题，都可以在牛客网找的到，当然**不同岗位面试题不一样，甚至每个人的面试问题都有很大不一样**，不要因为别人面经上的题不会，你就有些失去信心，但是**对于同一个岗位的频繁出现的面试问题还是必须掌握**。机器学习、算法工程师面试考点汇总，参考[这里](https://www.nowcoder.com/discuss/165930) +5. **简历要有亮点**。paper、算法比赛、项目、实习必须要有一个啊，博客、github一定要有，这真的很给简历加分！ ## 面试过程建议 1. 不要紧张，表达要清楚流利，要记住，绝大部分面试官都是很友好和亲切的，尤其是大公司的面试官真的超级温和，**这里为阿里和地平线机器人面试官点赞**！对于那种不尊重人和看不起人的面试官，我个人觉得没必要去他们公司了，一般这种面试官会出现在小公司，一个面试官连对面试者基本的尊重都做不到，我难以想象这家公司的文化是什么样子。 2. 要对自己有信心，但是**千万不能撒谎和装逼**，一般面试过程中不会的问题，面试官也会跳过或者安慰你没事的，不用太紧张，碰到１、2个不会的问题也属正常。 From 4c3f9424d43861f5de764cb0d7f07216c817187b Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 15 May 2019 20:53:15 +0800 Subject: [PATCH 128/386] =?UTF-8?q?Update=20=E6=9A=91=E6=9C=9F=E8=AE=A1?= =?UTF-8?q?=E7=AE=97=E6=9C=BA=E8=A7=86=E8=A7=89=E5=AE=9E=E4=B9=A0=E5=BA=94?= =?UTF-8?q?=E8=81=98=E6=80=BB=E7=BB=93.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" index 20713f4f..5b875d0b 100644 --- "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" +++ "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" @@ -8,7 +8,7 @@ 2. **实习要趁早**。建议有条件的研一或者大三就去实习，对于暑期实习来说，简历投递一定要趁早，最好2月份就开始，我很晚才投简历，很可能就会错过内推时间和岗位hc没有的情况，我4月份开始投简历已经算是很晚了，所以也直接导致我投了海康威视之后，一直是简历复选中，很有可能就是hc已经没了。 3. **数据结构与算法题必须刷**。虽然这是老生常谈的建议，但是我们必须记住很多大公司一定会有笔试题，就算是内推免笔试的，面试过程中也很有可能会出数据结构与算法题。这里我建议去**leetcode**或者牛客网上刷**剑指offer**，一般把简单和中等难度题刷会就可以，笔试题一般都不会很难，除非是谷歌、MSRA那种公司可能会对笔试题有更高的要求。刷题的话，第一次刷不会可以去看参考解题思路和答案，**看完后要自己写出代码**。 4. **常见面试题要掌握**。其实关于计算机基础和计算机视觉算法原理的面试题，可以提前准备下，有些题频繁的问到，可以提前准备下，比如：1*1卷积作用，链式求导公式，多线程与多进程区别，tcp/udp通信原理等。这些常见面试题，都可以在牛客网找的到，当然**不同岗位面试题不一样，甚至每个人的面试问题都有很大不一样**，不要因为别人面经上的题不会，你就有些失去信心，但是**对于同一个岗位的频繁出现的面试问题还是必须掌握**。机器学习、算法工程师面试考点汇总，参考[这里](https://www.nowcoder.com/discuss/165930) -5. **简历要有亮点**。paper、算法比赛、项目、实习必须要有一个啊，博客、github一定要有，这真的很给简历加分！ +5. **简历要有亮点**。paper、算法比赛、项目、实习必须要有一个啊，博客、github一定要有，这真的很给简历加分！实验室没有paper和项目条件的，可以考虑去**[天池](https://tianchi.aliyun.com/home/)、[Datafountain](https://www.datafountain.cn/)、[kaggle](https://www.kaggle.com/)**上打比赛，真的可以学到很多东西。 ## 面试过程建议 1. 不要紧张，表达要清楚流利，要记住，绝大部分面试官都是很友好和亲切的，尤其是大公司的面试官真的超级温和，**这里为阿里和地平线机器人面试官点赞**！对于那种不尊重人和看不起人的面试官，我个人觉得没必要去他们公司了，一般这种面试官会出现在小公司，一个面试官连对面试者基本的尊重都做不到，我难以想象这家公司的文化是什么样子。 2. 要对自己有信心，但是**千万不能撒谎和装逼**，一般面试过程中不会的问题，面试官也会跳过或者安慰你没事的，不用太紧张，碰到１、2个不会的问题也属正常。 From 24d42c5d57d1dc53d7d4c08474e2dc1b354718a6 Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 15 May 2019 23:27:05 +0800 Subject: [PATCH 129/386] =?UTF-8?q?Update=20=E6=9A=91=E6=9C=9F=E8=AE=A1?= =?UTF-8?q?=E7=AE=97=E6=9C=BA=E8=A7=86=E8=A7=89=E5=AE=9E=E4=B9=A0=E5=BA=94?= =?UTF-8?q?=E8=81=98=E6=80=BB=E7=BB=93.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" index 5b875d0b..7cd41c59 100644 --- "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" +++ "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" @@ -13,7 +13,7 @@ 1. 不要紧张，表达要清楚流利，要记住，绝大部分面试官都是很友好和亲切的，尤其是大公司的面试官真的超级温和，**这里为阿里和地平线机器人面试官点赞**！对于那种不尊重人和看不起人的面试官，我个人觉得没必要去他们公司了，一般这种面试官会出现在小公司，一个面试官连对面试者基本的尊重都做不到，我难以想象这家公司的文化是什么样子。 2. 要对自己有信心，但是**千万不能撒谎和装逼**，一般面试过程中不会的问题，面试官也会跳过或者安慰你没事的，不用太紧张，碰到１、2个不会的问题也属正常。 ## 计算机视觉岗找实习心得 -1. 首先，我的水平真的算是很一般的，真正的大佬都是很轻松的拿到数个满意offer。我自己是本科是双非大学自动化专业，研究生是中等211大学控制工程专业，本科主要搞嵌入式方向，研究生才转为计算机视觉方向和深度学习方向，这里也给后来者一个建议，如果不是真心热爱、喜欢你所从事的计算机视觉方向，只是为了钱的话那就真的没必要了，CV岗竞争真的很激烈，nlp和开发岗好很多，而且现在开发岗工资真的很高啊！ +1. 首先，我的水平真的算是很一般的，真正的大佬都是很轻松的拿到数个满意offer。我自己是本科是双非大学自动化专业，研究生是中等211大学控制工程专业，本科主要搞嵌入式方向，研究生才转为计算机视觉方向和深度学习方向，这里也给后来者一个建议，**如果不是真心热爱、喜欢你所从事的计算机视觉方向，只是为了钱的话那就真的没必要了**，**CV岗竞争真的很激烈**，nlp和开发岗好很多，而且现在开发岗工资真的很高啊！ 2. 其次，最开始找实习的时候，我没有刷过数据结构和算法题，导致我华为(程序写出来了，但是输入输出格式没注意)、百度、腾讯笔试统统挂了，那段时间真的超级难过，后悔没有提前刷题。4月20号之后，我开始在boss直聘上投简历，这里陆陆续续收到2家小公司面试通知和offer，也算是给了我些信心。 3. 最后，在这段时间一边把之前项目细节搞清楚，一边开始复习**栈、队列、链表、二叉树和经典数据结构算法原理**，并在[leetcode](https://leetcode-cn.com/)上刷题，因为时间关系刷的不多，然后就是把Faster RCNN、FPN、Mask RCNN、retinanet、ResNet、VGG、InceptionV3等网络结构原理和细节部分彻底搞清楚，并去看了部分检测框架源码，然后就是深度学习算法的一些基本原理：链式求导过程、BP反向传播、SGD优化器原理、激活函数公式及原理、常见图像处理算法等，这里涉及一些公式，还就是C/C++/Python编程基础，和计算机基础等。每经过一次面试，我都会自我总结，这使得我后期对面试也开始有了些自己的经验和心得。 对于CV实习岗，面试涉及到的知识可能有以下部分： From e64fa4479f3547fc9e6d0eb4a4b71c194658c3c6 Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 15 May 2019 23:44:47 +0800 Subject: [PATCH 130/386] =?UTF-8?q?Update=20=E6=9A=91=E6=9C=9F=E8=AE=A1?= =?UTF-8?q?=E7=AE=97=E6=9C=BA=E8=A7=86=E8=A7=89=E5=AE=9E=E4=B9=A0=E5=BA=94?= =?UTF-8?q?=E8=81=98=E6=80=BB=E7=BB=93.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...71\240\345\272\224\350\201\230\346\200\273\347\273\223.md" | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" index 7cd41c59..2ce77b49 100644 --- "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" +++ "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" @@ -7,7 +7,7 @@ 1. **心态第一，坚持为胜，找实习是一个持久战**。对于技术实力一般的人来说，真的要保持良好的心态，不抛弃、不放弃，这中间也许你会经历很多失败，但是真的只要坚持下来，我不保证你能拿到很好公司的offer，但是你自己本省一定能从面试中学到很多东西，尤其是很多大公司的面试官会给你一些很恳切的建议，可以让你受益匪浅。 2. **实习要趁早**。建议有条件的研一或者大三就去实习，对于暑期实习来说，简历投递一定要趁早，最好2月份就开始，我很晚才投简历，很可能就会错过内推时间和岗位hc没有的情况，我4月份开始投简历已经算是很晚了，所以也直接导致我投了海康威视之后，一直是简历复选中，很有可能就是hc已经没了。 3. **数据结构与算法题必须刷**。虽然这是老生常谈的建议，但是我们必须记住很多大公司一定会有笔试题，就算是内推免笔试的，面试过程中也很有可能会出数据结构与算法题。这里我建议去**leetcode**或者牛客网上刷**剑指offer**，一般把简单和中等难度题刷会就可以，笔试题一般都不会很难，除非是谷歌、MSRA那种公司可能会对笔试题有更高的要求。刷题的话，第一次刷不会可以去看参考解题思路和答案，**看完后要自己写出代码**。 -4. **常见面试题要掌握**。其实关于计算机基础和计算机视觉算法原理的面试题，可以提前准备下，有些题频繁的问到，可以提前准备下，比如：1*1卷积作用，链式求导公式，多线程与多进程区别，tcp/udp通信原理等。这些常见面试题，都可以在牛客网找的到，当然**不同岗位面试题不一样，甚至每个人的面试问题都有很大不一样**，不要因为别人面经上的题不会，你就有些失去信心，但是**对于同一个岗位的频繁出现的面试问题还是必须掌握**。机器学习、算法工程师面试考点汇总，参考[这里](https://www.nowcoder.com/discuss/165930) +4. **常见面试题要掌握**。其实关于计算机基础和计算机视觉算法原理的面试题，可以提前准备下，有些题频繁的问到，可以提前准备下，比如：1*1卷积作用，链式求导公式，多线程与多进程区别，tcp/udp通信原理等。这些常见面试题，都可以在牛客网找的到，当然**不同岗位面试题不一样，甚至每个人的面试问题都有很大不一样**，不要因为别人面经上的题不会，你就有些失去信心，但是**对于同一个岗位的频繁出现的面试问题还是必须掌握**。机器学习、算法工程师面试考点汇总，参考[这里](https://www.nowcoder.com/discuss/165930)。 5. **简历要有亮点**。paper、算法比赛、项目、实习必须要有一个啊，博客、github一定要有，这真的很给简历加分！实验室没有paper和项目条件的，可以考虑去**[天池](https://tianchi.aliyun.com/home/)、[Datafountain](https://www.datafountain.cn/)、[kaggle](https://www.kaggle.com/)**上打比赛，真的可以学到很多东西。 ## 面试过程建议 1. 不要紧张，表达要清楚流利，要记住，绝大部分面试官都是很友好和亲切的，尤其是大公司的面试官真的超级温和，**这里为阿里和地平线机器人面试官点赞**！对于那种不尊重人和看不起人的面试官，我个人觉得没必要去他们公司了，一般这种面试官会出现在小公司，一个面试官连对面试者基本的尊重都做不到，我难以想象这家公司的文化是什么样子。 @@ -56,4 +56,4 @@ |上海拼多多|朋友内推|简历过，没参加笔试| |北京合心科技|拉勾网投递|**技术面，面试官极其不尊重人，双方放弃**| ## 我的面经 -文章地址在[这里](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89%E5%B2%97%E5%AE%9E%E4%B9%A0%E9%9D%A2%E7%BB%8F.md) +文章地址在[这里](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89%E5%B2%97%E5%AE%9E%E4%B9%A0%E9%9D%A2%E7%BB%8F.md)。 From bf10c0de41b6faf9ba392852f67fb658268996ad Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 15 May 2019 23:46:53 +0800 Subject: [PATCH 131/386] =?UTF-8?q?Update=20=E6=9A=91=E6=9C=9F=E8=AE=A1?= =?UTF-8?q?=E7=AE=97=E6=9C=BA=E8=A7=86=E8=A7=89=E5=AE=9E=E4=B9=A0=E5=BA=94?= =?UTF-8?q?=E8=81=98=E6=80=BB=E7=BB=93.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...71\240\345\272\224\350\201\230\346\200\273\347\273\223.md" | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" index 2ce77b49..d51104f8 100644 --- "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" +++ "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" @@ -8,9 +8,9 @@ 2. **实习要趁早**。建议有条件的研一或者大三就去实习，对于暑期实习来说，简历投递一定要趁早，最好2月份就开始，我很晚才投简历，很可能就会错过内推时间和岗位hc没有的情况，我4月份开始投简历已经算是很晚了，所以也直接导致我投了海康威视之后，一直是简历复选中，很有可能就是hc已经没了。 3. **数据结构与算法题必须刷**。虽然这是老生常谈的建议，但是我们必须记住很多大公司一定会有笔试题，就算是内推免笔试的，面试过程中也很有可能会出数据结构与算法题。这里我建议去**leetcode**或者牛客网上刷**剑指offer**，一般把简单和中等难度题刷会就可以，笔试题一般都不会很难，除非是谷歌、MSRA那种公司可能会对笔试题有更高的要求。刷题的话，第一次刷不会可以去看参考解题思路和答案，**看完后要自己写出代码**。 4. **常见面试题要掌握**。其实关于计算机基础和计算机视觉算法原理的面试题，可以提前准备下，有些题频繁的问到，可以提前准备下，比如：1*1卷积作用，链式求导公式，多线程与多进程区别，tcp/udp通信原理等。这些常见面试题，都可以在牛客网找的到，当然**不同岗位面试题不一样，甚至每个人的面试问题都有很大不一样**，不要因为别人面经上的题不会，你就有些失去信心，但是**对于同一个岗位的频繁出现的面试问题还是必须掌握**。机器学习、算法工程师面试考点汇总，参考[这里](https://www.nowcoder.com/discuss/165930)。 -5. **简历要有亮点**。paper、算法比赛、项目、实习必须要有一个啊，博客、github一定要有，这真的很给简历加分！实验室没有paper和项目条件的，可以考虑去**[天池](https://tianchi.aliyun.com/home/)、[Datafountain](https://www.datafountain.cn/)、[kaggle](https://www.kaggle.com/)**上打比赛，真的可以学到很多东西。 +5. **简历要有亮点**。**paper、算法比赛、项目、实习必须要有一个啊，博客、github一定要有，这真的很给简历加分**！实验室没有paper和项目条件的，可以考虑去**[天池](https://tianchi.aliyun.com/home/)、[Datafountain](https://www.datafountain.cn/)、[kaggle](https://www.kaggle.com/)**上打比赛，真的可以学到很多东西。 ## 面试过程建议 -1. 不要紧张，表达要清楚流利，要记住，绝大部分面试官都是很友好和亲切的，尤其是大公司的面试官真的超级温和，**这里为阿里和地平线机器人面试官点赞**！对于那种不尊重人和看不起人的面试官，我个人觉得没必要去他们公司了，一般这种面试官会出现在小公司，一个面试官连对面试者基本的尊重都做不到，我难以想象这家公司的文化是什么样子。 +1. **不要紧张，表达要清楚流利**，要记住，**绝大部分面试官都是很友好和亲切的**，尤其是大公司的面试官真的超级温和，**这里为阿里和地平线机器人面试官点赞**！对于那种不尊重人和看不起人的面试官，我个人觉得没必要去他们公司了，一般这种面试官会出现在小公司，一个面试官连对面试者基本的尊重都做不到，我难以想象这家公司的文化是什么样子。 2. 要对自己有信心，但是**千万不能撒谎和装逼**，一般面试过程中不会的问题，面试官也会跳过或者安慰你没事的，不用太紧张，碰到１、2个不会的问题也属正常。 ## 计算机视觉岗找实习心得 1. 首先，我的水平真的算是很一般的，真正的大佬都是很轻松的拿到数个满意offer。我自己是本科是双非大学自动化专业，研究生是中等211大学控制工程专业，本科主要搞嵌入式方向，研究生才转为计算机视觉方向和深度学习方向，这里也给后来者一个建议，**如果不是真心热爱、喜欢你所从事的计算机视觉方向，只是为了钱的话那就真的没必要了**，**CV岗竞争真的很激烈**，nlp和开发岗好很多，而且现在开发岗工资真的很高啊！ From 1d00c2cfaf1b5db8e31236a9980e7a4a1f6a927b Mon Sep 17 00:00:00 2001 From: Zhang Date: Sat, 18 May 2019 13:46:33 +0800 Subject: [PATCH 132/386] =?UTF-8?q?Update=20=E6=9A=91=E6=9C=9F=E8=AE=A1?= =?UTF-8?q?=E7=AE=97=E6=9C=BA=E8=A7=86=E8=A7=89=E5=AE=9E=E4=B9=A0=E5=BA=94?= =?UTF-8?q?=E8=81=98=E6=80=BB=E7=BB=93.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" index d51104f8..86aa5686 100644 --- "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" +++ "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" @@ -16,7 +16,7 @@ 1. 首先，我的水平真的算是很一般的，真正的大佬都是很轻松的拿到数个满意offer。我自己是本科是双非大学自动化专业，研究生是中等211大学控制工程专业，本科主要搞嵌入式方向，研究生才转为计算机视觉方向和深度学习方向，这里也给后来者一个建议，**如果不是真心热爱、喜欢你所从事的计算机视觉方向，只是为了钱的话那就真的没必要了**，**CV岗竞争真的很激烈**，nlp和开发岗好很多，而且现在开发岗工资真的很高啊！ 2. 其次，最开始找实习的时候，我没有刷过数据结构和算法题，导致我华为(程序写出来了，但是输入输出格式没注意)、百度、腾讯笔试统统挂了，那段时间真的超级难过，后悔没有提前刷题。4月20号之后，我开始在boss直聘上投简历，这里陆陆续续收到2家小公司面试通知和offer，也算是给了我些信心。 3. 最后，在这段时间一边把之前项目细节搞清楚，一边开始复习**栈、队列、链表、二叉树和经典数据结构算法原理**，并在[leetcode](https://leetcode-cn.com/)上刷题，因为时间关系刷的不多，然后就是把Faster RCNN、FPN、Mask RCNN、retinanet、ResNet、VGG、InceptionV3等网络结构原理和细节部分彻底搞清楚，并去看了部分检测框架源码，然后就是深度学习算法的一些基本原理：链式求导过程、BP反向传播、SGD优化器原理、激活函数公式及原理、常见图像处理算法等，这里涉及一些公式，还就是C/C++/Python编程基础，和计算机基础等。每经过一次面试，我都会自我总结，这使得我后期对面试也开始有了些自己的经验和心得。 -对于CV实习岗，面试涉及到的知识可能有以下部分： +对于CV实习岗，面试涉及到的知识可参考下面的部分。 ## 计算机视觉面试问题分类总结 1. 目标检测算法原理和网络结构细节: + 经典的two-stage算法: Faster RCNN、FPN、Mask RCNN等 From ca23946269225d344cdabc2696f25a7cc0ff9da0 Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 22 May 2019 19:55:43 +0800 Subject: [PATCH 133/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 7 +++++++ 1 file changed, 7 insertions(+) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index daaa4497..ca4282f4 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -9,6 +9,13 @@ + 权值共享大大降低了网络的训练难度，一个Filter只提取一个特征，在整个图片（或者语音／文本） 中进行卷积 + 池化操作与多层次结构一起，实现了数据的降维，将低层次的局部特征组合成为较高层次的特征，从而对整个图片进行表示。 +## pooling层作用 +1. 增加特征平移不变性。汇合可以提高网络对微小位移的容忍能力。 +2. 减小特征图大小。汇合层对空间局部区域进行下采样，使下一层需要的参数量和计算量减少，并降低过拟合风险。 +3. 最大汇合可以带来非线性。这是目前最大汇合更常用的原因之一。 +### Reference +[(二)计算机视觉四大基本任务(分类、定位、检测、分割](https://zhuanlan.zhihu.com/p/31727402) + ## 什么样的数据集不适合深度学习 + 数据集太小，数据样本不足时，深度学习相对其它机器学习算法，没有明显优势。 + 数据集没有局部相关特性，目前深度学习表现比较好的领域主要是图像／语音／自然语言处理等领域，这些领域的一个共性是局部相关性。图像中像素组成物体，语音信号中音位组合成单词，文本数据中单词组合成句子，这些特征元素的组合一旦被打乱，表示的含义同时也被改变。对于没有这样的局部相关性的数据集，不适于使用深度学习算法进行处理。举个例子：预测一个人的健康状况，相关的参数会有年龄、职业、收入、家庭状况等各种元素，将这些元素打乱，并不会影响相关的结果。 From c366451a327c928aaa71006b7cd3437fc66e907b Mon Sep 17 00:00:00 2001 From: Zhang Date: Thu, 30 May 2019 17:59:11 +0800 Subject: [PATCH 134/386] =?UTF-8?q?Create=20MXNet=E5=AD=A6=E4=B9=A0?= =?UTF-8?q?=E7=AC=94=E8=AE=B0.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 2019/5/30更新MXNet初步学习笔记 --- ...46\344\271\240\347\254\224\350\256\260.md" | 110 ++++++++++++++++++ 1 file changed, 110 insertions(+) create mode 100644 "MXNet\345\255\246\344\271\240\347\254\224\350\256\260.md" diff --git "a/MXNet\345\255\246\344\271\240\347\254\224\350\256\260.md" "b/MXNet\345\255\246\344\271\240\347\254\224\350\256\260.md" new file mode 100644 index 00000000..83414637 --- /dev/null +++ "b/MXNet\345\255\246\344\271\240\347\254\224\350\256\260.md" @@ -0,0 +1,110 @@ +## 制作MXNet数据集 +与tf不同，MXNet也有自己的专属图像数据格式，MXNet读取图像有两种方式： ++ 读.rec格式文件，包含文件路径、标签和图像信息 ++ 读.lst和图像结合方式，.lst文件其实就是图像路径和标签的对应列表，有点类似.csv文件 +# MXNet-深度学习基础 +## 数据操作-NDArray +NDArray功能类似numpy库的多维数组操作，NDArray提供了GPU计算和自动求梯度等更多应用于深度学习的功能，用法如下： +```Python +from mcnet import nd # 导入NDArray(ndarray, nd)模块 +x = arange(10) # arange函数创建一个行向量 +``` +### 创建NDArray +这里的操作和numpy类似，创建零元素张量、1元素张量、改变张量形状等 +```Python +x = nd.arrange(12) +x.size # 12 +x.reshape((3,4)) +x.zeros((3, 4, 5)) # 创建元素为0，形状为(3, 4, 5)的张量 +x.ones((3, 4, 5)) +``` +### NDArray实例运算 +和numpy类似，矩阵乘法`np.dot(X, Y.T)`，***矩阵连结操作（concatenate）***。 +下面分别在行上（***维度0，即形状中的最左边元素***）和列上（维度1，即形状中左起第二个元素）连结两个矩阵。可以看到，输出的第一个NDArray在维度0的长度（ 6 ）为两个输入矩阵在维度0的长度之和（ 3+3 ），而输出的第二个NDArray在维度1的长度（ 8 ）为两个输入矩阵在维度1的长度之和（ 4+4 ） +```Python +X = nd.arrange(12).reshape(3,4) +Y = nd.arange(12).reshape(3,4) +Z1 = nd.concat(X, Y, dim=0) # Z1.shape (6,4) +Z2 = nd.concat(X, Y, dim=1) # Z1.shape (3,8) +``` +### NDArray其他操作 +常用的还有广播机制、索引、运算的内存开销和NDArray和Numpy相互互换。NDArray实例和NumPy实例互换如下： +```Python +import numpy as np +from mxnet import nd +# 将NumPy实例变换成NDArray实例 +P = np.ones((3,4)) +D = nd.array(P) +# 将NDArray实例变换成NumPy实例 +D.asnumpy +``` +## 对函数求梯度(gradient)-autograd模块 +`from mxnet import autograd, nd` +### 简单使用 +主要是两个函数： ++ attach_grad()：申请存储梯度所需内存 ++ record()：求MXNet记录与求梯度有关的计算 ++ backward()：自动求梯度 +### 训练模式和预测模式 +调用record函数后，MXNet会记录并计算梯度。此外，默认情况下autograd还会将运行模式从预测模式转为训练模式。这可以通过调用is_training函数来查看。 +# MXNet深度学习计算 +涉及使用MXNet框架完成模型构造、参数的访问和初始化、自定义层构建、模型读取、加载和使用GPU等。 +# MXNet用于构建CNN的模块-Symbol、gluon +## Symbol模块学习 +> MXNet提供了符号接口，用于符号编程的交互。它和一步步的解释的命令式编程不同，我们首先要定义一个计算图。这个图包括了输入的占位符和设计的输出。之后编译这个图，产生一个可以绑定到NDArray s并运行的函数。Symbol API类似于caffe中的网络配置或者Theano中的符号编程。 +symbol构建神经网络有点类似于tensorflow的静态图api。 +### 基本神经网络构建 +除了基本操作符，symbol模块提供了丰富的神经网络api，以下示例构建了一个两层的全连接层，通过给定输入数据大小实例化该结构： +```python +import mxnet as mx +net = mx.sym.Variable('data') +net = mx.sym.FullyConnected(data=net, num_hidden=128, name='fc1') +net = mx.sym.relu(net, name='relu1', act_type='relu') +net = mx.sym.FullyConnected(data=net, name='fc2', num_hidden=2) +net = mx.sym.SoftmaxOutput(data=net, name='out') +mx.viz.plot_network(net, shape={'data': (100, 200)}) +print(net.list_arguments()) # 遍历参数 +``` +输出如下： +> ['data', 'fc1_weight', 'fc1_bias', 'fc2_weight', 'fc2_bias', 'out_label'] +注意：FullyConnected 层有三个输入：数据，权值，偏置。任何，任何没有指定输入的将自动生成一个变量。***这里一般权值w，偏置b参数不用指定***。 +### 深度网络模块化构建 +对于一些深度的CNN网络，可用模块化的方式将其构建，下面一个示例将卷积层、批标准化层和relu激活层捆绑在一起形成一个新的函数。 +```python +def Conv(data, num_filter, kernel=(1, 1), stride=(1, 1), pad=(0, 0), name=None, suffix=''): + conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=kernel, stride=stride, pad=pad, no_bias=True, name='%s%s_conv2d' %(name, suffix)) + bn = mx.sym.BatchNorm(data=conv, name='%s%s_batchnorm' %(name, suffix), fix_gamma=False) + act = mx.sym.Activation(data=bn, act_type='relu', name='%s%s_relu' %(name, suffix)) + return act +``` +注意这里的mx.sym.Convolution函数需要自己指定`data`输入张量、`num_filter`滤波器（卷积核）数量、`kernel`卷积核尺寸、`stride`移动步长、`pad`填充shape。 +对于2-D convolution, 各参数对应shape是： +- **data**: *(batch_size, channel, height, width)* +- **weight**: *(num_filter, channel, kernel[0], kernel[1])* +- **bias**: *(num_filter,)* +- **out**: *(batch_size, num_filter, out_height, out_width)*. +### mxnet.sym.Group +组合两个输出层： +```python +net = mx.sym.Variable('data') +fc1 = mx.sym.FullyConnected(data=net, name='fc1', num_hidden=128) +net = mx.sym.Activation(data=fc1, name='relu1', act_type="relu") +out1 = mx.sym.SoftmaxOutput(data=net, name='softmax') +out2 = mx.sym.LinearRegressionOutput(data=net, name='regression') +group = mx.sym.Group([out1, out2]) +print(group.list_outputs()) +``` +输出如下： +> ['softmax_output', 'regression_output'] +# MXNet数据读取类：基础类mxnet.io.DataIter、高级类mxnet.io.MXDataIter、高级类mxnet.image.ImageIter +## mxnet.io.DataIter +***mxnet.io.DataIter是MXNet框架中构造数据迭代器的基础类***，在MXNet框架下只要和数据读取相关的接口基本上都继承该类，比如我们常用的图像算法相关的***mxnet.io.ImageRecordIter***类或***mxnet.image.ImageIter***类都直接或间接继承mxnet.io.DataIter类进行封装。 +## mxnet.io.MXDataIter +初始化一个mxnet.io.ImageRecordIter类时会得到一个MXDataIter实例，然后当你调用该实例的时候就会调用MXDataIter类的底层C++数据迭代器读取数据（后面会介绍是通过next方法实现的）。 +## mxnet.image.ImageIter +Imageter类是纯python实现，继承自DataIter类，与mxnet.io.imageRecordIter类不同，该接口是Python代码实现的图像数据迭代器，既可以读取.rec文件，也可以以图像+.lst方式来读取数据。 +> 由于mxnet.image.ImageIter接口在以原图像+.lst文件形式读取数据时是基于python代码实现的，因此在速度上会比基于C++代码实现的mxnet.io.ImageRecordIter接口效率低，尤其是当数据是存储在机械硬盘上时。 +## MXNet读取图像数据总结 +MXNet的图像数据导入模块主要有mxnet.io.ImageRecordIter和mxnet.image.ImageIter两个类，前者主要用来读取.rec格式的数据，***后者既可以读.rec格式文件，也可以读原图像数据***。 +注意：***在MXNet框架中，数据存储为NDArray格式，图像数据也是如此，因此mxnet.image中的很多函数的输入输出都是NDArray格式*** +当我们使用mxnet.io.ImageRecordIter这个类读取图像时，必须先用im2rec.py生成lst和rec文件，然后采用mxnet.io.ImageRecordIter类读取rec文件。 From 1438a00e8db3536ad062a8004104ebca061fa3c8 Mon Sep 17 00:00:00 2001 From: Zhang Date: Fri, 14 Jun 2019 15:21:48 +0800 Subject: [PATCH 135/386] =?UTF-8?q?Update=20=E6=9A=91=E6=9C=9F=E8=AE=A1?= =?UTF-8?q?=E7=AE=97=E6=9C=BA=E8=A7=86=E8=A7=89=E5=AE=9E=E4=B9=A0=E5=BA=94?= =?UTF-8?q?=E8=81=98=E6=80=BB=E7=BB=93.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 更新一些信心 --- ...40\345\272\224\350\201\230\346\200\273\347\273\223.md" | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" index 86aa5686..1cc3f930 100644 --- "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" +++ "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" @@ -45,10 +45,10 @@ |北京百度|网申|笔试挂| |深圳腾讯|朋友内推|笔试挂| |川渝华为|官网投递|笔试挂| -|北京小鹏汽车|boss直聘投递|二面过| -|南京地平线机器人|boss直聘投递|口头offer| -|深圳平安科技|boss直聘投递|简历过，等待面试| -|深圳pony.ai|boss直聘投递|简历过，等待笔试| +|北京小鹏汽车|boss直聘投递|技术面通过| +|南京地平线机器人|boss直聘投递|正式offer| +|深圳平安科技|boss直聘投递|简历过，拒绝面试| +|深圳pony.ai|boss直聘投递|简历过，拒绝笔试| |深圳中科类脑|boss直聘投递|简历过，婉拒面试| |杭州新再灵|拉钩网|技术面过，hr面挂| |康尚生物医疗|boss直聘投递|offer，婉拒| From 0c1e487706833a86ba2de7d157de8764b71d0a3c Mon Sep 17 00:00:00 2001 From: Zhang Date: Sat, 22 Jun 2019 23:53:01 +0800 Subject: [PATCH 136/386] =?UTF-8?q?Update=20=E8=AE=A1=E7=AE=97=E6=9C=BA?= =?UTF-8?q?=E8=A7=86=E8=A7=89=E5=B2=97=E5=AE=9E=E4=B9=A0=E9=9D=A2=E7=BB=8F?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" index 083a42c1..fb472394 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" @@ -66,7 +66,7 @@ + Python一些基础还是要搞清楚，向迭代器、深拷贝、浅拷贝，我之前都看过面经和用法，都还是忘了，真是不应该。 + 地平线机器人面试真的问的很广，偏工程向，碰到不会的也不要太紧张，之后一定要去补课。 + 自己要加强Python基础的一些**技术盲点**。 - + 以后面试表达要有针对性，不要拓展太多。 + + 以后面试表达要有针对性，可以引导面试官往自己熟悉的方向，但不要拓展太多。 ### 二面（70分钟） 1. 项目介绍 From 1212b6c9f08f006e6f6c24bb97ae3a8089d0fdf7 Mon Sep 17 00:00:00 2001 From: Zhang Date: Sat, 22 Jun 2019 23:54:11 +0800 Subject: [PATCH 137/386] =?UTF-8?q?Update=20=E8=AE=A1=E7=AE=97=E6=9C=BA?= =?UTF-8?q?=E8=A7=86=E8=A7=89=E5=B2=97=E5=AE=9E=E4=B9=A0=E9=9D=A2=E7=BB=8F?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" index fb472394..726a99ef 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" @@ -111,7 +111,8 @@ + 数据增强用了哪些，为什么用 3. 拓展问题 + 图像分割结果，如果边缘信息本来是直线的，但是分割出来效果线确是弯的，怎么解决（有点记不清了） -4. 你有什么想问我的 + +4. 你有什么想问我的没 ### 二面（27分钟） 1. 项目介绍 2. 你平时看过哪些论文，最新的论文看过哪些 From 6edfb1fb53cbb1c195fcacc46bb37d14194838dd Mon Sep 17 00:00:00 2001 From: Zhang Date: Sat, 22 Jun 2019 23:56:46 +0800 Subject: [PATCH 138/386] =?UTF-8?q?Update=20=E8=AE=A1=E7=AE=97=E6=9C=BA?= =?UTF-8?q?=E8=A7=86=E8=A7=89=E5=B2=97=E5=AE=9E=E4=B9=A0=E9=9D=A2=E7=BB=8F?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...2\227\345\256\236\344\271\240\351\235\242\347\273\217.md" | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" index 726a99ef..dd7984d4 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" @@ -111,13 +111,12 @@ + 数据增强用了哪些，为什么用 3. 拓展问题 + 图像分割结果，如果边缘信息本来是直线的，但是分割出来效果线确是弯的，怎么解决（有点记不清了） - 4. 你有什么想问我的没 ### 二面（27分钟） 1. 项目介绍 2. 你平时看过哪些论文，最新的论文看过哪些 -3. 平时几点钟回去, 代码量多少，平时用C、C++还是Python编程 -4．你有什么想问的 +3. 平时几点钟回去, 代码量多少，平时用C、C++还是Python编程. +4. 你有什么想问的 ### 面试总结 二面的面试官提到我最新的论文看的不多，其实**最新的论文是一定要看的**，紧跟行业发展，了解技术的最新发展动向，而且也有助于拓展自己的思维，学习下别人的idea。 ## 合心科技算法实习生（一家不尊重面试者、面试体验极其糟糕的公司）(不到10分钟) From 79b22d174b0de1aee70a026661293e31260db310 Mon Sep 17 00:00:00 2001 From: Zhang Date: Thu, 11 Jul 2019 14:52:52 +0800 Subject: [PATCH 139/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 更新多线程与多进程区别 --- ...\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 2 ++ 1 file changed, 2 insertions(+) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index ca4282f4..d8b478ef 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -307,6 +307,8 @@ faster rcnn将roi pooling替换为roi align效果有所提升 + 线程是进程的一部分，一个进程至少有一个线程； + 对于操作系统来说，一个任务就是一个进程，进程内的“子任务”称为线程； + 多线程和多进程最大的不同在于，多进程中，同一个变量，各自有一份拷贝存在于每个进程中，互不影响，而**多线程中，所有变量都由所有线程共享**，所以，任何一个变量都可以被任何一个线程修改，因此，线程之间共享数据最大的危险在于多个线程同时改一个变量，把内容给改乱了。 ++ 进程间调用、通讯和切换开销均比多线程大，单个线程的崩溃会导致整个应用的退出。 ++ 存在大量IO，网络耗时或者需要和用户交互等操作时，使用多线程有利于提高系统的并发性和用户界面快速响应从而提高友好性。 ## map与reduce函数用法解释下 1. map函数接收两个参数，一个是函数，一个是Iterable，map将传入的函数依次作用到序列的**每个元素**，并将结果作为新的Iterator返回，简单示例代码如下： ```Python3 From a98c474029b642d973f8c8a327064cb1d187d1ad Mon Sep 17 00:00:00 2001 From: Zhang Date: Thu, 11 Jul 2019 15:53:24 +0800 Subject: [PATCH 140/386] Add files via upload --- ...253\345\205\205\346\226\271\345\274\217.png" | Bin 0 -> 30299 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 "images/\345\215\267\347\247\257\345\241\253\345\205\205\346\226\271\345\274\217.png" diff --git "a/images/\345\215\267\347\247\257\345\241\253\345\205\205\346\226\271\345\274\217.png" "b/images/\345\215\267\347\247\257\345\241\253\345\205\205\346\226\271\345\274\217.png" new file mode 100644 index 0000000000000000000000000000000000000000..c21bc1d0aff8f1ee0f43a9710f6b843c145c749a GIT binary patch literal 30299 zcmdSBd0f(I_cv}e)l^eXGo`7em6chVrJ`9XO=Xr#re@|!<%+nZXadA`&9<`=Hf$-*?mf{C?lx^Lk#-AH~x3xvuM+>zs4F z-{&0q?bJ!@)hpFkDk&+gK5_i0lakV6G4SWyx66QU)@-yp1iUPQJ6RuADsSI741D+| z;3xZ^l$0tGSIwMX3VdF1@wgit82U`{XHiqwLtiDOsP`w1{&f1X_ZV>>b>L-Mx!Lq8 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z2_9vp=U8(6-Po^2YaSC0t!onF`MN1dKYxJm)^1dRl@l2fk0e*ao6BV#n_<_6-0R5h ztRk=eD(EN-7CG$7m<5XtxVh=9Fr8x8Oegx9!y%IbBr*VNTbBvR9F^QJO=$0cooZ}u zS5<(~tuz@b7>ig2h_<~Pl+TEgPUcSCOXY?St>cbG7{lKqbQ(ZnB6KNy8qDg5;4jeK z_v^iqeU5b3_1*lhn~MuGi9VPOM4%jkDgjolg0G&T-j@zSvZ0XyJSWcpUzSW6x`$A( z=7Pz2C2X?s4Nl*$M1c^N7=r$Q=8+wF27J)`*|$jZdAD)^X1`Bt;3$6YR$J>lm#r@H z6;56gE7Nh&$++ly!t_X%l z2nQKdNO`aTeB$a2ieUa1U4P&K1Ov|ra4Rtb^qB5)-cH2Z2_`i?+~i-LPt&riUkdD8 zlJ-XnM+7M>l@FcHHw27|lOL-o5yC`JgrhZUG^DW{fWqNRbxd8vcCd~cF=m1JRo4R< zRZ#-Sw&V?P-@YQwR@tHig{{wG@G#&4gjtZhB#$~cW(MockS8gvhBU!-yjB7g%?$3` zg(a{@5v>$*O3^0_dtf!P*79_WndCEwW6|fW((hSIAk7BO!fpQK?Or%_?+FrW;EtWS z%mgHP#4ie70Hr-m9=PszPnN1qs>0I<3Kzb4>W{dOz0`BX6KCCgkovfC$ zz;%x}oBir_z~?Bta6S|RD4dvnHC|}uwwah)x)%Zeq||X>j^|y)m}yi$&FRVOkK1P+-^?_y8+GwnL~bLthC4cJN~dNS}K&!i5;_)J5B6(2YRgYwp~sojsd6s E45bIN-2eap literal 0 HcmV?d00001 From 0bce81408e40a21c2f32a236728ebde6307bc6a3 Mon Sep 17 00:00:00 2001 From: Zhang Date: Thu, 11 Jul 2019 15:59:33 +0800 Subject: [PATCH 141/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...46\344\271\240\351\235\242\350\257\225\351\242\230.md" | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index d8b478ef..505ad832 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -208,6 +208,14 @@ HOG特征提取方法就是将一个image： 2. **经过卷积层节点矩阵深度会改变**，池化层不会改变节点矩阵的深度，但是它可以缩小节点矩阵的大小 ## 卷积层参数数量计算方法 假设输入层矩阵维度是96*96*3，第一层卷积层使用尺寸为5*5、深度为16的过滤器（卷积核尺寸为5*5、卷积核数量为16），那么这层卷积层的参数个数为５*5*3*16+16=1216个 + +## 卷积层输出大小计算 +卷积中的特征图大小计算方式有两种，分别是‘VALID’和‘SAME’，卷积和池化都适用，除不尽的结果都向下取整。公式：`O = (W-F+2P)/S+1`，输入图片（Input）大小为I=W*W，卷积核（Filter）大小为F*F，步长（stride）为S，填充（Padding）的像素数为P。 ++ `SAME`填充方式：不填充像素。 ++ `VALID`填充方式：填充，"SAME"卷积方式，对于输入5*5图像，图像的每一个点都作为卷积核的中心。最后得到5*5的结果，通俗的来说：首先在原图外层补一圈0，将原图的第一点作为卷积核中心，若一圈0不够，继续补一圈0。如下图所示： + +![卷积填充方式](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E5%8D%B7%E7%A7%AF%E5%A1%AB%E5%85%85%E6%96%B9%E5%BC%8F.png) + ## 神经网络为什么用交叉熵损失函数 判断一个输出向量和期望的向量有多接近，交叉熵（cross entroy）是常用的评判方法之一。交叉熵刻画了两个概率分布之间的距离，是分类问题中使用比较广泛的一种损失函数。 给定两个概率分布p和q，通过q来表示p的交叉熵公式为： From 1818ee74019edc531e39f06ba7adb9ed7e2e8a0e Mon Sep 17 00:00:00 2001 From: Zhang Date: Thu, 11 Jul 2019 17:39:09 +0800 Subject: [PATCH 142/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 更新C++面试题 --- ...1\240\351\235\242\350\257\225\351\242\230.md" | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 505ad832..11ed596a 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -211,8 +211,8 @@ HOG特征提取方法就是将一个image： ## 卷积层输出大小计算 卷积中的特征图大小计算方式有两种，分别是‘VALID’和‘SAME’，卷积和池化都适用，除不尽的结果都向下取整。公式：`O = (W-F+2P)/S+1`，输入图片（Input）大小为I=W*W，卷积核（Filter）大小为F*F，步长（stride）为S，填充（Padding）的像素数为P。 -+ `SAME`填充方式：不填充像素。 -+ `VALID`填充方式：填充，"SAME"卷积方式，对于输入5*5图像，图像的每一个点都作为卷积核的中心。最后得到5*5的结果，通俗的来说：首先在原图外层补一圈0，将原图的第一点作为卷积核中心，若一圈0不够，继续补一圈0。如下图所示： ++ `SAME`填充方式：不填充像素。`conv2d`函数常用。 ++ `VALID`填充方式：填充像素，`Maxpooling2D`函数常用。"SAME"卷积方式，对于输入5*5图像，图像的每一个点都作为卷积核的中心。最后得到5*5的结果，通俗的来说：首先在原图外层补一圈0，将原图的第一点作为卷积核中心，若一圈0不够，继续补一圈0。如下图所示： ![卷积填充方式](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E5%8D%B7%E7%A7%AF%E5%A1%AB%E5%85%85%E6%96%B9%E5%BC%8F.png) @@ -305,10 +305,22 @@ faster rcnn将roi pooling替换为roi align效果有所提升 2. [深度学习中 Batch Normalization为什么效果好](https://blog.csdn.net/happynear/article/details/44238541) 3. [详解机器学习中的梯度消失、爆炸原因及其解决方法](https://blog.csdn.net/qq_25737169/article/details/78847691) # Python/C/C++/计算机基础/图像处理基础 +## static关键字作用 ++ 在全局变量前加上关键字static，全局变量就定义为一个全局静态变量，全局静态变量在声明它的文件之外是不可见的，作用域范围为从定义之处开始，到文件结尾。 ++ 在函数返回类型前加`static`，函数就变为静态函数，静态函数只在声明它的文件中使用，不被其他文件所用。 ## C++指针和引用的区别 ++ 指针有自己的内存空间，而引用只是一个别名，类似于Python浅拷贝和深拷贝的区别 + 不存在空引用, 引用必须链接到一块合法的内存地址； + 一旦引用被初始化为一个对象，就不能指向另一个对象。指针可以在任何时候指向任何一个对象； + 引用必须在创建时被初始化。指针可以在任何时间初始化。 +## C++中析构函数的作用 +析构函数与构造函数对应，类的析构函数是类的一种特殊的成员函数，**它会在每次删除所创建的对象时执行**。析构函数的名称与类的名称是完全相同的，只是在前面加了个波浪号（~）作为前缀，它不会返回任何值，也不能带有任何参数。析构函数有助于在跳出程序（比如关闭文件、释放内存等）前释放资源。 +## C++静态函数和虚函数的区别 +静态函数在编译的时候就已经确定运行时机，虚函数在运行的时候动态绑定。虚函数因为用了虚函数表机制，调用的时候会增加一次内存开销。 +## ++i和i++区别 +++i 先自增1，再返回，i++，先返回 i，再自增1. +## const关键字作用 +`const`类型的对象在程序执行期间不能被修改改变。 ## Python装饰器解释 装饰器本质上是一个 Python 函数或类，**它可以让其他函数或类在不需要做任何代码修改的前提下增加额外功能**，装饰器的返回值也是一个函数/类对象。它经常用于有切面需求的场景，比如：插入日志、性能测试、事务处理、缓存、权限校验等场景，装饰器是解决这类问题的绝佳设计。有了装饰器，我们就可以**抽离出大量与函数功能本身无关的雷同代码到装饰器中并继续重用**。概括的讲，**装饰器的作用就是为已经存在的对象添加额外的功能**。 ## 多进程与多线程区别 From 26fab422f205579cf2a51902580d65c298d1252f Mon Sep 17 00:00:00 2001 From: Zhang Date: Thu, 11 Jul 2019 21:26:42 +0800 Subject: [PATCH 143/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...55\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 11ed596a..9134402e 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -211,8 +211,8 @@ HOG特征提取方法就是将一个image： ## 卷积层输出大小计算 卷积中的特征图大小计算方式有两种，分别是‘VALID’和‘SAME’，卷积和池化都适用，除不尽的结果都向下取整。公式：`O = (W-F+2P)/S+1`，输入图片（Input）大小为I=W*W，卷积核（Filter）大小为F*F，步长（stride）为S，填充（Padding）的像素数为P。 -+ `SAME`填充方式：不填充像素。`conv2d`函数常用。 -+ `VALID`填充方式：填充像素，`Maxpooling2D`函数常用。"SAME"卷积方式，对于输入5*5图像，图像的每一个点都作为卷积核的中心。最后得到5*5的结果，通俗的来说：首先在原图外层补一圈0，将原图的第一点作为卷积核中心，若一圈0不够，继续补一圈0。如下图所示： ++ `SAME`填充方式：填充像素。`conv2d`函数常用。 ++ `VALID`填充方式：不填充像素，`Maxpooling2D`函数常用。"SAME"卷积方式，对于输入5*5图像，图像的每一个点都作为卷积核的中心。最后得到5*5的结果，通俗的来说：首先在原图外层补一圈0，将原图的第一点作为卷积核中心，若一圈0不够，继续补一圈0。如下图所示： ![卷积填充方式](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E5%8D%B7%E7%A7%AF%E5%A1%AB%E5%85%85%E6%96%B9%E5%BC%8F.png) From ff7b817472cc6ecafc8058cb8333b1e5b796ec64 Mon Sep 17 00:00:00 2001 From: Zhang Date: Mon, 15 Jul 2019 14:50:47 +0800 Subject: [PATCH 144/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 7 +++++++ 1 file changed, 7 insertions(+) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 9134402e..2ef5fb65 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -1,4 +1,8 @@ # 深度学习 + +## 卷积操作作用 ++ 卷积网络中的卷积核参数是通过网络训练出来的 ++ 通过卷积核的组合以及随着网络后续操作的进行，基本而一般的模式会逐渐被抽象为具有高层语义的“概念”表示，也就是自动学习到图像的高层特征 ## CNN权值共享问题 首先**权值共享就是滤波器共享**，滤波器的参数是固定的，即是用相同的滤波器去扫一遍图像，提取一次特征特征，得到feature map。在卷积网络中，学好了一个滤波器，就相当于掌握了一种特征，这个滤波器在图像中滑动，进行特征提取，然后所有进行这样操作的区域都会被采集到这种特征，就好比上面的水平线。 @@ -109,6 +113,9 @@ RNN的输入有两个部分，一部分为上一时刻的状态，另一部分 此回答可以参考TensorFlow实战p110，网上很多回答都说的不全。 ## Relu比Sigmoid效果好在哪里？ +Sigmoid函数公式如下： +$\sigma (x)=\frac{1}{1+exp(-x)}$ + ReLU激活函数公式如下： ![relu激活函数](http://latex.codecogs.com/gif.latex?%24%24ReLu%28x%29%3D%5Cleft%5C%7B%5Cbegin%7Bmatrix%7D%20x%20%26%20x%3E0%5C%5C%200%20%26%20x%3C%3D0%20%5Cend%7Bmatrix%7D%5Cright.%24%24) From 9861cecdf8330b5b0319f6ade8d95a8c348ec6a6 Mon Sep 17 00:00:00 2001 From: Zhang Date: Mon, 15 Jul 2019 14:57:42 +0800 Subject: [PATCH 145/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 2 ++ 1 file changed, 2 insertions(+) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 2ef5fb65..9397a3e0 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -122,9 +122,11 @@ ReLU激活函数公式如下： relu函数方程 ReLU 的输出要么是 0, 要么是输入本身。虽然方程简单，但实际上效果更好。在网上看了很多版本的解释，有从程序实例分析也有从数学上分析，我找了个相对比较直白的回答，如下： + (1). ReLU函数计算简单，可以减少很多计算量。反向传播求误差梯度时，涉及除法，计算量相对较大，采用ReLU激活函数，可以节省很多计算量； (2). **避免梯度消失问题**。对于深层网络，sigmoid函数反向传播时，很容易就会出现梯度消失问题（在sigmoid接近饱和区时，变换太缓慢，导数趋于0，这种情况会造成信息丢失），从而无法完成深层网络的训练。 (3). 可以缓解过拟合问题的发生。Relu会使一部分神经元的输出为0，这样就造成了网络的稀疏性，并且减少了参数的相互依存关系，缓解了过拟合问题的发生。 +(4). 相比`sigmoid`型函数，`ReLU`函数有助于随机梯度下降方法收敛。 #### 参考链接 [ReLU为什么比Sigmoid效果好](https://www.twblogs.net/a/5c2dd30fbd9eee35b21c4337/zh-cn) From 001c482db7e6c2df1740588d93e6b2493415686a Mon Sep 17 00:00:00 2001 From: Zhang Date: Mon, 15 Jul 2019 14:58:58 +0800 Subject: [PATCH 146/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...46\344\271\240\351\235\242\350\257\225\351\242\230.md" | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 9397a3e0..ccc7df3e 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -123,10 +123,10 @@ ReLU激活函数公式如下： relu函数方程 ReLU 的输出要么是 0, 要么是输入本身。虽然方程简单，但实际上效果更好。在网上看了很多版本的解释，有从程序实例分析也有从数学上分析，我找了个相对比较直白的回答，如下： -(1). ReLU函数计算简单，可以减少很多计算量。反向传播求误差梯度时，涉及除法，计算量相对较大，采用ReLU激活函数，可以节省很多计算量； -(2). **避免梯度消失问题**。对于深层网络，sigmoid函数反向传播时，很容易就会出现梯度消失问题（在sigmoid接近饱和区时，变换太缓慢，导数趋于0，这种情况会造成信息丢失），从而无法完成深层网络的训练。 -(3). 可以缓解过拟合问题的发生。Relu会使一部分神经元的输出为0，这样就造成了网络的稀疏性，并且减少了参数的相互依存关系，缓解了过拟合问题的发生。 -(4). 相比`sigmoid`型函数，`ReLU`函数有助于随机梯度下降方法收敛。 +1. ReLU函数计算简单，可以减少很多计算量。反向传播求误差梯度时，涉及除法，计算量相对较大，采用ReLU激活函数，可以节省很多计算量； +2. **避免梯度消失问题**。对于深层网络，sigmoid函数反向传播时，很容易就会出现梯度消失问题（在sigmoid接近饱和区时，变换太缓慢，导数趋于0，这种情况会造成信息丢失），从而无法完成深层网络的训练。 +3. 可以缓解过拟合问题的发生。Relu会使一部分神经元的输出为0，这样就造成了网络的稀疏性，并且减少了参数的相互依存关系，缓解了过拟合问题的发生。 +4. 相比`sigmoid`型函数，`ReLU`函数有助于随机梯度下降方法收敛。 #### 参考链接 [ReLU为什么比Sigmoid效果好](https://www.twblogs.net/a/5c2dd30fbd9eee35b21c4337/zh-cn) From 2351f3939ab54e9314df91b70f6a5f898010c9f8 Mon Sep 17 00:00:00 2001 From: Zhang Date: Mon, 15 Jul 2019 15:22:40 +0800 Subject: [PATCH 147/386] Add files via upload --- ...27\351\207\216\347\251\272\351\227\264.jpg" | Bin 0 -> 100349 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 "images/\346\204\237\345\217\227\351\207\216\347\251\272\351\227\264.jpg" diff --git "a/images/\346\204\237\345\217\227\351\207\216\347\251\272\351\227\264.jpg" "b/images/\346\204\237\345\217\227\351\207\216\347\251\272\351\227\264.jpg" new file mode 100644 index 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zx3f8{667(HeidNY*axLU_0haiiyM6lpdYnsRUZ(q2T#-51zHylb0=EkH27fG*y)cLwF^ c>)JgSOa#Oh@%{JD#ees#{J+oL`M2=D0H+M34gdfE literal 0 HcmV?d00001 From 515b78c4bccf6db891f00f0b0c3fe044d97e95e0 Mon Sep 17 00:00:00 2001 From: Zhang Date: Mon, 15 Jul 2019 15:23:40 +0800 Subject: [PATCH 148/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 7 +++++++ 1 file changed, 7 insertions(+) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index ccc7df3e..d4f2c5fe 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -1,5 +1,12 @@ # 深度学习 +## 感受野 +后一层神经元在前一层神经元的感受空间，如下图所示： +![感受野大小](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E6%84%9F%E5%8F%97%E9%87%8E%E7%A9%BA%E9%97%B4.jpg) + +注意：小卷积核（如3*3）通过多层叠加可取得与大卷积核（如7*7）同等规模的感受野，此外采用小卷积核有两个优势： +1. 小卷积核需多层叠加，加深了网络深度进而增强了网络容量(model capacity)和复杂度（model complexity） +2. 增强了网络容量的同时减少了参数个数。 ## 卷积操作作用 + 卷积网络中的卷积核参数是通过网络训练出来的 + 通过卷积核的组合以及随着网络后续操作的进行，基本而一般的模式会逐渐被抽象为具有高层语义的“概念”表示，也就是自动学习到图像的高层特征 From 2e56dcb07f9b34660f02703d36c849e858d10099 Mon Sep 17 00:00:00 2001 From: Zhang Date: Mon, 15 Jul 2019 15:58:50 +0800 Subject: [PATCH 149/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...5\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index d4f2c5fe..6e4c7d34 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -9,7 +9,7 @@ 2. 增强了网络容量的同时减少了参数个数。 ## 卷积操作作用 + 卷积网络中的卷积核参数是通过网络训练出来的 -+ 通过卷积核的组合以及随着网络后续操作的进行，基本而一般的模式会逐渐被抽象为具有高层语义的“概念”表示，也就是自动学习到图像的高层特征 ++ 通过卷积核的组合以及随着网络后续操作的进行，卷积操作可获取图像区域不同类型特征；基本而一般的模式会逐渐被抽象为具有高层语义的“概念”表示，也就是自动学习到图像的高层特征 ## CNN权值共享问题 首先**权值共享就是滤波器共享**，滤波器的参数是固定的，即是用相同的滤波器去扫一遍图像，提取一次特征特征，得到feature map。在卷积网络中，学好了一个滤波器，就相当于掌握了一种特征，这个滤波器在图像中滑动，进行特征提取，然后所有进行这样操作的区域都会被采集到这种特征，就好比上面的水平线。 @@ -27,6 +27,9 @@ ### Reference [(二)计算机视觉四大基本任务(分类、定位、检测、分割](https://zhuanlan.zhihu.com/p/31727402) +## 深度特征的层次性 +卷积操作可获取图像区域不同类型特征，而汇合等操作可对这些特征进行融合和抽象，随着若干卷积、汇合等操作的堆叠，各层得到的深度特征逐渐从泛化特征（如边缘、纹理等）过渡到高层语义表示（躯干、头部等模式）。 + ## 什么样的数据集不适合深度学习 + 数据集太小，数据样本不足时，深度学习相对其它机器学习算法，没有明显优势。 + 数据集没有局部相关特性，目前深度学习表现比较好的领域主要是图像／语音／自然语言处理等领域，这些领域的一个共性是局部相关性。图像中像素组成物体，语音信号中音位组合成单词，文本数据中单词组合成句子，这些特征元素的组合一旦被打乱，表示的含义同时也被改变。对于没有这样的局部相关性的数据集，不适于使用深度学习算法进行处理。举个例子：预测一个人的健康状况，相关的参数会有年龄、职业、收入、家庭状况等各种元素，将这些元素打乱，并不会影响相关的结果。 From 88472bf6e917a5a7725e488cf01ed98df2c58a29 Mon Sep 17 00:00:00 2001 From: Zhang Date: Tue, 16 Jul 2019 15:36:00 +0800 Subject: [PATCH 150/386] =?UTF-8?q?Create=20=E7=A5=9E=E7=BB=8F=E7=BD=91?= =?UTF-8?q?=E7=BB=9C=E5=8E=8B=E7=BC=A9=E7=AE=97=E6=B3=95=E6=80=BB=E7=BB=93?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 添加神经网络压缩算法总结笔记 --- ...27\346\263\225\346\200\273\347\273\223.md" | 45 +++++++++++++++++++ 1 file changed, 45 insertions(+) create mode 100644 "\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" diff --git "a/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" "b/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" new file mode 100644 index 00000000..9610deb8 --- /dev/null +++ "b/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" @@ -0,0 +1,45 @@ +## 神经网络的压缩方法 +我们知道，在一定程度上，网络越深，参数越多，模型越复杂，其最终效果越好。神经网络的压缩算法是，旨在将一个庞大而复杂的预训练模型（pre-trained model）转化为一个精简的小模型。 +按照压缩过程对网络结构的破坏程度，我们将模型压缩技术分为“前端压缩”和“后端压缩”两部分。 ++ 前端压缩，是指在不改变原网络结构的压缩技术，主要包括`知识蒸馏`、紧凑的模型结构涉及以及`滤波器（filter）层面的剪枝`等； ++ 后端压缩，是指包括`低秩近似`、未加限制的剪枝、`参数量化`以及二值网络等，目标在于尽可能减少模型大小，会对原始网络结构造成极大程度的改造。 + +总结：前端压缩几乎不改变原有网络结构（仅仅只是在原模型基础上减少了网络的层数或者滤波器个数），后端压缩对网络结构有不可逆的大幅度改变，造成原有深度学习库、甚至硬件设备不兼容改变之后的网络。其维护成本很高。 +## 低秩近似 +简单理解就是，卷积神经网络的权重矩阵往往稠密且巨大，从而计算开销大，有一种办法是**采用低秩近似的技术**将该稠密矩阵由若干个小规模矩阵近似重构出来，这种方法归类为低秩近似算法。 +> 一般地，行阶梯型矩阵的秩等于其“台阶数”-非零行的行数。 + +**低秩近似算法能减小计算开销的原理**如下： +给定权重矩阵$W\epsilon R^{m*n}$，若能将其表示为若干个低秩矩阵的组合，即$W=\sum_{i=1}^{n}\alpha _{i}M_{i}$，其中$W\epsilon R^{m*n}$为低秩矩阵，其秩为$r_{i}$，并满足$r_{i}< Date: Sun, 28 Jul 2019 20:24:44 +0800 Subject: [PATCH 151/386] Update README.md MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 更新2020年校招可投递公司-计算机视觉/机器学习算法工程师表格 --- README.md | 24 ++++++++++++++++++++++++ 1 file changed, 24 insertions(+) diff --git a/README.md b/README.md index 28c76e7a..787e8210 100644 --- a/README.md +++ b/README.md @@ -37,3 +37,27 @@ |爱奇艺智能平台部-视频分析组|视频理解算法实习生|北京中关村|未知|内推邮箱:zhangyuntao@qiyi.com|简历命名格式：CVer推荐+学校+研究方向+实习开始时间+毕业时间|CVer公众号| ## [深度学习面试题](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E9%9D%A2%E8%AF%95%E9%A2%98.md) + +## 2020年校招可投递公司-计算机视觉/机器学习算法工程师 +侧重于于计算机视觉/图像/机器学习算法工程师岗位 +|神级公司|互联网公司|AI独角兽公司|其他大公司|是否投递| +|------------|---------------|---------------|-------------|----------------| +|百度|美团|地平线机器人|顺丰科技|否| +|阿里巴巴|滴滴出行|图森未来|招银网络科技|否| +|腾讯|拼多多|momenta|平安科技|，是，| +|字节跳动|京东|小马智行|cvte|是，，| +|微软|网易|蔚来汽车|海康威视|，是，| +|谷歌|快手|小鹏汽车|虹软科技|否| +|商汤|爱奇艺|科大讯飞|华为|否| +|旷世|小米|寒武纪|吉利汽车|否| +|依图|陌陌|格灵深瞳|中兴|否| +|大疆无人机|美图|文行知远|联想|否| +|蚂蚁金服|360安全|云天励飞|汇顶科技|否| +|云从科技|英语流利说|松鼠AI|美的中央研究院|，是，| +|无|新浪|驭势科技||否| +|无|搜狐|码隆科技||否| +|无|小红书|追一科技||否| +|无|作业帮|星环科技||否| +|无|猿辅导|||否| +|无|oppo/vivo|||否| +|无|虎牙/斗鱼直播|||否| From 2133e3b97a944415d9860bf9d0a9e9983a044fa0 Mon Sep 17 00:00:00 2001 From: Zhang Date: Sun, 28 Jul 2019 20:25:28 +0800 Subject: [PATCH 152/386] Update README.md --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 787e8210..23b0432c 100644 --- a/README.md +++ b/README.md @@ -40,8 +40,9 @@ ## 2020年校招可投递公司-计算机视觉/机器学习算法工程师 侧重于于计算机视觉/图像/机器学习算法工程师岗位 + |神级公司|互联网公司|AI独角兽公司|其他大公司|是否投递| -|------------|---------------|---------------|-------------|----------------| +|-------|---------|-----------|---------|--------| |百度|美团|地平线机器人|顺丰科技|否| |阿里巴巴|滴滴出行|图森未来|招银网络科技|否| |腾讯|拼多多|momenta|平安科技|，是，| From 88ae859eab516885306945bbfc47f7982f88c5ab Mon Sep 17 00:00:00 2001 From: Zhang Date: Sun, 28 Jul 2019 20:34:50 +0800 Subject: [PATCH 153/386] Update README.md --- README.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 23b0432c..fa537231 100644 --- a/README.md +++ b/README.md @@ -39,10 +39,10 @@ ## [深度学习面试题](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E9%9D%A2%E8%AF%95%E9%A2%98.md) ## 2020年校招可投递公司-计算机视觉/机器学习算法工程师 -侧重于于计算机视觉/图像/机器学习算法工程师岗位 +侧重于于计算机视觉/图像/机器学习算法工程师岗位,公司排名几乎部分先后。 |神级公司|互联网公司|AI独角兽公司|其他大公司|是否投递| -|-------|---------|-----------|---------|--------| +|------------|---------------|---------------|-------------|----------------| |百度|美团|地平线机器人|顺丰科技|否| |阿里巴巴|滴滴出行|图森未来|招银网络科技|否| |腾讯|拼多多|momenta|平安科技|，是，| @@ -54,11 +54,11 @@ |依图|陌陌|格灵深瞳|中兴|否| |大疆无人机|美图|文行知远|联想|否| |蚂蚁金服|360安全|云天励飞|汇顶科技|否| -|云从科技|英语流利说|松鼠AI|美的中央研究院|，是，| -|无|新浪|驭势科技||否| -|无|搜狐|码隆科技||否| +|云从科技|搜狗|松鼠AI|美的中央研究院|，是，| +|无|新浪|驭势科技|锐明技术|否| +|无|搜狐/金山|码隆科技||否| |无|小红书|追一科技||否| |无|作业帮|星环科技||否| -|无|猿辅导|||否| +|无|猿辅导/英语流利说|深睿医疗||否| |无|oppo/vivo|||否| |无|虎牙/斗鱼直播|||否| From 2a939d592545ed85d34b51d9c262ce28cb654748 Mon Sep 17 00:00:00 2001 From: Zhang Date: Sat, 3 Aug 2019 18:27:56 +0800 Subject: [PATCH 154/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...5\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 1 + 1 file changed, 1 insertion(+) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 6e4c7d34..54a554ff 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -67,6 +67,7 @@ Tensorflow 是一个通过计算图的形式来表述计算的编程系统，计 (5). **Batch Normalization调整了数据的分布，不考虑激活函数，它让每一层的输出归一化到了均值为0方差为1的分布**，这保证了梯度的有效性，可以解决反向传播过程中的梯度问题。目前大部分资料都这样解释，比如BN的原始论文认为的缓解了Internal Covariate Shift(ICS)问题。 ## 什么是梯度消失和爆炸，怎么解决？ +在反向传播过程中需要对激活han函数进行求导，如果导数大于1，那么随着网络层数的增加梯度更新将会朝着指数爆炸的方式增加这就是梯度爆炸。同样如果导数小于1，那么随着网络层数的增加梯度更新信息会朝着指数衰减的方式减少这就是梯度消失。因此，梯度消失、爆炸，其根本原因在于反向传播训练法则，属于先天不足。 当训练较多层数的模型时，一般会出现梯度消失问题（gradient vanishing problem）和梯度爆炸问题（gradient exploding problem）。注意在反向传播中，当网络模型层数较多时，梯度消失和梯度爆炸是不可避免的。 **深度神经网络中的梯度不稳定性，根本原因在于前面层上的梯度是来自于后面层上梯度的乘积**。当存在过多的层次时，就出现了内在本质上的不稳定场景。前面的层比后面的层梯度变化更小，故变化更慢，故引起了梯度消失问题。前面层比后面层梯度变化更快，故引起梯度爆炸问题。 From 3c7dc2d842e4dbd79cf547a24e4976d8cb1bd45d Mon Sep 17 00:00:00 2001 From: Zhang Date: Tue, 6 Aug 2019 20:05:05 +0800 Subject: [PATCH 155/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 添加dropout和优化算法题目 --- ...246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 54a554ff..427705e7 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -1,5 +1,6 @@ # 深度学习 - +## 优化算法理解 +`Adam`、`AdaGrad`、`RMSProp`优化算法具有自适应性 ## 感受野 后一层神经元在前一层神经元的感受空间，如下图所示： ![感受野大小](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E6%84%9F%E5%8F%97%E9%87%8E%E7%A9%BA%E9%97%B4.jpg) @@ -160,7 +161,9 @@ ReLU 的输出要么是 0, 要么是输入本身。虽然方程简单，但实 Python深度学习p127. ## 什么是dropout? -dropout可以防止过拟合，dropout简单来说就是：我们在前向传播的时候，**让某个神经元的激活值以一定的概率p停止工作**，这样可以使模型的泛化性更强，因为它不会依赖某些局部的特征。 ++ dropout可以防止过拟合，dropout简单来说就是：我们在前向传播的时候，**让某个神经元的激活值以一定的概率p停止工作**，这样可以使模型的泛化性更强，因为它不会依赖某些局部的特征。 ++ `dropout`效果跟`bagging`效果类似（bagging是减少方差variance，而boosting是减少偏差bias） ++ 加入dropout会使神经网络训练时间边长，模型预测时不需要dropout，记得关掉。 ![dropou直观展示](https://www.armcvai.com/wp-content/uploads/2019/03/dropout.png) From 5641caf88dbd82ccc4bee5e84ff441dd3f03e01c Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 7 Aug 2019 15:48:52 +0800 Subject: [PATCH 156/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\271\240\351\235\242\350\257\225\351\242\230.md" | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 427705e7..7ce93cea 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -1,4 +1,17 @@ # 深度学习 +## softmax回归与交叉熵损失函数 +分类问题中，直接使用输出层的输出有两个问题： ++ 神经网络输出层的输出值的范围不确定，我们难以直观上判断这些值的意义 ++ 由于真实标签是离散值，这些离散值与不确定范围的输出值之间的误差难以衡量 + +`softmax回归`解决了以上两个问题，它**将输出值变换为值为正且和为1的概率分布**，公式如下： +$$softmax(y)_{i} = y_{i}^{'} = \frac{e^{yi}}{\sum_{j=1}^{n}e^{yj}}$$ +交叉熵刻画了两个概率分布之间的距离，它是分类问题中使用比较广泛的一种损失函数，交叉熵一般会与softmax回归一起使用，公式如下： +$$L = -\sum_{c=1}^{M}y_{c}log(p_{c})或者H(p,q)=-\sum p(x)logq(x)$$（p代表正确答案，q代表预测值） +- $M$ ——类别的数量； +- $y_{c}$ ——指示变量（0或1）,如果该类别和样本的类别相同就是1，否则是0； +- $p_{c}$ ——对于观测样本属于类别 $c$ 的预测概率。 + ## 优化算法理解 `Adam`、`AdaGrad`、`RMSProp`优化算法具有自适应性 ## 感受野 From 5637398b76eb779d061afa28d2d1591a0b303d29 Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 7 Aug 2019 15:49:48 +0800 Subject: [PATCH 157/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 7ce93cea..f63da9eb 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -1,13 +1,18 @@ # 深度学习 -## softmax回归与交叉熵损失函数 +## softmax回归 分类问题中，直接使用输出层的输出有两个问题： + 神经网络输出层的输出值的范围不确定，我们难以直观上判断这些值的意义 + 由于真实标签是离散值，这些离散值与不确定范围的输出值之间的误差难以衡量 `softmax回归`解决了以上两个问题，它**将输出值变换为值为正且和为1的概率分布**，公式如下： + $$softmax(y)_{i} = y_{i}^{'} = \frac{e^{yi}}{\sum_{j=1}^{n}e^{yj}}$$ + +## 交叉熵损失函数 交叉熵刻画了两个概率分布之间的距离，它是分类问题中使用比较广泛的一种损失函数，交叉熵一般会与softmax回归一起使用，公式如下： + $$L = -\sum_{c=1}^{M}y_{c}log(p_{c})或者H(p,q)=-\sum p(x)logq(x)$$（p代表正确答案，q代表预测值） + - $M$ ——类别的数量； - $y_{c}$ ——指示变量（0或1）,如果该类别和样本的类别相同就是1，否则是0； - $p_{c}$ ——对于观测样本属于类别 $c$ 的预测概率。 From 30a7ef0e9af272edb46ec775b7ccdd3586f2c79e Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 7 Aug 2019 15:54:35 +0800 Subject: [PATCH 158/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...344\271\240\351\235\242\350\257\225\351\242\230.md" | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index f63da9eb..b55470da 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -57,9 +57,13 @@ $$L = -\sum_{c=1}^{M}y_{c}log(p_{c})或者H(p,q)=-\sum p(x)logq(x)$$（p代表 + 梯度消失会造成权值更新缓慢，模型训练难度增加。造成梯度消失的一个原因是，许多激活函数将输出值挤压在很小的区间内，在激活函数两端较大范围的定义域内梯度为0，造成学习停止。 ## Overfitting怎么解决 首先所谓过拟合，指的是一个模型过于复杂之后，它可以很好地“记忆”每一个训练数据中随机噪音的部分而忘记了去“训练”数据中的通用趋势。 -**过拟合具体表现在：模型在训练数据上损失函数较小，预测准确率较高；但是在测试数据上损失函数比较大，预测准确率较低**。 - -Parameter Norm Penalties(参数范数惩罚)；Dataset Augmentation (数据集增强)；Early Stopping(提前终止)；Parameter Tying and Parameter Sharing (参数绑定与参数共享)；Bagging and Other Ensemble Methods(Bagging 和其他集成方法)；dropout；regularization； batch normalizatin。是解决Overfitting的常用手段。 +**过拟合具体表现在：模型在训练数据上损失函数较小，预测准确率较高；但是在测试数据上损失函数比较大，预测准确率较低**。解决办法如下： ++ 数据增强。增加数据多样性 ++ Parameter Norm Penalties(参数范数惩罚), L1, L2正则化 ++ dropout ++ 模型融合, 比如Bagging 和其他集成方法 ++ BN ,batch normalization ++ Early Stopping(提前终止训练) ## L1和L2区别 L1 范数（L1 norm）是指向量中各个元素绝对值之和，也有个美称叫“稀疏规则算子”（Lasso regularization）。 比如 向量 A=[1，-1，3]， 那么 A 的 L1 范数为 |1|+|-1|+|3|。简单总结一下就是： From 3f4a153130fe9f74b3d781e4639c03710a2bf383 Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 7 Aug 2019 16:31:14 +0800 Subject: [PATCH 159/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...6\344\271\240\351\235\242\350\257\225\351\242\230.md" | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index b55470da..ff02b3ba 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -58,11 +58,11 @@ $$L = -\sum_{c=1}^{M}y_{c}log(p_{c})或者H(p,q)=-\sum p(x)logq(x)$$（p代表 ## Overfitting怎么解决 首先所谓过拟合，指的是一个模型过于复杂之后，它可以很好地“记忆”每一个训练数据中随机噪音的部分而忘记了去“训练”数据中的通用趋势。 **过拟合具体表现在：模型在训练数据上损失函数较小，预测准确率较高；但是在测试数据上损失函数比较大，预测准确率较低**。解决办法如下： -+ 数据增强。增加数据多样性 -+ Parameter Norm Penalties(参数范数惩罚), L1, L2正则化 -+ dropout ++ `数据增强`, 增加数据多样性 ++ Parameter Norm Penalties(参数范数惩罚), `L1, L2正则化` ++ `dropout` + 模型融合, 比如Bagging 和其他集成方法 -+ BN ,batch normalization ++ `BN` ,batch normalization + Early Stopping(提前终止训练) ## L1和L2区别 @@ -71,6 +71,7 @@ L1 范数（L1 norm）是指向量中各个元素绝对值之和，也有个美 + L1 范数: 为 x 向量各个元素绝对值之和。 + L2 范数: 为 x 向量各个元素平方和的 1/2 次方，L2 范数又称 Euclidean 范数或 Frobenius 范数 + Lp 范数: 为 x 向量各个元素绝对值 p 次方和的 1/p 次方. + 在支持向量机学习过程中，L1 范数实际是一种对于成本函数求解最优的过程，因此，L1 范数正则化通过向成本函数中添加 L1 范数，使得学习得到的结果满足稀疏化，从而方便人类提取特征。 L1 范数可以使权值参数稀疏，方便特征提取。 L2 范数可以防止过拟合，提升模型的泛化能力。 From e78880daf9352a5d331a4ffdc6e685225b2888ca Mon Sep 17 00:00:00 2001 From: MTlab Date: Thu, 8 Aug 2019 13:04:53 +0800 Subject: [PATCH 160/386] Update README.md --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index fa537231..edfdba29 100644 --- a/README.md +++ b/README.md @@ -4,6 +4,7 @@ |公司|招聘岗位|工作地点|截止时间|投递方式|内推方式|是否投递|信息来源| |---|-------|-------|------|-------|-------|------|-------| +|美图影像实验室MTlab|深度学习/计算机视觉实习生|北京五道口|长期|内推邮箱：lsr@meitu.com|邮件主题：CVer推荐+姓名+电话号码+实习生|无|ai.meitu.com| |文行知远|感知算法实习生|北京/广州|未知|内推邮箱：hzshuai@gmail.com|简历命名格式：CVer推荐+姓名+电话号码+实习生|无|CVer公众号| |商汤科技|深度学习/计算机视觉见习研究员等|北京/上海/深圳/杭州|未知|内推邮箱：wwlwenli@163.com|邮件主题：CVer推荐+商汤2019春招内推、商汤2020实习内推，简历附件命名：姓名+岗位名称+到岗时间+可见习时长|无|CVer公众号| |阿里达摩院|计算机视觉算法实习生|北京/杭州/深圳|未知|内推邮箱：zhoujing.zjh@alibaba-inc.com|简历命名格式：CVer推荐+姓名+电话号码+方向(工程/算法)|无|CVer公众号| @@ -52,7 +53,7 @@ |商汤|爱奇艺|科大讯飞|华为|否| |旷世|小米|寒武纪|吉利汽车|否| |依图|陌陌|格灵深瞳|中兴|否| -|大疆无人机|美图|文行知远|联想|否| +|大疆无人机|美图MTlab|文行知远|联想|否| |蚂蚁金服|360安全|云天励飞|汇顶科技|否| |云从科技|搜狗|松鼠AI|美的中央研究院|，是，| |无|新浪|驭势科技|锐明技术|否| From 2580dda7ae84a3f55d347a1e8ec7942d48dd4d89 Mon Sep 17 00:00:00 2001 From: Zhang Date: Sun, 11 Aug 2019 13:02:49 +0800 Subject: [PATCH 161/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index ff02b3ba..834d219b 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -330,7 +330,7 @@ def calculateIoU(candidateBound, groundTruthBound): + 翻转：Fliplr,Flipud。不同于旋转180度，这是类似镜面的翻折，跟人在镜子中的映射类似，常用水平、上下镜面翻转。 + 旋转：rotate。顺时针/逆时针旋转，最好旋转90-180度，否则会出现边缘缺失或者超出问题，如旋转45度。 + 缩放：zoom。图像可以被放大或缩小，imgaug库可用Scal函数实现。 -+ 裁剪：crop。一般叫随机裁剪，操作步骤是：随机从图像中选择一部分，然后降这部分图像裁剪出来，然后调整为原图像的大小。 ++ 裁剪：crop。一般叫随机裁剪，操作步骤是：随机从图像中选择一部分，然后降这部分图像裁剪出来，然后调整为原图像的大小。根本上理解，图像crop就是指从图像中移除不需要的信息，只保留需要的部分 + 平移：translation。平移是将图像沿着x或者y方向（或者两个方向）移动。我们在平移的时候需对背景进行假设，比如说假设为黑色等等，因为平移的时候有一部分图像是空的，由于图片中的物体可能出现在任意的位置，所以说平移增强方法十分有用。 + 放射变换：Affine。包含：平移(Translation)、旋转(Rotation)、放缩(zoom)、错切(shear)。 + 添加噪声：过拟合通常发生在神经网络学习高频特征的时候，为消除高频特征的过拟合，可以随机加入噪声数据来消除这些高频特征。imgaug库使用GaussianBlur函数。 From 2994b7daf85bf647a0cb821a54cf9a04ffdadf7a Mon Sep 17 00:00:00 2001 From: Zhang Date: Mon, 12 Aug 2019 22:18:05 +0800 Subject: [PATCH 162/386] =?UTF-8?q?Create=20=E7=9B=AE=E6=A0=87=E6=A3=80?= =?UTF-8?q?=E6=B5=8B=E4=B8=8E=E5=9B=BE=E5=83=8F=E5=88=86=E7=B1=BB=E6=8C=87?= =?UTF-8?q?=E6=A0=87.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 上传图像分类与目标检测指标理解及代码实现文档 --- ...06\347\261\273\346\214\207\346\240\207.md" | 281 ++++++++++++++++++ 1 file changed, 281 insertions(+) create mode 100644 "\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" diff --git "a/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" "b/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" new file mode 100644 index 00000000..0ddb4ff3 --- /dev/null +++ "b/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" @@ -0,0 +1,281 @@ +## 交并比IOU +交并比（Intersection-over-Union，IoU），目标检测中使用的一个概念，是产生的候选框（candidate bound）与原标记框（ground truth bound）的交叠率，即它们的交集与并集的比值。最理想情况是完全重叠，即比值为1。 +计算公式如下： +https://www.armcvai.com/wp-content/uploads/2019/04/image-12.png +代码实现如下： +```python +import numpy as np +def iou(bbox1, bbox2): + """ + 计算两个bbox(两框的交并比)的iou值 + :param bbox1: (x1,y1,x2,y2), type: ndarray or list + :param bbox2: (x1,y1,x2,y2), type: ndarray or list + :return: iou, type float + """ + if type(bbox1) or type(bbox2) != 'ndarray': + bbox1 = np.array(bbox1) + bbox2 = np.array(bbox2) + assert bbox1.size == 4 and bbox2.size == 4, "bounding box coordinate size must be 4" + xx1 = np.max((bbox1[0], bbox2[0])) + yy1 = np.max((bbox1[1], bbox1[1])) + xx2 = np.min((bbox1[2], bbox2[2])) + yy2 = np.min((bbox1[3], bbox2[3])) + bwidth = xx2 - xx1 + bheight = yy2 - yy1 + area = bwidth * bheight # 求两个矩形框的交集 + union = (bbox1[2] - bbox1[0])*(bbox1[3] - bbox1[1]) + (bbox2[2] - bbox2[0])*(bbox2[3] - bbox2[1]) - area # 求两个矩形框的并集 + iou = area / union + return iou +if __name__=='__main__': + rect1 = (661, 27, 679, 47) + # (top, left, bottom, right) + rect2 = (662, 27, 682, 47) + iou = iou(rect1, rect2) + print(iou) +``` +## 非极大值抑制算法NMS +### [NMS介绍](https://juejin.im/entry/5bdbc26151882516da0ddd25) +在目标检测中，常会利用非极大值抑制算法(NMS，non maximum suppression)对生成的大量候选框进行后处理，去除冗余的候选框，得到最佳检测框，以加快目标检测的效率。其本质思想是其思想是搜素局部最大值，抑制非极大值。非极大值抑制，在计算机视觉任务中得到了广泛的应用，例如边缘检测、人脸检测、目标检测（DPM，YOLO，SSD，Faster R-CNN）等。即如图 2所示实现效果，消除多余的候选框，找到最佳的bbox。 + +以上图为例，每个选出来的Bounding Box检测框（既BBox）用（x,y,h,w, confidence score，Pdog,Pcat）表示，confidence score表示background和foreground的置信度得分，取值范围[0,1]。Pdog,Pcat分布代表类别是狗和猫的概率。如果是100类的目标检测模型，BBox输出向量为5+100=105。 +### [NMS算法](https://juejin.im/entry/5bdbc26151882516da0ddd25) +NMS主要就是通过迭代的形式，不断地以最大得分的框去与其他框做IoU操作，并过滤那些IoU较大的框。 +其实现的思想主要是将各个框的置信度进行排序，然后选择其中置信度最高的框A，将其作为标准选择其他框，同时设置一个阈值，[当其他框B与A的重合程度超过阈值就将B舍弃掉](https://blog.csdn.net/williamyi96/article/details/77996167 )，然后在剩余的框中选择置信度最大的框，重复上述操作。算法过程如下： +1. 根据候选框类别分类概率排序：F>E>D>C>B>A，并标记最大概率的矩形框F作为标准框。 +2. 分别判断A~E与F的重叠度IOU(两框的交并比)是否大于某个设定的阈值，假设B、D与F的重叠度超过阈值，那么就扔掉B、D； +3. 从剩下的矩形框A、C、E中，选择概率最大的E，标记为要保留下来的，然后判读E与A、C的重叠度，扔掉重叠度超过设定阈值的矩形框； +4. 对剩下的bbx，循环执行(2)和(3)直到所有的bbx均满足要求（即不能再移除bbx） + +**nms的python代码如下**： +```Python +import numpy as np + +def py_nms(dets, thresh): + """Pure Python NMS baseline.注意，这里的计算都是在矩阵层面上计算的 + greedily select boxes with high confidence and overlap with current maximum <= thresh + rule out overlap >= thresh + :param dets: [[x1, y1, x2, y2 score],] # ndarray, shape(-1,5) + :param thresh: retain overlap < thresh + :return: indexes to keep + """ + # x1、y1、x2、y2、以及score赋值 + x1 = dets[:, 0] + y1 = dets[:, 1] + x2 = dets[:, 2] + y2 = dets[:, 3] + + # 计算每一个候选框的面积, 纯矩阵加和乘法运算,为何加1？ + areas = (x2 - x1 + 1) * (y2 - y1 + 1) + # order是将confidence降序排序后得到的矩阵索引 + order = np.argsort(dets[:, 4])[::-1] + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + # 计算当前概率最大矩形框与其他矩形框的相交框的坐标，会用到numpy的broadcast机制，得到的是向量 + xx1 = np.maximum(x1[i], x1[order[1:]]) + yy1 = np.maximum(y1[i], y1[order[1:]]) + xx2 = np.minimum(x2[i], x2[order[1:]]) + yy2 = np.minimum(y2[i], y2[order[1:]]) + + # 计算相交框的面积,注意矩形框不相交时w或h算出来会是负数，用0代替 + w = np.maximum(0.0, xx2 - xx1 + 1) + h = np.maximum(0.0, yy2 - yy1 + 1) + inter = w * h + # 计算重叠度IOU：重叠面积/（面积1+面积2-重叠面积） + iou = inter / (areas[i] + areas[order[1:]] - inter) + # 找到重叠度不高于阈值的矩形框索引 + inds = np.where(iou < thresh)[0] + # 将order序列更新，由于前面得到的矩形框索引要比矩形框在原order序列中的索引小1，所以要把这个1加回来 + order = order[inds + 1] + return keep + +# test +if __name__ == "__main__": + dets = np.array([[30, 20, 230, 200, 1], + [50, 50, 260, 220, 0.9], + [210, 30, 420, 5, 0.8], + [430, 280, 460, 360, 0.7]]) + thresh = 0.35 + keep_dets = py_nms(dets, thresh) + print(keep_dets) + print(dets[keep_dets]) +``` +程序输出如下： +> [0, 2, 3] +[[ 30. 20. 230. 200. 1. ] + [210. 30. 420. 5. 0.8] + [430. 280. 460. 360. 0.7]] + +## Soft NMS算法 +Soft NMS算法是对NMS算法的改进，是发表在ICCV2017的文章中提出的。`NMS`算法存在一个问题是可能会把一些目标框给过滤掉，从而导致目标的`recall`指标比较低。原来的NMS可以描述如下：将IOU大于阈值的窗口的得分全部置为0。 + +文章的改进有两种形式，一种是`线性加权`的: + +另一种是`高斯加权`的： + +注意，这两种形式，思想都是M为当前得分最高框，$b_{i}$为待处理框， $b_{i}$和M的IOU越大，bbox的得分$s_{i}$就下降的越厉害($N_{t}$为给定阈值)。更多细节可以参考原[论文](https://arxiv.org/pdf/1704.04503.pdf) +soft nms的python代码如下： +```Python +def soft_nms(dets, thresh, type='gaussian'): + x1 = dets[:, 0] + y1 = dets[:, 1] + x2 = dets[:, 2] + y2 = dets[:, 3] + scores = dets[:, 4] + + areas = (x2 - x1 + 1) * (y2 - y1 + 1) + order = scores.argsort()[::-1] + scores = scores[order] + + keep = [] + while order.size > 0: + i = order[0] + dets[i, 4] = scores[0] + keep.append(i) + + xx1 = np.maximum(x1[i], x1[order[1:]]) + yy1 = np.maximum(y1[i], y1[order[1:]]) + xx2 = np.minimum(x2[i], x2[order[1:]]) + yy2 = np.minimum(y2[i], y2[order[1:]]) + + w = np.maximum(0.0, xx2 - xx1 + 1) + h = np.maximum(0.0, yy2 - yy1 + 1) + inter = w * h + # 计算重叠度IOU：重叠面积/（面积1+面积2-重叠面积） + ovr = inter / (areas[i] + areas[order[1:]] - inter) + + order = order[1:] + scores = scores[1:] + if type == 'linear': + inds = np.where(ovr >= thresh)[0] + scores[inds] *= (1 - ovr[inds]) + else: + scores *= np.exp(- ovr ** 2 / thresh) + inds = np.where(scores > 1e-3)[0] + order = order[inds] + scores = scores[inds] + + tmp = scores.argsort()[::-1] + order = order[tmp] + scores = scores[tmp] + return keep +``` +## 类别AP计算 +目标检测领域常用的评估标准是：`mAP(mean average precision)`，计算mAP需要先计算`AP`，计算`AP`需涉及到`precision`和`recall`的计算，而这两者的计算又需设计`TP`、`FP`、`FN`的计算。 +`AP`的计算一般先设计到`P-R`曲线（precision-recall curve）的绘制，P-R曲线下面与x轴围成的面积称为`average precison（AP）`。下图是一个二分类问题的P-R曲线： +1527520824881 +### 近似计算AP(approximated average precision) +$$AP = \sum_{k=1}^{N}P(k)\Delta r(k)$$ ++ 这个计算方式称为 approximated 形式的，而插值计算的方式里面这个是最精确的，每个样本点都参与了计算 ++ 很显然位于一条竖直线上的点对计算AP没有贡献 ++ 这里N为数据总量，k为每个样本点的索引， Δr(k)=r(k)−r(k−1) +### 插值计算(Interpolated average precision) +插值计算AP的公式的演变过程我不写了，详情可以参考这篇[文章](https://arleyzhang.github.io/articles/c521a01c/)，我这里的公式和图也是参考此文章的。公式如下： + ++ 这是通常意义上的 11points_Interpolated 形式的 AP，选取固定的 {0,0.1,0.2,…,1.0} 11个阈值，这个在PASCAL2007中有使用 ++ 这里因为参与计算的只有11个点，所以 K=11，称为11points_Interpolated，k为阈值索引 ++ $P_{interp}(k)$ 取第 k 个阈值所对应的样本点之后的样本中的最大值，只不过这里的阈值被限定在了 {0,0.1,0.2,…,1.0} 范围内。 + + +从曲线上看，真实 `AP< approximated AP < Interpolated AP`，11-points Interpolated AP 可能大也可能小，当数据量很多的时候会接近于 Interpolated AP，与 Interpolated AP不同，前面的公式中计算AP时都是对PR曲线的面积估计，PASCAL的论文里给出的公式就更加简单粗暴了，直接计算11个阈值处的precision的平均值。如下： + +### AP计算代码实现 +近似计算`AP`和绘制·PR`曲线代码如下： +```Python +import numpy as np +import matplotlib.pyplot as plt + +class_names = ["car", "pedestrians", "bicycle"] + +def draw_PR_curve(predict_scores, eval_labels, name, cls_idx=1): + """calculate AP and draw PR curve, there 3 types + Parameters: + @all_scores: single test dataset predict scores array, (-1, 3) + @all_labels: single test dataset predict label array, (-1, 3) + """ + # print('sklearn Macro-F1-Score:', f1_score(predict_scores, eval_labels, average='macro')) + global class_names + fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(15, 10)) + # Rank the predicted scores from large to small, extract their corresponding index(index number), and generate an array + idx = predict_scores[:, cls_idx].argsort()[::-1] + eval_labels_descend = eval_labels[idx] + pos_gt_num = np.sum(eval_labels == cls_idx) # number of all gt + + predict_results = np.ones_like(eval_labels) + tp_arr = np.logical_and(predict_results == cls_idx, eval_labels_descend == cls_idx) # ndarray + fp_arr = np.logical_and(predict_results == cls_idx, eval_labels_descend != cls_idx) + + tp_cum = np.cumsum(tp_arr).astype(float) # ndarray, Cumulative sum of array elements. + fp_cum = np.cumsum(fp_arr).astype(float) + + precision_arr = tp_cum / (tp_cum + fp_cum) # ndarray + recall_arr = tp_cum / pos_gt_num + ap = 0.0 + prev_recall = 0 + for p, r in zip(precision_arr, recall_arr): + ap += p * (r - prev_recall) + # pdb.set_trace() + prev_recall = r + print("------%s, ap: %f-----" % (name, ap)) + + fig_label = '[%s, %s] ap=%f' % (name, class_names[cls_idx], ap) + ax.plot(recall_arr, precision_arr, label=fig_label) + + ax.legend(loc="lower left") + ax.set_title("PR curve about class: %s" % (class_names[cls_idx])) + ax.set(xticks=np.arange(0., 1, 0.05), yticks=np.arange(0., 1, 0.05)) + ax.set(xlabel="recall", ylabel="precision", xlim=[0, 1], ylim=[0, 1]) + + fig.savefig("./pr-curve-%s.png" % class_names[cls_idx]) + plt.close(fig) +``` +voc中计算ap的代码（用的是插值计算方法，代码出自[这里](https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/datasets/voc_eval.py)）如下： +```Python +def voc_ap(rec, prec, use_07_metric=False): + """ ap = voc_ap(rec, prec, [use_07_metric]) + Compute VOC AP given precision and recall. + If use_07_metric is true, uses the + VOC 07 11 point method (default:False). + """ + if use_07_metric: + # 11 point metric + ap = 0. + for t in np.arange(0., 1.1, 0.1): + if np.sum(rec >= t) == 0: + p = 0 + else: + p = np.max(prec[rec >= t]) + ap = ap + p / 11. + else: + # correct AP calculation + # first append sentinel values at the end + mrec = np.concatenate(([0.], rec, [1.])) + mpre = np.concatenate(([0.], prec, [0.])) + + # compute the precision envelope + for i in range(mpre.size - 1, 0, -1): + mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) + + # to calculate area under PR curve, look for points + # where X axis (recall) changes value + i = np.where(mrec[1:] != mrec[:-1])[0] + + # and sum (\Delta recall) * prec + ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) + return ap +``` +## FLOPs计算 +> 先解释一下FLOPs：floating point operations 指的是浮点运算次数，理解为计算量，可以用来衡量算法/模型的复杂度。此处区分一下FLOPS（全部大写），FLOPS指的是每秒运算的浮点数，理解为计算速度，衡量一个硬件的标准。我们要的是衡量模型的复杂度的指标，所以选择FLOPs。 + +### 卷积层FLOPs计算 +$FLOPs=(2*Ci*k*K-1）*H*W*Co$（不考虑bias） +$FLOPs=(2*Ci*k*K）*H*W*Co$（考虑bias） +**Ci为输入特征图通道数，K为过滤器尺寸，H,W,Co为输出特征图的高，宽和通道数**。 + +公式解释，参考[这里](https://zhuanlan.zhihu.com/p/70306015?utm_source=wechat_session&utm_medium=social&utm_oi=571954943427219456)，如下： +最后得到的Co张输出特征图，每张特征图上有H*W个像素点，而这其中的每个像素点的值都是由过滤器与输入特征图卷积得到的，过滤器中Ci*k*K个点，每个点都要和输入特征图对应点作一次相乘操作（浮点操作数为ci*k*k），然后将这些过滤器和输入特征图对应点相乘所得的数相加起来（浮点操作数为ci*k*k-1，n个数相加，所需要的浮点操作数为n-1），得到一个值，对应于一张输出特征图中的一个像素，输出特征图有Co张，故有Co个过滤器参与卷积运算，所以卷积层的**$FLOPs=(2*Ci*k*K-1）*H*W*Co$** +(Ci为输入特征图通道数，K为过滤器尺寸，H,W,Co为输出特征图的高，宽和通道数) +## 参考资料 +[目标检测评价标准-AP mAP](https://arleyzhang.github.io/articles/c521a01c/) +[目标检测的性能评价指标](https://zhuanlan.zhihu.com/p/70306015?utm_source=wechat_session&utm_medium=social&utm_oi=571954943427219456) From 9309dbe2642572e0f2f15b9060ec9f487f2c20f3 Mon Sep 17 00:00:00 2001 From: Zhang Date: Mon, 12 Aug 2019 22:30:28 +0800 Subject: [PATCH 163/386] Add files via upload MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 上传目标检测指标计算的一些图 --- ...\347\256\227AP\345\205\254\345\274\217.png" | Bin 0 -> 7491 bytes 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=?UTF-8?q?=E6=B5=8B=E4=B8=8E=E5=9B=BE=E5=83=8F=E5=88=86=E7=B1=BB=E6=8C=87?= =?UTF-8?q?=E6=A0=87.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 更新图片 --- ...\210\206\347\261\273\346\214\207\346\240\207.md" | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git "a/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" "b/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" index 0ddb4ff3..f82a3fcc 100644 --- "a/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" +++ "b/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" @@ -1,7 +1,8 @@ ## 交并比IOU 交并比（Intersection-over-Union，IoU），目标检测中使用的一个概念，是产生的候选框（candidate bound）与原标记框（ground truth bound）的交叠率，即它们的交集与并集的比值。最理想情况是完全重叠，即比值为1。 计算公式如下： -https://www.armcvai.com/wp-content/uploads/2019/04/image-12.png +！IOU计算公式(https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/IOU%E8%AE%A1%E7%AE%97%E5%85%AC%E5%BC%8F.png) + 代码实现如下： ```python import numpy as np @@ -36,6 +37,7 @@ if __name__=='__main__': ## 非极大值抑制算法NMS ### [NMS介绍](https://juejin.im/entry/5bdbc26151882516da0ddd25) 在目标检测中，常会利用非极大值抑制算法(NMS，non maximum suppression)对生成的大量候选框进行后处理，去除冗余的候选框，得到最佳检测框，以加快目标检测的效率。其本质思想是其思想是搜素局部最大值，抑制非极大值。非极大值抑制，在计算机视觉任务中得到了广泛的应用，例如边缘检测、人脸检测、目标检测（DPM，YOLO，SSD，Faster R-CNN）等。即如图 2所示实现效果，消除多余的候选框，找到最佳的bbox。 +!NMS过程(https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/NMS%E8%BF%87%E7%A8%8B.png) 以上图为例，每个选出来的Bounding Box检测框（既BBox）用（x,y,h,w, confidence score，Pdog,Pcat）表示，confidence score表示background和foreground的置信度得分，取值范围[0,1]。Pdog,Pcat分布代表类别是狗和猫的概率。如果是100类的目标检测模型，BBox输出向量为5+100=105。 ### [NMS算法](https://juejin.im/entry/5bdbc26151882516da0ddd25) @@ -108,11 +110,14 @@ if __name__ == "__main__": [430. 280. 460. 360. 0.7]] ## Soft NMS算法 -Soft NMS算法是对NMS算法的改进，是发表在ICCV2017的文章中提出的。`NMS`算法存在一个问题是可能会把一些目标框给过滤掉，从而导致目标的`recall`指标比较低。原来的NMS可以描述如下：将IOU大于阈值的窗口的得分全部置为0。 +Soft NMS算法是对NMS算法的改进，是发表在ICCV2017的文章中提出的。`NMS`算法存在一个问题是可能会把一些目标框给过滤掉，从而导致目标的`recall`指标比较低。原来的NMS可以描述如下：将IOU大于阈值的窗口的得分全部置为0，计算公式如下： +!硬NMS算法(https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E7%A1%ACNMS%E7%AE%97%E6%B3%95.jpg) -文章的改进有两种形式，一种是`线性加权`的: +文章的改进有两种形式，一种是`线性加权`的。设 si 为第 i 个 box 的 score, 则在应用 SoftNMS 时各个 box score 的计算公式如下： +!线性加权形式的soft NMS算法(https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E7%BA%BF%E6%80%A7%E5%8A%A0%E6%9D%83%E5%BD%A2%E5%BC%8F%E7%9A%84%E8%BD%AFNMS.jpg) -另一种是`高斯加权`的： +另一种是`高斯加权`的，高斯惩罚系数(与上面的线性截断惩罚不同的是, 高斯惩罚会对其他所有的 box 作用)，计算公式图如下： +!高斯加权形式的soft NMS算法(https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E9%AB%98%E6%96%AF%E5%8A%A0%E6%9D%83%E5%BD%A2%E5%BC%8F%E7%9A%84NMS.jpg) 注意，这两种形式，思想都是M为当前得分最高框，$b_{i}$为待处理框， $b_{i}$和M的IOU越大，bbox的得分$s_{i}$就下降的越厉害($N_{t}$为给定阈值)。更多细节可以参考原[论文](https://arxiv.org/pdf/1704.04503.pdf) soft nms的python代码如下： From bea054871ea0621a5b21734d66153e86be578f7d Mon Sep 17 00:00:00 2001 From: Zhang Date: Mon, 12 Aug 2019 22:41:34 +0800 Subject: [PATCH 165/386] =?UTF-8?q?Update=20=E7=9B=AE=E6=A0=87=E6=A3=80?= =?UTF-8?q?=E6=B5=8B=E4=B8=8E=E5=9B=BE=E5=83=8F=E5=88=86=E7=B1=BB=E6=8C=87?= =?UTF-8?q?=E6=A0=87.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 更新图 --- ...06\347\261\273\346\214\207\346\240\207.md" | 23 +++++++++++-------- 1 file changed, 14 insertions(+), 9 deletions(-) diff --git "a/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" "b/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" index f82a3fcc..eee4ddeb 100644 --- "a/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" +++ "b/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" @@ -1,7 +1,7 @@ ## 交并比IOU 交并比（Intersection-over-Union，IoU），目标检测中使用的一个概念，是产生的候选框（candidate bound）与原标记框（ground truth bound）的交叠率，即它们的交集与并集的比值。最理想情况是完全重叠，即比值为1。 计算公式如下： -！IOU计算公式(https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/IOU%E8%AE%A1%E7%AE%97%E5%85%AC%E5%BC%8F.png) +![IOU计算公式](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/IOU%E8%AE%A1%E7%AE%97%E5%85%AC%E5%BC%8F.png) 代码实现如下： ```python @@ -37,7 +37,7 @@ if __name__=='__main__': ## 非极大值抑制算法NMS ### [NMS介绍](https://juejin.im/entry/5bdbc26151882516da0ddd25) 在目标检测中，常会利用非极大值抑制算法(NMS，non maximum suppression)对生成的大量候选框进行后处理，去除冗余的候选框，得到最佳检测框，以加快目标检测的效率。其本质思想是其思想是搜素局部最大值，抑制非极大值。非极大值抑制，在计算机视觉任务中得到了广泛的应用，例如边缘检测、人脸检测、目标检测（DPM，YOLO，SSD，Faster R-CNN）等。即如图 2所示实现效果，消除多余的候选框，找到最佳的bbox。 -!NMS过程(https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/NMS%E8%BF%87%E7%A8%8B.png) +![NMS过程](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/NMS%E8%BF%87%E7%A8%8B.png) 以上图为例，每个选出来的Bounding Box检测框（既BBox）用（x,y,h,w, confidence score，Pdog,Pcat）表示，confidence score表示background和foreground的置信度得分，取值范围[0,1]。Pdog,Pcat分布代表类别是狗和猫的概率。如果是100类的目标检测模型，BBox输出向量为5+100=105。 ### [NMS算法](https://juejin.im/entry/5bdbc26151882516da0ddd25) @@ -111,13 +111,13 @@ if __name__ == "__main__": ## Soft NMS算法 Soft NMS算法是对NMS算法的改进，是发表在ICCV2017的文章中提出的。`NMS`算法存在一个问题是可能会把一些目标框给过滤掉，从而导致目标的`recall`指标比较低。原来的NMS可以描述如下：将IOU大于阈值的窗口的得分全部置为0，计算公式如下： -!硬NMS算法(https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E7%A1%ACNMS%E7%AE%97%E6%B3%95.jpg) +![硬NMS算法](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E7%A1%ACNMS%E7%AE%97%E6%B3%95.jpg) 文章的改进有两种形式，一种是`线性加权`的。设 si 为第 i 个 box 的 score, 则在应用 SoftNMS 时各个 box score 的计算公式如下： -!线性加权形式的soft NMS算法(https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E7%BA%BF%E6%80%A7%E5%8A%A0%E6%9D%83%E5%BD%A2%E5%BC%8F%E7%9A%84%E8%BD%AFNMS.jpg) +![线性加权形式的soft NMS算法](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E7%BA%BF%E6%80%A7%E5%8A%A0%E6%9D%83%E5%BD%A2%E5%BC%8F%E7%9A%84%E8%BD%AFNMS.jpg) 另一种是`高斯加权`的，高斯惩罚系数(与上面的线性截断惩罚不同的是, 高斯惩罚会对其他所有的 box 作用)，计算公式图如下： -!高斯加权形式的soft NMS算法(https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E9%AB%98%E6%96%AF%E5%8A%A0%E6%9D%83%E5%BD%A2%E5%BC%8F%E7%9A%84NMS.jpg) +![高斯加权形式的soft NMS算法](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E9%AB%98%E6%96%AF%E5%8A%A0%E6%9D%83%E5%BD%A2%E5%BC%8F%E7%9A%84NMS.jpg) 注意，这两种形式，思想都是M为当前得分最高框，$b_{i}$为待处理框， $b_{i}$和M的IOU越大，bbox的得分$s_{i}$就下降的越厉害($N_{t}$为给定阈值)。更多细节可以参考原[论文](https://arxiv.org/pdf/1704.04503.pdf) soft nms的python代码如下： @@ -169,22 +169,26 @@ def soft_nms(dets, thresh, type='gaussian'): ## 类别AP计算 目标检测领域常用的评估标准是：`mAP(mean average precision)`，计算mAP需要先计算`AP`，计算`AP`需涉及到`precision`和`recall`的计算，而这两者的计算又需设计`TP`、`FP`、`FN`的计算。 `AP`的计算一般先设计到`P-R`曲线（precision-recall curve）的绘制，P-R曲线下面与x轴围成的面积称为`average precison（AP）`。下图是一个二分类问题的P-R曲线： -1527520824881 +![分类问题的PR曲线图](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E5%88%86%E7%B1%BBPR%E6%9B%B2%E7%BA%BF%E5%9B%BE.png) + ### 近似计算AP(approximated average precision) $$AP = \sum_{k=1}^{N}P(k)\Delta r(k)$$ + 这个计算方式称为 approximated 形式的，而插值计算的方式里面这个是最精确的，每个样本点都参与了计算 + 很显然位于一条竖直线上的点对计算AP没有贡献 + 这里N为数据总量，k为每个样本点的索引， Δr(k)=r(k)−r(k−1) + ### 插值计算(Interpolated average precision) -插值计算AP的公式的演变过程我不写了，详情可以参考这篇[文章](https://arleyzhang.github.io/articles/c521a01c/)，我这里的公式和图也是参考此文章的。公式如下： +插值计算AP的公式的演变过程我不写了，详情可以参考这篇[文章](https://arleyzhang.github.io/articles/c521a01c/)，我这里的公式和图也是参考此文章的。`11点插值计算方式计算AP公式`如下： +![11点插值计算方式计算AP公式](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E6%8F%92%E5%80%BC%E8%AE%A1%E7%AE%97AP%E5%85%AC%E5%BC%8F.png) + 这是通常意义上的 11points_Interpolated 形式的 AP，选取固定的 {0,0.1,0.2,…,1.0} 11个阈值，这个在PASCAL2007中有使用 + 这里因为参与计算的只有11个点，所以 K=11，称为11points_Interpolated，k为阈值索引 + $P_{interp}(k)$ 取第 k 个阈值所对应的样本点之后的样本中的最大值，只不过这里的阈值被限定在了 {0,0.1,0.2,…,1.0} 范围内。 +![插值计算方式计算AP的PR曲线图](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E6%8F%92%E5%80%BC%E8%AE%A1%E7%AE%97AP%E7%9A%84PR%E6%9B%B2%E7%BA%BF%E5%9B%BE.png) 从曲线上看，真实 `AP< approximated AP < Interpolated AP`，11-points Interpolated AP 可能大也可能小，当数据量很多的时候会接近于 Interpolated AP，与 Interpolated AP不同，前面的公式中计算AP时都是对PR曲线的面积估计，PASCAL的论文里给出的公式就更加简单粗暴了，直接计算11个阈值处的precision的平均值。如下： - +![PASCAL论文给出的11点计算AP公式](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/11%E7%82%B9%E8%AE%A1%E7%AE%97AP%E5%85%AC%E5%BC%8F.png) ### AP计算代码实现 近似计算`AP`和绘制·PR`曲线代码如下： ```Python @@ -277,9 +281,10 @@ def voc_ap(rec, prec, use_07_metric=False): $FLOPs=(2*Ci*k*K-1）*H*W*Co$（不考虑bias） $FLOPs=(2*Ci*k*K）*H*W*Co$（考虑bias） **Ci为输入特征图通道数，K为过滤器尺寸，H,W,Co为输出特征图的高，宽和通道数**。 +![卷积过程](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E5%8D%B7%E7%A7%AF%E8%BF%87%E7%A8%8B%E5%9B%BE.png) 公式解释，参考[这里](https://zhuanlan.zhihu.com/p/70306015?utm_source=wechat_session&utm_medium=social&utm_oi=571954943427219456)，如下： -最后得到的Co张输出特征图，每张特征图上有H*W个像素点，而这其中的每个像素点的值都是由过滤器与输入特征图卷积得到的，过滤器中Ci*k*K个点，每个点都要和输入特征图对应点作一次相乘操作（浮点操作数为ci*k*k），然后将这些过滤器和输入特征图对应点相乘所得的数相加起来（浮点操作数为ci*k*k-1，n个数相加，所需要的浮点操作数为n-1），得到一个值，对应于一张输出特征图中的一个像素，输出特征图有Co张，故有Co个过滤器参与卷积运算，所以卷积层的**$FLOPs=(2*Ci*k*K-1）*H*W*Co$** +最后得到的Co张输出特征图，每张特征图上有H\*W个像素点，而这其中的每个像素点的值都是由过滤器与输入特征图卷积得到的，过滤器中Ci\*k\*K个点，每个点都要和输入特征图对应点作一次相乘操作（浮点操作数为ci\*k\*k），然后将这些过滤器和输入特征图对应点相乘所得的数相加起来（浮点操作数为ci\*k\*k-1，n个数相加，所需要的浮点操作数为n-1），得到一个值，对应于一张输出特征图中的一个像素，输出特征图有Co张，故有Co个过滤器参与卷积运算，所以卷积层的**$FLOPs=(2*Ci*k*K-1）*H*W*Co$** (Ci为输入特征图通道数，K为过滤器尺寸，H,W,Co为输出特征图的高，宽和通道数) ## 参考资料 [目标检测评价标准-AP mAP](https://arleyzhang.github.io/articles/c521a01c/) From f956f6eb363b514a9ad3d763d0bed8ede6dd5bfc Mon Sep 17 00:00:00 2001 From: Zhang Date: Mon, 12 Aug 2019 22:43:39 +0800 Subject: [PATCH 166/386] =?UTF-8?q?Update=20=E7=9B=AE=E6=A0=87=E6=A3=80?= =?UTF-8?q?=E6=B5=8B=E4=B8=8E=E5=9B=BE=E5=83=8F=E5=88=86=E7=B1=BB=E6=8C=87?= =?UTF-8?q?=E6=A0=87.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 更新图片显示错误 --- ...10\206\347\261\273\346\214\207\346\240\207.md" | 15 +++++++++++++-- 1 file changed, 13 insertions(+), 2 deletions(-) diff --git "a/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" "b/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" index eee4ddeb..588449fd 100644 --- "a/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" +++ "b/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" @@ -36,7 +36,8 @@ if __name__=='__main__': ``` ## 非极大值抑制算法NMS ### [NMS介绍](https://juejin.im/entry/5bdbc26151882516da0ddd25) -在目标检测中，常会利用非极大值抑制算法(NMS，non maximum suppression)对生成的大量候选框进行后处理，去除冗余的候选框，得到最佳检测框，以加快目标检测的效率。其本质思想是其思想是搜素局部最大值，抑制非极大值。非极大值抑制，在计算机视觉任务中得到了广泛的应用，例如边缘检测、人脸检测、目标检测（DPM，YOLO，SSD，Faster R-CNN）等。即如图 2所示实现效果，消除多余的候选框，找到最佳的bbox。 +在目标检测中，常会利用非极大值抑制算法(NMS，non maximum suppression)对生成的大量候选框进行后处理，去除冗余的候选框，得到最佳检测框，以加快目标检测的效率。其本质思想是其思想是搜素局部最大值，抑制非极大值。非极大值抑制，在计算机视觉任务中得到了广泛的应用，例如边缘检测、人脸检测、目标检测（DPM，YOLO，SSD，Faster R-CNN）等。即如图 2所示实现效果，消除多余的候选框，找到最佳的bbox。`NMS过程`如下图所示： + ![NMS过程](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/NMS%E8%BF%87%E7%A8%8B.png) 以上图为例，每个选出来的Bounding Box检测框（既BBox）用（x,y,h,w, confidence score，Pdog,Pcat）表示，confidence score表示background和foreground的置信度得分，取值范围[0,1]。Pdog,Pcat分布代表类别是狗和猫的概率。如果是100类的目标检测模型，BBox输出向量为5+100=105。 @@ -111,12 +112,16 @@ if __name__ == "__main__": ## Soft NMS算法 Soft NMS算法是对NMS算法的改进，是发表在ICCV2017的文章中提出的。`NMS`算法存在一个问题是可能会把一些目标框给过滤掉，从而导致目标的`recall`指标比较低。原来的NMS可以描述如下：将IOU大于阈值的窗口的得分全部置为0，计算公式如下： + ![硬NMS算法](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E7%A1%ACNMS%E7%AE%97%E6%B3%95.jpg) 文章的改进有两种形式，一种是`线性加权`的。设 si 为第 i 个 box 的 score, 则在应用 SoftNMS 时各个 box score 的计算公式如下： -![线性加权形式的soft NMS算法](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E7%BA%BF%E6%80%A7%E5%8A%A0%E6%9D%83%E5%BD%A2%E5%BC%8F%E7%9A%84%E8%BD%AFNMS.jpg) + +![线性加权形式的soft NMS算法] +(https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E7%BA%BF%E6%80%A7%E5%8A%A0%E6%9D%83%E5%BD%A2%E5%BC%8F%E7%9A%84%E8%BD%AFNMS.jpg) 另一种是`高斯加权`的，高斯惩罚系数(与上面的线性截断惩罚不同的是, 高斯惩罚会对其他所有的 box 作用)，计算公式图如下： + ![高斯加权形式的soft NMS算法](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E9%AB%98%E6%96%AF%E5%8A%A0%E6%9D%83%E5%BD%A2%E5%BC%8F%E7%9A%84NMS.jpg) 注意，这两种形式，思想都是M为当前得分最高框，$b_{i}$为待处理框， $b_{i}$和M的IOU越大，bbox的得分$s_{i}$就下降的越厉害($N_{t}$为给定阈值)。更多细节可以参考原[论文](https://arxiv.org/pdf/1704.04503.pdf) @@ -169,6 +174,7 @@ def soft_nms(dets, thresh, type='gaussian'): ## 类别AP计算 目标检测领域常用的评估标准是：`mAP(mean average precision)`，计算mAP需要先计算`AP`，计算`AP`需涉及到`precision`和`recall`的计算，而这两者的计算又需设计`TP`、`FP`、`FN`的计算。 `AP`的计算一般先设计到`P-R`曲线（precision-recall curve）的绘制，P-R曲线下面与x轴围成的面积称为`average precison（AP）`。下图是一个二分类问题的P-R曲线： + ![分类问题的PR曲线图](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E5%88%86%E7%B1%BBPR%E6%9B%B2%E7%BA%BF%E5%9B%BE.png) ### 近似计算AP(approximated average precision) @@ -179,6 +185,7 @@ $$AP = \sum_{k=1}^{N}P(k)\Delta r(k)$$ ### 插值计算(Interpolated average precision) 插值计算AP的公式的演变过程我不写了，详情可以参考这篇[文章](https://arleyzhang.github.io/articles/c521a01c/)，我这里的公式和图也是参考此文章的。`11点插值计算方式计算AP公式`如下： + ![11点插值计算方式计算AP公式](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E6%8F%92%E5%80%BC%E8%AE%A1%E7%AE%97AP%E5%85%AC%E5%BC%8F.png) + 这是通常意义上的 11points_Interpolated 形式的 AP，选取固定的 {0,0.1,0.2,…,1.0} 11个阈值，这个在PASCAL2007中有使用 @@ -187,8 +194,11 @@ $$AP = \sum_{k=1}^{N}P(k)\Delta r(k)$$ ![插值计算方式计算AP的PR曲线图](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E6%8F%92%E5%80%BC%E8%AE%A1%E7%AE%97AP%E7%9A%84PR%E6%9B%B2%E7%BA%BF%E5%9B%BE.png) + 从曲线上看，真实 `AP< approximated AP < Interpolated AP`，11-points Interpolated AP 可能大也可能小，当数据量很多的时候会接近于 Interpolated AP，与 Interpolated AP不同，前面的公式中计算AP时都是对PR曲线的面积估计，PASCAL的论文里给出的公式就更加简单粗暴了，直接计算11个阈值处的precision的平均值。如下： + ![PASCAL论文给出的11点计算AP公式](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/11%E7%82%B9%E8%AE%A1%E7%AE%97AP%E5%85%AC%E5%BC%8F.png) + ### AP计算代码实现 近似计算`AP`和绘制·PR`曲线代码如下： ```Python @@ -281,6 +291,7 @@ def voc_ap(rec, prec, use_07_metric=False): $FLOPs=(2*Ci*k*K-1）*H*W*Co$（不考虑bias） $FLOPs=(2*Ci*k*K）*H*W*Co$（考虑bias） **Ci为输入特征图通道数，K为过滤器尺寸，H,W,Co为输出特征图的高，宽和通道数**。 + ![卷积过程](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E5%8D%B7%E7%A7%AF%E8%BF%87%E7%A8%8B%E5%9B%BE.png) 公式解释，参考[这里](https://zhuanlan.zhihu.com/p/70306015?utm_source=wechat_session&utm_medium=social&utm_oi=571954943427219456)，如下： From 5b4f34bc8d210c7ac7ec7621375f98a6da1331be Mon Sep 17 00:00:00 2001 From: Zhang Date: Mon, 12 Aug 2019 22:47:17 +0800 Subject: [PATCH 167/386] =?UTF-8?q?Update=20=E7=9B=AE=E6=A0=87=E6=A3=80?= =?UTF-8?q?=E6=B5=8B=E4=B8=8E=E5=9B=BE=E5=83=8F=E5=88=86=E7=B1=BB=E6=8C=87?= =?UTF-8?q?=E6=A0=87.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...17\345\210\206\347\261\273\346\214\207\346\240\207.md" | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git "a/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" "b/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" index 588449fd..3704f24e 100644 --- "a/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" +++ "b/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" @@ -117,8 +117,7 @@ Soft NMS算法是对NMS算法的改进，是发表在ICCV2017的文章中提出 文章的改进有两种形式，一种是`线性加权`的。设 si 为第 i 个 box 的 score, 则在应用 SoftNMS 时各个 box score 的计算公式如下： -![线性加权形式的soft NMS算法] -(https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E7%BA%BF%E6%80%A7%E5%8A%A0%E6%9D%83%E5%BD%A2%E5%BC%8F%E7%9A%84%E8%BD%AFNMS.jpg) +![线性加权形式的soft NMS算法](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E7%BA%BF%E6%80%A7%E5%8A%A0%E6%9D%83%E5%BD%A2%E5%BC%8F%E7%9A%84%E8%BD%AFNMS.jpg) 另一种是`高斯加权`的，高斯惩罚系数(与上面的线性截断惩罚不同的是, 高斯惩罚会对其他所有的 box 作用)，计算公式图如下： @@ -290,12 +289,13 @@ def voc_ap(rec, prec, use_07_metric=False): ### 卷积层FLOPs计算 $FLOPs=(2*Ci*k*K-1）*H*W*Co$（不考虑bias） $FLOPs=(2*Ci*k*K）*H*W*Co$（考虑bias） -**Ci为输入特征图通道数，K为过滤器尺寸，H,W,Co为输出特征图的高，宽和通道数**。 +**Ci为输入特征图通道数，K为过滤器尺寸，H,W,Co为输出特征图的高，宽和通道数**。`卷积过程`如下图所示： ![卷积过程](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E5%8D%B7%E7%A7%AF%E8%BF%87%E7%A8%8B%E5%9B%BE.png) 公式解释，参考[这里](https://zhuanlan.zhihu.com/p/70306015?utm_source=wechat_session&utm_medium=social&utm_oi=571954943427219456)，如下： -最后得到的Co张输出特征图，每张特征图上有H\*W个像素点，而这其中的每个像素点的值都是由过滤器与输入特征图卷积得到的，过滤器中Ci\*k\*K个点，每个点都要和输入特征图对应点作一次相乘操作（浮点操作数为ci\*k\*k），然后将这些过滤器和输入特征图对应点相乘所得的数相加起来（浮点操作数为ci\*k\*k-1，n个数相加，所需要的浮点操作数为n-1），得到一个值，对应于一张输出特征图中的一个像素，输出特征图有Co张，故有Co个过滤器参与卷积运算，所以卷积层的**$FLOPs=(2*Ci*k*K-1）*H*W*Co$** +最后得到的Co张输出特征图，每张特征图上有H\*W个像素点，而这其中的每个像素点的值都是由过滤器与输入特征图卷积得到的，过滤器中Ci\*k\*K个点，每个点都要和输入特征图对应点作一次相乘操作（浮点操作数为ci\*k\*k），然后将这些过滤器和输入特征图对应点相乘所得的数相加起来（浮点操作数为ci\*k\*k-1，n个数相加，所需要的浮点操作数为n-1），得到一个值，对应于一张输出特征图中的一个像素，输出特征图有Co张，故有Co个过滤器参与卷积运算，所以卷积层的**$FLOPs=(2\*Ci\*k\*K-1）\*H\*W\*Co$** + (Ci为输入特征图通道数，K为过滤器尺寸，H,W,Co为输出特征图的高，宽和通道数) ## 参考资料 [目标检测评价标准-AP mAP](https://arleyzhang.github.io/articles/c521a01c/) From b9938dad564e922e60c08307094c48d725d7fd9e Mon Sep 17 00:00:00 2001 From: Zhang Date: Mon, 12 Aug 2019 22:48:36 +0800 Subject: [PATCH 168/386] =?UTF-8?q?Update=20=E7=9B=AE=E6=A0=87=E6=A3=80?= =?UTF-8?q?=E6=B5=8B=E4=B8=8E=E5=9B=BE=E5=83=8F=E5=88=86=E7=B1=BB=E6=8C=87?= =?UTF-8?q?=E6=A0=87.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 更新参考资料显示错误 --- ...3\217\345\210\206\347\261\273\346\214\207\346\240\207.md" | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git "a/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" "b/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" index 3704f24e..ee522455 100644 --- "a/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" +++ "b/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" @@ -298,5 +298,6 @@ $FLOPs=(2*Ci*k*K）*H*W*Co$（考虑bias） (Ci为输入特征图通道数，K为过滤器尺寸，H,W,Co为输出特征图的高，宽和通道数) ## 参考资料 -[目标检测评价标准-AP mAP](https://arleyzhang.github.io/articles/c521a01c/) -[目标检测的性能评价指标](https://zhuanlan.zhihu.com/p/70306015?utm_source=wechat_session&utm_medium=social&utm_oi=571954943427219456) ++ [目标检测评价标准-AP mAP](https://arleyzhang.github.io/articles/c521a01c/) ++ [目标检测的性能评价指标](https://zhuanlan.zhihu.com/p/70306015?utm_source=wechat_session&utm_medium=social&utm_oi=571954943427219456) ++ [Soft-NMS](https://hellozhaozheng.github.io/z_post/%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89-SoftNMS-ICCV2017/) From f2a6a0f18c6ef82fc19ef70cd7d05495d035b04b Mon Sep 17 00:00:00 2001 From: Zhang Date: Tue, 13 Aug 2019 00:05:47 +0800 Subject: [PATCH 169/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 更新卷积输出大小计算 --- ...40\351\235\242\350\257\225\351\242\230.md" | 27 +++++++++++++++++++ 1 file changed, 27 insertions(+) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 834d219b..32e8420c 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -1,4 +1,31 @@ # 深度学习 +## 卷积输出大小计算 +### CNN中术语解释 +CNN网络的主要参数有下面这么几个： + ++ 卷积核Kernal（在Tensorflow中称为filter）； ++ 填充Padding； ++ 滑动步长Strides； ++ 池化核Kernal（在Tensorflow中称为filter）； ++ 通道数Channels。 + +### 卷积输出大小计算 +1.图片经卷积后输出大小计算公式如下： +`N = (W − F + 2P )/S+1` ++ 输入图片大小 W×W ++ Filter大小 F×F ++ 步长 S ++ padding的像素数 P + +输出通道数 = 卷积核/池化核数量 +2.反卷积得到的图片大小计算方式： +**反卷积的大小是由卷积核大小与滑动步长决定**， in是输入大小， k是卷积核大小， s是滑动步长， out是输出大小。 +得到 `out = (in - 1) * s + k` +例如 输入：2x2， 卷积核：4x4， 滑动步长：3， 输出：7x7 ，其计算过程就是， (2 - 1) * 3 + 4 = 7 +3.池化得到的特征图大小计算方式： +**卷积向下取整，池化向上取整**。 +### 参考资料 ++ [CNN中的参数解释及计算](https://flat2010.github.io/2018/06/15/%E6%89%8B%E7%AE%97CNN%E4%B8%AD%E7%9A%84%E5%8F%82%E6%95%B0/) ## softmax回归 分类问题中，直接使用输出层的输出有两个问题： + 神经网络输出层的输出值的范围不确定，我们难以直观上判断这些值的意义 From 2d9e03b306e3dc0507f0fcecc5ce1e91c7badc52 Mon Sep 17 00:00:00 2001 From: Zhang Date: Tue, 13 Aug 2019 00:11:08 +0800 Subject: [PATCH 170/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...55\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 32e8420c..e83ce598 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -18,12 +18,14 @@ CNN网络的主要参数有下面这么几个： + padding的像素数 P 输出通道数 = 卷积核/池化核数量 + 2.反卷积得到的图片大小计算方式： **反卷积的大小是由卷积核大小与滑动步长决定**， in是输入大小， k是卷积核大小， s是滑动步长， out是输出大小。 得到 `out = (in - 1) * s + k` 例如 输入：2x2， 卷积核：4x4， 滑动步长：3， 输出：7x7 ，其计算过程就是， (2 - 1) * 3 + 4 = 7 + 3.池化得到的特征图大小计算方式： -**卷积向下取整，池化向上取整**。 +**卷积向下取整，池化向上取整**。注意：stride为1的时候，当kernel为 3 padding为1或者kernel为5 padding为2，这种情况可直接得出卷积前后尺寸不变。 ### 参考资料 + [CNN中的参数解释及计算](https://flat2010.github.io/2018/06/15/%E6%89%8B%E7%AE%97CNN%E4%B8%AD%E7%9A%84%E5%8F%82%E6%95%B0/) ## softmax回归 From eb8d19161e384792b96e1a0cced8151b8cbd329e Mon Sep 17 00:00:00 2001 From: Zhang Date: Tue, 13 Aug 2019 00:17:53 +0800 Subject: [PATCH 171/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index e83ce598..1c801ffe 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -25,7 +25,7 @@ CNN网络的主要参数有下面这么几个： 例如 输入：2x2， 卷积核：4x4， 滑动步长：3， 输出：7x7 ，其计算过程就是， (2 - 1) * 3 + 4 = 7 3.池化得到的特征图大小计算方式： -**卷积向下取整，池化向上取整**。注意：stride为1的时候，当kernel为 3 padding为1或者kernel为5 padding为2，这种情况可直接得出卷积前后尺寸不变。 +**卷积向下取整，池化向上取整**。**卷积层一般填充像素(`SAME`)，池化层一般不填充像素(`VALID`)？**注意：stride为1的时候，当kernel为 3 padding为1或者kernel为5 padding为2，这种情况可直接得出卷积前后尺寸不变。 ### 参考资料 + [CNN中的参数解释及计算](https://flat2010.github.io/2018/06/15/%E6%89%8B%E7%AE%97CNN%E4%B8%AD%E7%9A%84%E5%8F%82%E6%95%B0/) ## softmax回归 From 577fd3283ae3ed55cc6578b30eaad15512a03924 Mon Sep 17 00:00:00 2001 From: Zhang Date: Tue, 13 Aug 2019 00:20:06 +0800 Subject: [PATCH 172/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...4\271\240\351\235\242\350\257\225\351\242\230.md" | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 1c801ffe..4a6996bd 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -1,7 +1,7 @@ # 深度学习 ## 卷积输出大小计算 ### CNN中术语解释 -CNN网络的主要参数有下面这么几个： +**CNN网络的主要参数**有下面这么几个： + 卷积核Kernal（在Tensorflow中称为filter）； + 填充Padding； @@ -10,7 +10,7 @@ CNN网络的主要参数有下面这么几个： + 通道数Channels。 ### 卷积输出大小计算 -1.图片经卷积后输出大小计算公式如下： +1.图片经卷积后**输出大小计算公式**如下： `N = (W − F + 2P )/S+1` + 输入图片大小 W×W + Filter大小 F×F @@ -25,7 +25,13 @@ CNN网络的主要参数有下面这么几个： 例如 输入：2x2， 卷积核：4x4， 滑动步长：3， 输出：7x7 ，其计算过程就是， (2 - 1) * 3 + 4 = 7 3.池化得到的特征图大小计算方式： -**卷积向下取整，池化向上取整**。**卷积层一般填充像素(`SAME`)，池化层一般不填充像素(`VALID`)？**注意：stride为1的时候，当kernel为 3 padding为1或者kernel为5 padding为2，这种情况可直接得出卷积前后尺寸不变。 + +其他知识点如下： + ++ **卷积向下取整，池化向上取整**。 ++ **卷积层一般填充像素(`SAME`)，池化层一般不填充像素(`VALID`)**？ ++ 注意：`stride`为1的时候，当`kernel`为 3 `padding`为1或者`kernel`为5 `padding`为2，这种情况可直接得出卷积前后尺寸不变。 + ### 参考资料 + [CNN中的参数解释及计算](https://flat2010.github.io/2018/06/15/%E6%89%8B%E7%AE%97CNN%E4%B8%AD%E7%9A%84%E5%8F%82%E6%95%B0/) ## softmax回归 From 6e18035effff0f81471594bc7e517b72af5260b0 Mon Sep 17 00:00:00 2001 From: Zhang Date: Tue, 13 Aug 2019 00:34:24 +0800 Subject: [PATCH 173/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 4a6996bd..81b15c80 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -25,12 +25,9 @@ 例如 输入：2x2， 卷积核：4x4， 滑动步长：3， 输出：7x7 ，其计算过程就是， (2 - 1) * 3 + 4 = 7 3.池化得到的特征图大小计算方式： +W=(W-F)/S+1，池化层一般不填充像素(`VALID`) -其他知识点如下： - -+ **卷积向下取整，池化向上取整**。 -+ **卷积层一般填充像素(`SAME`)，池化层一般不填充像素(`VALID`)**？ -+ 注意：`stride`为1的时候，当`kernel`为 3 `padding`为1或者`kernel`为5 `padding`为2，这种情况可直接得出卷积前后尺寸不变。 +**卷积向下取整，池化向上取整**。**卷积层一般填充像素(`SAME`)，池化层一般不填充像素(`VALID`)**？注意：`stride`为1的时候，当`kernel`为 3 `padding`为1或者`kernel`为5 `padding`为2，这种情况可直接得出卷积前后尺寸不变。 ### 参考资料 + [CNN中的参数解释及计算](https://flat2010.github.io/2018/06/15/%E6%89%8B%E7%AE%97CNN%E4%B8%AD%E7%9A%84%E5%8F%82%E6%95%B0/) From 2abe0cfad60f73f5a57b5e0c570cdfce44676ff7 Mon Sep 17 00:00:00 2001 From: Zhang Date: Tue, 13 Aug 2019 00:35:19 +0800 Subject: [PATCH 174/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 81b15c80..8bfdbd83 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -25,7 +25,7 @@ 例如 输入：2x2， 卷积核：4x4， 滑动步长：3， 输出：7x7 ，其计算过程就是， (2 - 1) * 3 + 4 = 7 3.池化得到的特征图大小计算方式： -W=(W-F)/S+1，池化层一般不填充像素(`VALID`) +`W=(W-F)/S+1`，池化层一般不填充像素(`VALID`) **卷积向下取整，池化向上取整**。**卷积层一般填充像素(`SAME`)，池化层一般不填充像素(`VALID`)**？注意：`stride`为1的时候，当`kernel`为 3 `padding`为1或者`kernel`为5 `padding`为2，这种情况可直接得出卷积前后尺寸不变。 From ea927ede149912e863131586665c23ab8a4a8d49 Mon Sep 17 00:00:00 2001 From: Zhang Date: Tue, 13 Aug 2019 00:40:15 +0800 Subject: [PATCH 175/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 8bfdbd83..80054b4e 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -1,7 +1,7 @@ # 深度学习 ## 卷积输出大小计算 ### CNN中术语解释 -**CNN网络的主要参数**有下面这么几个： +`CNN`网络的主要参数有下面这么几个： + 卷积核Kernal（在Tensorflow中称为filter）； + 填充Padding； From 909c91feed3ff026ad3a38a6134d66f0dea27ed4 Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 14 Aug 2019 11:44:41 +0800 Subject: [PATCH 176/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 更正3*3卷积参数量计算错误 --- ...\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 80054b4e..9fa4c210 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -175,7 +175,7 @@ RNN的输入有两个部分，一部分为上一时刻的状态，另一部分 (2). **更多的非线性变换**。2个3*3卷积层拥有比1个5*5卷积层更多的非线性变换（前者可以使用两次ReLU激活函数，而后者只有一次），使得卷积神经网络对特征的学习能力更强。 -***paper中给出的相关解释***：三个这样的层具有7×7的有效感受野。那么我们获得了什么？例如通过使用三个3×3卷积层的堆叠来替换单个7×7层。首先，我们结合了三个非线性修正层，而不是单一的，这使得决策函数更具判别性。其次，我们减少参数的数量：假设三层3×3卷积堆叠的输入和输出有C个通道，堆叠卷积层的参数为3(32C2)=27C2个权重；同时，单个7×7卷积层将需要72C2=49C2个参数，即参数多81％。这可以看作是对7×7卷积滤波器进行正则化，迫使它们通过3×3滤波器（在它们之间注入非线性）进行分解。 +***paper中给出的相关解释***：三个这样的层具有7×7的有效感受野。那么我们获得了什么？例如通过使用三个3×3卷积层的堆叠来替换单个7×7层。首先，我们结合了三个非线性修正层，而不是单一的，这使得决策函数更具判别性。其次，我们减少参数的数量：假设三层3×3卷积堆叠的输入和输出有C个通道，堆叠卷积层的参数为3×(3×3C) = `27C` 个权重；同时，单个7×7卷积层将需要7×7×C = `49C` 个参数，即参数多81％。这可以看作是对7×7卷积滤波器进行正则化，迫使它们通过3×3滤波器（在它们之间注入非线性）进行分解。 此回答可以参考TensorFlow实战p110，网上很多回答都说的不全。 From 183b0f44869e2f5c1d6c3198eda70e8a7ced629d Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 14 Aug 2019 11:47:16 +0800 Subject: [PATCH 177/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...5\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 1 + 1 file changed, 1 insertion(+) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 9fa4c210..92a05a75 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -209,6 +209,7 @@ ReLU 的输出要么是 0, 要么是输入本身。虽然方程简单，但实 使用预训练模型的好处，在于利用训练好的SOTA模型权重去做特征提取，可以节省我们训练模型和调参的时间。 至于为什么只微调最后几层神经网络权重，是因为： + (1). CNN中更靠近底部的层（定义模型时先添加到模型中的层）编码的是更加通用的可复用特征，而更靠近顶部的层（最后添加到模型中的层）编码的是更专业业化的特征。微调这些更专业化的特征更加有用，它更代表了新数据集上的有用特征。 (2). 训练的参数越多，过拟合的风险越大。很多SOTA模型拥有超过千万的参数，在一个不大的数据集上训练这么多参数是有过拟合风险的，除非你的数据集像Imagenet那样大。 From 7fec2a4a3e2d47d155d712f6d45ea50fd6fdd93c Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 14 Aug 2019 11:49:30 +0800 Subject: [PATCH 178/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 92a05a75..b7fd751b 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -226,9 +226,10 @@ Python深度学习p127. #### dropout具体工作流程 以 标准神经网络为例，正常的流程是：我们首先把输入数据x通过网络前向传播，然后把误差反向传播一决定如何更新参数让网络进行学习。使用dropout之后，过程变成如下： -(1). 首先随机（临时）删掉网络中一半的隐藏神经元，输入输出神经元保持不变（图3中虚线为部分临时被删除的神经元）； -(2). 然后把输入x通过修改后的网络进行前向传播计算，然后把得到的损失结果通过修改的网络反向传播。一小批训练样本执行完这个过程后，在没有被删除的神经元上按照随机梯度下降法更新对应的参数（w，b）； -(3). 然后重复这一过程： +1. 首先随机（临时）删掉网络中一半的隐藏神经元，输入输出神经元保持不变（图3中虚线为部分临时被删除的神经元）； +2. 然后把输入x通过修改后的网络进行前向传播计算，然后把得到的损失结果通过修改的网络反向传播。一小批训练样本执行完这个过程后，在没有被删除的神经元上按照随机梯度下降法更新对应的参数（w，b）； +3. 然后重复这一过程： + + 恢复被删掉的神经元（此时被删除的神经元保持原样没有更新w参数，而没有被删除的神经元已经有所更新） + 从隐藏层神经元中随机选择一个一半大小的子集临时删除掉（同时备份被删除神经元的参数）。 + 对一小批训练样本，先前向传播然后反向传播损失并根据随机梯度下降法更新参数（w，b） （没有被删除的那一部分参数得到更新，删除的神经元参数保持被删除前的结果）。 From 142d018c2cf4b72e5cecc5e0bcb0b3aff8525b82 Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 14 Aug 2019 11:50:22 +0800 Subject: [PATCH 179/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index b7fd751b..92a05a75 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -226,10 +226,9 @@ Python深度学习p127. #### dropout具体工作流程 以 标准神经网络为例，正常的流程是：我们首先把输入数据x通过网络前向传播，然后把误差反向传播一决定如何更新参数让网络进行学习。使用dropout之后，过程变成如下： -1. 首先随机（临时）删掉网络中一半的隐藏神经元，输入输出神经元保持不变（图3中虚线为部分临时被删除的神经元）； -2. 然后把输入x通过修改后的网络进行前向传播计算，然后把得到的损失结果通过修改的网络反向传播。一小批训练样本执行完这个过程后，在没有被删除的神经元上按照随机梯度下降法更新对应的参数（w，b）； -3. 然后重复这一过程： - +(1). 首先随机（临时）删掉网络中一半的隐藏神经元，输入输出神经元保持不变（图3中虚线为部分临时被删除的神经元）； +(2). 然后把输入x通过修改后的网络进行前向传播计算，然后把得到的损失结果通过修改的网络反向传播。一小批训练样本执行完这个过程后，在没有被删除的神经元上按照随机梯度下降法更新对应的参数（w，b）； +(3). 然后重复这一过程： + 恢复被删掉的神经元（此时被删除的神经元保持原样没有更新w参数，而没有被删除的神经元已经有所更新） + 从隐藏层神经元中随机选择一个一半大小的子集临时删除掉（同时备份被删除神经元的参数）。 + 对一小批训练样本，先前向传播然后反向传播损失并根据随机梯度下降法更新参数（w，b） （没有被删除的那一部分参数得到更新，删除的神经元参数保持被删除前的结果）。 From e9e6a1faab827549168833fe086e1653e674f6a3 Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 14 Aug 2019 11:53:53 +0800 Subject: [PATCH 180/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 92a05a75..b1078693 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -286,7 +286,7 @@ HOG特征提取方法就是将一个image： 1. 卷积层有参数，池化层没有参数 2. **经过卷积层节点矩阵深度会改变**，池化层不会改变节点矩阵的深度，但是它可以缩小节点矩阵的大小 ## 卷积层参数数量计算方法 -假设输入层矩阵维度是96*96*3，第一层卷积层使用尺寸为5*5、深度为16的过滤器（卷积核尺寸为5*5、卷积核数量为16），那么这层卷积层的参数个数为５*5*3*16+16=1216个 +假设输入层矩阵维度是96\*96\*3，第一层卷积层使用尺寸为5\*5、深度为16的过滤器（卷积核尺寸为5\*5、卷积核数量为16），那么这层卷积层的参数个数为５\*5\*3\*16+16=1216个 ## 卷积层输出大小计算 卷积中的特征图大小计算方式有两种，分别是‘VALID’和‘SAME’，卷积和池化都适用，除不尽的结果都向下取整。公式：`O = (W-F+2P)/S+1`，输入图片（Input）大小为I=W*W，卷积核（Filter）大小为F*F，步长（stride）为S，填充（Padding）的像素数为P。 From f004f6aacf5d51f6e176526ce8dde8c57a165733 Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 14 Aug 2019 15:09:30 +0800 Subject: [PATCH 181/386] Add files via upload --- 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zais5Q+F51o$2$d{rSWc7+a;M%DxVk_d6734>(;b@@SqF_Pl#~&(4a9hUshU{|2+yo zlrrF`h*5LK|CdUn=vgQ;7*Zy;bML0Z6a#RAcS!f!X`&@SNd>@!)U%ictC5WrVRr2c z3N;dHACAQxNFfNeB?w!1y^!S9B1-@gz|G&XADR~DF9it`|JpnjQucqjajfV!&&A(&~f+{P@L+Em`S?=CnC0S(OaVPff1n=h^ zb%*u+ph*gEIH;3eIVkyw>aCG^?Wi--!r8+-9Y8NXcNa4!wK6*siyy3MbOu!{w~ri? zM<{aAIq-&ZEhV;H}P(j5b(xNR9n)*n7m@ zP3$;0>Q)-a5W`qNg;z}jO5oyye@BNbJx^YY%)@3tpZChwZ9obG?s<~hV*qi13zfH7)wdvtJ^ z*Fi~Zn+`AYjgf0Idgii#liupMfBIa0ce?xsrLR8bKXW(cG&=vk59I%PO#08nshl1g zz~cWQAt>3Nmy-)>{@(R__kY>$Ip-t+#7h2sBfr7?68VMy9`5;H^pC%xApeRq|NZ Date: Wed, 14 Aug 2019 15:11:25 +0800 Subject: [PATCH 182/386] =?UTF-8?q?Update=20=E7=9B=AE=E6=A0=87=E6=A3=80?= =?UTF-8?q?=E6=B5=8B=E4=B8=8E=E5=9B=BE=E5=83=8F=E5=88=86=E7=B1=BB=E6=8C=87?= =?UTF-8?q?=E6=A0=87.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 更新目标检测度量标准汇总图 --- ...03\217\345\210\206\347\261\273\346\214\207\346\240\207.md" | 4 ++++ 1 file changed, 4 insertions(+) diff --git "a/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" "b/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" index ee522455..dc02f84f 100644 --- "a/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" +++ "b/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" @@ -297,7 +297,11 @@ $FLOPs=(2*Ci*k*K）*H*W*Co$（考虑bias） 最后得到的Co张输出特征图，每张特征图上有H\*W个像素点，而这其中的每个像素点的值都是由过滤器与输入特征图卷积得到的，过滤器中Ci\*k\*K个点，每个点都要和输入特征图对应点作一次相乘操作（浮点操作数为ci\*k\*k），然后将这些过滤器和输入特征图对应点相乘所得的数相加起来（浮点操作数为ci\*k\*k-1，n个数相加，所需要的浮点操作数为n-1），得到一个值，对应于一张输出特征图中的一个像素，输出特征图有Co张，故有Co个过滤器参与卷积运算，所以卷积层的**$FLOPs=(2\*Ci\*k\*K-1）\*H\*W\*Co$** (Ci为输入特征图通道数，K为过滤器尺寸，H,W,Co为输出特征图的高，宽和通道数) + +## 目标检测度量标准汇总 +![目标检测指标汇总](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E7%9B%AE%E6%A0%87%E6%A3%80%E6%B5%8B%E5%BA%A6%E9%87%8F%E6%A0%87%E5%87%86%E6%B1%87%E6%80%BB.jpg) ## 参考资料 + [目标检测评价标准-AP mAP](https://arleyzhang.github.io/articles/c521a01c/) + [目标检测的性能评价指标](https://zhuanlan.zhihu.com/p/70306015?utm_source=wechat_session&utm_medium=social&utm_oi=571954943427219456) + [Soft-NMS](https://hellozhaozheng.github.io/z_post/%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89-SoftNMS-ICCV2017/) ++ [Recent Advances in Deep Learning for Object Detection](https://arxiv.org/abs/1908.03673v1) From 8417577c583e0dcff2dcb89d6d8dcb3fa928c279 Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 14 Aug 2019 20:28:48 +0800 Subject: [PATCH 183/386] =?UTF-8?q?Update=20=E7=9B=AE=E6=A0=87=E6=A3=80?= =?UTF-8?q?=E6=B5=8B=E4=B8=8E=E5=9B=BE=E5=83=8F=E5=88=86=E7=B1=BB=E6=8C=87?= =?UTF-8?q?=E6=A0=87.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 增加generate_anchors.py代码 --- ...06\347\261\273\346\214\207\346\240\207.md" | 79 +++++++++++++++++++ 1 file changed, 79 insertions(+) diff --git "a/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" "b/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" index dc02f84f..7e938c28 100644 --- "a/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" +++ "b/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" @@ -297,6 +297,84 @@ $FLOPs=(2*Ci*k*K）*H*W*Co$（考虑bias） 最后得到的Co张输出特征图，每张特征图上有H\*W个像素点，而这其中的每个像素点的值都是由过滤器与输入特征图卷积得到的，过滤器中Ci\*k\*K个点，每个点都要和输入特征图对应点作一次相乘操作（浮点操作数为ci\*k\*k），然后将这些过滤器和输入特征图对应点相乘所得的数相加起来（浮点操作数为ci\*k\*k-1，n个数相加，所需要的浮点操作数为n-1），得到一个值，对应于一张输出特征图中的一个像素，输出特征图有Co张，故有Co个过滤器参与卷积运算，所以卷积层的**$FLOPs=(2\*Ci\*k\*K-1）\*H\*W\*Co$** (Ci为输入特征图通道数，K为过滤器尺寸，H,W,Co为输出特征图的高，宽和通道数) +## anchors +所谓anchors，实际上就是一组由generate_anchors.py生成的矩形。其中每行的4个值 `(x1,y1,x2,y2)` 表矩形左上和右下角点坐标。9个矩形共有3种形状，长宽比为大约为 `{1:1, 1:2, 2:1}` 三种, 实际上通过anchors就引入了检测中常用到的多尺度方法。generate_anchors.py的代码如下： +```Python +import numpy as np +import six +from six import __init__ + + + +def generate_anchor_base(base_size=16, ratios=[0.5, 1, 2], + anchor_scales=[8, 16, 32]): + """Generate anchor base windows by enumerating aspect ratio and scales. + + Generate anchors that are scaled and modified to the given aspect ratios. + Area of a scaled anchor is preserved when modifying to the given aspect + ratio. + + :obj:`R = len(ratios) * len(anchor_scales)` anchors are generated by this + function. + The :obj:`i * len(anchor_scales) + j` th anchor corresponds to an anchor + generated by :obj:`ratios[i]` and :obj:`anchor_scales[j]`. + + For example, if the scale is :math:`8` and the ratio is :math:`0.25`, + the width and the height of the base window will be stretched by :math:`8`. + For modifying the anchor to the given aspect ratio, + the height is halved and the width is doubled. + + Args: + base_size (number): The width and the height of the reference window. + ratios (list of floats): This is ratios of width to height of + the anchors. + anchor_scales (list of numbers): This is areas of anchors. + Those areas will be the product of the square of an element in + :obj:`anchor_scales` and the original area of the reference + window. + + Returns: + ~numpy.ndarray: + An array of shape :math:`(R, 4)`. + Each element is a set of coordinates of a bounding box. + The second axis corresponds to + :math:`(y_{min}, x_{min}, y_{max}, x_{max})` of a bounding box. + + """ + import numpy as np + py = base_size / 2. + px = base_size / 2. + + anchor_base = np.zeros((len(ratios) * len(anchor_scales), 4), + dtype=np.float32) + for i in six.moves.range(len(ratios)): + for j in six.moves.range(len(anchor_scales)): + h = base_size * anchor_scales[j] * np.sqrt(ratios[i]) + w = base_size * anchor_scales[j] * np.sqrt(1. / ratios[i]) + + index = i * len(anchor_scales) + j + anchor_base[index, 0] = py - h / 2. + anchor_base[index, 1] = px - w / 2. + anchor_base[index, 2] = py + h / 2. + anchor_base[index, 3] = px + w / 2. + return anchor_base + + +# test +if __name__ == "__main__": + bbox_list = generate_anchor_base() + print(bbox_list) +``` +程序运行输出如下： +> [[ -37.254833 -82.50967 53.254833 98.50967 ] + [ -82.50967 -173.01933 98.50967 189.01933 ] + [-173.01933 -354.03867 189.01933 370.03867 ] + [ -56. -56. 72. 72. ] + [-120. -120. 136. 136. ] + [-248. -248. 264. 264. ] + [ -82.50967 -37.254833 98.50967 53.254833] + [-173.01933 -82.50967 189.01933 98.50967 ] + [-354.03867 -173.01933 370.03867 189.01933 ]] ## 目标检测度量标准汇总 ![目标检测指标汇总](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E7%9B%AE%E6%A0%87%E6%A3%80%E6%B5%8B%E5%BA%A6%E9%87%8F%E6%A0%87%E5%87%86%E6%B1%87%E6%80%BB.jpg) @@ -305,3 +383,4 @@ $FLOPs=(2*Ci*k*K）*H*W*Co$（考虑bias） + [目标检测的性能评价指标](https://zhuanlan.zhihu.com/p/70306015?utm_source=wechat_session&utm_medium=social&utm_oi=571954943427219456) + [Soft-NMS](https://hellozhaozheng.github.io/z_post/%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89-SoftNMS-ICCV2017/) + [Recent Advances in Deep Learning for Object Detection](https://arxiv.org/abs/1908.03673v1) ++ [A Simple and Fast Implementation of Faster R-CNN](https://github.com/chenyuntc/simple-faster-rcnn-pytorch) From 30cfde0c98ee60c3c47d26d90af60d1ae9f8c720 Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 14 Aug 2019 20:33:45 +0800 Subject: [PATCH 184/386] =?UTF-8?q?Update=20=E7=9B=AE=E6=A0=87=E6=A3=80?= =?UTF-8?q?=E6=B5=8B=E4=B8=8E=E5=9B=BE=E5=83=8F=E5=88=86=E7=B1=BB=E6=8C=87?= =?UTF-8?q?=E6=A0=87.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git "a/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" "b/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" index 7e938c28..1a3cfd9f 100644 --- "a/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" +++ "b/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" @@ -302,8 +302,7 @@ $FLOPs=(2*Ci*k*K）*H*W*Co$（考虑bias） ```Python import numpy as np import six -from six import __init__ - +from six import __init__ # 兼容python2和python3模块 def generate_anchor_base(base_size=16, ratios=[0.5, 1, 2], From d29bb107055ef0e35e9fa80c30c58cfbe9512e31 Mon Sep 17 00:00:00 2001 From: Zhang Date: Thu, 15 Aug 2019 15:05:40 +0800 Subject: [PATCH 185/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...344\271\240\351\235\242\350\257\225\351\242\230.md" | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index b1078693..c4c1bddc 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -122,9 +122,13 @@ Tensorflow 是一个通过计算图的形式来表述计算的编程系统，计 (5). **Batch Normalization调整了数据的分布，不考虑激活函数，它让每一层的输出归一化到了均值为0方差为1的分布**，这保证了梯度的有效性，可以解决反向传播过程中的梯度问题。目前大部分资料都这样解释，比如BN的原始论文认为的缓解了Internal Covariate Shift(ICS)问题。 -## 什么是梯度消失和爆炸，怎么解决？ -在反向传播过程中需要对激活han函数进行求导，如果导数大于1，那么随着网络层数的增加梯度更新将会朝着指数爆炸的方式增加这就是梯度爆炸。同样如果导数小于1，那么随着网络层数的增加梯度更新信息会朝着指数衰减的方式减少这就是梯度消失。因此，梯度消失、爆炸，其根本原因在于反向传播训练法则，属于先天不足。 -当训练较多层数的模型时，一般会出现梯度消失问题（gradient vanishing problem）和梯度爆炸问题（gradient exploding problem）。注意在反向传播中，当网络模型层数较多时，梯度消失和梯度爆炸是不可避免的。 +## 什么是梯度消失和爆炸，什么原因造成了梯度消失和梯度爆炸，怎么解决？ ++ 梯度消失是指在深度学习训练的过程中，梯度随着链式求导逐层传递逐层减小，最后趋近于0，导致对某些层的训练失效； ++ 梯度爆炸与梯度消失相反，梯度随着链式求导逐层传递逐层增大，最后趋于无穷，导致某些层无法收敛； + +> 在反向传播过程中需要对激活han函数进行求导，如果导数大于1，那么随着网络层数的增加，梯度更新将会朝着指数爆炸的方式增加这就是梯度爆炸。同样如果导数小于1，那么随着网络层数的增加梯度更新信息会朝着指数衰减的方式减少这就是梯度消失。 + +**出现梯度消失和梯度爆炸的问题主要是因为参数初始化不当以及激活函数选择不当造成的**。其根本原因在于反向传播训练法则，属于先天不足。当训练较多层数的模型时，一般会出现梯度消失问题（gradient vanishing problem）和梯度爆炸问题（gradient exploding problem）。注意在反向传播中，当网络模型层数较多时，梯度消失和梯度爆炸是不可避免的。 **深度神经网络中的梯度不稳定性，根本原因在于前面层上的梯度是来自于后面层上梯度的乘积**。当存在过多的层次时，就出现了内在本质上的不稳定场景。前面的层比后面的层梯度变化更小，故变化更慢，故引起了梯度消失问题。前面层比后面层梯度变化更快，故引起梯度爆炸问题。 From 1ddc5897657eb3da7355ec777750f31b779f473a Mon Sep 17 00:00:00 2001 From: Zhang Date: Fri, 16 Aug 2019 10:22:06 +0800 Subject: [PATCH 186/386] =?UTF-8?q?Update=20=E7=9B=AE=E6=A0=87=E6=A3=80?= =?UTF-8?q?=E6=B5=8B=E4=B8=8E=E5=9B=BE=E5=83=8F=E5=88=86=E7=B1=BB=E6=8C=87?= =?UTF-8?q?=E6=A0=87.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...3\217\345\210\206\347\261\273\346\214\207\346\240\207.md" | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git "a/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" "b/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" index 1a3cfd9f..5b55cfa7 100644 --- "a/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" +++ "b/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" @@ -287,8 +287,9 @@ def voc_ap(rec, prec, use_07_metric=False): > 先解释一下FLOPs：floating point operations 指的是浮点运算次数，理解为计算量，可以用来衡量算法/模型的复杂度。此处区分一下FLOPS（全部大写），FLOPS指的是每秒运算的浮点数，理解为计算速度，衡量一个硬件的标准。我们要的是衡量模型的复杂度的指标，所以选择FLOPs。 ### 卷积层FLOPs计算 -$FLOPs=(2*Ci*k*K-1）*H*W*Co$（不考虑bias） -$FLOPs=(2*Ci*k*K）*H*W*Co$（考虑bias） ++ $FLOPs=(2\*Ci\*k\*K-1)\*H\*W\*Co$（不考虑bias） ++ $FLOPs=(2\*Ci\*k\*K）\*H\*W\*Co$（考虑bias） + **Ci为输入特征图通道数，K为过滤器尺寸，H,W,Co为输出特征图的高，宽和通道数**。`卷积过程`如下图所示： ![卷积过程](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E5%8D%B7%E7%A7%AF%E8%BF%87%E7%A8%8B%E5%9B%BE.png) From df5f884b5006c9d5b5de5e1a9aec9f101bdefd6a Mon Sep 17 00:00:00 2001 From: Zhang Date: Sat, 17 Aug 2019 12:12:55 +0800 Subject: [PATCH 187/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 3 +++ 1 file changed, 3 insertions(+) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index c4c1bddc..bcf40fbd 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -481,6 +481,9 @@ print('d = ', d) # [1, 2, 3, ['a', 'b', 'c']] b = [1, 2, 3, ['a', 'b', 'c', 'd'], 4] c = [1, 2, 3, ['a', 'b', 'c', 'd']] d = [1, 2, 3, ['a', 'b', 'c']] + +## 其他面试题 +参考[这里](https://zhuanlan.zhihu.com/p/23526961) ## 图像锐化方法 **锐化主要影响图像中的低频分量，不影响图像中的高频分量**像锐化的主要目的有两个： 1. 增强图像边缘，使模糊的图像变得更加清晰，颜色变得鲜明突出，图像的质量有所改善，产生更适合人眼观察和识别的图像； From 8eddaa5bfeaa35b0aa582b4140684a2fea9d117a Mon Sep 17 00:00:00 2001 From: Zhang Date: Sat, 17 Aug 2019 13:02:05 +0800 Subject: [PATCH 188/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 2 ++ 1 file changed, 2 insertions(+) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index bcf40fbd..c202c166 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -456,6 +456,8 @@ print(str2int('0012345')) # 0012345 1. 直接赋值：其实就是对象的引用（别名）。 2. 浅拷贝(copy)：拷贝父对象，不会拷贝对象的内部的子对象。**copy浅拷贝，没有拷贝子对象，所以原始数据改变，子对象会改变。** 3. 深拷贝(deepcopy)： copy 模块的 deepcopy 方法，完全拷贝了父对象及其子对象，两者是完全独立的。**深拷贝，包含对象里面的自对象的拷贝，所以原始对象的改变不会造成深拷贝里任何子元素的改变**。 + +**注意：浅拷贝和深拷贝的不同仅仅是对组合对象来说，所谓的组合对象（容器）就是包含了其它对象的对象，如列表，类实例。而对于数字、字符串以及其它“原子”类型，没有拷贝一说，产生的都是原对象的引用。** 看一个示例程序，就能明白浅拷贝与深拷贝的区别了： ```Python3 #!/usr/bin/Python3 From f0dbc2588a83ac1bf0e9245ff439c0a374f65014 Mon Sep 17 00:00:00 2001 From: Zhang Date: Sat, 17 Aug 2019 13:25:01 +0800 Subject: [PATCH 189/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index c202c166..13f1c8a3 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -454,8 +454,11 @@ print(str2int('0012345')) # 0012345 ``` ## Python深拷贝、浅拷贝区别 1. 直接赋值：其实就是对象的引用（别名）。 -2. 浅拷贝(copy)：拷贝父对象，不会拷贝对象的内部的子对象。**copy浅拷贝，没有拷贝子对象，所以原始数据改变，子对象会改变。** -3. 深拷贝(deepcopy)： copy 模块的 deepcopy 方法，完全拷贝了父对象及其子对象，两者是完全独立的。**深拷贝，包含对象里面的自对象的拷贝，所以原始对象的改变不会造成深拷贝里任何子元素的改变**。 +2. 浅拷贝(copy)：拷贝父对象，不会拷贝对象的内部的子对象。（拷贝可以理解为创建内存）可以产生浅拷贝的操作有以下几种： ++ 使用切片[:]操作 ++ 使用工厂函数（如list/dir/set）,工厂函数看上去像函数，实质上是类，调用时实际上是生成了该类型的一个实例，就像工厂生产货物一样. ++ 使用copy模块中的copy()函数 +3. 深拷贝(deepcopy)： copy 模块的 deepcopy 方法，完全拷贝了父对象及其子对象，两者是完全独立的。**深拷贝，包含对象里面的子对象的拷贝，所以原始对象的改变不会造成深拷贝里任何子元素的改变**。 **注意：浅拷贝和深拷贝的不同仅仅是对组合对象来说，所谓的组合对象（容器）就是包含了其它对象的对象，如列表，类实例。而对于数字、字符串以及其它“原子”类型，没有拷贝一说，产生的都是原对象的引用。** 看一个示例程序，就能明白浅拷贝与深拷贝的区别了： From e675c8bf8b44e1e3ca058d250b4e2a151e77d783 Mon Sep 17 00:00:00 2001 From: Zhang Date: Sat, 17 Aug 2019 13:31:01 +0800 Subject: [PATCH 190/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 13f1c8a3..7e5c91b3 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -455,9 +455,9 @@ print(str2int('0012345')) # 0012345 ## Python深拷贝、浅拷贝区别 1. 直接赋值：其实就是对象的引用（别名）。 2. 浅拷贝(copy)：拷贝父对象，不会拷贝对象的内部的子对象。（拷贝可以理解为创建内存）可以产生浅拷贝的操作有以下几种： -+ 使用切片[:]操作 -+ 使用工厂函数（如list/dir/set）,工厂函数看上去像函数，实质上是类，调用时实际上是生成了该类型的一个实例，就像工厂生产货物一样. -+ 使用copy模块中的copy()函数 + + 使用切片[:]操作 + + 使用工厂函数（如list/dir/set）,工厂函数看上去像函数，实质上是类，调用时实际上是生成了该类型的一个实例，就像工厂生产货物一样. + + 使用copy模块中的copy()函数 3. 深拷贝(deepcopy)： copy 模块的 deepcopy 方法，完全拷贝了父对象及其子对象，两者是完全独立的。**深拷贝，包含对象里面的子对象的拷贝，所以原始对象的改变不会造成深拷贝里任何子元素的改变**。 **注意：浅拷贝和深拷贝的不同仅仅是对组合对象来说，所谓的组合对象（容器）就是包含了其它对象的对象，如列表，类实例。而对于数字、字符串以及其它“原子”类型，没有拷贝一说，产生的都是原对象的引用。** From ab7308e56a7dee48815090709d539cbd4b6b1bba Mon Sep 17 00:00:00 2001 From: Zhang Date: Mon, 19 Aug 2019 22:09:23 +0800 Subject: [PATCH 191/386] =?UTF-8?q?Update=20=E7=9B=AE=E6=A0=87=E6=A3=80?= =?UTF-8?q?=E6=B5=8B=E4=B8=8E=E5=9B=BE=E5=83=8F=E5=88=86=E7=B1=BB=E6=8C=87?= =?UTF-8?q?=E6=A0=87.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 更新MACC指标计算 --- ...\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" | 2 ++ 1 file changed, 2 insertions(+) diff --git "a/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" "b/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" index 5b55cfa7..a0463a16 100644 --- "a/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" +++ "b/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" @@ -284,6 +284,8 @@ def voc_ap(rec, prec, use_07_metric=False): return ap ``` ## FLOPs计算 ++ MACC：multiply-accumulate，乘法累加。 ++ FLOPS：Floating-point Operations Per Second，每秒所执行的浮点运算次数。 > 先解释一下FLOPs：floating point operations 指的是浮点运算次数，理解为计算量，可以用来衡量算法/模型的复杂度。此处区分一下FLOPS（全部大写），FLOPS指的是每秒运算的浮点数，理解为计算速度，衡量一个硬件的标准。我们要的是衡量模型的复杂度的指标，所以选择FLOPs。 ### 卷积层FLOPs计算 From 16736e015ffad36b52f0da489d6c2b48674b75b1 Mon Sep 17 00:00:00 2001 From: Zhang Date: Mon, 19 Aug 2019 22:09:39 +0800 Subject: [PATCH 192/386] =?UTF-8?q?Update=20=E7=9B=AE=E6=A0=87=E6=A3=80?= =?UTF-8?q?=E6=B5=8B=E4=B8=8E=E5=9B=BE=E5=83=8F=E5=88=86=E7=B1=BB=E6=8C=87?= =?UTF-8?q?=E6=A0=87.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...5\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" | 1 + 1 file changed, 1 insertion(+) diff --git "a/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" "b/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" index a0463a16..b9e309f8 100644 --- "a/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" +++ "b/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" @@ -286,6 +286,7 @@ def voc_ap(rec, prec, use_07_metric=False): ## FLOPs计算 + MACC：multiply-accumulate，乘法累加。 + FLOPS：Floating-point Operations Per Second，每秒所执行的浮点运算次数。 + > 先解释一下FLOPs：floating point operations 指的是浮点运算次数，理解为计算量，可以用来衡量算法/模型的复杂度。此处区分一下FLOPS（全部大写），FLOPS指的是每秒运算的浮点数，理解为计算速度，衡量一个硬件的标准。我们要的是衡量模型的复杂度的指标，所以选择FLOPs。 ### 卷积层FLOPs计算 From b00014fe64cbe67a75d0a302ab1b6681db5a2757 Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 21 Aug 2019 00:24:41 +0800 Subject: [PATCH 193/386] =?UTF-8?q?Update=20=E7=9B=AE=E6=A0=87=E6=A3=80?= =?UTF-8?q?=E6=B5=8B=E4=B8=8E=E5=9B=BE=E5=83=8F=E5=88=86=E7=B1=BB=E6=8C=87?= =?UTF-8?q?=E6=A0=87.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 更新MACC（乘加操作次数）计算公式 --- ...5\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" | 1 + 1 file changed, 1 insertion(+) diff --git "a/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" "b/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" index b9e309f8..fa9e7606 100644 --- "a/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" +++ "b/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" @@ -292,6 +292,7 @@ def voc_ap(rec, prec, use_07_metric=False): ### 卷积层FLOPs计算 + $FLOPs=(2\*Ci\*k\*K-1)\*H\*W\*Co$（不考虑bias） + $FLOPs=(2\*Ci\*k\*K）\*H\*W\*Co$（考虑bias） ++ $MACC=(\*Ci\*k\*K）\*H\*W\*Co$（考虑bias） **Ci为输入特征图通道数，K为过滤器尺寸，H,W,Co为输出特征图的高，宽和通道数**。`卷积过程`如下图所示： From 3fa195c64ef23f6b5d2a207e742ab256adb45659 Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 21 Aug 2019 00:26:42 +0800 Subject: [PATCH 194/386] =?UTF-8?q?Update=20=E7=9B=AE=E6=A0=87=E6=A3=80?= =?UTF-8?q?=E6=B5=8B=E4=B8=8E=E5=9B=BE=E5=83=8F=E5=88=86=E7=B1=BB=E6=8C=87?= =?UTF-8?q?=E6=A0=87.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...03\217\345\210\206\347\261\273\346\214\207\346\240\207.md" | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git "a/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" "b/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" index fa9e7606..5124723a 100644 --- "a/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" +++ "b/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" @@ -291,8 +291,8 @@ def voc_ap(rec, prec, use_07_metric=False): ### 卷积层FLOPs计算 + $FLOPs=(2\*Ci\*k\*K-1)\*H\*W\*Co$（不考虑bias） -+ $FLOPs=(2\*Ci\*k\*K）\*H\*W\*Co$（考虑bias） -+ $MACC=(\*Ci\*k\*K）\*H\*W\*Co$（考虑bias） ++ $FLOPs=(2\*Ci\*k\*K)\*H\*W\*Co$（考虑bias） ++ $MACC=(Ci\*k\*K)\*H\*W\*Co$（考虑bias） **Ci为输入特征图通道数，K为过滤器尺寸，H,W,Co为输出特征图的高，宽和通道数**。`卷积过程`如下图所示： From e61b9d8d65d7437bc79bc18589114018a1ee0901 Mon Sep 17 00:00:00 2001 From: Jun Wang Date: Thu, 22 Aug 2019 10:28:07 +0800 Subject: [PATCH 195/386] Add files via upload --- ...\241\346\213\233\345\206\205\346\216\250.png" | Bin 0 -> 4051 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 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z+fD5jez1>wN1F!k=?Kc{DdkGyPO#7O$2O=wMOU9Zq&Wu)K&vRWjy%U$T$3?l)l#8zpr4jzB>H~rvG7EufyUU{?ET*JoEFT azUQXL`t$nXEpvuu-2}HgB3Kxhkn>LioU}0j literal 0 HcmV?d00001 From d9a5592aa54703ffb9ac19a9c20aef9cb1ba8e1d Mon Sep 17 00:00:00 2001 From: Jun Wang Date: Thu, 22 Aug 2019 10:28:34 +0800 Subject: [PATCH 196/386] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index edfdba29..740257ab 100644 --- a/README.md +++ b/README.md @@ -36,6 +36,7 @@ |图森未来|感知算法实习生等|北京|未知|内推邮箱:yifyang29@gmail.com|简历命名格式：姓名+电话号码+应聘岗位|无|我爱计算机视觉公众号| |最右|视频图像算法工程师实习|北京|未知|内推邮箱:sunhaiyong2014@xiaochuankeji.cn|简历命名格式：CVer推荐+姓名+电话+社招/校招/实习|CVer公众号| |爱奇艺智能平台部-视频分析组|视频理解算法实习生|北京中关村|未知|内推邮箱:zhangyuntao@qiyi.com|简历命名格式：CVer推荐+学校+研究方向+实习开始时间+毕业时间|CVer公众号| +|依图科技|校招、实习、社招|上海、北京、杭州|长期|![依图科技-校招内推](images/依图科技-校招内推.png)||| ## [深度学习面试题](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E9%9D%A2%E8%AF%95%E9%A2%98.md) From 6a62985f61d1d73108ac9363f4fa2c515d28d319 Mon Sep 17 00:00:00 2001 From: Jun Wang Date: Thu, 22 Aug 2019 10:31:00 +0800 Subject: [PATCH 197/386] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 740257ab..999d5984 100644 --- a/README.md +++ b/README.md @@ -36,7 +36,7 @@ |图森未来|感知算法实习生等|北京|未知|内推邮箱:yifyang29@gmail.com|简历命名格式：姓名+电话号码+应聘岗位|无|我爱计算机视觉公众号| |最右|视频图像算法工程师实习|北京|未知|内推邮箱:sunhaiyong2014@xiaochuankeji.cn|简历命名格式：CVer推荐+姓名+电话+社招/校招/实习|CVer公众号| |爱奇艺智能平台部-视频分析组|视频理解算法实习生|北京中关村|未知|内推邮箱:zhangyuntao@qiyi.com|简历命名格式：CVer推荐+学校+研究方向+实习开始时间+毕业时间|CVer公众号| -|依图科技|校招、实习、社招|上海、北京、杭州|长期|![依图科技-校招内推](images/依图科技-校招内推.png)||| +|依图科技|校招、实习、社招|上海、北京、杭州|长期|![依图科技-校招内推](images/依图科技-校招内推.png)|微信扫描下方二维码（长期有效），链接中的岗位均可投递|| ## [深度学习面试题](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E9%9D%A2%E8%AF%95%E9%A2%98.md) From b448cf28dbf07cc32102d277c5ff5a59a26f57b4 Mon Sep 17 00:00:00 2001 From: Jun Wang Date: Thu, 22 Aug 2019 10:31:34 +0800 Subject: [PATCH 198/386] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 999d5984..cb18444e 100644 --- a/README.md +++ b/README.md @@ -36,7 +36,7 @@ |图森未来|感知算法实习生等|北京|未知|内推邮箱:yifyang29@gmail.com|简历命名格式：姓名+电话号码+应聘岗位|无|我爱计算机视觉公众号| |最右|视频图像算法工程师实习|北京|未知|内推邮箱:sunhaiyong2014@xiaochuankeji.cn|简历命名格式：CVer推荐+姓名+电话+社招/校招/实习|CVer公众号| |爱奇艺智能平台部-视频分析组|视频理解算法实习生|北京中关村|未知|内推邮箱:zhangyuntao@qiyi.com|简历命名格式：CVer推荐+学校+研究方向+实习开始时间+毕业时间|CVer公众号| -|依图科技|校招、实习、社招|上海、北京、杭州|长期|![依图科技-校招内推](images/依图科技-校招内推.png)|微信扫描下方二维码（长期有效），链接中的岗位均可投递|| +|依图科技|校招、实习、社招|上海、北京、杭州|长期|![依图科技-校招内推](images/依图科技-校招内推.png)|微信扫描二维码（长期有效），链接中的岗位均可投递|| ## [深度学习面试题](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E9%9D%A2%E8%AF%95%E9%A2%98.md) From 229262a6450636d694acdfdf634a59e352ce2ea5 Mon Sep 17 00:00:00 2001 From: Zhang Date: Tue, 27 Aug 2019 14:23:36 +0800 Subject: [PATCH 199/386] =?UTF-8?q?Update=20=E7=A5=9E=E7=BB=8F=E7=BD=91?= =?UTF-8?q?=E7=BB=9C=E5=8E=8B=E7=BC=A9=E7=AE=97=E6=B3=95=E6=80=BB=E7=BB=93?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 更新神经网络压缩算法总结的内容和markdown语法错误 --- ...27\346\263\225\346\200\273\347\273\223.md" | 124 +++++++++++++++++- 1 file changed, 119 insertions(+), 5 deletions(-) diff --git "a/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" "b/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" index 9610deb8..70069fe5 100644 --- "a/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" +++ "b/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" @@ -1,4 +1,11 @@ ## 神经网络的压缩方法 ++ 低秩近似 ++ 剪枝与稀疏约束 ++ 参数量化 ++ 二值网络 ++ 知识蒸馏 ++ 紧凑的网络结构 + 我们知道，在一定程度上，网络越深，参数越多，模型越复杂，其最终效果越好。神经网络的压缩算法是，旨在将一个庞大而复杂的预训练模型（pre-trained model）转化为一个精简的小模型。 按照压缩过程对网络结构的破坏程度，我们将模型压缩技术分为“前端压缩”和“后端压缩”两部分。 + 前端压缩，是指在不改变原网络结构的压缩技术，主要包括`知识蒸馏`、紧凑的模型结构涉及以及`滤波器（filter）层面的剪枝`等； @@ -10,7 +17,7 @@ > 一般地，行阶梯型矩阵的秩等于其“台阶数”-非零行的行数。 **低秩近似算法能减小计算开销的原理**如下： -给定权重矩阵$W\epsilon R^{m*n}$，若能将其表示为若干个低秩矩阵的组合，即$W=\sum_{i=1}^{n}\alpha _{i}M_{i}$，其中$W\epsilon R^{m*n}$为低秩矩阵，其秩为$r_{i}$，并满足$r_{i}<批标准化(BNorm)->激活(Activ)->池化(Pool)这样的顺序操作组成的。对于异或神经网络，设计出的模块是由**批标准化(BNorm)->二值化激活(BinActiv)->二值化卷积(BinConv)->池化(Pool)**的顺序操作完成。这样做的原因是批标准化以后，保证了输入均值为0，然后进行二值化激活，保证了数据为-1或者+1，然后进行二值化卷积，这样能最大程度上减少特征信息的损失。二值化残差网络结构定义实例代码如下： +```Pyhon +def residual_unit(data, num_filter, stride, dim_match, num_bits=1): + bnAct1 = bnn.BatchNorm(data=data, num_bits=num_bits) + conv1 = bnn.Convolution(data=bnAct1, num_filter=num_filter, kernel=(3, 3), stride=stride, pad=(1, 1)) + convBn1 = bnn.BatchNorm(data=conv1, num_bits=num_bits) + conv2 = bnn.Convolution(data=convBn1, num_filter=num_filter, kernel=(3, 3), stride=(1, 1), pad=(1, 1)) + if dim_match: + shortcut = data + else: + shortcut = bnn.Convolution(data=bnAct1, num_filter=num_filter, kernel=(3, 3), stride=stride, pad=(1, 1)) + return conv2 + shortcut +``` +### 二值网络的梯度下降 +现在的神经网络几乎都是基于梯度下降算法来训练的，但是二值网络的权重只有 $\pm 1$，无法直接计算梯度信息，也无法进行权重更新。为解决这个问题，[Courbariaux](https://arxiv.org/pdf/1602.02830v1.pdf)等人提出二值连接（binary connect）算法，该算法采取单精度与二值结合的方式来训练二值神经网络（），这是第一次给出了关于如何对网络进行二值化和如何训练二值化神经网络的方法。过程如下： +1. 权重 weight 初始化为浮点 +2. 前向传播 Forward Pass: + + 利用决定化方式（`sign(x)函数`）把 Weight 量化为 `+1/-1`, 以0为阈值 + + 利用量化后的 Weight (只有+1/-1)来计算前向传播，由二值权重与输入进行卷积运算（实际上只涉及加法），获得卷积层输出。 +3. 反向传播 Backward Pass: + + 把梯度更新到浮点的 Weight 上（根据放松后的符号函数，计算相应梯度值，并根据该梯度的值对单精度的权重进行参数更新） + + 训练结束： 把 Weight 永久性转化为+1/-1, 以便 inference 使用 +### 两个问题 +网络二值化需要解决两个问题：如何对权重进行二值化和如何计算二值权重的梯度。 +**1，如何对权重进行二值化？** +权重二值化一般有两种选择： ++ 直接根据权重的正负进行二值化：$x^{b}=sign(x)$。符号函数sign(x)定义如下： +$$sign(x) = \left\{\begin{matrix} +-1 & x < 0 \\ +0 & x = 0\\ +1 & x > 0 +\end{matrix}\right.$$ ++ 进行随机的二值化，即对每一个权重，以一定概率取 $\pm 1$ + +**2，如何计算二值权重的梯度？** +二值权重的梯度为0，无法进行参数更新。为解决这个问题，需要**对符号函数进行放松**，即用 $Htanh(x) = max(-1, min(1,x))$来代替 $sinx(x)$。当 x 在区间 [-1,1] 时，存在梯度值1，否则梯度为 0 。 +### 二值连接算法改进 +之前的二值连接算法只对权重进行了二值化，但是网络的中间输出值依然是单精度的，于是 Rastegari 等人对此进行了改进，提出用**单精度对角阵与二值矩阵之积来近似表示原矩阵的算法**，以提升二值网络的分类性能，弥补二值网络在精度上弱势。该算法将原卷积运算分解为如下过程： +$$I*W\approx (I*B)\alpha$$ +其中$I \epsilon \mathbb{R}^{c\times w_{in}\times h_{in}}$为该层的输入张量，$I \epsilon \mathbb{R}^{c\times w\times h}$为该层的一个滤波器，$B=sign(W)\epsilon \{+1, -1\}^{c \times w\times h}$为该滤波器所对应的二值权重。 +这里，Rastegari 等人认为单靠二值运算，很难大的原单精度卷积元素的结果，于是他们使用了一个单精度放缩因子 $\alpha \epsilon \mathbb{R}^{+}$ 来对二至滤波器卷积后的结果进行放缩。$\alpha$ 的取值，可根据优化目标： +$$min||W-\alpha B||^{2}$$ +得到$\alpha = \frac{1}{n}\left \| W \right \|_{\iota _{1}}$。二值连接改进的算法训练过程与之前的算法大致相同，不同的地方在于梯度的计算过程还考虑了 $\alpha$ 的影响。由于 $\alpha$ 这个单精度的缩放因子的存在，有效降低了重构误差，并首次在 ImageNet 数据集上取得了与 Alex-Net相当的精度。如下图所示： + +可以看到的是`权重二值化神经网络（BWN）`和全精度神经网络的精确度几乎一样，但是异或神经网络（XNOR-Net）相比而言，Top-1 和 Top-5 都有 10+% 的损失。 +> 相比于权重二值化神经网络，异或神经网络将网络的输入也转化为二进制值，所以，异或神经网络中的乘法加法 (Multiplication and ACcumulation) 运算用按位异或 (bitwise xnor) 和数 1 的个数 (popcount) 来代替。 + +**更多内容，可以看这两篇文章：** ++ [https://github.com/Ewenwan/MVision/tree/master/CNN/Deep_Compression/quantization/BNN](https://github.com/Ewenwan/MVision/tree/master/CNN/Deep_Compression/quantization/BNN) ++ [二值神经网络（Binary Neural Network，BNN）](https://blog.csdn.net/stdcoutzyx/article/details/50926174) +### 二值网络设计注意事项 ++ 不要使用 kernel = (1, 1) 的 Convolution （包括 resnet 的 bottleneck）：二值网络中的 weight 都为 1bit， 如果再是 1x1 大小， 会极大地降低表达能力 ++ 增大 Channel 数目 + 增大 activation bit 数 要协同配合：如果一味增大 channel 数， 最终 feature map 因为 bit 数过低， 还是浪费了模型容量。 同理反过来也是。 ++ 建议使用 4bit 及以下的 activation bit， 过高带来的精度收益变小， 而会显著提高 inference 计算量 ## 知识蒸馏 知识蒸馏（knowledge distillation），是迁移学习（transfer learning）的一种，简单来说就是训练一个大模型和一个小模型，将庞大而复杂的大模型学习到的知识，通过一定技术手段迁移到精简的小模型上，从而使小模型能够获得与大模型相近的性能。 +student模型最终的损失函数由两部分组成： ++ 第一项是由小模型的预测结果与大模型的“软标签”所构成的交叉熵（cross entroy）; ++ 第二项为预测结果与普通类别标签的交叉熵。 + +软标签计算公式：$$q_{i} = \frac{z_{i}/T}{\sum_{j}z_{j}/T}$$ + +这两个损失函数的重要程度可通过一定的权重进行调节，在实际应用中， `T` 的取值会影响最终的结果，一般而言，较大的 T 能够获得较高的准确度，T（蒸馏温度参数） 属于知识蒸馏模型训练超参数的一种。**T是一个可调节的超参数、T值越大、概率分布越软（论文中的描述），曲线便越平滑**，相当于在迁移学习的过程中添加了扰动，从而使得学生网络在借鉴学习的时候更有效、泛化能力更强，这其实就是一种抑制过拟合的策略。知识蒸馏的整个过程如下图： + +student模型的实际模型结构和小模型一样，但是损失函数包含了两部分，`mxnet` 代码示例如下： +```Pyhon +def get_symbol(data, class_labels, resnet_layer_num,Temperature,mimic_weight,num_classes=2): + conv1 = ConvFactory(data, 48, kernel=(3, 3), stride=(2, 2), pad=(1, 1), name="conv1_3x3") + conv2 = InceptionFactoryB(conv1, 10, 12, 10, 12, 16, (1,1),'conv2_block') + conv3 = InceptionFactoryB(conv2, 12, 24, 12, 24, 24, (0,0),'conv3_block') + conv4 = InceptionFactoryC(conv3, 24, 96, 96, (0,0),(2,2),'max','conv4_block') + conv5 = InceptionFactoryC(conv4, 72, 288, 288, (0,0),(1,1),'avg','con5_block') + flatten = mx.symbol.Flatten(data=conv5, name="flatten") + fc_class_score_s = mx.symbol.FullyConnected(data=flatten, num_hidden=num_classes, name='fc_class_score') + softmax1 = mx.symbol.SoftmaxOutput(data=fc_class_score_s, label=class_labels, name='softmax_hard') + + import symbol_resnet + fc_class_score_t = symbol_resnet.get_symbol(net_depth=resnet_layer_num, num_class=num_classes,data=data) + s_input_for_softmax=fc_class_score_s/Temperature + t_input_for_softmax=fc_class_score_t/Temperature + t_soft_labels=mx.symbol.softmax(t_input_for_softmax,name='teacher_soft_labels') + softmax2 = mx.symbol.SoftmaxOutput(data=s_input_for_softmax, label=t_soft_labels, name='softmax_soft',grad_scale=mimic_weight) + group=mx.symbol.Group([softmax1,softmax2]) + group.save('group2-symbol.json') + return group +``` +tensorflow代码示例如下： +```Python +# 将类别标签进行one-hot编码 +one_hot = tf.one_hot(y, n_classes,1.0,0.0) # n_classes为类别总数, n为类别标签 +#one_hot = tf.cast(one_hot_int, tf.float32) +teacher_tau = tf.scalar_mul(1.0/args.tau, teacher) # teacher为teacher模型直接输出张量, tau为温度系数T +student_tau = tf.scalar_mul(1.0/args.tau, student) # 将模型直接输出logits张量student处于温度系数T +objective1 = tf.nn.sigmoid_cross_entropy_with_logits(student_tau, one_hot) +objective2 = tf.scalar_mul(0.5, tf.square(student_tau-teacher_tau)) +# student模型最终的损失函数由两部分组成： +# 第一项是由小模型的预测结果与大模型的“软标签”所构成的交叉熵（cross entroy）; +# 第二项为预测结果与普通类别标签的交叉熵。 +tf_loss = (args.lamda*tf.reduce_sum(objective1) + (1-args.lamda)*tf.reduce_sum(objective2))/batch_size +``` + `tf.scalar_mul` 函数为对 tf 张量进行固定倍率 scalar 缩放函数。**一般T的取值在 1 - 20 之间，这里我参考了开源代码，取值为 3**。我发现在开源代码中 `student` 模型的训练，有些是和 `teacher` 模型一起训练的，有些是 teacher 模型训练好后直接指导 student 模型训练。 +## 浅层网络 +浅层网络：通过设计一个更浅（层数较少）结构更紧凑的网络来实现对复杂模型效果的逼近, 但是浅层网络的表达能力很难与深层网络相匹敌。因此，这种设计方法的局限性在于只能应用解决在较为简单问题上。如分类问题中类别数较少的task。 ## 参考资料 -魏秀参-CNN解析神经网络 +[神经网络模型压缩和加速之知识蒸馏](https://www.cnblogs.com/dyl222/p/11079489.html) +[https://github.com/chengshengchan/model_compression/blob/master/teacher-student.py](https://github.com/chengshengchan/model_compression/blob/master/teacher-student.py) +[https://github.com/dkozlov/awesome-knowledge-distillation](https://github.com/dkozlov/awesome-knowledge-distillation) +[XNOR-Nethttps://arxiv.org/abs/1603.05279]() From 187840ac9c7c8d2452d575f4a6791e65829a7406 Mon Sep 17 00:00:00 2001 From: Zhang Date: Tue, 27 Aug 2019 14:30:19 +0800 Subject: [PATCH 200/386] Add files via upload --- ...27\346\263\225\347\262\276\345\272\246.png" | Bin 0 -> 214235 bytes ...41\345\236\213\350\277\207\347\250\213.png" | Bin 0 -> 293402 bytes 2 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 "images/\344\272\214\345\200\274\345\214\226\347\256\227\346\263\225\347\262\276\345\272\246.png" create mode 100644 "images/\347\237\245\350\257\206\350\222\270\351\246\217\346\250\241\345\236\213\350\277\207\347\250\213.png" diff --git 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11c069d70a122f059499deb7c23b02cb53a1be2c Mon Sep 17 00:00:00 2001 From: Zhang Date: Tue, 27 Aug 2019 14:32:27 +0800 Subject: [PATCH 201/386] =?UTF-8?q?Update=20=E7=A5=9E=E7=BB=8F=E7=BD=91?= =?UTF-8?q?=E7=BB=9C=E5=8E=8B=E7=BC=A9=E7=AE=97=E6=B3=95=E6=80=BB=E7=BB=93?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...27\346\263\225\346\200\273\347\273\223.md" | 22 ++++++++++--------- 1 file changed, 12 insertions(+), 10 deletions(-) diff --git "a/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" "b/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" index 70069fe5..7ca361a4 100644 --- "a/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" +++ "b/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" @@ -47,9 +47,10 @@ w_{i}-c_{j} ### 总结 参数量化是一种常用的后端压缩技术，能够以很小的性能损失实现模型体积的大幅下降，不足之处在于，量化的网络是“固定”的，很难对其做任何改变，同时这种方法通用性差，需要配套专门的深度学习库来运行网络。 ## 二值化网络 -二值化网络可以视为量化方法的一种极端情况：所有的权重参数取值只能为 $\pm 1$ ，也就是使用 `1bit`来存储`Weight` 和 `Feature`。在普通神经网络中，一个参数是由单精度浮点数来表示的，参数的二值化能将存储开销降低为原来的 `1/32`。 -二值化神经网络以其高的模型压缩率和在前传中计算速度上的优势，近几年格外受到重视和发展，成为神经网络模型研究中的非常热门的一个研究方向。但是，第一篇真正意义上将神经网络中的权重值和激活函数值同时做到二值化的是 Courbariaux 等人 2016 年发表的名为《Binarynet: Training deep neural networks with weights and activations constrained to +1 or -1》的一篇论文。**这篇论文第一次给出了关于如何对网络进行二值化和如何训练二值化神经网络的方法。** -CNN 网络一个典型的模块是由卷积(Conv)->批标准化(BNorm)->激活(Activ)->池化(Pool)这样的顺序操作组成的。对于异或神经网络，设计出的模块是由**批标准化(BNorm)->二值化激活(BinActiv)->二值化卷积(BinConv)->池化(Pool)**的顺序操作完成。这样做的原因是批标准化以后，保证了输入均值为0，然后进行二值化激活，保证了数据为-1或者+1，然后进行二值化卷积，这样能最大程度上减少特征信息的损失。二值化残差网络结构定义实例代码如下： +1. 二值化网络可以视为量化方法的一种极端情况：所有的权重参数取值只能为 $\pm 1$ ，也就是使用 `1bit`来存储`Weight` 和 `Feature`。在普通神经网络中，一个参数是由单精度浮点数来表示的，参数的二值化能将存储开销降低为原来的 `1/32`。 +2. 二值化神经网络以其高的模型压缩率和在前传中计算速度上的优势，近几年格外受到重视和发展，成为神经网络模型研究中的非常热门的一个研究方向。但是，第一篇真正意义上将神经网络中的权重值和激活函数值同时做到二值化的是 Courbariaux 等人 2016 年发表的名为《Binarynet: Training deep neural networks with weights and activations constrained to +1 or -1》的一篇论文。**这篇论文第一次给出了关于如何对网络进行二值化和如何训练二值化神经网络的方法。** +3. CNN 网络一个典型的模块是由卷积(Conv)->批标准化(BNorm)->激活(Activ)->池化(Pool)这样的顺序操作组成的。对于异或神经网络，设计出的模块是由**批标准化(BNorm)->二值化激活(BinActiv)->二值化卷积(BinConv)->池化(Pool)**的顺序操作完成。这样做的原因是批标准化以后，保证了输入均值为0，然后进行二值化激活，保证了数据为-1或者+1，然后进行二值化卷积，这样能最大程度上减少特征信息的损失。二值化残差网络结构定义实例代码如下： + ```Pyhon def residual_unit(data, num_filter, stride, dim_match, num_bits=1): bnAct1 = bnn.BatchNorm(data=data, num_bits=num_bits) @@ -93,6 +94,7 @@ $$I*W\approx (I*B)\alpha$$ $$min||W-\alpha B||^{2}$$ 得到$\alpha = \frac{1}{n}\left \| W \right \|_{\iota _{1}}$。二值连接改进的算法训练过程与之前的算法大致相同，不同的地方在于梯度的计算过程还考虑了 $\alpha$ 的影响。由于 $\alpha$ 这个单精度的缩放因子的存在，有效降低了重构误差，并首次在 ImageNet 数据集上取得了与 Alex-Net相当的精度。如下图所示： +![二值化网络精度对比](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E4%BA%8C%E5%80%BC%E5%8C%96%E7%AE%97%E6%B3%95%E7%B2%BE%E5%BA%A6.png) 可以看到的是`权重二值化神经网络（BWN）`和全精度神经网络的精确度几乎一样，但是异或神经网络（XNOR-Net）相比而言，Top-1 和 Top-5 都有 10+% 的损失。 > 相比于权重二值化神经网络，异或神经网络将网络的输入也转化为二进制值，所以，异或神经网络中的乘法加法 (Multiplication and ACcumulation) 运算用按位异或 (bitwise xnor) 和数 1 的个数 (popcount) 来代替。 @@ -112,7 +114,7 @@ student模型最终的损失函数由两部分组成： 软标签计算公式：$$q_{i} = \frac{z_{i}/T}{\sum_{j}z_{j}/T}$$ 这两个损失函数的重要程度可通过一定的权重进行调节，在实际应用中， `T` 的取值会影响最终的结果，一般而言，较大的 T 能够获得较高的准确度，T（蒸馏温度参数） 属于知识蒸馏模型训练超参数的一种。**T是一个可调节的超参数、T值越大、概率分布越软（论文中的描述），曲线便越平滑**，相当于在迁移学习的过程中添加了扰动，从而使得学生网络在借鉴学习的时候更有效、泛化能力更强，这其实就是一种抑制过拟合的策略。知识蒸馏的整个过程如下图： - +![知识蒸馏模型训练过程](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E7%9F%A5%E8%AF%86%E8%92%B8%E9%A6%8F%E6%A8%A1%E5%9E%8B%E8%BF%87%E7%A8%8B.png) student模型的实际模型结构和小模型一样，但是损失函数包含了两部分，`mxnet` 代码示例如下： ```Pyhon def get_symbol(data, class_labels, resnet_layer_num,Temperature,mimic_weight,num_classes=2): @@ -135,7 +137,7 @@ def get_symbol(data, class_labels, resnet_layer_num,Temperature,mimic_weight,num group.save('group2-symbol.json') return group ``` -tensorflow代码示例如下： +`tensorflow`代码示例如下： ```Python # 将类别标签进行one-hot编码 one_hot = tf.one_hot(y, n_classes,1.0,0.0) # n_classes为类别总数, n为类别标签 @@ -149,11 +151,11 @@ objective2 = tf.scalar_mul(0.5, tf.square(student_tau-teacher_tau)) # 第二项为预测结果与普通类别标签的交叉熵。 tf_loss = (args.lamda*tf.reduce_sum(objective1) + (1-args.lamda)*tf.reduce_sum(objective2))/batch_size ``` - `tf.scalar_mul` 函数为对 tf 张量进行固定倍率 scalar 缩放函数。**一般T的取值在 1 - 20 之间，这里我参考了开源代码，取值为 3**。我发现在开源代码中 `student` 模型的训练，有些是和 `teacher` 模型一起训练的，有些是 teacher 模型训练好后直接指导 student 模型训练。 +`tf.scalar_mul` 函数为对 tf 张量进行固定倍率 scalar 缩放函数。**一般T的取值在 1 - 20 之间，这里我参考了开源代码，取值为 3**。我发现在开源代码中 `student` 模型的训练，有些是和 `teacher` 模型一起训练的，有些是 teacher 模型训练好后直接指导 student 模型训练。 ## 浅层网络 浅层网络：通过设计一个更浅（层数较少）结构更紧凑的网络来实现对复杂模型效果的逼近, 但是浅层网络的表达能力很难与深层网络相匹敌。因此，这种设计方法的局限性在于只能应用解决在较为简单问题上。如分类问题中类别数较少的task。 ## 参考资料 -[神经网络模型压缩和加速之知识蒸馏](https://www.cnblogs.com/dyl222/p/11079489.html) -[https://github.com/chengshengchan/model_compression/blob/master/teacher-student.py](https://github.com/chengshengchan/model_compression/blob/master/teacher-student.py) -[https://github.com/dkozlov/awesome-knowledge-distillation](https://github.com/dkozlov/awesome-knowledge-distillation) -[XNOR-Nethttps://arxiv.org/abs/1603.05279]() +1. [神经网络模型压缩和加速之知识蒸馏](https://www.cnblogs.com/dyl222/p/11079489.html) +2. [https://github.com/chengshengchan/model_compression/blob/master/teacher-student.py](https://github.com/chengshengchan/model_compression/blob/master/teacher-student.py) +3. [https://github.com/dkozlov/awesome-knowledge-distillation](https://github.com/dkozlov/awesome-knowledge-distillation) +4. [XNOR-Nethttps://arxiv.org/abs/1603.05279]() From 5f7b8fcf2fedfb3623fe66dbd9cf8641fb90eded Mon Sep 17 00:00:00 2001 From: Zhang Date: Tue, 27 Aug 2019 14:33:57 +0800 Subject: [PATCH 202/386] =?UTF-8?q?Update=20=E7=A5=9E=E7=BB=8F=E7=BD=91?= =?UTF-8?q?=E7=BB=9C=E5=8E=8B=E7=BC=A9=E7=AE=97=E6=B3=95=E6=80=BB=E7=BB=93?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" "b/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" index 7ca361a4..1f8f1157 100644 --- "a/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" +++ "b/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" @@ -158,4 +158,4 @@ tf_loss = (args.lamda*tf.reduce_sum(objective1) + (1-args.lamda)*tf.reduce_sum(o 1. [神经网络模型压缩和加速之知识蒸馏](https://www.cnblogs.com/dyl222/p/11079489.html) 2. [https://github.com/chengshengchan/model_compression/blob/master/teacher-student.py](https://github.com/chengshengchan/model_compression/blob/master/teacher-student.py) 3. [https://github.com/dkozlov/awesome-knowledge-distillation](https://github.com/dkozlov/awesome-knowledge-distillation) -4. [XNOR-Nethttps://arxiv.org/abs/1603.05279]() +4. [XNOR-Net](https://arxiv.org/abs/1603.05279]) From a10d66073368c38e7f324bffa15d9ff69e403b46 Mon Sep 17 00:00:00 2001 From: Zhang Date: Tue, 27 Aug 2019 14:37:27 +0800 Subject: [PATCH 203/386] =?UTF-8?q?Update=20=E7=A5=9E=E7=BB=8F=E7=BD=91?= =?UTF-8?q?=E7=BB=9C=E5=8E=8B=E7=BC=A9=E7=AE=97=E6=B3=95=E6=80=BB=E7=BB=93?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...51\347\256\227\346\263\225\346\200\273\347\273\223.md" | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git "a/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" "b/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" index 1f8f1157..d26c19c4 100644 --- "a/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" +++ "b/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" @@ -18,6 +18,7 @@ **低秩近似算法能减小计算开销的原理**如下： 给定权重矩阵$W\epsilon R^{m*n}$，若能将其表示为若干个低秩矩阵的组合，即$W=\sum_{i=1}^{n}\alpha _{i}M_{i}$，其中$W\epsilon R^{m*n}$为低秩矩阵，其秩为$r_{i}$，并满足$r_{i}<批标准化(BNorm)->激活(Activ)->池化(Pool)这样的顺序操作组成的。对于异或神经网络，设计出的模块是由**批标准化(BNorm)->二值化激活(BinActiv)->二值化卷积(BinConv)->池化(Pool)**的顺序操作完成。这样做的原因是批标准化以后，保证了输入均值为0，然后进行二值化激活，保证了数据为-1或者+1，然后进行二值化卷积，这样能最大程度上减少特征信息的损失。二值化残差网络结构定义实例代码如下： +2. 二值化神经网络以其高的模型压缩率和在前传中计算速度上的优势，近几年格外受到重视和发展，成为神经网络模型研究中的非常热门的一个研究方向。但是，第一篇真正意义上将神经网络中的权重值和激活函数值同时做到二值化的是 Courbariaux 等人 2016 年发表的名为《Binarynet: Training deep neural networks with weights and activations constrained to +1 or -1》的一篇论文。**这篇论文第一次给出了关于如何对网络进行二值化和如何训练二值化神经网络的方法**。 +3. CNN 网络一个典型的模块是由卷积(Conv)->批标准化(BNorm)->激活(Activ)->池化(Pool)这样的顺序操作组成的。对于异或神经网络，设计出的模块是由批标准化(BNorm)->**二值化激活(BinActiv)**->二值化卷积(BinConv)->池化(Pool)的顺序操作完成。这样做的原因是批标准化以后，保证了输入均值为0，然后进行二值化激活，保证了数据为-1或者+1，然后进行二值化卷积，这样能最大程度上减少特征信息的损失。二值化残差网络结构定义实例代码如下： ```Pyhon def residual_unit(data, num_filter, stride, dim_match, num_bits=1): @@ -89,9 +91,11 @@ $$sign(x) = \left\{\begin{matrix} ### 二值连接算法改进 之前的二值连接算法只对权重进行了二值化，但是网络的中间输出值依然是单精度的，于是 Rastegari 等人对此进行了改进，提出用**单精度对角阵与二值矩阵之积来近似表示原矩阵的算法**，以提升二值网络的分类性能，弥补二值网络在精度上弱势。该算法将原卷积运算分解为如下过程： $$I*W\approx (I*B)\alpha$$ + 其中$I \epsilon \mathbb{R}^{c\times w_{in}\times h_{in}}$为该层的输入张量，$I \epsilon \mathbb{R}^{c\times w\times h}$为该层的一个滤波器，$B=sign(W)\epsilon \{+1, -1\}^{c \times w\times h}$为该滤波器所对应的二值权重。 这里，Rastegari 等人认为单靠二值运算，很难大的原单精度卷积元素的结果，于是他们使用了一个单精度放缩因子 $\alpha \epsilon \mathbb{R}^{+}$ 来对二至滤波器卷积后的结果进行放缩。$\alpha$ 的取值，可根据优化目标： $$min||W-\alpha B||^{2}$$ + 得到$\alpha = \frac{1}{n}\left \| W \right \|_{\iota _{1}}$。二值连接改进的算法训练过程与之前的算法大致相同，不同的地方在于梯度的计算过程还考虑了 $\alpha$ 的影响。由于 $\alpha$ 这个单精度的缩放因子的存在，有效降低了重构误差，并首次在 ImageNet 数据集上取得了与 Alex-Net相当的精度。如下图所示： ![二值化网络精度对比](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E4%BA%8C%E5%80%BC%E5%8C%96%E7%AE%97%E6%B3%95%E7%B2%BE%E5%BA%A6.png) From 7f6e8f7b84ab7c837fb4863b5a18778aa5b4de3f Mon Sep 17 00:00:00 2001 From: Zhang Date: Tue, 27 Aug 2019 14:43:21 +0800 Subject: [PATCH 204/386] =?UTF-8?q?Update=20=E7=A5=9E=E7=BB=8F=E7=BD=91?= =?UTF-8?q?=E7=BB=9C=E5=8E=8B=E7=BC=A9=E7=AE=97=E6=B3=95=E6=80=BB=E7=BB=93?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\227\346\263\225\346\200\273\347\273\223.md" | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git "a/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" "b/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" index d26c19c4..3cd0cc17 100644 --- "a/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" +++ "b/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" @@ -17,9 +17,9 @@ > 一般地，行阶梯型矩阵的秩等于其“台阶数”-非零行的行数。 **低秩近似算法能减小计算开销的原理**如下： -给定权重矩阵$W\epsilon R^{m*n}$，若能将其表示为若干个低秩矩阵的组合，即$W=\sum_{i=1}^{n}\alpha _{i}M_{i}$，其中$W\epsilon R^{m*n}$为低秩矩阵，其秩为$r_{i}$，并满足$r_{i}< 0 \end{matrix}\right.$$ + + 进行随机的二值化，即对每一个权重，以一定概率取 $\pm 1$ **2，如何计算二值权重的梯度？** -二值权重的梯度为0，无法进行参数更新。为解决这个问题，需要**对符号函数进行放松**，即用 $Htanh(x) = max(-1, min(1,x))$来代替 $sinx(x)$。当 x 在区间 [-1,1] 时，存在梯度值1，否则梯度为 0 。 +二值权重的梯度为0，无法进行参数更新。为解决这个问题，需要**对符号函数进行放松**，即用 $Htanh(x) = max(-1, min(1,x))$ 来代替 $sinx(x)$。当 x 在区间 [-1,1] 时，存在梯度值1，否则梯度为 0 。 ### 二值连接算法改进 之前的二值连接算法只对权重进行了二值化，但是网络的中间输出值依然是单精度的，于是 Rastegari 等人对此进行了改进，提出用**单精度对角阵与二值矩阵之积来近似表示原矩阵的算法**，以提升二值网络的分类性能，弥补二值网络在精度上弱势。该算法将原卷积运算分解为如下过程： $$I*W\approx (I*B)\alpha$$ From 22a834a43d24e5643a24ff6c47a37c019174e83b Mon Sep 17 00:00:00 2001 From: Zhang Date: Tue, 27 Aug 2019 14:46:59 +0800 Subject: [PATCH 205/386] =?UTF-8?q?Update=20=E7=A5=9E=E7=BB=8F=E7=BD=91?= =?UTF-8?q?=E7=BB=9C=E5=8E=8B=E7=BC=A9=E7=AE=97=E6=B3=95=E6=80=BB=E7=BB=93?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...51\347\256\227\346\263\225\346\200\273\347\273\223.md" | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git "a/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" "b/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" index 3cd0cc17..345c73d8 100644 --- "a/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" +++ "b/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" @@ -17,7 +17,7 @@ > 一般地，行阶梯型矩阵的秩等于其“台阶数”-非零行的行数。 **低秩近似算法能减小计算开销的原理**如下： -给定权重矩阵 $W\epsilon R^{m\*n}$, 若能将其表示为若干个低秩矩阵的组合，即$W=\sum_{i=1}^{n}\alpha _{i}M_{i}$，其中 $W\epsilon R^{m\*n}$ 为低秩矩阵，其秩为 $r_{i}$，并满足 $r_{i}< Date: Tue, 27 Aug 2019 14:50:00 +0800 Subject: [PATCH 206/386] =?UTF-8?q?Update=20=E7=A5=9E=E7=BB=8F=E7=BD=91?= =?UTF-8?q?=E7=BB=9C=E5=8E=8B=E7=BC=A9=E7=AE=97=E6=B3=95=E6=80=BB=E7=BB=93?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...74\251\347\256\227\346\263\225\346\200\273\347\273\223.md" | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git "a/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" "b/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" index 345c73d8..e0de0c7e 100644 --- "a/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" +++ "b/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" @@ -83,7 +83,7 @@ def residual_unit(data, num_filter, stride, dim_match, num_bits=1): + 直接根据权重的正负进行二值化：$x^{b}=sign(x)$。符号函数 `sign(x)` 定义如下： $$sign(x) = \left\{\begin{matrix} -1 & x < 0 \\ -0 & x = 0\\ +0 & x = 0 \\ 1 & x > 0 \end{matrix}\right.$$ @@ -97,7 +97,7 @@ $$I\*W\approx (I\*B)\alpha$$ 其中 $I\epsilon \mathbb{R}^{c\times w_{in}\times h_{in}}$ 为该层的输入张量，$I \epsilon \mathbb{R}^{c\times w\times h}$ 为该层的一个滤波器，$B=sign(W)\epsilon \{+1, -1\}^{c \times w\times h}$为该滤波器所对应的二值权重。 这里，Rastegari 等人认为单靠二值运算，很难大的原单精度卷积元素的结果，于是他们使用了一个单精度放缩因子 $\alpha \epsilon \mathbb{R}^{+}$ 来对二至滤波器卷积后的结果进行放缩。$\alpha$ 的取值，可根据优化目标： -$$min||W-\alphaB||^{2}$$ +$$min \left \| W -\alpha B \right \|^{2}$$ 得到$\alpha = \frac{1}{n}\left \| W \right \|_{\iota _{1}}$。二值连接改进的算法训练过程与之前的算法大致相同，不同的地方在于梯度的计算过程还考虑了 $\alpha$ 的影响。由于 $\alpha$ 这个单精度的缩放因子的存在，有效降低了重构误差，并首次在 ImageNet 数据集上取得了与 Alex-Net相当的精度。如下图所示： From aee4146163043c5a51c5ead81719e4c03458f7d5 Mon Sep 17 00:00:00 2001 From: Zhang Date: Tue, 27 Aug 2019 14:51:42 +0800 Subject: [PATCH 207/386] =?UTF-8?q?Update=20=E7=A5=9E=E7=BB=8F=E7=BD=91?= =?UTF-8?q?=E7=BB=9C=E5=8E=8B=E7=BC=A9=E7=AE=97=E6=B3=95=E6=80=BB=E7=BB=93?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...4\251\347\256\227\346\263\225\346\200\273\347\273\223.md" | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git "a/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" "b/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" index e0de0c7e..ec23042b 100644 --- "a/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" +++ "b/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" @@ -85,7 +85,8 @@ $$sign(x) = \left\{\begin{matrix} -1 & x < 0 \\ 0 & x = 0 \\ 1 & x > 0 -\end{matrix}\right.$$ +\end{matrix} +\right$$ + 进行随机的二值化，即对每一个权重，以一定概率取 $\pm 1$ @@ -97,7 +98,7 @@ $$I\*W\approx (I\*B)\alpha$$ 其中 $I\epsilon \mathbb{R}^{c\times w_{in}\times h_{in}}$ 为该层的输入张量，$I \epsilon \mathbb{R}^{c\times w\times h}$ 为该层的一个滤波器，$B=sign(W)\epsilon \{+1, -1\}^{c \times w\times h}$为该滤波器所对应的二值权重。 这里，Rastegari 等人认为单靠二值运算，很难大的原单精度卷积元素的结果，于是他们使用了一个单精度放缩因子 $\alpha \epsilon \mathbb{R}^{+}$ 来对二至滤波器卷积后的结果进行放缩。$\alpha$ 的取值，可根据优化目标： -$$min \left \| W -\alpha B \right \|^{2}$$ +$$min \left \|| W -\alpha B \right \||^{2}$$ 得到$\alpha = \frac{1}{n}\left \| W \right \|_{\iota _{1}}$。二值连接改进的算法训练过程与之前的算法大致相同，不同的地方在于梯度的计算过程还考虑了 $\alpha$ 的影响。由于 $\alpha$ 这个单精度的缩放因子的存在，有效降低了重构误差，并首次在 ImageNet 数据集上取得了与 Alex-Net相当的精度。如下图所示： From 2e2e8d2bcf92d10c8b9786799a7b9781a6e378ba Mon Sep 17 00:00:00 2001 From: Zhang Date: Tue, 27 Aug 2019 14:54:43 +0800 Subject: [PATCH 208/386] =?UTF-8?q?Update=20=E7=A5=9E=E7=BB=8F=E7=BD=91?= =?UTF-8?q?=E7=BB=9C=E5=8E=8B=E7=BC=A9=E7=AE=97=E6=B3=95=E6=80=BB=E7=BB=93?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...4\251\347\256\227\346\263\225\346\200\273\347\273\223.md" | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git "a/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" "b/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" index ec23042b..04ab8cac 100644 --- "a/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" +++ "b/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" @@ -85,8 +85,7 @@ $$sign(x) = \left\{\begin{matrix} -1 & x < 0 \\ 0 & x = 0 \\ 1 & x > 0 -\end{matrix} -\right$$ +\end{matrix}\right.$$ + 进行随机的二值化，即对每一个权重，以一定概率取 $\pm 1$ @@ -100,7 +99,7 @@ $$I\*W\approx (I\*B)\alpha$$ 这里，Rastegari 等人认为单靠二值运算，很难大的原单精度卷积元素的结果，于是他们使用了一个单精度放缩因子 $\alpha \epsilon \mathbb{R}^{+}$ 来对二至滤波器卷积后的结果进行放缩。$\alpha$ 的取值，可根据优化目标： $$min \left \|| W -\alpha B \right \||^{2}$$ -得到$\alpha = \frac{1}{n}\left \| W \right \|_{\iota _{1}}$。二值连接改进的算法训练过程与之前的算法大致相同，不同的地方在于梯度的计算过程还考虑了 $\alpha$ 的影响。由于 $\alpha$ 这个单精度的缩放因子的存在，有效降低了重构误差，并首次在 ImageNet 数据集上取得了与 Alex-Net相当的精度。如下图所示： +得到$\alpha = \frac{1}{n}\left \|| W \right \||_{\iota _{1}}$。二值连接改进的算法训练过程与之前的算法大致相同，不同的地方在于梯度的计算过程还考虑了 $\alpha$ 的影响。由于 $\alpha$ 这个单精度的缩放因子的存在，有效降低了重构误差，并首次在 ImageNet 数据集上取得了与 Alex-Net相当的精度。如下图所示： ![二值化网络精度对比](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E4%BA%8C%E5%80%BC%E5%8C%96%E7%AE%97%E6%B3%95%E7%B2%BE%E5%BA%A6.png) 可以看到的是`权重二值化神经网络（BWN）`和全精度神经网络的精确度几乎一样，但是异或神经网络（XNOR-Net）相比而言，Top-1 和 Top-5 都有 10+% 的损失。 From a6f8107c3aba7ea404d68ca395f9dba6029a19d0 Mon Sep 17 00:00:00 2001 From: Zhang Date: Tue, 27 Aug 2019 14:55:58 +0800 Subject: [PATCH 209/386] Add files via upload --- "images/sign(x)\345\205\254\345\274\217.png" | Bin 0 -> 5969 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 "images/sign(x)\345\205\254\345\274\217.png" diff --git 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=?UTF-8?q?=E7=BB=9C=E5=8E=8B=E7=BC=A9=E7=AE=97=E6=B3=95=E6=80=BB=E7=BB=93?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" "b/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" index 04ab8cac..c0e323dd 100644 --- "a/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" +++ "b/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" @@ -86,7 +86,7 @@ $$sign(x) = \left\{\begin{matrix} 0 & x = 0 \\ 1 & x > 0 \end{matrix}\right.$$ - +![sign(x)公式](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/sign(x)%E5%85%AC%E5%BC%8F.png) + 进行随机的二值化，即对每一个权重，以一定概率取 $\pm 1$ **2，如何计算二值权重的梯度？** From c18141e892f06e446bd254efa968b6c669c6f42c Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 28 Aug 2019 23:22:25 +0800 Subject: [PATCH 211/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 更新Python为什么是动态语言的问题 --- ...6\344\271\240\351\235\242\350\257\225\351\242\230.md" | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 7e5c91b3..085dc9a5 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -404,6 +404,15 @@ faster rcnn将roi pooling替换为roi align效果有所提升 ++i 先自增1，再返回，i++，先返回 i，再自增1. ## const关键字作用 `const`类型的对象在程序执行期间不能被修改改变。 +## 为什么说Python是动态语言 +在 `Python` 中，等号 `=` 是赋值语句，可以把`任意数据类型`赋值给变量，同样一个变量可以反复赋值，而且可以是不同类型的变量，例如： +```Python +a = 100 # a是int型变量 +print(a) +a = 'ABC' # a 是str型变量 +print(a) +``` +Pyhon 这种变量本身类型不固定，可以反复赋值不同类型的变量称为动态语言，与之对应的是静态语言。静态语言在定义变量时必须指定变量类型，如果赋值的时候类型不匹配，就会报错，Java/C++ 都是静态语言（`int a; a = 100`） ## Python装饰器解释 装饰器本质上是一个 Python 函数或类，**它可以让其他函数或类在不需要做任何代码修改的前提下增加额外功能**，装饰器的返回值也是一个函数/类对象。它经常用于有切面需求的场景，比如：插入日志、性能测试、事务处理、缓存、权限校验等场景，装饰器是解决这类问题的绝佳设计。有了装饰器，我们就可以**抽离出大量与函数功能本身无关的雷同代码到装饰器中并继续重用**。概括的讲，**装饰器的作用就是为已经存在的对象添加额外的功能**。 ## 多进程与多线程区别 From dee48403b89981605d1c2d937fa2fb7c4e0bba5e Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 28 Aug 2019 23:31:15 +0800 Subject: [PATCH 212/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 更新Python怎么实现对函数参数类型做检查 --- ...\240\351\235\242\350\257\225\351\242\230.md" | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 085dc9a5..6d87aa7e 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -404,6 +404,23 @@ faster rcnn将roi pooling替换为roi align效果有所提升 ++i 先自增1，再返回，i++，先返回 i，再自增1. ## const关键字作用 `const`类型的对象在程序执行期间不能被修改改变。 +## Python怎么实现对函数参数类型做检查 +`Python` 自带的函数一般都会有对函数参数类型做检查，自定义的函数参数类型检查可以用函数 `isinstance()` 实现，例如： +```Python +def my_abs(x): + """ + 自定义的绝对值函数 + :param x: int or float + :return: positive number, int or float + """ + if not isinstance(x, (int, float)): + raise TypeError('bad operand type') + if x > 0: + return x + else: + return -x +``` +添加了参数检查后，如果传入错误的参数类型，函数就可以抛出一个 `TypeError` 错误。 ## 为什么说Python是动态语言 在 `Python` 中，等号 `=` 是赋值语句，可以把`任意数据类型`赋值给变量，同样一个变量可以反复赋值，而且可以是不同类型的变量，例如： ```Python From a2c5f8546329bccd72075e7210e75efdb913b7dc Mon Sep 17 00:00:00 2001 From: Zhang Date: Wed, 28 Aug 2019 23:32:42 +0800 Subject: [PATCH 213/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 6d87aa7e..96a97873 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -404,7 +404,7 @@ faster rcnn将roi pooling替换为roi align效果有所提升 ++i 先自增1，再返回，i++，先返回 i，再自增1. ## const关键字作用 `const`类型的对象在程序执行期间不能被修改改变。 -## Python怎么实现对函数参数类型做检查 +## Python实现对函参做类型检查 `Python` 自带的函数一般都会有对函数参数类型做检查，自定义的函数参数类型检查可以用函数 `isinstance()` 实现，例如： ```Python def my_abs(x): From c43e91778d562ff41b84a8bd930e6867734d2ee5 Mon Sep 17 00:00:00 2001 From: Zhang Date: Thu, 29 Aug 2019 00:14:10 +0800 Subject: [PATCH 214/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...6\344\271\240\351\235\242\350\257\225\351\242\230.md" | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 96a97873..3b39db29 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -404,6 +404,15 @@ faster rcnn将roi pooling替换为roi align效果有所提升 ++i 先自增1，再返回，i++，先返回 i，再自增1. ## const关键字作用 `const`类型的对象在程序执行期间不能被修改改变。 +## Python的函数参数传递 +> 参考这两个链接，stackoverflow的最高赞那个讲得很详细 +[How do I pass a variable by reference?](https://stackoverflow.com/questions/986006/how-do-i-pass-a-variable-by-reference) +[Python 面试题](https://github.com/taizilongxu/interview_python#python%E8%AF%AD%E8%A8%80%E7%89%B9%E6%80%A7) + +个人总结（有点不好）： ++ 将可变对象：列表list、字典dict、NumPy数组ndarray和用户定义的类型（类），作为参数传递给函数，函数内部将其改变后，函数外部这个变量也会改变 ++ 将不可变对象：字符串string、元组tuple、数值numbers，作为参数传递给函数，函数内部将其改变后，函数外部这个变量不会改变 + ## Python实现对函参做类型检查 `Python` 自带的函数一般都会有对函数参数类型做检查，自定义的函数参数类型检查可以用函数 `isinstance()` 实现，例如： ```Python From 3aa0b38d9f475eaa586eca3f8af1408a535928da Mon Sep 17 00:00:00 2001 From: Zhang Date: Thu, 29 Aug 2019 00:15:12 +0800 Subject: [PATCH 215/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...55\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 3b39db29..bb212253 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -406,8 +406,8 @@ faster rcnn将roi pooling替换为roi align效果有所提升 `const`类型的对象在程序执行期间不能被修改改变。 ## Python的函数参数传递 > 参考这两个链接，stackoverflow的最高赞那个讲得很详细 -[How do I pass a variable by reference?](https://stackoverflow.com/questions/986006/how-do-i-pass-a-variable-by-reference) -[Python 面试题](https://github.com/taizilongxu/interview_python#python%E8%AF%AD%E8%A8%80%E7%89%B9%E6%80%A7) ++ [How do I pass a variable by reference?](https://stackoverflow.com/questions/986006/how-do-i-pass-a-variable-by-reference) ++ [Python 面试题](https://github.com/taizilongxu/interview_python#python%E8%AF%AD%E8%A8%80%E7%89%B9%E6%80%A7) 个人总结（有点不好）： + 将可变对象：列表list、字典dict、NumPy数组ndarray和用户定义的类型（类），作为参数传递给函数，函数内部将其改变后，函数外部这个变量也会改变 From 72456bff5a6f166729505d48c36befb41af64ca6 Mon Sep 17 00:00:00 2001 From: Zhang Date: Thu, 29 Aug 2019 00:17:27 +0800 Subject: [PATCH 216/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index bb212253..9540c9d3 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -406,11 +406,11 @@ faster rcnn将roi pooling替换为roi align效果有所提升 `const`类型的对象在程序执行期间不能被修改改变。 ## Python的函数参数传递 > 参考这两个链接，stackoverflow的最高赞那个讲得很详细 -+ [How do I pass a variable by reference?](https://stackoverflow.com/questions/986006/how-do-i-pass-a-variable-by-reference) -+ [Python 面试题](https://github.com/taizilongxu/interview_python#python%E8%AF%AD%E8%A8%80%E7%89%B9%E6%80%A7) +[How do I pass a variable by reference?](https://stackoverflow.com/questions/986006/how-do-i-pass-a-variable-by-reference) +[Python 面试题](https://github.com/taizilongxu/interview_python#python%E8%AF%AD%E8%A8%80%E7%89%B9%E6%80%A7) 个人总结（有点不好）： -+ 将可变对象：列表list、字典dict、NumPy数组ndarray和用户定义的类型（类），作为参数传递给函数，函数内部将其改变后，函数外部这个变量也会改变 ++ 将可变对象：列表list、字典dict、NumPy数组ndarray和用户定义的类型（类），作为参数传递给函数，函数内部将其改变后，函数外部这个变量也会改变（对变量进行重新赋值除外 `rebind the reference in the method`） + 将不可变对象：字符串string、元组tuple、数值numbers，作为参数传递给函数，函数内部将其改变后，函数外部这个变量不会改变 ## Python实现对函参做类型检查 From e74dae0a7b3c611dbf90bc5aa8ec449e1fc1a678 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Fri, 30 Aug 2019 12:23:13 +0800 Subject: [PATCH 217/386] =?UTF-8?q?=E9=87=8D=E6=96=B0=E5=88=9B=E5=BB=BA?= =?UTF-8?q?=E5=88=86=E7=B1=BB=E7=9B=AE=E5=BD=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...3\345\215\267\347\247\257\345\261\202.png" | Bin ...46\344\271\240\347\254\224\350\256\260.md" | 0 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"\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" (100%) diff --git "a/images/2\344\270\2523*3\345\215\267\347\247\257\345\261\202.png" "b/images/2\344\270\2523_3\345\215\267\347\247\257\345\261\202.png" similarity index 100% rename from "images/2\344\270\2523*3\345\215\267\347\247\257\345\261\202.png" rename to "images/2\344\270\2523_3\345\215\267\347\247\257\345\261\202.png" diff --git "a/MXNet\345\255\246\344\271\240\347\254\224\350\256\260.md" "b/ml-dl-\346\241\206\346\236\266/MXNet\345\255\246\344\271\240\347\254\224\350\256\260.md" similarity index 100% rename from "MXNet\345\255\246\344\271\240\347\254\224\350\256\260.md" rename to "ml-dl-\346\241\206\346\236\266/MXNet\345\255\246\344\271\240\347\254\224\350\256\260.md" diff --git 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"b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" index 1cc3f930..588ca486 100644 --- "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" +++ "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" @@ -12,6 +12,7 @@ ## 面试过程建议 1. **不要紧张，表达要清楚流利**，要记住，**绝大部分面试官都是很友好和亲切的**，尤其是大公司的面试官真的超级温和，**这里为阿里和地平线机器人面试官点赞**！对于那种不尊重人和看不起人的面试官，我个人觉得没必要去他们公司了，一般这种面试官会出现在小公司，一个面试官连对面试者基本的尊重都做不到，我难以想象这家公司的文化是什么样子。 2. 要对自己有信心，但是**千万不能撒谎和装逼**，一般面试过程中不会的问题，面试官也会跳过或者安慰你没事的，不用太紧张，碰到１、2个不会的问题也属正常。 + ## 计算机视觉岗找实习心得 1. 首先，我的水平真的算是很一般的，真正的大佬都是很轻松的拿到数个满意offer。我自己是本科是双非大学自动化专业，研究生是中等211大学控制工程专业，本科主要搞嵌入式方向，研究生才转为计算机视觉方向和深度学习方向，这里也给后来者一个建议，**如果不是真心热爱、喜欢你所从事的计算机视觉方向，只是为了钱的话那就真的没必要了**，**CV岗竞争真的很激烈**，nlp和开发岗好很多，而且现在开发岗工资真的很高啊！ 2. 其次，最开始找实习的时候，我没有刷过数据结构和算法题，导致我华为(程序写出来了，但是输入输出格式没注意)、百度、腾讯笔试统统挂了，那段时间真的超级难过，后悔没有提前刷题。4月20号之后，我开始在boss直聘上投简历，这里陆陆续续收到2家小公司面试通知和offer，也算是给了我些信心。 diff --git "a/\346\234\272\345\231\250\345\255\246\344\271\240/\346\234\272\345\231\250\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\234\272\345\231\250\345\255\246\344\271\240/\346\234\272\345\231\250\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" new file mode 100644 index 00000000..a8e8ce8f --- /dev/null +++ "b/\346\234\272\345\231\250\345\255\246\344\271\240/\346\234\272\345\231\250\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -0,0 +1,15 @@ +# 机器学习 +## 机器学习常用算法 ++ 支持向量机 `SVM` ++ 贝叶斯分类器 ++ 决策树 ++ 集成学习 ++ 梯度提升决策树 `GBDT` ++ 随机森林 ++ 聚类与降维 + +## 数据不平衡怎么办? + +## AUC的理解 + +## AUC的计算公式 \ No newline at end of file diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" similarity index 73% rename from "\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" rename to "\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 9540c9d3..85635e44 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -161,17 +161,6 @@ RNN的输入有两个部分，一部分为上一时刻的状态，另一部分 + 网络太深。隐藏层神经元数量错误。 + 更多回答，参考此链接。 -## 图像处理中平滑和锐化操作是什么？ -**平滑处理（smoothing）也称模糊处理（bluring）**，主要用于**消除图像中的噪声部分**，平滑处理常用的用途是用来减少图像上的噪点或失真，平滑主要使用图像滤波。在这里，我个人认为可以把图像平滑和图像滤波联系起来，因为图像平滑常用的方法就是图像滤波器。 -在OpenCV3中常用的图像滤波器有以下几种： - -+ 方框滤波——BoxBlur函数 -+ 均值滤波（邻域平均滤波）——Blur函数 -+ 高斯滤波——GaussianBlur函数（高斯低通滤波是模糊，高斯高通滤波是锐化） -+ 中值滤波——medianBlur函数 -+ 双边滤波——bilateralFilter函数 -图像锐化操作是为了突出显示图像的边界和其他细节，而图像锐化实现的方法是通过各种算子和滤波器实现的——Canny算子、Sobel算子、Laplacian算子以及Scharr滤波器。 - ## VGG使用2个3*3卷积的优势在哪里？ (1). **减少网络层参数**。用两个3*3卷积比用1个5*5卷积拥有更少的参数量，只有后者的2∗3∗35∗5=0.72。但是起到的效果是一样的，两个3*3的卷积层串联相当于一个5*5的卷积层，感受野的大小都是5×5，即1个像素会跟周围5*5的像素产生关联。把下图当成动态图看，很容易看到两个3×3卷积层堆叠（没有空间池化）有5×5的有效感受野。 @@ -387,163 +376,8 @@ faster rcnn将roi pooling替换为roi align效果有所提升 1. [《Batch Normalization Accelerating Deep Network Training by Reducing Internal Covariate Shift》阅读笔记与实现](https://blog.csdn.net/happynear/article/details/44238541) 2. [深度学习中 Batch Normalization为什么效果好](https://blog.csdn.net/happynear/article/details/44238541) 3. [详解机器学习中的梯度消失、爆炸原因及其解决方法](https://blog.csdn.net/qq_25737169/article/details/78847691) -# Python/C/C++/计算机基础/图像处理基础 -## static关键字作用 -+ 在全局变量前加上关键字static，全局变量就定义为一个全局静态变量，全局静态变量在声明它的文件之外是不可见的，作用域范围为从定义之处开始，到文件结尾。 -+ 在函数返回类型前加`static`，函数就变为静态函数，静态函数只在声明它的文件中使用，不被其他文件所用。 -## C++指针和引用的区别 -+ 指针有自己的内存空间，而引用只是一个别名，类似于Python浅拷贝和深拷贝的区别 -+ 不存在空引用, 引用必须链接到一块合法的内存地址； -+ 一旦引用被初始化为一个对象，就不能指向另一个对象。指针可以在任何时候指向任何一个对象； -+ 引用必须在创建时被初始化。指针可以在任何时间初始化。 -## C++中析构函数的作用 -析构函数与构造函数对应，类的析构函数是类的一种特殊的成员函数，**它会在每次删除所创建的对象时执行**。析构函数的名称与类的名称是完全相同的，只是在前面加了个波浪号（~）作为前缀，它不会返回任何值，也不能带有任何参数。析构函数有助于在跳出程序（比如关闭文件、释放内存等）前释放资源。 -## C++静态函数和虚函数的区别 -静态函数在编译的时候就已经确定运行时机，虚函数在运行的时候动态绑定。虚函数因为用了虚函数表机制，调用的时候会增加一次内存开销。 -## ++i和i++区别 -++i 先自增1，再返回，i++，先返回 i，再自增1. -## const关键字作用 -`const`类型的对象在程序执行期间不能被修改改变。 -## Python的函数参数传递 -> 参考这两个链接，stackoverflow的最高赞那个讲得很详细 -[How do I pass a variable by reference?](https://stackoverflow.com/questions/986006/how-do-i-pass-a-variable-by-reference) -[Python 面试题](https://github.com/taizilongxu/interview_python#python%E8%AF%AD%E8%A8%80%E7%89%B9%E6%80%A7) - -个人总结（有点不好）： -+ 将可变对象：列表list、字典dict、NumPy数组ndarray和用户定义的类型（类），作为参数传递给函数，函数内部将其改变后，函数外部这个变量也会改变（对变量进行重新赋值除外 `rebind the reference in the method`） -+ 将不可变对象：字符串string、元组tuple、数值numbers，作为参数传递给函数，函数内部将其改变后，函数外部这个变量不会改变 - -## Python实现对函参做类型检查 -`Python` 自带的函数一般都会有对函数参数类型做检查，自定义的函数参数类型检查可以用函数 `isinstance()` 实现，例如： -```Python -def my_abs(x): - """ - 自定义的绝对值函数 - :param x: int or float - :return: positive number, int or float - """ - if not isinstance(x, (int, float)): - raise TypeError('bad operand type') - if x > 0: - return x - else: - return -x -``` -添加了参数检查后，如果传入错误的参数类型，函数就可以抛出一个 `TypeError` 错误。 -## 为什么说Python是动态语言 -在 `Python` 中，等号 `=` 是赋值语句，可以把`任意数据类型`赋值给变量，同样一个变量可以反复赋值，而且可以是不同类型的变量，例如： -```Python -a = 100 # a是int型变量 -print(a) -a = 'ABC' # a 是str型变量 -print(a) -``` -Pyhon 这种变量本身类型不固定，可以反复赋值不同类型的变量称为动态语言，与之对应的是静态语言。静态语言在定义变量时必须指定变量类型，如果赋值的时候类型不匹配，就会报错，Java/C++ 都是静态语言（`int a; a = 100`） -## Python装饰器解释 -装饰器本质上是一个 Python 函数或类，**它可以让其他函数或类在不需要做任何代码修改的前提下增加额外功能**，装饰器的返回值也是一个函数/类对象。它经常用于有切面需求的场景，比如：插入日志、性能测试、事务处理、缓存、权限校验等场景，装饰器是解决这类问题的绝佳设计。有了装饰器，我们就可以**抽离出大量与函数功能本身无关的雷同代码到装饰器中并继续重用**。概括的讲，**装饰器的作用就是为已经存在的对象添加额外的功能**。 -## 多进程与多线程区别 -+ 线程是进程的一部分，一个进程至少有一个线程； -+ 对于操作系统来说，一个任务就是一个进程，进程内的“子任务”称为线程； -+ 多线程和多进程最大的不同在于，多进程中，同一个变量，各自有一份拷贝存在于每个进程中，互不影响，而**多线程中，所有变量都由所有线程共享**，所以，任何一个变量都可以被任何一个线程修改，因此，线程之间共享数据最大的危险在于多个线程同时改一个变量，把内容给改乱了。 -+ 进程间调用、通讯和切换开销均比多线程大，单个线程的崩溃会导致整个应用的退出。 -+ 存在大量IO，网络耗时或者需要和用户交互等操作时，使用多线程有利于提高系统的并发性和用户界面快速响应从而提高友好性。 -## map与reduce函数用法解释下 -1. map函数接收两个参数，一个是函数，一个是Iterable，map将传入的函数依次作用到序列的**每个元素**，并将结果作为新的Iterator返回，简单示例代码如下： -```Python3 -# 示例１ -def square(x): - return x ** 2 -r = map(square, [1, 2, 3, 4, 5, 6, 7]) -squareed_list = list(r) -print(squareed_list) # [1, 4, 9, 16, 25, 36, 49] -# 使用lambda匿名函数简化为一行代码 -list(map(lambda x: x * x, [1, 2, 3, 4, 5, 6, 7, 8, 9])) -# 示例２ -list(map(str, [1, 2, 3, 4, 5, 6, 7, 8, 9]))　＃　['1', '2', '3', '4', '5', '6', '7', '8', '9'] -``` -**注意map函数返回的是一个Iterator（惰性序列），要通过list函数转化为常用列表结构**。map()作为高阶函数，事实上它是把运算规则抽象了。 - -2. reduce()函数也接受两个参数，一个是函数（**两个参数**），一个是序列，与map不同的是**reduce把结果继续和序列的下一个元素做累积计算**,效果如下： -`reduce(f, [x1, x2, x3, x4]) = f(f(f(x1, x2), x3), x4)` -示例代码如下： -```Python3 -from functools import reduce -CHAR_TO_INT = { - '0': 0, - '1': 1, - '2': 2, - '3': 3, - '4': 4, - '5': 5, - '6': 6, - '7': 7, - '8': 8, - '9': 9 -} -def str2int(str): - ints = map(lambda x:CHAR_TO_INT[x], str) # str对象是Iterable对象 - return reduce(lambda x,y:10*x + y, ints) -print(str2int('0')) -print(str2int('12300')) -print(str2int('0012345')) # 0012345 -``` -## Python深拷贝、浅拷贝区别 -1. 直接赋值：其实就是对象的引用（别名）。 -2. 浅拷贝(copy)：拷贝父对象，不会拷贝对象的内部的子对象。（拷贝可以理解为创建内存）可以产生浅拷贝的操作有以下几种： - + 使用切片[:]操作 - + 使用工厂函数（如list/dir/set）,工厂函数看上去像函数，实质上是类，调用时实际上是生成了该类型的一个实例，就像工厂生产货物一样. - + 使用copy模块中的copy()函数 -3. 深拷贝(deepcopy)： copy 模块的 deepcopy 方法，完全拷贝了父对象及其子对象，两者是完全独立的。**深拷贝，包含对象里面的子对象的拷贝，所以原始对象的改变不会造成深拷贝里任何子元素的改变**。 - -**注意：浅拷贝和深拷贝的不同仅仅是对组合对象来说，所谓的组合对象（容器）就是包含了其它对象的对象，如列表，类实例。而对于数字、字符串以及其它“原子”类型，没有拷贝一说，产生的都是原对象的引用。** -看一个示例程序，就能明白浅拷贝与深拷贝的区别了： -```Python3 -#!/usr/bin/Python3 -# -*-coding:utf-8 -*- - -import copy -a = [1, 2, 3, ['a', 'b', 'c']] - -b = a # 赋值，传对象的引用 -c = copy.copy(a) # 浅拷贝 -d = copy.deepcopy(a) # 深拷贝 - -a.append(4) -a[3].append('d') - -print('a = ', a) -print('b = ', b) -print('c = ', c) -print('d = ', d) # [1, 2, 3, ['a', 'b', 'c']] -``` -程序输出如下： -> a = [1, 2, 3, ['a', 'b', 'c', 'd'], 4] -b = [1, 2, 3, ['a', 'b', 'c', 'd'], 4] -c = [1, 2, 3, ['a', 'b', 'c', 'd']] -d = [1, 2, 3, ['a', 'b', 'c']] - -## 其他面试题 -参考[这里](https://zhuanlan.zhihu.com/p/23526961) -## 图像锐化方法 -**锐化主要影响图像中的低频分量，不影响图像中的高频分量**像锐化的主要目的有两个： -1. 增强图像边缘，使模糊的图像变得更加清晰，颜色变得鲜明突出，图像的质量有所改善，产生更适合人眼观察和识别的图像； -2. 过锐化处理后，目标物体的边缘鲜明，以便于提取目标的边缘、对图像进行分割、目标区域识别、区域形状提取等，进一步的图像理解与分析奠定基础。 - -图像锐化一般有两种方法： -1. 微分法 -2. 高通滤波法 - -一般来说，图像的能量主要集中在其低频部分，噪声所在的频段主要在高频段，同时图像边缘信息也主要集中在其高频部分。这将导致原始图像在平滑处理之后，图像边缘和图像轮廓模糊的情况出现。为了减少这类不利效果的影响，就需要利用图像锐化技术，使图像的边缘变得清晰。图像锐化处理的目的是为了使图像的边缘、轮廓线以及图像的细节变得清晰，经过平滑的图像变得模糊的根本原因是因为图像受到了平均或积分运算，因此可以对其进行逆运算(如微分运算)就可以使图像变得清晰。微分运算是求信号的变化率，由傅立叶变换的微分性质可知，微分运算具有较强高频分量作用。从频率域来考虑，图像模糊的实质是因为其高频分量被衰减，因此可以用高通滤波器来使图像清晰。但要注意能够进行锐化处理的图像必须有较高的性噪比，否则锐化后图像性噪比反而更低，从而使得噪声增加的比信号还要多，因此一般是先去除或减轻噪声后再进行锐化处理。 -### Reference -[图像增强－图像锐化](https://www.cnblogs.com/BYTEMAN/archive/2012/07/21/2603021.html) -## numpy手撕代码 -人脸识别的场景下，输入105*12的feature map，用这个在1000512的特征库当中用欧氏距离去匹配105*12的feature map，用这个在1000*512的特征库当中用欧氏距离去匹配10*1000的特征，得到这个output。 -# 机器学习 -## Focal Loss 介绍一下 -## 数据不平衡怎么办? +## Focal Loss 介绍一下 -## AUC的理解 -## AUC的计算公式 diff --git "a/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" similarity index 100% rename from "\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" rename to "\346\267\261\345\272\246\345\255\246\344\271\240/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" diff --git "a/\347\274\226\347\250\213\350\257\255\350\250\200/cpp-python-shell\351\235\242\350\257\225\351\242\230.md" "b/\347\274\226\347\250\213\350\257\255\350\250\200/cpp-python-shell\351\235\242\350\257\225\351\242\230.md" new file mode 100644 index 00000000..1f5d5a85 --- /dev/null +++ "b/\347\274\226\347\250\213\350\257\255\350\250\200/cpp-python-shell\351\235\242\350\257\225\351\242\230.md" @@ -0,0 +1,137 @@ +# Python/C/C++/计算机基础/图像处理基础 +## static关键字作用 ++ 在全局变量前加上关键字static，全局变量就定义为一个全局静态变量，全局静态变量在声明它的文件之外是不可见的，作用域范围为从定义之处开始，到文件结尾。 ++ 在函数返回类型前加`static`，函数就变为静态函数，静态函数只在声明它的文件中使用，不被其他文件所用。 +## C++指针和引用的区别 ++ 指针有自己的内存空间，而引用只是一个别名，类似于Python浅拷贝和深拷贝的区别 ++ 不存在空引用, 引用必须链接到一块合法的内存地址； ++ 一旦引用被初始化为一个对象，就不能指向另一个对象。指针可以在任何时候指向任何一个对象； ++ 引用必须在创建时被初始化。指针可以在任何时间初始化。 +## C++中析构函数的作用 +析构函数与构造函数对应，类的析构函数是类的一种特殊的成员函数，**它会在每次删除所创建的对象时执行**。析构函数的名称与类的名称是完全相同的，只是在前面加了个波浪号（~）作为前缀，它不会返回任何值，也不能带有任何参数。析构函数有助于在跳出程序（比如关闭文件、释放内存等）前释放资源。 +## C++静态函数和虚函数的区别 +静态函数在编译的时候就已经确定运行时机，虚函数在运行的时候动态绑定。虚函数因为用了虚函数表机制，调用的时候会增加一次内存开销。 +## ++i和i++区别 +++i 先自增1，再返回，i++，先返回 i，再自增1. +## const关键字作用 +`const`类型的对象在程序执行期间不能被修改改变。 +## Python的函数参数传递 +> 参考这两个链接，stackoverflow的最高赞那个讲得很详细 +[How do I pass a variable by reference?](https://stackoverflow.com/questions/986006/how-do-i-pass-a-variable-by-reference) +[Python 面试题](https://github.com/taizilongxu/interview_python#python%E8%AF%AD%E8%A8%80%E7%89%B9%E6%80%A7) + +个人总结（有点不好）： ++ 将可变对象：列表list、字典dict、NumPy数组ndarray和用户定义的类型（类），作为参数传递给函数，函数内部将其改变后，函数外部这个变量也会改变（对变量进行重新赋值除外 `rebind the reference in the method`） ++ 将不可变对象：字符串string、元组tuple、数值numbers，作为参数传递给函数，函数内部将其改变后，函数外部这个变量不会改变 + +## Python实现对函参做类型检查 +`Python` 自带的函数一般都会有对函数参数类型做检查，自定义的函数参数类型检查可以用函数 `isinstance()` 实现，例如： +```Python +def my_abs(x): + """ + 自定义的绝对值函数 + :param x: int or float + :return: positive number, int or float + """ + if not isinstance(x, (int, float)): + raise TypeError('bad operand type') + if x > 0: + return x + else: + return -x +``` +添加了参数检查后，如果传入错误的参数类型，函数就可以抛出一个 `TypeError` 错误。 +## 为什么说Python是动态语言 +在 `Python` 中，等号 `=` 是赋值语句，可以把`任意数据类型`赋值给变量，同样一个变量可以反复赋值，而且可以是不同类型的变量，例如： +```Python +a = 100 # a是int型变量 +print(a) +a = 'ABC' # a 是str型变量 +print(a) +``` +Pyhon 这种变量本身类型不固定，可以反复赋值不同类型的变量称为动态语言，与之对应的是静态语言。静态语言在定义变量时必须指定变量类型，如果赋值的时候类型不匹配，就会报错，Java/C++ 都是静态语言（`int a; a = 100`） +## Python装饰器解释 +装饰器本质上是一个 Python 函数或类，**它可以让其他函数或类在不需要做任何代码修改的前提下增加额外功能**，装饰器的返回值也是一个函数/类对象。它经常用于有切面需求的场景，比如：插入日志、性能测试、事务处理、缓存、权限校验等场景，装饰器是解决这类问题的绝佳设计。有了装饰器，我们就可以**抽离出大量与函数功能本身无关的雷同代码到装饰器中并继续重用**。概括的讲，**装饰器的作用就是为已经存在的对象添加额外的功能**。 +## 多进程与多线程区别 ++ 线程是进程的一部分，一个进程至少有一个线程； ++ 对于操作系统来说，一个任务就是一个进程，进程内的“子任务”称为线程； ++ 多线程和多进程最大的不同在于，多进程中，同一个变量，各自有一份拷贝存在于每个进程中，互不影响，而**多线程中，所有变量都由所有线程共享**，所以，任何一个变量都可以被任何一个线程修改，因此，线程之间共享数据最大的危险在于多个线程同时改一个变量，把内容给改乱了。 ++ 进程间调用、通讯和切换开销均比多线程大，单个线程的崩溃会导致整个应用的退出。 ++ 存在大量IO，网络耗时或者需要和用户交互等操作时，使用多线程有利于提高系统的并发性和用户界面快速响应从而提高友好性。 +## map与reduce函数用法解释下 +1. map函数接收两个参数，一个是函数，一个是Iterable，map将传入的函数依次作用到序列的**每个元素**，并将结果作为新的Iterator返回，简单示例代码如下： +```Python3 +# 示例１ +def square(x): + return x ** 2 +r = map(square, [1, 2, 3, 4, 5, 6, 7]) +squareed_list = list(r) +print(squareed_list) # [1, 4, 9, 16, 25, 36, 49] +# 使用lambda匿名函数简化为一行代码 +list(map(lambda x: x * x, [1, 2, 3, 4, 5, 6, 7, 8, 9])) +# 示例２ +list(map(str, [1, 2, 3, 4, 5, 6, 7, 8, 9]))　＃　['1', '2', '3', '4', '5', '6', '7', '8', '9'] +``` +**注意map函数返回的是一个Iterator（惰性序列），要通过list函数转化为常用列表结构**。map()作为高阶函数，事实上它是把运算规则抽象了。 + +2. reduce()函数也接受两个参数，一个是函数（**两个参数**），一个是序列，与map不同的是**reduce把结果继续和序列的下一个元素做累积计算**,效果如下： +`reduce(f, [x1, x2, x3, x4]) = f(f(f(x1, x2), x3), x4)` +示例代码如下： +```Python3 +from functools import reduce +CHAR_TO_INT = { + '0': 0, + '1': 1, + '2': 2, + '3': 3, + '4': 4, + '5': 5, + '6': 6, + '7': 7, + '8': 8, + '9': 9 +} +def str2int(str): + ints = map(lambda x:CHAR_TO_INT[x], str) # str对象是Iterable对象 + return reduce(lambda x,y:10*x + y, ints) +print(str2int('0')) +print(str2int('12300')) +print(str2int('0012345')) # 0012345 +``` +## Python深拷贝、浅拷贝区别 +1. 直接赋值：其实就是对象的引用（别名）。 +2. 浅拷贝(copy)：拷贝父对象，不会拷贝对象的内部的子对象。（拷贝可以理解为创建内存）可以产生浅拷贝的操作有以下几种： + + 使用切片[:]操作 + + 使用工厂函数（如list/dir/set）,工厂函数看上去像函数，实质上是类，调用时实际上是生成了该类型的一个实例，就像工厂生产货物一样. + + 使用copy模块中的copy()函数 +3. 深拷贝(deepcopy)： copy 模块的 deepcopy 方法，完全拷贝了父对象及其子对象，两者是完全独立的。**深拷贝，包含对象里面的子对象的拷贝，所以原始对象的改变不会造成深拷贝里任何子元素的改变**。 + +**注意：浅拷贝和深拷贝的不同仅仅是对组合对象来说，所谓的组合对象（容器）就是包含了其它对象的对象，如列表，类实例。而对于数字、字符串以及其它“原子”类型，没有拷贝一说，产生的都是原对象的引用。** +看一个示例程序，就能明白浅拷贝与深拷贝的区别了： +```Python3 +#!/usr/bin/Python3 +# -*-coding:utf-8 -*- + +import copy +a = [1, 2, 3, ['a', 'b', 'c']] + +b = a # 赋值，传对象的引用 +c = copy.copy(a) # 浅拷贝 +d = copy.deepcopy(a) # 深拷贝 + +a.append(4) +a[3].append('d') + +print('a = ', a) +print('b = ', b) +print('c = ', c) +print('d = ', d) # [1, 2, 3, ['a', 'b', 'c']] +``` +程序输出如下： +> a = [1, 2, 3, ['a', 'b', 'c', 'd'], 4] +b = [1, 2, 3, ['a', 'b', 'c', 'd'], 4] +c = [1, 2, 3, ['a', 'b', 'c', 'd']] +d = [1, 2, 3, ['a', 'b', 'c']] + +## 其他面试题 +参考[这里](https://zhuanlan.zhihu.com/p/23526961) diff --git "a/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200\347\237\245\350\257\206.md" "b/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200\347\237\245\350\257\206.md" new file mode 100644 index 00000000..df5478fa --- /dev/null +++ "b/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200\347\237\245\350\257\206.md" @@ -0,0 +1,9 @@ +# 计算机基础知识 +## 知识点目录 ++ 数据结构与算法 ++ 操作系统 ++ 计算机网络 ++ 面向对象 ++ 数据库 ++ Linux系统开发 ++ 常用工具（Cmake/Git/Docker/正则表达式） \ No newline at end of file diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206\345\237\272\347\241\200.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206\345\237\272\347\241\200.md" new file mode 100644 index 00000000..4a360c7c --- /dev/null +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206\345\237\272\347\241\200.md" @@ -0,0 +1,25 @@ +# 数字图像处理 +## 图像处理中平滑和锐化操作是什么？ +**平滑处理（smoothing）也称模糊处理（bluring）**，主要用于**消除图像中的噪声部分**，平滑处理常用的用途是用来减少图像上的噪点或失真，平滑主要使用图像滤波。在这里，我个人认为可以把图像平滑和图像滤波联系起来，因为图像平滑常用的方法就是图像滤波器。在OpenCV3中常用的图像滤波器有以下几种： + ++ 方框滤波——BoxBlur函数 ++ 均值滤波（邻域平均滤波）——Blur函数 ++ 高斯滤波——GaussianBlur函数（高斯低通滤波是模糊，高斯高通滤波是锐化） ++ 中值滤波——medianBlur函数 ++ 双边滤波——bilateralFilter函数 + +图像锐化操作是为了突出显示图像的边界和其他细节，而图像锐化实现的方法是通过各种算子和滤波器实现的——`Canny` 算子、`Sobel` 算子、`Laplacian` 算子以及 `Scharr` 滤波器。 +## 图像锐化方法 +**锐化主要影响图像中的低频分量，不影响图像中的高频分量**像锐化的主要目的有两个： +1. 增强图像边缘，使模糊的图像变得更加清晰，颜色变得鲜明突出，图像的质量有所改善，产生更适合人眼观察和识别的图像； +2. 过锐化处理后，目标物体的边缘鲜明，以便于提取目标的边缘、对图像进行分割、目标区域识别、区域形状提取等，进一步的图像理解与分析奠定基础。 + +图像锐化一般有两种方法： +1. 微分法 +2. 高通滤波法 + +一般来说，图像的能量主要集中在其低频部分，噪声所在的频段主要在高频段，同时图像边缘信息也主要集中在其高频部分。这将导致原始图像在平滑处理之后，图像边缘和图像轮廓模糊的情况出现。为了减少这类不利效果的影响，就需要利用图像锐化技术，使图像的边缘变得清晰。图像锐化处理的目的是为了使图像的边缘、轮廓线以及图像的细节变得清晰，经过平滑的图像变得模糊的根本原因是因为图像受到了平均或积分运算，因此可以对其进行逆运算(如微分运算)就可以使图像变得清晰。微分运算是求信号的变化率，由傅立叶变换的微分性质可知，微分运算具有较强高频分量作用。从频率域来考虑，图像模糊的实质是因为其高频分量被衰减，因此可以用高通滤波器来使图像清晰。但要注意能够进行锐化处理的图像必须有较高的性噪比，否则锐化后图像性噪比反而更低，从而使得噪声增加的比信号还要多，因此一般是先去除或减轻噪声后再进行锐化处理。 +### Reference +[图像增强－图像锐化](https://www.cnblogs.com/BYTEMAN/archive/2012/07/21/2603021.html) +## numpy手撕代码 +人脸识别的场景下，输入105*12的feature map，用这个在1000512的特征库当中用欧氏距离去匹配105*12的feature map，用这个在1000*512的特征库当中用欧氏距离去匹配10*1000的特征，得到这个output。 \ No newline at end of file diff --git "a/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" similarity index 100% rename from "\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" rename to "\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" From 6bf57d526c6073f11778311bf7dc1f2613aaa05b Mon Sep 17 00:00:00 2001 From: Zhang Date: Fri, 30 Aug 2019 12:25:27 +0800 Subject: [PATCH 218/386] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index cb18444e..8ea948b6 100644 --- a/README.md +++ b/README.md @@ -38,7 +38,7 @@ |爱奇艺智能平台部-视频分析组|视频理解算法实习生|北京中关村|未知|内推邮箱:zhangyuntao@qiyi.com|简历命名格式：CVer推荐+学校+研究方向+实习开始时间+毕业时间|CVer公众号| |依图科技|校招、实习、社招|上海、北京、杭州|长期|![依图科技-校招内推](images/依图科技-校招内推.png)|微信扫描二维码（长期有效），链接中的岗位均可投递|| -## [深度学习面试题](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E9%9D%A2%E8%AF%95%E9%A2%98.md) +## [深度学习面试题](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E9%9D%A2%E8%AF%95%E9%A2%98.md) ## 2020年校招可投递公司-计算机视觉/机器学习算法工程师 侧重于于计算机视觉/图像/机器学习算法工程师岗位,公司排名几乎部分先后。 From fbfc674fcc3f6b51271389301a5fea21c1a13edd Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Fri, 30 Aug 2019 12:31:20 +0800 Subject: [PATCH 219/386] =?UTF-8?q?=E5=88=A0=E9=99=A4=E5=88=86=E7=B1=BB?= =?UTF-8?q?=E7=9B=AE=E5=BD=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ... => "MXNet\345\255\246\344\271\240\347\254\224\350\256\260.md" | 0 ..." => "cpp-python-shell\351\235\242\350\257\225\351\242\230.md" | 0 ...45\203\217\345\244\204\347\220\206\345\237\272\347\241\200.md" | 0 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"\347\274\226\347\250\213\350\257\255\350\250\200/cpp-python-shell\351\235\242\350\257\225\351\242\230.md" diff --git "a/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200\347\237\245\350\257\206.md" "b/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200\347\237\245\350\257\206.md" similarity index 100% rename from "\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200\347\237\245\350\257\206.md" rename to "\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200\347\237\245\350\257\206.md" diff --git "a/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" similarity index 100% rename from "\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" rename to "\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" From ec1bf6abec229408a6bb91c6d2e55ed4df68a053 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Fri, 30 Aug 2019 12:54:09 +0800 Subject: [PATCH 221/386] RoadMap and introduction in README.md --- README.md | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/README.md b/README.md index 8ea948b6..8dff9750 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,19 @@ +## 算法/图像/CS基础/编程语言(`RoadMap`) ++ [计算机基础](https://github.com/HarleysZhang/2019_algorithm_intern_information/tree/master/%E8%AE%A1%E7%AE%97%E6%9C%BA%E5%9F%BA%E7%A1%80) ++ [数据结构与算法](https://github.com/HarleysZhang/2019_algorithm_intern_information/tree/master/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95) ++ [数字图像处理](https://github.com/HarleysZhang/2019_algorithm_intern_information/tree/master/%E6%95%B0%E5%AD%97%E5%9B%BE%E5%83%8F%E5%A4%84%E7%90%86) ++ [编程语言](https://github.com/HarleysZhang/2019_algorithm_intern_information/tree/master/%E7%BC%96%E7%A8%8B%E8%AF%AD%E8%A8%80) ++ [机器学习](https://github.com/HarleysZhang/2019_algorithm_intern_information/tree/master/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0) ++ [深度学习](https://github.com/HarleysZhang/2019_algorithm_intern_information/tree/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0) ++ [计算机视觉](https://github.com/HarleysZhang/2019_algorithm_intern_information/tree/master/%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89) + +## 前言 +本仓库的知识点和文章对于我主要用于我个人复习和作知识点总结使用，部分内容参考自`github项目/网络资料/书籍`等，由于时间和能力有限，有些知识点其实掌握得还不够深和全面, 我推荐阅读以下github仓库项目（有些已经停止更新, 当作参考） ++ [Algorithm_Interview_Notes-Chinese](https://github.com/imhuay/Algorithm_Interview_Notes-Chinese) ++ [关于Python的面试题](https://github.com/taizilongxu/interview_python) ++ [CS-Notes](https://github.com/CyC2018/CS-Notes) ++ [用动画的形式呈现解LeetCode题目的思路](https://github.com/MisterBooo/LeetCodeAnimation) + ## 2019春招算法实习岗位表 2019年春招近期实习岗位汇总，还有待完善，持续更新，也希望各位一起更新。 注意：大部分岗位都是面向2020年毕业的学生。 From db2c9e2ee217a007d318d9176a58ade9a28d7a76 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Fri, 30 Aug 2019 12:54:41 +0800 Subject: [PATCH 222/386] RoadMap and introduction in README.md --- README.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 8dff9750..cd0647af 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,10 @@ +## 前言 +本仓库的知识点和文章对于我主要用于我个人复习和作知识点总结使用，部分内容参考自`github项目/网络资料/书籍`等，由于时间和能力有限，有些知识点其实掌握得还不够深和全面, 我推荐阅读以下github仓库项目（有些已经停止更新, 当作参考） ++ [Algorithm_Interview_Notes-Chinese](https://github.com/imhuay/Algorithm_Interview_Notes-Chinese) ++ [关于Python的面试题](https://github.com/taizilongxu/interview_python) ++ [CS-Notes](https://github.com/CyC2018/CS-Notes) ++ [用动画的形式呈现解LeetCode题目的思路](https://github.com/MisterBooo/LeetCodeAnimation) + ## 算法/图像/CS基础/编程语言(`RoadMap`) + [计算机基础](https://github.com/HarleysZhang/2019_algorithm_intern_information/tree/master/%E8%AE%A1%E7%AE%97%E6%9C%BA%E5%9F%BA%E7%A1%80) + [数据结构与算法](https://github.com/HarleysZhang/2019_algorithm_intern_information/tree/master/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95) @@ -7,13 +14,6 @@ + [深度学习](https://github.com/HarleysZhang/2019_algorithm_intern_information/tree/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0) + [计算机视觉](https://github.com/HarleysZhang/2019_algorithm_intern_information/tree/master/%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89) -## 前言 -本仓库的知识点和文章对于我主要用于我个人复习和作知识点总结使用，部分内容参考自`github项目/网络资料/书籍`等，由于时间和能力有限，有些知识点其实掌握得还不够深和全面, 我推荐阅读以下github仓库项目（有些已经停止更新, 当作参考） -+ [Algorithm_Interview_Notes-Chinese](https://github.com/imhuay/Algorithm_Interview_Notes-Chinese) -+ [关于Python的面试题](https://github.com/taizilongxu/interview_python) -+ [CS-Notes](https://github.com/CyC2018/CS-Notes) -+ [用动画的形式呈现解LeetCode题目的思路](https://github.com/MisterBooo/LeetCodeAnimation) - ## 2019春招算法实习岗位表 2019年春招近期实习岗位汇总，还有待完善，持续更新，也希望各位一起更新。 注意：大部分岗位都是面向2020年毕业的学生。 From d1d5feacbb8e474a8f946a233a48439d34b240d8 Mon Sep 17 00:00:00 2001 From: Zhang Date: Fri, 30 Aug 2019 14:42:36 +0800 Subject: [PATCH 223/386] Add files via upload From 1460ef36072c092be97ce6b98b7aff0f7822f736 Mon Sep 17 00:00:00 2001 From: Zhang Date: Fri, 30 Aug 2019 14:44:55 +0800 Subject: [PATCH 224/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 85635e44..f7cb41b0 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -164,7 +164,7 @@ RNN的输入有两个部分，一部分为上一时刻的状态，另一部分 ## VGG使用2个3*3卷积的优势在哪里？ (1). **减少网络层参数**。用两个3*3卷积比用1个5*5卷积拥有更少的参数量，只有后者的2∗3∗35∗5=0.72。但是起到的效果是一样的，两个3*3的卷积层串联相当于一个5*5的卷积层，感受野的大小都是5×5，即1个像素会跟周围5*5的像素产生关联。把下图当成动态图看，很容易看到两个3×3卷积层堆叠（没有空间池化）有5×5的有效感受野。 -![2个３*3卷积层](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/2%E4%B8%AA3*3%E5%8D%B7%E7%A7%AF%E5%B1%82.png) +![2个３*3卷积层](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/2%E4%B8%AA3_3%E5%8D%B7%E7%A7%AF%E5%B1%82.png) (2). **更多的非线性变换**。2个3*3卷积层拥有比1个5*5卷积层更多的非线性变换（前者可以使用两次ReLU激活函数，而后者只有一次），使得卷积神经网络对特征的学习能力更强。 From 8d4b79e54b6537f16e94ab1fddd44f00e8a09905 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Sat, 31 Aug 2019 00:44:25 +0800 Subject: [PATCH 225/386] add python interview problem: class method and __init__ information --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index cd0647af..0e50ea11 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@ ## 前言 -本仓库的知识点和文章对于我主要用于我个人复习和作知识点总结使用，部分内容参考自`github项目/网络资料/书籍`等，由于时间和能力有限，有些知识点其实掌握得还不够深和全面, 我推荐阅读以下github仓库项目（有些已经停止更新, 当作参考） +本仓库的知识点和文章对于我主要用于我个人复习和作知识点总结使用，部分内容参考自`github项目/网络资料/书籍`等，由于时间和能力有限，有些知识点其实掌握得还不够深和全面, 我推荐阅读以下 `github` 仓库项目（有些已经停止更新, 当作参考） + [Algorithm_Interview_Notes-Chinese](https://github.com/imhuay/Algorithm_Interview_Notes-Chinese) + [关于Python的面试题](https://github.com/taizilongxu/interview_python) + [CS-Notes](https://github.com/CyC2018/CS-Notes) From fbbaba82dce95a88ace3269e6179e88eed60dad3 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Sat, 31 Aug 2019 00:48:49 +0800 Subject: [PATCH 226/386] add python interview problem: class method and __init__ information --- ...ll\351\235\242\350\257\225\351\242\230.md" | 42 +++++++++++++++++++ 1 file changed, 42 insertions(+) diff --git "a/\347\274\226\347\250\213\350\257\255\350\250\200/cpp-python-shell\351\235\242\350\257\225\351\242\230.md" "b/\347\274\226\347\250\213\350\257\255\350\250\200/cpp-python-shell\351\235\242\350\257\225\351\242\230.md" index 1f5d5a85..646c4686 100644 --- "a/\347\274\226\347\250\213\350\257\255\350\250\200/cpp-python-shell\351\235\242\350\257\225\351\242\230.md" +++ "b/\347\274\226\347\250\213\350\257\255\350\250\200/cpp-python-shell\351\235\242\350\257\225\351\242\230.md" @@ -15,6 +15,48 @@ ++i 先自增1，再返回，i++，先返回 i，再自增1. ## const关键字作用 `const`类型的对象在程序执行期间不能被修改改变。 +## 实例方法/静态方法/类方法 +`python` 类语法中有三种方法，**实例方法，静态方法，类方法**，它们的区别如下： ++ 实例方法只能被实例对象调用，静态方法(由 `@staticmethod` 装饰器来声明)、类方法(由 `@classmethod` 装饰器来声明)，可以被类或类的实例对象调用; ++ 实例方法，第一个参数必须要默认传实例对象，一般习惯用self。静态方法，参数没有要求。类方法，第一个参数必须要默认传类，一般习惯用 `cls` . + +实例代码如下： +```Python +class Foo(object): + """类三种方法语法形式 + """ + def instance_method(self): + print("是类{}的实例方法，只能被实例对象调用".format(Foo)) + + @staticmethod + def static_method(): + print("是静态方法") + + @classmethod + def class_method(cls): + print("是类方法") + + +foo = Foo() +foo.instance_method() +foo.static_method() +foo.class_method() +print('##############') +Foo.static_method() +Foo.class_method() +``` +程序执行后输出如下： +> 是类的实例方法，只能被实例对象调用 +是静态方法 +是类方法 +############## +是静态方法 +是类方法 + +## __new__和__init__方法的区别 ++ `__init__` 方法并不是真正意义上的构造函数, `__new__` 方法才是(类的构造函数是类的一种特殊的成员函数，它会**在每次创建类的新对象时执行**); ++ `__new__` 方法用于创建对象并返回对象，当返回对象时会自动调用 `__init__` 方法进行初始化, `__new__` 方法比 `__init__` 方法更早执行; ++ `__new__` 方法是**静态方法**，而 `__init__` 是**实例方法**。 ## Python的函数参数传递 > 参考这两个链接，stackoverflow的最高赞那个讲得很详细 [How do I pass a variable by reference?](https://stackoverflow.com/questions/986006/how-do-i-pass-a-variable-by-reference) From d92c189e047d1748f44e1297d99fc1e49b9c247a Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Sat, 31 Aug 2019 01:27:15 +0800 Subject: [PATCH 227/386] add python interview problem --- ...ll\351\235\242\350\257\225\351\242\230.md" | 33 ++++++++++++++++--- 1 file changed, 28 insertions(+), 5 deletions(-) diff --git "a/\347\274\226\347\250\213\350\257\255\350\250\200/cpp-python-shell\351\235\242\350\257\225\351\242\230.md" "b/\347\274\226\347\250\213\350\257\255\350\250\200/cpp-python-shell\351\235\242\350\257\225\351\242\230.md" index 646c4686..b5acef11 100644 --- "a/\347\274\226\347\250\213\350\257\255\350\250\200/cpp-python-shell\351\235\242\350\257\225\351\242\230.md" +++ "b/\347\274\226\347\250\213\350\257\255\350\250\200/cpp-python-shell\351\235\242\350\257\225\351\242\230.md" @@ -11,14 +11,35 @@ 析构函数与构造函数对应，类的析构函数是类的一种特殊的成员函数，**它会在每次删除所创建的对象时执行**。析构函数的名称与类的名称是完全相同的，只是在前面加了个波浪号（~）作为前缀，它不会返回任何值，也不能带有任何参数。析构函数有助于在跳出程序（比如关闭文件、释放内存等）前释放资源。 ## C++静态函数和虚函数的区别 静态函数在编译的时候就已经确定运行时机，虚函数在运行的时候动态绑定。虚函数因为用了虚函数表机制，调用的时候会增加一次内存开销。 -## ++i和i++区别 +## ++i 和 i++区别 ++i 先自增1，再返回，i++，先返回 i，再自增1. -## const关键字作用 +## const 关键字作用 `const`类型的对象在程序执行期间不能被修改改变。 + +## 提高python运行效率的方法 +1. 使用生成器，因为可以节约大量内存; +2. 循环代码优化，避免过多重复代码的执行; +3. 核心模块用 `Cython PyPy` 等，提高效率; +4. 多进程、多线程、协程; +5. 多个 `if elif` 条件判断，可以把最有可能先发生的条件放到前面写，这样可以减少程序判断的次数，提高效率。 + +## Python 单例模式 +## 为什么 python 不提供函数重载 +> 参考知乎[为什么 Python 不支持函数重载？其他函数大部分都支持的？](https://www.zhihu.com/question/20053359) + +我们知道 `函数重载` 主要是为了解决两个问题。 +1. 可变参数类型。 +2. 可变参数个数。 + +另外，一个函数重载基本的设计原则是，仅仅当两个函数除了参数类型和参数个数不同以外，其功能是完全相同的，此时才使用函数重载，如果两个函数的功能其实不同，那么不应当使用重载，而应当使用一个名字不同的函数。 + +1. 对于情况 1 ，函数功能相同，但是参数类型不同，python 如何处理？答案是根本不需要处理，因为 `python` 可以接受任何类型的参数，如果函数的功能相同，那么不同的参数类型在 python 中很可能是相同的代码，没有必要做成两个不同函数。 +2. 对于情况 2 ，函数功能相同，但参数个数不同，python 如何处理？大家知道，答案就是**缺省参数(默认参数)**。对那些缺少的参数设定为缺省参数(默认参数)即可解决问题。因为你假设函数功能相同，那么那些缺少的参数终归是需要用的。所以，鉴于情况 1 跟 情况 2 都有了解决方案，python 自然就不需要函数重载了。 + ## 实例方法/静态方法/类方法 `python` 类语法中有三种方法，**实例方法，静态方法，类方法**，它们的区别如下： + 实例方法只能被实例对象调用，静态方法(由 `@staticmethod` 装饰器来声明)、类方法(由 `@classmethod` 装饰器来声明)，可以被类或类的实例对象调用; -+ 实例方法，第一个参数必须要默认传实例对象，一般习惯用self。静态方法，参数没有要求。类方法，第一个参数必须要默认传类，一般习惯用 `cls` . ++ `实例方法`，第一个参数必须要默认传实例对象，一般习惯用self。`静态方法`，参数没有要求。`类方法`，第一个参数必须要默认传类，一般习惯用 `cls` . 实例代码如下： ```Python @@ -53,7 +74,7 @@ Foo.class_method() 是静态方法 是类方法 -## __new__和__init__方法的区别 +## `__new__` 和 `__init__` 方法的区别 + `__init__` 方法并不是真正意义上的构造函数, `__new__` 方法才是(类的构造函数是类的一种特殊的成员函数，它会**在每次创建类的新对象时执行**); + `__new__` 方法用于创建对象并返回对象，当返回对象时会自动调用 `__init__` 方法进行初始化, `__new__` 方法比 `__init__` 方法更早执行; + `__new__` 方法是**静态方法**，而 `__init__` 是**实例方法**。 @@ -176,4 +197,6 @@ c = [1, 2, 3, ['a', 'b', 'c', 'd']] d = [1, 2, 3, ['a', 'b', 'c']] ## 其他面试题 -参考[这里](https://zhuanlan.zhihu.com/p/23526961) +1. 参考[这里](https://zhuanlan.zhihu.com/p/23526961) +2. [110道Python面试题（真题）](https://zhuanlan.zhihu.com/p/54430650) +3. [关于Python的面试题](https://github.com/taizilongxu/interview_python#15-__new__%E5%92%8C__init__%E7%9A%84%E5%8C%BA%E5%88%AB) From 4bc0a43fd5e7611d685810bcd54d43c256c7a292 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Sat, 31 Aug 2019 01:28:18 +0800 Subject: [PATCH 228/386] add python interview problem --- .../cpp-python-shell\351\235\242\350\257\225\351\242\230.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/\347\274\226\347\250\213\350\257\255\350\250\200/cpp-python-shell\351\235\242\350\257\225\351\242\230.md" "b/\347\274\226\347\250\213\350\257\255\350\250\200/cpp-python-shell\351\235\242\350\257\225\351\242\230.md" index b5acef11..e746a562 100644 --- "a/\347\274\226\347\250\213\350\257\255\350\250\200/cpp-python-shell\351\235\242\350\257\225\351\242\230.md" +++ "b/\347\274\226\347\250\213\350\257\255\350\250\200/cpp-python-shell\351\235\242\350\257\225\351\242\230.md" @@ -74,7 +74,7 @@ Foo.class_method() 是静态方法 是类方法 -## `__new__` 和 `__init__` 方法的区别 +## __new__和__init__方法的区别 + `__init__` 方法并不是真正意义上的构造函数, `__new__` 方法才是(类的构造函数是类的一种特殊的成员函数，它会**在每次创建类的新对象时执行**); + `__new__` 方法用于创建对象并返回对象，当返回对象时会自动调用 `__init__` 方法进行初始化, `__new__` 方法比 `__init__` 方法更早执行; + `__new__` 方法是**静态方法**，而 `__init__` 是**实例方法**。 From 4931af08d5dc26fb3747fe42ce18dd1e37fb923d Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Sat, 31 Aug 2019 01:58:00 +0800 Subject: [PATCH 229/386] add python interview problem --- ...ll\351\235\242\350\257\225\351\242\230.md" | 59 +++++++++++++++---- 1 file changed, 47 insertions(+), 12 deletions(-) diff --git "a/\347\274\226\347\250\213\350\257\255\350\250\200/cpp-python-shell\351\235\242\350\257\225\351\242\230.md" "b/\347\274\226\347\250\213\350\257\255\350\250\200/cpp-python-shell\351\235\242\350\257\225\351\242\230.md" index e746a562..3811e669 100644 --- "a/\347\274\226\347\250\213\350\257\255\350\250\200/cpp-python-shell\351\235\242\350\257\225\351\242\230.md" +++ "b/\347\274\226\347\250\213\350\257\255\350\250\200/cpp-python-shell\351\235\242\350\257\225\351\242\230.md" @@ -16,7 +16,34 @@ ## const 关键字作用 `const`类型的对象在程序执行期间不能被修改改变。 -## 提高python运行效率的方法 +## lambda匿名函数好处 +精简代码，`lambda`省去了定义函数，`map` 省去了写 `for` 循环过程: +```Python +str_1 = ["中国", "美国", "法国", "", "", "英国"] +res = list(map(lambda x: "填充值" if x=="" else x, str_1)) +print(res) # ['中国', '美国', '法国', '填充值', '填充值', '英国'] +``` +## Python 错误处理 +和其他高级语言一样，`Python` 也内置了一套`try...except...finally...` 的错误处理机制。 +当我们认为某些代码可能会出错时，就可以用 `try` 来运行这段代码，如果执行出错，则后续代码不会继续执行，而是直接跳转至跳转至错误处理代码，即 `except` 语句块，执行完 `except` 后，如果有 `finally` 语句块，则执行。 +finally 语句块，至此，执行完毕。跳转至错误处理代码， +## Python 内置错误类型 ++ `IOError`：输入输出异常 ++ `AttributeError`：试图访问一个对象没有的属性 ++ `ImportError`：无法引入模块或包，基本是路径问题 ++ `IndentationError`：语法错误，代码没有正确的对齐 ++ `IndexError`：下标索引超出序列边界 ++ `KeyError`: 试图访问你字典里不存在的键 ++ `SyntaxError`: Python 代码逻辑语法出错，不能执行 ++ `NameError`: 使用一个还未赋予对象的变量 + + +## 简述any()和all()方法 ++ `any()`: 只要迭代器中有一个元素为真就为真; ++ `all()`: 迭代器中所有的判断项返回都是真，结果才为真. +## Python中什么元素为假？ +答案：（0，空字符串，空列表、空字典、空元组、None, False） +## 提高Python运行效率的方法 1. 使用生成器，因为可以节约大量内存; 2. 循环代码优化，避免过多重复代码的执行; 3. 核心模块用 `Cython PyPy` 等，提高效率; @@ -24,7 +51,8 @@ 5. 多个 `if elif` 条件判断，可以把最有可能先发生的条件放到前面写，这样可以减少程序判断的次数，提高效率。 ## Python 单例模式 -## 为什么 python 不提供函数重载 + +## 为什么 Python 不提供函数重载 > 参考知乎[为什么 Python 不支持函数重载？其他函数大部分都支持的？](https://www.zhihu.com/question/20053359) 我们知道 `函数重载` 主要是为了解决两个问题。 @@ -33,11 +61,11 @@ 另外，一个函数重载基本的设计原则是，仅仅当两个函数除了参数类型和参数个数不同以外，其功能是完全相同的，此时才使用函数重载，如果两个函数的功能其实不同，那么不应当使用重载，而应当使用一个名字不同的函数。 -1. 对于情况 1 ，函数功能相同，但是参数类型不同，python 如何处理？答案是根本不需要处理，因为 `python` 可以接受任何类型的参数，如果函数的功能相同，那么不同的参数类型在 python 中很可能是相同的代码，没有必要做成两个不同函数。 -2. 对于情况 2 ，函数功能相同，但参数个数不同，python 如何处理？大家知道，答案就是**缺省参数(默认参数)**。对那些缺少的参数设定为缺省参数(默认参数)即可解决问题。因为你假设函数功能相同，那么那些缺少的参数终归是需要用的。所以，鉴于情况 1 跟 情况 2 都有了解决方案，python 自然就不需要函数重载了。 +1. 对于情况 1 ，函数功能相同，但是参数类型不同，Python 如何处理？答案是根本不需要处理，因为 `Python` 可以接受任何类型的参数，如果函数的功能相同，那么不同的参数类型在 Python 中很可能是相同的代码，没有必要做成两个不同函数。 +2. 对于情况 2 ，函数功能相同，但参数个数不同，Python 如何处理？大家知道，答案就是**缺省参数(默认参数)**。对那些缺少的参数设定为缺省参数(默认参数)即可解决问题。因为你假设函数功能相同，那么那些缺少的参数终归是需要用的。所以，鉴于情况 1 跟 情况 2 都有了解决方案，Python 自然就不需要函数重载了。 ## 实例方法/静态方法/类方法 -`python` 类语法中有三种方法，**实例方法，静态方法，类方法**，它们的区别如下： +`Python` 类语法中有三种方法，**实例方法，静态方法，类方法**，它们的区别如下： + 实例方法只能被实例对象调用，静态方法(由 `@staticmethod` 装饰器来声明)、类方法(由 `@classmethod` 装饰器来声明)，可以被类或类的实例对象调用; + `实例方法`，第一个参数必须要默认传实例对象，一般习惯用self。`静态方法`，参数没有要求。`类方法`，第一个参数必须要默认传类，一般习惯用 `cls` . @@ -81,7 +109,7 @@ Foo.class_method() ## Python的函数参数传递 > 参考这两个链接，stackoverflow的最高赞那个讲得很详细 [How do I pass a variable by reference?](https://stackoverflow.com/questions/986006/how-do-i-pass-a-variable-by-reference) -[Python 面试题](https://github.com/taizilongxu/interview_python#python%E8%AF%AD%E8%A8%80%E7%89%B9%E6%80%A7) +[Python 面试题](https://github.com/taizilongxu/interview_Python#Python%E8%AF%AD%E8%A8%80%E7%89%B9%E6%80%A7) 个人总结（有点不好）： + 将可变对象：列表list、字典dict、NumPy数组ndarray和用户定义的类型（类），作为参数传递给函数，函数内部将其改变后，函数外部这个变量也会改变（对变量进行重新赋值除外 `rebind the reference in the method`） @@ -116,14 +144,14 @@ Pyhon 这种变量本身类型不固定，可以反复赋值不同类型的变 ## Python装饰器解释 装饰器本质上是一个 Python 函数或类，**它可以让其他函数或类在不需要做任何代码修改的前提下增加额外功能**，装饰器的返回值也是一个函数/类对象。它经常用于有切面需求的场景，比如：插入日志、性能测试、事务处理、缓存、权限校验等场景，装饰器是解决这类问题的绝佳设计。有了装饰器，我们就可以**抽离出大量与函数功能本身无关的雷同代码到装饰器中并继续重用**。概括的讲，**装饰器的作用就是为已经存在的对象添加额外的功能**。 ## 多进程与多线程区别 -+ 线程是进程的一部分，一个进程至少有一个线程； -+ 对于操作系统来说，一个任务就是一个进程，进程内的“子任务”称为线程； -+ 多线程和多进程最大的不同在于，多进程中，同一个变量，各自有一份拷贝存在于每个进程中，互不影响，而**多线程中，所有变量都由所有线程共享**，所以，任何一个变量都可以被任何一个线程修改，因此，线程之间共享数据最大的危险在于多个线程同时改一个变量，把内容给改乱了。 ++ 多个进程之间相互独立，一个任务就是一个进程，进程内的“子任务”称为线程；线程是进程的一部分，一个进程至少有一个线程； ++ 进程是操作系统进行资源分配和调度的基本单位，线程是 `CPU` 进行资源分配和调度的基本单位； ++ 多线程和多进程最大的不同在于，多进程中，同一个变量，各自有一份拷贝存在于每个进程中，互不影响，多个进程之间相互独立；而**多线程中，所有变量都由所有线程共享**，所以，任何一个变量都可以被任何一个线程修改，因此，线程之间共享数据最大的危险在于多个线程同时改一个变量，把内容给改乱了。 + 进程间调用、通讯和切换开销均比多线程大，单个线程的崩溃会导致整个应用的退出。 + 存在大量IO，网络耗时或者需要和用户交互等操作时，使用多线程有利于提高系统的并发性和用户界面快速响应从而提高友好性。 ## map与reduce函数用法解释下 1. map函数接收两个参数，一个是函数，一个是Iterable，map将传入的函数依次作用到序列的**每个元素**，并将结果作为新的Iterator返回，简单示例代码如下： -```Python3 +```Python # 示例１ def square(x): return x ** 2 @@ -140,7 +168,7 @@ list(map(str, [1, 2, 3, 4, 5, 6, 7, 8, 9]))　＃　['1', '2', '3', '4', '5', '6 2. reduce()函数也接受两个参数，一个是函数（**两个参数**），一个是序列，与map不同的是**reduce把结果继续和序列的下一个元素做累积计算**,效果如下： `reduce(f, [x1, x2, x3, x4]) = f(f(f(x1, x2), x3), x4)` 示例代码如下： -```Python3 +```Python from functools import reduce CHAR_TO_INT = { '0': 0, @@ -162,6 +190,13 @@ print(str2int('12300')) print(str2int('0012345')) # 0012345 ``` ## Python深拷贝、浅拷贝区别 +> Python中的大多数对象，比如列表、字典、NumPy数组，和用户定义的类型（类），都是可变的。意味着这些对象或包含的值可以被修改。但也有些对象是不可变的，例如数值型、字符串型string和元组tuple。 + +**1、复制不可变数据类型：** + +复制不可变数据类型，不管copy还是deepcopy,都是同一个地址当浅复制的值是不可变对象（数值，字符串，元组）时和=“赋值”的情况一样，对象的id值与浅复制原来的值相同。 + +**2、复制可变数据类型：** 1. 直接赋值：其实就是对象的引用（别名）。 2. 浅拷贝(copy)：拷贝父对象，不会拷贝对象的内部的子对象。（拷贝可以理解为创建内存）可以产生浅拷贝的操作有以下几种： + 使用切片[:]操作 @@ -199,4 +234,4 @@ d = [1, 2, 3, ['a', 'b', 'c']] ## 其他面试题 1. 参考[这里](https://zhuanlan.zhihu.com/p/23526961) 2. [110道Python面试题（真题）](https://zhuanlan.zhihu.com/p/54430650) -3. [关于Python的面试题](https://github.com/taizilongxu/interview_python#15-__new__%E5%92%8C__init__%E7%9A%84%E5%8C%BA%E5%88%AB) +3. [关于Python的面试题](https://github.com/taizilongxu/interview_Python#15-__new__%E5%92%8C__init__%E7%9A%84%E5%8C%BA%E5%88%AB) From ac7a8571dbeff0b5b7a543a34bd230d54dbb7020 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Tue, 3 Sep 2019 21:36:20 +0800 Subject: [PATCH 230/386] updata internship information --- README.md | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 0e50ea11..7543aac7 100644 --- a/README.md +++ b/README.md @@ -74,9 +74,10 @@ |蚂蚁金服|360安全|云天励飞|汇顶科技|否| |云从科技|搜狗|松鼠AI|美的中央研究院|，是，| |无|新浪|驭势科技|锐明技术|否| -|无|搜狐/金山|码隆科技||否| +|无|搜狐/金山|码隆科技|联发科|否| |无|小红书|追一科技||否| -|无|作业帮|星环科技||否| -|无|猿辅导/英语流利说|深睿医疗||否| +|无|作业帮/猿辅导|星环科技||否| +|无|英语流利说|深睿医疗||否| |无|oppo/vivo|||否| -|无|虎牙/斗鱼直播|||否| +|无|欢聚时代/虎牙/斗鱼直播|||否| +|无|keep|用友科技||否| From 30aff0e0da9cdf96871855e381bbfae9c61b5943 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Wed, 4 Sep 2019 01:29:19 +0800 Subject: [PATCH 231/386] updata internship information --- README.md | 26 ++++++++++++++++++-------- 1 file changed, 18 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 7543aac7..1d08b49c 100644 --- a/README.md +++ b/README.md @@ -73,11 +73,21 @@ |大疆无人机|美图MTlab|文行知远|联想|否| |蚂蚁金服|360安全|云天励飞|汇顶科技|否| |云从科技|搜狗|松鼠AI|美的中央研究院|，是，| -|无|新浪|驭势科技|锐明技术|否| -|无|搜狐/金山|码隆科技|联发科|否| -|无|小红书|追一科技||否| -|无|作业帮/猿辅导|星环科技||否| -|无|英语流利说|深睿医疗||否| -|无|oppo/vivo|||否| -|无|欢聚时代/虎牙/斗鱼直播|||否| -|无|keep|用友科技||否| +|英伟达|新浪|驭势科技|锐明技术|否| +|Intel|搜狐/金山|码隆科技|联发科|否| +|亚马逊|小红书/蘑菇街|优必选|远景智能|否| +|博士中国|作业帮/猿辅导/好未来/跟谁学|星环科技|戴尔|否| +|无|英语流利说|深睿医疗|TP-LINK|否| +|无|oppo/vivo/一加|趋势科技|ZOOM|否| +|无|欢聚时代/虎牙/斗鱼直播/BIGO|追一科技|广联达|否| +|无|keep/shopee|用友科技|深信服|否| +|无|酷狗/瓜子二手车|度小满|中国电信云|否| +|无|巨人网络/盛大游戏/|京东数科|三星电子研究所|否| +|无|搜狐畅游/tap2fun|猎豹移动|苏宁|否| +|无|阅文集团/58集团|数美科技|微众银行|是，否| +|无|B站/酷狗音乐当当网|思必驰||否| +|无|最右/快看漫画/猫眼娱乐|随手科技||否| +|无|乐信/有赞|智加科技||否| +|无|去哪儿/携程/途家|||否| +|无|同花顺/老虎证券|||否| +|无|转转/珍爱网/知乎|||否| From 276728707863693a2365829c67674c247e06ab66 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Wed, 4 Sep 2019 01:32:19 +0800 Subject: [PATCH 232/386] updata internship information --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 1d08b49c..e8609fd5 100644 --- a/README.md +++ b/README.md @@ -88,6 +88,7 @@ |无|B站/酷狗音乐当当网|思必驰||否| |无|最右/快看漫画/猫眼娱乐|随手科技||否| |无|乐信/有赞|智加科技||否| -|无|去哪儿/携程/途家|||否| +|无|去哪儿/携程/途家|暗物智能DMAI||否| |无|同花顺/老虎证券|||否| |无|转转/珍爱网/知乎|||否| +|无|金蝶软件(中国)|||否| From cd431fcc1dce2b040a40f035b47b00b52401e5c5 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Wed, 4 Sep 2019 14:29:23 +0800 Subject: [PATCH 233/386] updata internship information --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index e8609fd5..2e4ddc43 100644 --- a/README.md +++ b/README.md @@ -85,7 +85,7 @@ |无|巨人网络/盛大游戏/|京东数科|三星电子研究所|否| |无|搜狐畅游/tap2fun|猎豹移动|苏宁|否| |无|阅文集团/58集团|数美科技|微众银行|是，否| -|无|B站/酷狗音乐当当网|思必驰||否| +|无|B站/酷狗音乐当当网|思必驰|中国移动|否| |无|最右/快看漫画/猫眼娱乐|随手科技||否| |无|乐信/有赞|智加科技||否| |无|去哪儿/携程/途家|暗物智能DMAI||否| From 3a283d21285cf0e61c56834544d443e222d36a9d Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Wed, 4 Sep 2019 15:08:38 +0800 Subject: [PATCH 234/386] updata internship information --- README.md | 38 +++++++++++++++++++++----------------- 1 file changed, 21 insertions(+), 17 deletions(-) diff --git a/README.md b/README.md index 2e4ddc43..035b1f7e 100644 --- a/README.md +++ b/README.md @@ -68,27 +68,31 @@ |微软|网易|蔚来汽车|海康威视|，是，| |谷歌|快手|小鹏汽车|虹软科技|否| |商汤|爱奇艺|科大讯飞|华为|否| -|旷世|小米|寒武纪|吉利汽车|否| +|旷世|小米|寒武纪|大华|否| |依图|陌陌|格灵深瞳|中兴|否| |大疆无人机|美图MTlab|文行知远|联想|否| |蚂蚁金服|360安全|云天励飞|汇顶科技|否| |云从科技|搜狗|松鼠AI|美的中央研究院|，是，| -|英伟达|新浪|驭势科技|锐明技术|否| +|英伟达|新浪|思必驰|锐明技术|否| |Intel|搜狐/金山|码隆科技|联发科|否| -|亚马逊|小红书/蘑菇街|优必选|远景智能|否| -|博士中国|作业帮/猿辅导/好未来/跟谁学|星环科技|戴尔|否| +|亚马逊|小红书/蘑菇街|优必选|奥比中光科技|否| +|博士中国|作业帮/猿辅导/好未来/跟谁学|度小满金融|戴尔|否| |无|英语流利说|深睿医疗|TP-LINK|否| -|无|oppo/vivo/一加|趋势科技|ZOOM|否| -|无|欢聚时代/虎牙/斗鱼直播/BIGO|追一科技|广联达|否| -|无|keep/shopee|用友科技|深信服|否| -|无|酷狗/瓜子二手车|度小满|中国电信云|否| -|无|巨人网络/盛大游戏/|京东数科|三星电子研究所|否| -|无|搜狐畅游/tap2fun|猎豹移动|苏宁|否| +|无|oppo/vivo/一加|京东数科|ZOOM|否| +|无|欢聚时代/虎牙/斗鱼直播/BIGO|猎豹移动|广联达|否| +|无|keep/shopee|京东数科|深信服|否| +|无|汽车之家/瓜子二手车|追一科技|中国电信云计算|否| +|无|巨人网络/盛大游戏/|深兰科技|三星电子研究所|否| +|无|搜狐畅游/tap2fun|用友科技|苏宁|否| |无|阅文集团/58集团|数美科技|微众银行|是，否| -|无|B站/酷狗音乐当当网|思必驰|中国移动|否| -|无|最右/快看漫画/猫眼娱乐|随手科技||否| -|无|乐信/有赞|智加科技||否| -|无|去哪儿/携程/途家|暗物智能DMAI||否| -|无|同花顺/老虎证券|||否| -|无|转转/珍爱网/知乎|||否| -|无|金蝶软件(中国)|||否| +|无|B站/酷狗音乐/当当网|驭势科技|中国移动成研院|否| +|无|最右/快看漫画/猫眼娱乐|随手科技|远景智能|否| +|无|乐信/有赞|智加科技|联影医疗|否| +|无|去哪儿/携程/途家|星环科技|中科创达|否| +|无|同花顺/老虎证券|趋势科技|恒润科技|否| +|无|转转/珍爱网/知乎|暗物智能DMAI|航天二院706所|否| +|无|金蝶软件(中国)|第四范式(有毁约风险)|吉利汽车|否| +|无|酷狗音乐/同花顺||碧桂园机器人|否| +|无|趣头条/一点资讯|||无| +|无|多牛传媒|||无| +|无|华米/绿米|||无| From 226774e9a8f2b978f4678492bbcfb98eac2780ed Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Wed, 4 Sep 2019 17:22:46 +0800 Subject: [PATCH 235/386] updata internship information --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 035b1f7e..5ec217cc 100644 --- a/README.md +++ b/README.md @@ -5,6 +5,7 @@ + [CS-Notes](https://github.com/CyC2018/CS-Notes) + [用动画的形式呈现解LeetCode题目的思路](https://github.com/MisterBooo/LeetCodeAnimation) + ## 算法/图像/CS基础/编程语言(`RoadMap`) + [计算机基础](https://github.com/HarleysZhang/2019_algorithm_intern_information/tree/master/%E8%AE%A1%E7%AE%97%E6%9C%BA%E5%9F%BA%E7%A1%80) + [数据结构与算法](https://github.com/HarleysZhang/2019_algorithm_intern_information/tree/master/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95) @@ -96,3 +97,4 @@ |无|趣头条/一点资讯|||无| |无|多牛传媒|||无| |无|华米/绿米|||无| + From 04af9c72bb9a87b8693438b84deedf0adeb94043 Mon Sep 17 00:00:00 2001 From: Zhang Date: Sun, 8 Sep 2019 23:40:36 +0800 Subject: [PATCH 236/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 更新反卷积公式 --- ...46\344\271\240\351\235\242\350\257\225\351\242\230.md" | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index f7cb41b0..7a7af55d 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -10,7 +10,7 @@ + 通道数Channels。 ### 卷积输出大小计算 -1.图片经卷积后**输出大小计算公式**如下： +1.图片经卷积 `Conv2D` 后**输出大小计算公式**如下： `N = (W − F + 2P )/S+1` + 输入图片大小 W×W + Filter大小 F×F @@ -19,9 +19,9 @@ 输出通道数 = 卷积核/池化核数量 -2.反卷积得到的图片大小计算方式： -**反卷积的大小是由卷积核大小与滑动步长决定**， in是输入大小， k是卷积核大小， s是滑动步长， out是输出大小。 -得到 `out = (in - 1) * s + k` +2.反卷积 `Conv2DTranspose` 得到的图片大小计算方式： +**反卷积的大小是由卷积核大小与滑动步长决定**， in是输入大小， k是卷积核大小， s是滑动步长， padding的像素数 P, out是输出大小。 +得到 `out = (in - 1) * s -2p + k`，还有另外一个写法：`W = (N - 1)*S - 2P + F`。 例如 输入：2x2， 卷积核：4x4， 滑动步长：3， 输出：7x7 ，其计算过程就是， (2 - 1) * 3 + 4 = 7 3.池化得到的特征图大小计算方式： From ce760ef7e13e8a3b6c15d42c2d985e66fdbc2234 Mon Sep 17 00:00:00 2001 From: Zhang Date: Sun, 8 Sep 2019 23:41:13 +0800 Subject: [PATCH 237/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...5\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 1 + 1 file changed, 1 insertion(+) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 7a7af55d..41dda464 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -22,6 +22,7 @@ 2.反卷积 `Conv2DTranspose` 得到的图片大小计算方式： **反卷积的大小是由卷积核大小与滑动步长决定**， in是输入大小， k是卷积核大小， s是滑动步长， padding的像素数 P, out是输出大小。 得到 `out = (in - 1) * s -2p + k`，还有另外一个写法：`W = (N - 1)*S - 2P + F`。 + 例如 输入：2x2， 卷积核：4x4， 滑动步长：3， 输出：7x7 ，其计算过程就是， (2 - 1) * 3 + 4 = 7 3.池化得到的特征图大小计算方式： From 5d6f16766798c281bea0ce05af5e0aaeb131da69 Mon Sep 17 00:00:00 2001 From: Zhang Date: Mon, 9 Sep 2019 16:40:58 +0800 Subject: [PATCH 238/386] =?UTF-8?q?Update=20=E7=9B=AE=E6=A0=87=E6=A3=80?= =?UTF-8?q?=E6=B5=8B=E4=B8=8E=E5=9B=BE=E5=83=8F=E5=88=86=E7=B1=BB=E6=8C=87?= =?UTF-8?q?=E6=A0=87.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 更新生成anchor程序，输出bbox为[x1,y1,x2,y2] --- ...06\347\261\273\346\214\207\346\240\207.md" | 23 ++++++++++--------- 1 file changed, 12 insertions(+), 11 deletions(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" index 5124723a..2598027f 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\344\270\216\345\233\276\345\203\217\345\210\206\347\261\273\346\214\207\346\240\207.md" @@ -342,7 +342,7 @@ def generate_anchor_base(base_size=16, ratios=[0.5, 1, 2], An array of shape :math:`(R, 4)`. Each element is a set of coordinates of a bounding box. The second axis corresponds to - :math:`(y_{min}, x_{min}, y_{max}, x_{max})` of a bounding box. + :math:`(x_{min}, y_{min}, x_{max}, y_{max})` of a bounding box. """ import numpy as np @@ -357,10 +357,11 @@ def generate_anchor_base(base_size=16, ratios=[0.5, 1, 2], w = base_size * anchor_scales[j] * np.sqrt(1. / ratios[i]) index = i * len(anchor_scales) + j - anchor_base[index, 0] = py - h / 2. - anchor_base[index, 1] = px - w / 2. - anchor_base[index, 2] = py + h / 2. - anchor_base[index, 3] = px + w / 2. + anchor_base[index, 0] = px - w / 2. + anchor_base[index, 1] = py - h / 2. + + anchor_base[index, 2] = px + h / 2. + anchor_base[index, 3] = py + w / 2. return anchor_base @@ -370,15 +371,15 @@ if __name__ == "__main__": print(bbox_list) ``` 程序运行输出如下： -> [[ -37.254833 -82.50967 53.254833 98.50967 ] - [ -82.50967 -173.01933 98.50967 189.01933 ] - [-173.01933 -354.03867 189.01933 370.03867 ] +> [[ -82.50967 -37.254833 53.254833 98.50967 ] + [-173.01933 -82.50967 98.50967 189.01933 ] + [-354.03867 -173.01933 189.01933 370.03867 ] [ -56. -56. 72. 72. ] [-120. -120. 136. 136. ] [-248. -248. 264. 264. ] - [ -82.50967 -37.254833 98.50967 53.254833] - [-173.01933 -82.50967 189.01933 98.50967 ] - [-354.03867 -173.01933 370.03867 189.01933 ]] + [ -37.254833 -82.50967 98.50967 53.254833] + [ -82.50967 -173.01933 189.01933 98.50967 ] + [-173.01933 -354.03867 370.03867 189.01933 ]] ## 目标检测度量标准汇总 ![目标检测指标汇总](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E7%9B%AE%E6%A0%87%E6%A3%80%E6%B5%8B%E5%BA%A6%E9%87%8F%E6%A0%87%E5%87%86%E6%B1%87%E6%80%BB.jpg) From 6f69826cb073023173b0df599a73dadd2d908dd0 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Mon, 9 Sep 2019 17:37:08 +0800 Subject: [PATCH 239/386] =?UTF-8?q?=E8=AE=A1=E7=AE=97=E6=9C=BA=E8=A7=86?= =?UTF-8?q?=E8=A7=89=E7=A7=8B=E6=8B=9B=E9=9D=A2=E7=BB=8F?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...72\347\241\200\347\237\245\350\257\206.md" | 16 ++++++++++- ...13\346\213\233\351\235\242\347\273\217.md" | 28 +++++++++++++++++++ 2 files changed, 43 insertions(+), 1 deletion(-) create mode 100644 "\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" diff --git "a/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200\347\237\245\350\257\206.md" "b/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200\347\237\245\350\257\206.md" index df5478fa..f9a32511 100644 --- "a/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200\347\237\245\350\257\206.md" +++ "b/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200\347\237\245\350\257\206.md" @@ -6,4 +6,18 @@ + 面向对象 + 数据库 + Linux系统开发 -+ 常用工具（Cmake/Git/Docker/正则表达式） \ No newline at end of file ++ 常用工具（Cmake/Git/Docker/正则表达式） + +## 操作系统 +### 并发与并行区别 ++ **并发**是指宏观上在一段时间内能同时运行多个程序，而**并行**则指在同一时刻能运行多个指令。 ++ 并行需要硬件支持，如多流水线、多核处理器或者分布式计算系统。 ++ 操作系统通过引入进程和线程，使得程序能够并发运行。 +## 数据库 +### 什么是事务 +事务是指满足 `ACID` 特性的一组操作，可以通过 `Commit` 提交一个事务，也可以用 `Rollback` 进行回滚。 +### 并发一致性问题 ++ 丢失修改 ++ 读脏数据 ++ 不可重复读 ++ 脏影读 \ No newline at end of file diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" new file mode 100644 index 00000000..5813cca1 --- /dev/null +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" @@ -0,0 +1,28 @@ +## 字节跳动AI Lab一面（挂） +1. 钢筋检测项目深挖 +2. `Roi Pooling` 和 `Roi Algin`区别 +3. `F1 Score` 如何计算 +4. `Siamese` 网络原理，`loss` 计算方式 +5. 算法题（忘了） + +## 地平线提前批一面（挂）(1个小时) +1. 两道算法题： + + 求二叉树的右视图 + + 输入一个数，找出含 7 的数字的数的最大个数 +2. 实习项目细节 +3. `Roi pooling` 和 `Roi Align` 怎么做的，`Roi Align`的反向传播公式写下 +4. 几种优化器说下 +5. `SGD` 公式和带动量`SGD`公式写下 + +## 元戎启行一面（50分钟） +1. 自我介绍 +2. 项目细节，深挖项目（图像增强怎么做的，过采样具体怎么实现，模型融合怎么融合等） +3. 实习项目，具体怎么解决了问题 +4. `Roi pooling` 为什么不如 `Roi Align`？ +5. `Focal loss`说下 +6. `git` 如何回退版本： + + `git log` 查看历史版本 + + `git reset -hard [版本id]` 恢复到历史版本 + + `git push -f -u origin master` 把修改推送到远程服务器 + +7. 一道编程题 \ No newline at end of file From 70e9fa5dce00ca89afb04c0c658e466246aed1fe Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Mon, 9 Sep 2019 17:38:29 +0800 Subject: [PATCH 240/386] =?UTF-8?q?=E8=AE=A1=E7=AE=97=E6=9C=BA=E8=A7=86?= =?UTF-8?q?=E8=A7=89=E7=A7=8B=E6=8B=9B=E9=9D=A2=E7=BB=8F?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...5\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" | 1 + 1 file changed, 1 insertion(+) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" index 5813cca1..8358c9fc 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" @@ -9,6 +9,7 @@ 1. 两道算法题： + 求二叉树的右视图 + 输入一个数，找出含 7 的数字的数的最大个数 + 2. 实习项目细节 3. `Roi pooling` 和 `Roi Align` 怎么做的，`Roi Align`的反向传播公式写下 4. 几种优化器说下 From 0b8cf0e2f0fdc1e69618dbe925eea263e3dac74d Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Mon, 9 Sep 2019 17:42:27 +0800 Subject: [PATCH 241/386] =?UTF-8?q?=E8=AE=A1=E7=AE=97=E6=9C=BA=E8=A7=86?= =?UTF-8?q?=E8=A7=89=E7=A7=8B=E6=8B=9B=E9=9D=A2=E7=BB=8F?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...27\347\247\213\346\213\233\351\235\242\347\273\217.md" | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" index 8358c9fc..c936c650 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" @@ -9,7 +9,7 @@ 1. 两道算法题： + 求二叉树的右视图 + 输入一个数，找出含 7 的数字的数的最大个数 - + 2. 实习项目细节 3. `Roi pooling` 和 `Roi Align` 怎么做的，`Roi Align`的反向传播公式写下 4. 几种优化器说下 @@ -25,5 +25,9 @@ + `git log` 查看历史版本 + `git reset -hard [版本id]` 恢复到历史版本 + `git push -f -u origin master` 把修改推送到远程服务器 +7. 多进程了解吗： + + `Python` 如何实现多进程; + + 多进程中如何对同一个变量进行操作; + + 进程之间的通信方式 -7. 一道编程题 \ No newline at end of file +8. 一道编程题 \ No newline at end of file From ea91c9009e06177f4ed100bf95a83b825dfc9639 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Mon, 9 Sep 2019 17:43:14 +0800 Subject: [PATCH 242/386] =?UTF-8?q?=E8=AE=A1=E7=AE=97=E6=9C=BA=E8=A7=86?= =?UTF-8?q?=E8=A7=89=E7=A7=8B=E6=8B=9B=E9=9D=A2=E7=BB=8F?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\227\347\247\213\346\213\233\351\235\242\347\273\217.md" | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" index c936c650..2ae1db18 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" @@ -1,11 +1,11 @@ -## 字节跳动AI Lab一面（挂） +### 字节跳动AI Lab一面（挂） 1. 钢筋检测项目深挖 2. `Roi Pooling` 和 `Roi Algin`区别 3. `F1 Score` 如何计算 4. `Siamese` 网络原理，`loss` 计算方式 5. 算法题（忘了） -## 地平线提前批一面（挂）(1个小时) +### 地平线提前批一面（挂）(1个小时) 1. 两道算法题： + 求二叉树的右视图 + 输入一个数，找出含 7 的数字的数的最大个数 @@ -15,7 +15,7 @@ 4. 几种优化器说下 5. `SGD` 公式和带动量`SGD`公式写下 -## 元戎启行一面（50分钟） +### 元戎启行一面（50分钟） 1. 自我介绍 2. 项目细节，深挖项目（图像增强怎么做的，过采样具体怎么实现，模型融合怎么融合等） 3. 实习项目，具体怎么解决了问题 From 6f79c956de47eab598dec6c833e595c5b6b347d8 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Mon, 9 Sep 2019 17:44:37 +0800 Subject: [PATCH 243/386] =?UTF-8?q?=E8=AE=A1=E7=AE=97=E6=9C=BA=E8=A7=86?= =?UTF-8?q?=E8=A7=89=E7=A7=8B=E6=8B=9B=E9=9D=A2=E7=BB=8F?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...5\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" | 1 - 1 file changed, 1 deletion(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" index 2ae1db18..55b2f28c 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" @@ -9,7 +9,6 @@ 1. 两道算法题： + 求二叉树的右视图 + 输入一个数，找出含 7 的数字的数的最大个数 - 2. 实习项目细节 3. `Roi pooling` 和 `Roi Align` 怎么做的，`Roi Align`的反向传播公式写下 4. 几种优化器说下 From 56b34a0dfea42c14e14408490017e477b6a2ee93 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Mon, 9 Sep 2019 17:45:22 +0800 Subject: [PATCH 244/386] =?UTF-8?q?=E8=AE=A1=E7=AE=97=E6=9C=BA=E8=A7=86?= =?UTF-8?q?=E8=A7=89=E7=A7=8B=E6=8B=9B=E9=9D=A2=E7=BB=8F?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" | 3 --- 1 file changed, 3 deletions(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" index 55b2f28c..08240aa5 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" @@ -4,7 +4,6 @@ 3. `F1 Score` 如何计算 4. `Siamese` 网络原理，`loss` 计算方式 5. 算法题（忘了） - ### 地平线提前批一面（挂）(1个小时) 1. 两道算法题： + 求二叉树的右视图 @@ -13,7 +12,6 @@ 3. `Roi pooling` 和 `Roi Align` 怎么做的，`Roi Align`的反向传播公式写下 4. 几种优化器说下 5. `SGD` 公式和带动量`SGD`公式写下 - ### 元戎启行一面（50分钟） 1. 自我介绍 2. 项目细节，深挖项目（图像增强怎么做的，过采样具体怎么实现，模型融合怎么融合等） @@ -28,5 +26,4 @@ + `Python` 如何实现多进程; + 多进程中如何对同一个变量进行操作; + 进程之间的通信方式 - 8. 一道编程题 \ No newline at end of file From 00120fb977d26d65c04d0f8b3fd11cb347f3d703 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Wed, 11 Sep 2019 14:29:05 +0800 Subject: [PATCH 245/386] fix some bug --- ...55\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 2 +- ...62\227\347\247\213\346\213\233\351\235\242\347\273\217.md" | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 41dda464..5a2ca463 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -23,7 +23,7 @@ **反卷积的大小是由卷积核大小与滑动步长决定**， in是输入大小， k是卷积核大小， s是滑动步长， padding的像素数 P, out是输出大小。 得到 `out = (in - 1) * s -2p + k`，还有另外一个写法：`W = (N - 1)*S - 2P + F`。 -例如 输入：2x2， 卷积核：4x4， 滑动步长：3， 输出：7x7 ，其计算过程就是， (2 - 1) * 3 + 4 = 7 +例如 输入：2x2， 卷积核：4x4， 滑动步长：3，填充像素为0， 输出：7x7 ，其计算过程就是， (2 - 1) * 3 + 4 = 7 3.池化得到的特征图大小计算方式： `W=(W-F)/S+1`，池化层一般不填充像素(`VALID`) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" index 08240aa5..416c7b62 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" @@ -10,9 +10,9 @@ + 输入一个数，找出含 7 的数字的数的最大个数 2. 实习项目细节 3. `Roi pooling` 和 `Roi Align` 怎么做的，`Roi Align`的反向传播公式写下 -4. 几种优化器说下 +4. 几种优化器说下，说下区别及优缺点 5. `SGD` 公式和带动量`SGD`公式写下 -### 元戎启行一面（50分钟） +### 元戎启行一面（50分钟）(挂) 1. 自我介绍 2. 项目细节，深挖项目（图像增强怎么做的，过采样具体怎么实现，模型融合怎么融合等） 3. 实习项目，具体怎么解决了问题 From ca3cdfddcc993cae347e59e0cc7033f7ff917e31 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Wed, 11 Sep 2019 14:33:28 +0800 Subject: [PATCH 246/386] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E4=B8=BA=E4=BB=80?= =?UTF-8?q?=E4=B9=88=E4=BA=A4=E5=8F=89=E7=86=B5=E5=8F=AF=E4=BB=A5=E7=94=A8?= =?UTF-8?q?=E4=BD=9C=E4=BB=A3=E4=BB=B7=E5=87=BD=E6=95=B0=E7=9A=84=E9=97=AE?= =?UTF-8?q?=E9=A2=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 2 ++ 1 file changed, 2 insertions(+) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 5a2ca463..d61d992c 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -50,6 +50,8 @@ $$L = -\sum_{c=1}^{M}y_{c}log(p_{c})或者H(p,q)=-\sum p(x)logq(x)$$（p代表 - $y_{c}$ ——指示变量（0或1）,如果该类别和样本的类别相同就是1，否则是0； - $p_{c}$ ——对于观测样本属于类别 $c$ 的预测概率。 +## 为什么交叉熵可以用作代价函数 +从数学上来理解就是，为了让学到的模型分布更接近真实数据的分布，我们需要最小化模型数据分布与训练数据之间的 `KL 散度`，而因为训练数据的分布是固定的，因此最小化 `KL 散度`等价于最小化交叉熵，而且交叉熵计算更简单，所以机器/深度学习中常用交叉熵 `cross-entroy` 作为分类问题的损失函数。 ## 优化算法理解 `Adam`、`AdaGrad`、`RMSProp`优化算法具有自适应性 ## 感受野 From f7cc6dfa2ff1517ffaa0bb34f7ff6e18ce5bd758 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Wed, 11 Sep 2019 14:57:20 +0800 Subject: [PATCH 247/386] fix some bug --- ...55\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index d61d992c..99eab1f0 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -54,7 +54,7 @@ $$L = -\sum_{c=1}^{M}y_{c}log(p_{c})或者H(p,q)=-\sum p(x)logq(x)$$（p代表 从数学上来理解就是，为了让学到的模型分布更接近真实数据的分布，我们需要最小化模型数据分布与训练数据之间的 `KL 散度`，而因为训练数据的分布是固定的，因此最小化 `KL 散度`等价于最小化交叉熵，而且交叉熵计算更简单，所以机器/深度学习中常用交叉熵 `cross-entroy` 作为分类问题的损失函数。 ## 优化算法理解 `Adam`、`AdaGrad`、`RMSProp`优化算法具有自适应性 -## 感受野 +## 感受野(Receptive Field) 后一层神经元在前一层神经元的感受空间，如下图所示： ![感受野大小](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E6%84%9F%E5%8F%97%E9%87%8E%E7%A9%BA%E9%97%B4.jpg) @@ -165,7 +165,7 @@ RNN的输入有两个部分，一部分为上一时刻的状态，另一部分 + 更多回答，参考此链接。 ## VGG使用2个3*3卷积的优势在哪里？ -(1). **减少网络层参数**。用两个3*3卷积比用1个5*5卷积拥有更少的参数量，只有后者的2∗3∗35∗5=0.72。但是起到的效果是一样的，两个3*3的卷积层串联相当于一个5*5的卷积层，感受野的大小都是5×5，即1个像素会跟周围5*5的像素产生关联。把下图当成动态图看，很容易看到两个3×3卷积层堆叠（没有空间池化）有5×5的有效感受野。 +(1). **减少网络层参数**。用两个3\*3卷积比用1个5\*5卷积拥有更少的参数量，只有后者的2∗3∗3/5∗5=0.72。但是起到的效果是一样的，两个3*3的卷积层串联相当于一个5*5的卷积层，感受野的大小都是5×5，即1个像素会跟周围5*5的像素产生关联。把下图当成动态图看，很容易看到两个3×3卷积层堆叠（没有空间池化）有5×5的有效感受野。 ![2个３*3卷积层](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/2%E4%B8%AA3_3%E5%8D%B7%E7%A7%AF%E5%B1%82.png) From a366b727fb2b7740c04294fd605cdb16db460e0d Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Wed, 11 Sep 2019 15:07:44 +0800 Subject: [PATCH 248/386] add some problem --- ...\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 99eab1f0..65adc7d4 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -55,7 +55,7 @@ $$L = -\sum_{c=1}^{M}y_{c}log(p_{c})或者H(p,q)=-\sum p(x)logq(x)$$（p代表 ## 优化算法理解 `Adam`、`AdaGrad`、`RMSProp`优化算法具有自适应性 ## 感受野(Receptive Field) -后一层神经元在前一层神经元的感受空间，如下图所示： +后一层神经元在前一层神经元的感受空间，即如下图所示： ![感受野大小](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E6%84%9F%E5%8F%97%E9%87%8E%E7%A9%BA%E9%97%B4.jpg) 注意：小卷积核（如3*3）通过多层叠加可取得与大卷积核（如7*7）同等规模的感受野，此外采用小卷积核有两个优势： @@ -64,6 +64,10 @@ $$L = -\sum_{c=1}^{M}y_{c}log(p_{c})或者H(p,q)=-\sum p(x)logq(x)$$（p代表 ## 卷积操作作用 + 卷积网络中的卷积核参数是通过网络训练出来的 + 通过卷积核的组合以及随着网络后续操作的进行，卷积操作可获取图像区域不同类型特征；基本而一般的模式会逐渐被抽象为具有高层语义的“概念”表示，也就是自动学习到图像的高层特征 ++ 卷积神经网络学到的模式具有平移不变性（translation invariant），卷积神经网络可以学到模式的空间层次结构。 +## 卷积层conv与全连接层dense的区别 ++ 卷积层学习到的是局部模式（对于图像，学到的就是在输入图像的二维小窗口中发现的模式） ++ 全连接层学习到的是全局模式（全局模式就算设计所有像素） ## CNN权值共享问题 首先**权值共享就是滤波器共享**，滤波器的参数是固定的，即是用相同的滤波器去扫一遍图像，提取一次特征特征，得到feature map。在卷积网络中，学好了一个滤波器，就相当于掌握了一种特征，这个滤波器在图像中滑动，进行特征提取，然后所有进行这样操作的区域都会被采集到这种特征，就好比上面的水平线。 From d8383a5196ba4b3816726fd13574066c33b65e76 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Wed, 11 Sep 2019 23:13:44 +0800 Subject: [PATCH 249/386] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E5=A6=82=E4=BD=95?= =?UTF-8?q?=E8=AE=A1=E7=AE=97=E6=84=9F=E5=8F=97=E9=87=8E=E5=A4=A7=E5=B0=8F?= =?UTF-8?q?=E7=9A=84=E4=BB=A3=E7=A0=81=E5=92=8C=E7=9F=A5=E8=AF=86?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...40\351\235\242\350\257\225\351\242\230.md" | 107 +++++++++++++++++- 1 file changed, 105 insertions(+), 2 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 65adc7d4..18c173db 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -55,16 +55,119 @@ $$L = -\sum_{c=1}^{M}y_{c}log(p_{c})或者H(p,q)=-\sum p(x)logq(x)$$（p代表 ## 优化算法理解 `Adam`、`AdaGrad`、`RMSProp`优化算法具有自适应性 ## 感受野(Receptive Field) -后一层神经元在前一层神经元的感受空间，即如下图所示： +后一层神经元在前一层神经元的感受空间，也可以定义为卷积神经网络中每层的特征图（`Feature Map`）上的像素点在原始图像中映射的区域大小，即如下图所示： ![感受野大小](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E6%84%9F%E5%8F%97%E9%87%8E%E7%A9%BA%E9%97%B4.jpg) -注意：小卷积核（如3*3）通过多层叠加可取得与大卷积核（如7*7）同等规模的感受野，此外采用小卷积核有两个优势： +注意：感受野在 `CNN` 中是呈指数级增加的。小卷积核（如3*3）通过多层叠加可取得与大卷积核（如7*7）同等规模的感受野，此外采用小卷积核有两个优势： 1. 小卷积核需多层叠加，加深了网络深度进而增强了网络容量(model capacity)和复杂度（model complexity） 2. 增强了网络容量的同时减少了参数个数。 + +### 感受野大小计算 +计算感受野时，我们需要知道： +> 参考[感受野（receptive file）计算](https://www.starlg.cn/blog/2017/06/13/Receptive-File/) + ++ 第一层卷积层的输出特征图像素的感受野的大小等于滤波器的大小 ++ 深层卷积层的感受野大小和它之前所有层的滤波器大小和步长有关系 ++ 计算感受野大小时，忽略了图像边缘的影响 + +感受野大小的计算采用 `top to down` 的方式， 即先计算最深层在前一层上的感受野，然后逐渐传递到第一层，使用的公式可以表示如下: $$RF = ((RF-)*stride + fsize +```　 +for layer in （top layer To down layer）: 　　　　 + RF = ((RF -1)* stride) + fsize +``` ++ `stride`: 表示卷积的步长； ++ `fsize`: 表示卷积层滤波器的大小 ++ `RF`: 待计算的feature map上的感受野大小,原图，RF = 1 + +以下 Python 代码可以实现计算 `Alexnet zf-5` 和 `VGG16` 网络每层输出 `feature map` 的感受野大小，卷积核大小和输入图像尺寸默认定义好了，代码如下： +```Python +#!/usr/bin/env python + +# [filter size, stride, padding] +net_struct = {'alexnet': {'net':[[11,4,0],[3,2,0],[5,1,2],[3,2,0],[3,1,1],[3,1,1],[3,1,1],[3,2,0]], + 'name':['conv1','pool1','conv2','pool2','conv3','conv4','conv5','pool5']}, + 'vgg16': {'net':[[3,1,1],[3,1,1],[2,2,0],[3,1,1],[3,1,1],[2,2,0],[3,1,1],[3,1,1],[3,1,1], + [2,2,0],[3,1,1],[3,1,1],[3,1,1],[2,2,0],[3,1,1],[3,1,1],[3,1,1],[2,2,0]], + 'name':['conv1_1','conv1_2','pool1','conv2_1','conv2_2','pool2','conv3_1','conv3_2', + 'conv3_3', 'pool3','conv4_1','conv4_2','conv4_3','pool4','conv5_1','conv5_2','conv5_3','pool5']}, + 'zf-5':{'net': [[7,2,3],[3,2,1],[5,2,2],[3,2,1],[3,1,1],[3,1,1],[3,1,1]], + 'name': ['conv1','pool1','conv2','pool2','conv3','conv4','conv5']}} + + + +def outFromIn(isz, net, layernum): + totstride = 1 + insize = isz + for layer in range(layernum): + fsize, stride, pad = net[layer] + outsize = (insize - fsize + 2*pad) / stride + 1 + insize = outsize + totstride = totstride * stride + return outsize, totstride + +def inFromOut(net, layernum): + RF = 1 + for layer in reversed(range(layernum)): + fsize, stride, pad = net[layer] + RF = ((RF -1)* stride) + fsize + return RF + +if __name__ == '__main__': + imsize = 224 + + print "layer output sizes given image = %dx%d" % (imsize, imsize) + + for net in net_struct.keys(): + print '************net structrue name is %s**************'% net + for i in range(len(net_struct[net]['net'])): + p = outFromIn(imsize,net_struct[net]['net'], i+1) + rf = inFromOut(net_struct[net]['net'], i+1) + print "Layer Name = %s, Output size = %3d, Stride = % 3d, RF size = %3d" % (net_struct[net]['name'][i], p[0], p[1], rf) +``` +输出结果如下： +> layer output sizes given image = 224x224 +************net structrue name is vgg16************** +Layer Name = conv1_1, Output size = 224, Stride = 1, RF size = 3 +Layer Name = conv1_2, Output size = 224, Stride = 1, RF size = 5 +Layer Name = pool1, Output size = 112, Stride = 2, RF size = 6 +Layer Name = conv2_1, Output size = 112, Stride = 2, RF size = 10 +Layer Name = conv2_2, Output size = 112, Stride = 2, RF size = 14 +Layer Name = pool2, Output size = 56, Stride = 4, RF size = 16 +Layer Name = conv3_1, Output size = 56, Stride = 4, RF size = 24 +Layer Name = conv3_2, Output size = 56, Stride = 4, RF size = 32 +Layer Name = conv3_3, Output size = 56, Stride = 4, RF size = 40 +Layer Name = pool3, Output size = 28, Stride = 8, RF size = 44 +Layer Name = conv4_1, Output size = 28, Stride = 8, RF size = 60 +Layer Name = conv4_2, Output size = 28, Stride = 8, RF size = 76 +Layer Name = conv4_3, Output size = 28, Stride = 8, RF size = 92 +Layer Name = pool4, Output size = 14, Stride = 16, RF size = 100 +Layer Name = conv5_1, Output size = 14, Stride = 16, RF size = 132 +Layer Name = conv5_2, Output size = 14, Stride = 16, RF size = 164 +Layer Name = conv5_3, Output size = 14, Stride = 16, RF size = 196 +Layer Name = pool5, Output size = 7, Stride = 32, RF size = 212 +************net structrue name is zf-5************** +Layer Name = conv1, Output size = 112, Stride = 2, RF size = 7 +Layer Name = pool1, Output size = 56, Stride = 4, RF size = 11 +Layer Name = conv2, Output size = 28, Stride = 8, RF size = 27 +Layer Name = pool2, Output size = 14, Stride = 16, RF size = 43 +Layer Name = conv3, Output size = 14, Stride = 16, RF size = 75 +Layer Name = conv4, Output size = 14, Stride = 16, RF size = 107 +Layer Name = conv5, Output size = 14, Stride = 16, RF size = 139 +************net structrue name is alexnet************** +Layer Name = conv1, Output size = 54, Stride = 4, RF size = 11 +Layer Name = pool1, Output size = 26, Stride = 8, RF size = 19 +Layer Name = conv2, Output size = 26, Stride = 8, RF size = 51 +Layer Name = pool2, Output size = 12, Stride = 16, RF size = 67 +Layer Name = conv3, Output size = 12, Stride = 16, RF size = 99 +Layer Name = conv4, Output size = 12, Stride = 16, RF size = 131 +Layer Name = conv5, Output size = 12, Stride = 16, RF size = 163 +Layer Name = pool5, Output size = 5, Stride = 32, RF size = 195 + ## 卷积操作作用 + 卷积网络中的卷积核参数是通过网络训练出来的 + 通过卷积核的组合以及随着网络后续操作的进行，卷积操作可获取图像区域不同类型特征；基本而一般的模式会逐渐被抽象为具有高层语义的“概念”表示，也就是自动学习到图像的高层特征 + 卷积神经网络学到的模式具有平移不变性（translation invariant），卷积神经网络可以学到模式的空间层次结构。 + ## 卷积层conv与全连接层dense的区别 + 卷积层学习到的是局部模式（对于图像，学到的就是在输入图像的二维小窗口中发现的模式） + 全连接层学习到的是全局模式（全局模式就算设计所有像素） From e69ca101c59af9187628ada4d70db75bcf423713 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Wed, 11 Sep 2019 23:21:51 +0800 Subject: [PATCH 250/386] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E5=A6=82=E4=BD=95?= =?UTF-8?q?=E8=AE=A1=E7=AE=97=E6=84=9F=E5=8F=97=E9=87=8E=E5=A4=A7=E5=B0=8F?= =?UTF-8?q?=E7=9A=84=E4=BB=A3=E7=A0=81=E5=92=8C=E7=9F=A5=E8=AF=86?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...55\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 18c173db..86075340 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -125,7 +125,8 @@ if __name__ == '__main__': print "Layer Name = %s, Output size = %3d, Stride = % 3d, RF size = %3d" % (net_struct[net]['name'][i], p[0], p[1], rf) ``` 输出结果如下： -> layer output sizes given image = 224x224 +```shell +layer output sizes given image = 224x224 ************net structrue name is vgg16************** Layer Name = conv1_1, Output size = 224, Stride = 1, RF size = 3 Layer Name = conv1_2, Output size = 224, Stride = 1, RF size = 5 @@ -162,6 +163,7 @@ Layer Name = conv3, Output size = 12, Stride = 16, RF size = 99 Layer Name = conv4, Output size = 12, Stride = 16, RF size = 131 Layer Name = conv5, Output size = 12, Stride = 16, RF size = 163 Layer Name = pool5, Output size = 5, Stride = 32, RF size = 195 +``` ## 卷积操作作用 + 卷积网络中的卷积核参数是通过网络训练出来的 From 3647dfb62feb424d131488f1b14a694fecca4f31 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Fri, 13 Sep 2019 00:33:26 +0800 Subject: [PATCH 251/386] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E6=AD=A3=E7=A1=AE?= =?UTF-8?q?=E7=9A=84=E5=BF=AB=E6=8E=92=E4=BB=A3=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...46\344\271\240\347\254\224\350\256\260.md" | 34 +++++++++++++++++++ 1 file changed, 34 insertions(+) diff --git "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" index 90cfbdb5..b0b284c9 100644 --- "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" +++ "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" @@ -94,6 +94,40 @@ def quicksort(array): return quicksort(less) + [pivot] + quicksort(greater) print(quicksort([4, 90, 0, 2, 17, 79, 12])) # [0, 2, 4, 12, 17, 79, 90] ``` +上面的代码空间复杂度很大，真正的快排是原地排序，空间复杂度为O(1),代码如下： +```python +# _*_ coding:utf-8 _*_ + +def quick_sort(L): + return q_sort(L, 0, len(L)-1) + +def q_sort(L, left, right): + if left < right: + pivot = Partition(L, left, right) + q_sort(L, left, pivot-1) + q_sort(L, pivot+1, right) + + return L + +def Partition(L, left, right): + pivotkey = L[left] + + while left < right: + while left < right and L[right] >= pivotkey: + right -= 1 + L[left] = L[right] + while left < right and L[left] <= pivotkey: + left += 1 + L[right] = L[left] # 遇到比基准大的数, 则覆盖在之前尾部指针的位置 + L[left] = pivotkey + return left + + +if __name__ == "__main__": + L = [5, 9, 1, 1, 11, 6, 7, 2, 4] + print(quick_sort(L)) +``` + ## 4.3 再谈大Ｏ表示法 快速排序的独特之处在于，其速度取决于选择的基准值。在讨论快速排序的运行时间前，我 们再来看看最常见的大O运行时间。 ![4.3 再谈大O表示法](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/4.3%E5%86%8D%E8%B0%88%E5%A4%A7O%E8%A1%A8%E7%A4%BA%E6%B3%95.png) From 39e00f66d8c8c23f81359b9c4840271b2d36be42 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Mon, 16 Sep 2019 22:57:54 +0800 Subject: [PATCH 252/386] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E6=B6=82=E9=B8=A6?= =?UTF-8?q?=E7=A7=BB=E5=8A=A8=E9=9D=A2=E7=BB=8F?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...347\247\213\346\213\233\351\235\242\347\273\217.md" | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" index 416c7b62..6215a2d9 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" @@ -26,4 +26,12 @@ + `Python` 如何实现多进程; + 多进程中如何对同一个变量进行操作; + 进程之间的通信方式 -8. 一道编程题 \ No newline at end of file +8. 一道编程题 + +### 涂鸦移动一面(45分钟) +1. 实习项目介绍 +2. Python多进程介绍下 +3. 面向对象讲解下 +4. 两道编程题 + + 二维数组矩阵 + + 求top k数 \ No newline at end of file From 1a2e635bfbef2af3ff4b1d4241028c69abac07b0 Mon Sep 17 00:00:00 2001 From: Zhang Date: Mon, 16 Sep 2019 23:20:00 +0800 Subject: [PATCH 253/386] Add files via upload --- ...264\345\244\215\346\235\202\345\272\246.jpg" | Bin 0 -> 56876 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 "images/\346\216\222\345\272\217\347\256\227\346\263\225\346\227\266\351\227\264\345\244\215\346\235\202\345\272\246.jpg" diff --git "a/images/\346\216\222\345\272\217\347\256\227\346\263\225\346\227\266\351\227\264\345\244\215\346\235\202\345\272\246.jpg" "b/images/\346\216\222\345\272\217\347\256\227\346\263\225\346\227\266\351\227\264\345\244\215\346\235\202\345\272\246.jpg" new file mode 100644 index 0000000000000000000000000000000000000000..a04cf60b37c1539016595fae00e1639441707c35 GIT binary patch literal 56876 zcmV)BK*PUMNk&E_-T(ksMM6+kP&gnM-T(mb&jOtRD#im$0zPdtl}Mx_p`s&|YEXa; 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zBT#j7GpNjUc?3^T*2x>nO%8rqwUdx{zCW9W*@9Xjg);8!FX?K1L;CfbL9XLIXL@us zV;W+hrrqFhaaZb9L-MZr^H#heb2_+O{Ra|l?|z;tgbr)ES~}DRcXM&&yx90msfz_Y ziU3>fK1x7bu5zMT9VUIm@&A0ZDq^bgFx1%JJ@@eGfoJ7_s)}M8<7n0TFX|?uz|7qR zaPWP=NAHq9y3k`+hubMw-CI{?lIlGw(F1x*#S}#TVgwAvjDJrRhaMsZMIP*DUX?T$ z3+&ZZHEt9#?`lH?```_cK;hlAFFbm7S^b-`@^6}DC@jxPUi=>dT`5C+>ZbACBBDSh zP39&cZLe0TK)}A``oMjm zw0p^9T!*(FNs=1J`*;!I6$&73To(ecB>#cF(CL@}n470+BLthjn!rAww8Y7^u`oNEw7y%)^BgT-U N5RemZ1!sha0031J<6{5- literal 0 HcmV?d00001 From affd3d059a740125738d71b65fd6919f747b03aa Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Mon, 16 Sep 2019 23:22:54 +0800 Subject: [PATCH 254/386] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E6=8E=92=E5=BA=8F?= =?UTF-8?q?=E7=AE=97=E6=B3=95=E5=A4=8D=E6=9D=82=E5=BA=A6=E5=88=86=E6=9E=90?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...345\211\221\346\214\207offer\351\242\230\350\247\243.md" | 0 ...\225\345\255\246\344\271\240\347\254\224\350\256\260.md" | 6 ++++++ 2 files changed, 6 insertions(+) create mode 100644 "\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\345\211\221\346\214\207offer\351\242\230\350\247\243.md" diff --git "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\345\211\221\346\214\207offer\351\242\230\350\247\243.md" "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\345\211\221\346\214\207offer\351\242\230\350\247\243.md" new file mode 100644 index 00000000..e69de29b diff --git "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" index b0b284c9..bacc9ca0 100644 --- "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" +++ "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" @@ -1,3 +1,9 @@ +# 前言知识 +> [十大经典排序算法动画与解析，看我就够了！（配代码完全版）](https://mp.weixin.qq.com/s/vn3KiV-ez79FmbZ36SX9lg) + +10大排序算法时间复杂度及空间复杂度如下图： +![10大排序算法时间复杂度及空间复杂度](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E6%8E%92%E5%BA%8F%E7%AE%97%E6%B3%95%E6%97%B6%E9%97%B4%E5%A4%8D%E6%9D%82%E5%BA%A6.jpg) + # 第一章，算法简介 ## 1.2，二分法查找元素 一般而言，对于包含n个元素的列表查找某个元素，使用二分法最多需要$log_{2}n$步(**时间复杂度为$log_{2}n$**)，简单查找最多需要n步。大O表示法指出了算法最糟糕情况下的运行时间 From a28e35e5ab7ce4855d0a57d5de51a4b336eb87f3 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Tue, 17 Sep 2019 20:08:36 +0800 Subject: [PATCH 255/386] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E5=8D=A1=E6=96=AF?= =?UTF-8?q?=E6=9F=AF=E5=92=8C=E5=B7=A5=E5=95=86=E9=93=B6=E8=A1=8C=E9=9D=A2?= =?UTF-8?q?=E7=BB=8F?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...6\214\207offer\351\242\230\350\247\243.md" | 11 ++++++++++ ...13\346\213\233\351\235\242\347\273\217.md" | 20 +++++++++++++++++-- 2 files changed, 29 insertions(+), 2 deletions(-) diff --git "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\345\211\221\346\214\207offer\351\242\230\350\247\243.md" "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\345\211\221\346\214\207offer\351\242\230\350\247\243.md" index e69de29b..790cb937 100644 --- "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\345\211\221\346\214\207offer\351\242\230\350\247\243.md" +++ "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\345\211\221\346\214\207offer\351\242\230\350\247\243.md" @@ -0,0 +1,11 @@ +## 030 最小的K个数 +> [华为OJ2051-最小的K个数（Top K问题）](https://songlee24.github.io/2015/03/21/hua-wei-OJ2051/) + +**题目描述**： +输入 `n` 个数，输出其中最小的 `k` 个 +### 方法１部分排序 +### 方法2 快速划分 +### 方法3 构建大根堆 ++ 遍历前 `1-k` 个数，创建大小为 `k` 的大根堆用来存储最小的 k 个数，大根堆堆顶元素是最小 k 个数中最大的一个； ++ 遍历 `k-n` 个数，得到元素　`X`，如果　`X`　比堆顶元素 `Y` 大，则不改变原来的堆，因为这个元素比大根堆中所有元素都大； ++ 如果　`X`　比堆顶元素 `Y` 小，那么用 `X` 替换堆顶的元素 `Y`。在 X 替换堆顶元素 Y 之后，大根堆的结构可能被破坏，需要进行向下调整(重新排序)。调整过程的时间复杂度是 $O(log_2k)$ 。 \ No newline at end of file diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" index 6215a2d9..5924a3d8 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" @@ -32,6 +32,22 @@ 1. 实习项目介绍 2. Python多进程介绍下 3. 面向对象讲解下 -4. 两道编程题 +4. 说说有哪些排序算法，讲下你最熟悉的几种，怎么实现的 +5. 两道编程题 + 二维数组矩阵 - + 求top k数 \ No newline at end of file + + 求top k数 + +## 卡斯柯信号一面(35分钟)(人工智能->C++开发) +1. 项目介绍 +2. 你是如何学习机器学习、深度学习的 +3. 指针和引用的区别 +4. `c++` 面向对象的三个特性：继承、多态、接口 +5. 基类和派生类析构函数有什么区别 +6. 讲下继承 +## 工商银行(15分钟)（软甲开发中心） +1. 核心项目介绍(围绕项目提了一些问题) +2. 你的本科和研究所成绩，为什么投成都岗 +3. 你的本科学校是什么类型的，研究所毕业设计打算做什么 +4. 你的博客主要写了什么内容 +5. 你的比赛经历介绍下 +6. 你有什么想问的 \ No newline at end of file From 39893e90b5ae1ec9d8925eea82e4728322e6adf6 Mon Sep 17 00:00:00 2001 From: Zhang Date: Tue, 17 Sep 2019 23:27:04 +0800 Subject: [PATCH 256/386] Add files via upload --- images/roi align.png | Bin 0 -> 122256 bytes images/roi pooling.png | Bin 0 -> 128007 bytes 2 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 images/roi align.png create mode 100644 images/roi pooling.png diff --git a/images/roi align.png b/images/roi align.png new file mode 100644 index 0000000000000000000000000000000000000000..e1de2cafdd2947d03776f9be8656dd388116b9b5 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channel 数， 最终 feature map 因为 bit 数过低， 还是浪费了模型容量。 同理反过来也是。 + 建议使用 4bit 及以下的 activation bit， 过高带来的精度收益变小， 而会显著提高 inference 计算量 ## 知识蒸馏 -知识蒸馏（knowledge distillation），是迁移学习（transfer learning）的一种，简单来说就是训练一个大模型和一个小模型，将庞大而复杂的大模型学习到的知识，通过一定技术手段迁移到精简的小模型上，从而使小模型能够获得与大模型相近的性能。 +知识蒸馏（knowledge distillation），是迁移学习（transfer learning）的一种，简单来说就是训练一个大模型（teacher）和一个小模型（student），将庞大而复杂的大模型学习到的知识，通过一定技术手段迁移到精简的小模型上，从而使小模型能够获得与大模型相近的性能。 student模型最终的损失函数由两部分组成： + 第一项是由小模型的预测结果与大模型的“软标签”所构成的交叉熵（cross entroy）; + 第二项为预测结果与普通类别标签的交叉熵。 diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" index 25339c0f..8084fe71 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" @@ -583,23 +583,26 @@ def voc_eval(detpath, ``` ## FLOPs计算 -+ MACC：multiply-accumulate，乘法累加。 -+ FLOPS：Floating-point Operations Per Second，每秒所执行的浮点运算次数。 ++ `FLOPs`：floating point operations 指的是浮点运算次数 ++ `MACC`：multiply-accumulate，乘法累加。 ++ `FLOPS`：Floating-point Operations Per Second，每秒所执行的浮点运算次数。 -> 先解释一下FLOPs：floating point operations 指的是浮点运算次数，理解为计算量，可以用来衡量算法/模型的复杂度。此处区分一下FLOPS（全部大写），FLOPS指的是每秒运算的浮点数，理解为计算速度，衡量一个硬件的标准。我们要的是衡量模型的复杂度的指标，所以选择FLOPs。 +> 先解释一下FLOPs：floating point operations 指的是浮点运算次数，理解为计算量，可以用来衡量算法/模型的复杂度。此处区分一下FLOPS（全部大写），FLOPS指的是每秒执行的浮点运算次数，理解为计算速度，衡量一个硬件的标准。我们要的是衡量模型的复杂度的指标，所以选择FLOPs。 ### 卷积层FLOPs计算 + $FLOPs=(2*C_i*K*K-1)*H*W*C_o$（不考虑bias） + $FLOPs=(2*C_i*K*K)*H*W*C_o$（考虑bias） + $MACC=(C_i*K*K)*H*W*C_o$（考虑bias） -**$C_i$为输入特征图通道数，$K$ 为过滤器尺寸，$H,W,C_o$ 为输出特征图的高，宽和通道数**。`卷积过程`如下图所示： +**$C_i$ 为输入特征图通道数，$K$ 为过滤器尺寸，$H,W,C_o$ 为输出特征图的高，宽和通道数**。`二维卷积过程`如下图所示： -![卷积过程](../images/卷积过程图.png) +> 二维卷积是一个相当简单的操作：从卷积核开始，这是一个小的权值矩阵。这个卷积核在 2 维输入数据上「滑动」，对当前输入的部分元素进行矩阵乘法，然后将结果汇为单个输出像素。 + +![卷积过程](../images/二维卷积操作动态图.gif) 公式解释，参考[这里](https://zhuanlan.zhihu.com/p/70306015?utm_source=wechat_session&utm_medium=social&utm_oi=571954943427219456)，如下： -最后得到的 $C_o$ 张输出特征图，每张特征图上有 HxW 个像素点，而这其中的每个像素点的值都是由过滤器与输入特征图卷积得到的，过滤器中有 $C_i*K*K$ 个点，每个点都要和输入特征图对应点作一次相乘操作（浮点操作数为 $C_i*K*K$），然后将这些过滤器和输入特征图对应点相乘所得的数相加起来（浮点操作数为 $C_i*K*K-1$，n个数相加，所需要的浮点操作数为 n-1），得到一个值，对应于一张输出特征图中的一个像素，输出特征图有 $C_o$ 张，故有 $C_o$ 个过滤器参与卷积运算。 +执行卷积操作后输出 $C_o$ 张输出特征图，每张特征图上有 HxW 个像素点，而这其中的每个像素点的值都是由过滤器与输入特征图卷积得到的，过滤器中有 $C_i*K*K$ 个点，每个点都要和输入特征图对应点作一次相乘操作（浮点操作数为 $C_i*K*K$），然后将这些过滤器和输入特征图对应点相乘所得的数相加起来（浮点操作数为 $C_i*K*K-1$，n个数相加，所需要的浮点操作数为 n-1），得到一个值，对应于一张输出特征图中的一个像素，输出特征图有 $C_o$ 张，故有 $C_o$ 个过滤器参与卷积运算。 所以卷积层的 $FLOPs=(2*C_{i}*K*K-1)*H*W*C_o$ ($C_i$ 为输入特征图通道数，$K$ 为过滤器尺寸，$H, W, C_o$为输出特征图的高，宽和通道数)。 From 2577a55332ac7b2a6492abe78c2b0b20356ec4cc Mon Sep 17 00:00:00 2001 From: unknown Date: Sat, 8 Aug 2020 17:08:17 +0800 Subject: [PATCH 327/386] =?UTF-8?q?=E6=9B=B4=E6=96=B0FLOPs=E7=9A=84?= =?UTF-8?q?=E8=A7=A3=E9=87=8A?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...267\346\214\207\346\240\207\350\256\241\347\256\227.md" | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" index 8084fe71..84b5892e 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" @@ -583,9 +583,10 @@ def voc_eval(detpath, ``` ## FLOPs计算 -+ `FLOPs`：floating point operations 指的是浮点运算次数 -+ `MACC`：multiply-accumulate，乘法累加。 -+ `FLOPS`：Floating-point Operations Per Second，每秒所执行的浮点运算次数。 ++ `FLOPs`：floating point operations 指的是浮点运算次数,**理解为计算量**，可以用来衡量算法/模型的复杂度。 ++ `FLOPS`：Floating-point Operations Per Second，每秒所执行的浮点运算次数，理解为计算速度,是一个衡量硬件性能的指标。 ++ `MACC`：multiply-accumulate，乘法累加次数。 + > 先解释一下FLOPs：floating point operations 指的是浮点运算次数，理解为计算量，可以用来衡量算法/模型的复杂度。此处区分一下FLOPS（全部大写），FLOPS指的是每秒执行的浮点运算次数，理解为计算速度，衡量一个硬件的标准。我们要的是衡量模型的复杂度的指标，所以选择FLOPs。 From 40f7e207475230058dba7509d95e938fb5513891 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Sat, 8 Aug 2020 17:25:51 +0800 Subject: [PATCH 328/386] =?UTF-8?q?=E6=9B=B4=E6=96=B0FLOPs=E7=9A=84?= =?UTF-8?q?=E8=A7=A3=E9=87=8A?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...3\267\346\214\207\346\240\207\350\256\241\347\256\227.md" | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" index 84b5892e..637eec98 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" @@ -584,12 +584,9 @@ def voc_eval(detpath, ## FLOPs计算 + `FLOPs`：floating point operations 指的是浮点运算次数,**理解为计算量**，可以用来衡量算法/模型的复杂度。 -+ `FLOPS`：Floating-point Operations Per Second，每秒所执行的浮点运算次数，理解为计算速度,是一个衡量硬件性能的指标。 ++ `FLOPS`：（全部大写）,Floating-point Operations Per Second，每秒所执行的浮点运算次数，理解为计算速度,是一个衡量硬件性能的指标。 + `MACC`：multiply-accumulate，乘法累加次数。 - -> 先解释一下FLOPs：floating point operations 指的是浮点运算次数，理解为计算量，可以用来衡量算法/模型的复杂度。此处区分一下FLOPS（全部大写），FLOPS指的是每秒执行的浮点运算次数，理解为计算速度，衡量一个硬件的标准。我们要的是衡量模型的复杂度的指标，所以选择FLOPs。 - ### 卷积层FLOPs计算 + $FLOPs=(2*C_i*K*K-1)*H*W*C_o$（不考虑bias） + $FLOPs=(2*C_i*K*K)*H*W*C_o$（考虑bias） From a7a7b8c29a9a6a73c94e64344be55f17d91e4cda Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Sat, 8 Aug 2020 22:41:59 +0800 Subject: [PATCH 329/386] =?UTF-8?q?=E6=9B=B4=E6=96=B0pytorch=E5=92=8Ckeras?= =?UTF-8?q?=E6=A1=86=E6=9E=B6=E7=9A=84=E5=8D=B7=E7=A7=AF=E5=B1=82=E5=92=8C?= =?UTF-8?q?=E6=B1=A0=E5=8C=96=E5=B1=82=E8=BE=93=E5=87=BAshape=E8=AE=A1?= =?UTF-8?q?=E7=AE=97=E5=85=AC=E5=BC=8F=E5=8D=B3=E4=BB=A3=E7=A0=81=E5=AE=9E?= =?UTF-8?q?=E4=BE=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...40\351\235\242\350\257\225\351\242\230.md" | 125 +++++++++++++++++- 1 file changed, 118 insertions(+), 7 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index af7a8bac..e00106a0 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -1,5 +1,6 @@ # 深度学习 -## 卷积输出大小计算 +## 卷积层和池化输出大小计算 +> 不管是TensorFlow、Keras、Caffe还是Pytorch，其卷积层和池化层的参数默认值可能有所不同，但是最终的卷积输出大小计算公式是一样的 ### CNN中术语解释 `CNN`网络的主要参数有下面这么几个： @@ -11,7 +12,7 @@ ### 卷积输出大小计算 1，图片经卷积 `Conv2D` 后**输出大小计算公式**如下： -`N = (W − F + 2P )/S+1` +$N = (W − F + 2P )/S+1$，**这里表示的是向下取整再加1**。 + 输入图片大小 W×W + Filter大小 F×F + 步长 S @@ -22,19 +23,129 @@ 2，反卷积 `Conv2DTranspose` 得到的图片大小计算方式： **反卷积的大小是由卷积核大小与滑动步长决定**， in是输入大小， k是卷积核大小， s是滑动步长， padding的像素数 P, out是输出大小。 -得到 `out = (in - 1) * s -2p + k`，还有另外一个写法：`W = (N - 1)*S - 2P + F`。 +得到 $out = (in - 1) * s -2p + k$，还有另外一个写法：$W = (N - 1)*S - 2P + F$。 -（`反卷积`也称为转置卷积，一般主要用来还原feature map的尺寸大小，在cnn可视化，fcn中达到pixel classification，以及gan中从特征生成图像都需要用到反卷积的操作。） +（`反卷积`也称为转置卷积，一般主要用来还原feature map的尺寸大小，在 cnn 可视化，fcn 中达到 pixel classification，以及 gan中从特征生成图像都需要用到反卷积的操作。） 反卷积输出结果计算实例。例如，输入：2x2， 卷积核：4x4， 滑动步长：3，填充像素为0， 输出：7x7 ，其计算过程就是， (2 - 1) * 3 + 4 = 7。 -3，池化得到的特征图大小计算方式： -`N=(W-F)/S+1`，池化层一般不填充像素(`VALID`) +3，池化层如果设置为填充像素(`VALID`)，对于 Pytorch 即参数padding = 0 的条件下，池化得到的特征图大小计算方式：$N=(W-F)/S+1$，这里表示的是向下取整再加1。 -**卷积向下取整，池化向上取整**。**卷积层一般填充像素(`SAME`)，池化层一般不填充像素(`VALID`)**？注意：`stride`为1的时候，当`kernel`为 3 `padding`为1或者`kernel`为5 `padding`为2，这种情况可直接得出卷积前后尺寸不变。 +**卷积向下取整，池化向上取整**。**在实际设计的卷积神经网络中，卷积层一般填充像素(`same`)，池化层一般不填充像素(`valid`)**？注意：`stride`为1的时候，当`kernel`为 3 `padding`为 1 或者`kernel`为5 `padding`为2，这种情况可直接得出卷积前后尺寸不变。 + +Keras 和 TensorFlow 设置卷积层的过程中可以设置 padding 参数值为 valid 或 same。“valid” 代表只进行有效的卷积，对边界数据不处理。‘same’ 代表 TensorFlow会自动对原图像进行补零（表示卷积核可以停留在图像边缘），也就是自动设置`padding` 值让输出与输入形状相同**。 ### 参考资料 + [CNN中的参数解释及计算](https://flat2010.github.io/2018/06/15/%E6%89%8B%E7%AE%97CNN%E4%B8%AD%E7%9A%84%E5%8F%82%E6%95%B0/) + +## Pytorch、Keras 的卷积层和池化层函数 + ++ 注意：`Pytorc` h的卷积层和池化层的输入 `shape` 格式为 **(N,C,H,W)**，`Keras` 的卷积层和池化层的输入 `shape` 格式为 **(N,H,W,C)** ++ 注意：`Pytorch、Keras` 的卷积层和池化层函数，**padding参数值都默认为不填充像素**，默认值为 `0`和 `valid`。 + +## Pytorch 和 Keras 的池化层函数 + +### torch.nn.MaxPool2d + +```python +class torch.nn.MaxPool2d(kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False) +``` + +二维池化层, 默认输入的尺度是`(N, C_in,H,W)`，输出尺度`（N,C_out,H_out,W_out）`。 + +**主要参数解释**： ++ `kernel_size`(int or tuple) - max pooling的窗口大小 ++ `stride`(int or tuple, optional) - max pooling的窗口移动的步长。**默认值是kernel_size** ++ `padding`(int or tuple, optional) - **默认值为0，即不填充像素**，输入的每一条边补充0的层数 ++ `ceil_mode` - **默认值为 False**，如果等于True，计算输出信号大小的时候，会使用向上取整，代替默认的向下取整的操作 + +**example** + +```python +import torch +import torch.nn as nn +import torch.autograd as autograd +# 大小为3，步幅为2的正方形窗口池 +m = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) +# pool of non-square window +input = autograd.Variable(torch.randn(20, 16, 50, 32)) +output = m(input) +print(output.shape) # torch.Size([20, 16, 25, 16]) +``` + +### keras.layers.MaxPooling2D + +```python +keras.layers.MaxPooling2D(pool_size=(2, 2), strides=None, padding='valid', data_format=None) +``` + +对于2D空间数据的最大池化。默认输入尺寸是 `(batch_size, rows, cols, channels)` 的 4D 张量，默认输出尺寸是 `(batch_size, pooled_rows, pooled_cols, channels)` 的 4D 张量。 + ++ `padding = valid`：池化层输出的特征图大小为：$N=(W-F)/S+1$, **这里表示的是向下取整再加1**。 ++ `padding = same`: 池化层输出的特征图大小为$N = W/S$, **这里表示向上取整**。 + +**主要参数解释** + ++ `pool_size`: 整数，或者 2 个整数表示的元组， 沿（垂直，水平）方向缩小比例的因数。 （2，2）会把输入张量的两个维度都缩小一半。 如果只使用一个整数，那么两个维度都会使用同样的窗口长度。 ++ `strides`: 整数，2 个整数表示的元组，或者是 None。 表示步长值。 如果是 None，那么默认值是 pool_size。 ++ `padding`: "valid" 或者 "same" （区分大小写）。 ++ `data_format`: 字符串，`channels_last` (默认)或 `channels_first` 之一。 表示输入各维度的顺序。 `channels_last` 代表尺寸是 `(batch, height, width, channels)` 的输入张量， 而 `channels_first` 代表尺寸是 `(batch, channels, height, width)` 的输入张量。 默认值根据 `Keras` 配置文件 `~/.keras/keras.json` 中的 `image_data_format` 值来设置。如果还没有设置过，那么**默认值就是 `"channels_last"`**。 + +## Pytorch 和 Keras 的卷积层函数 + +### torch.nn.Conv2d +```python +class torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True) +``` + +二维卷积层, 输入的尺度是`(N, C_in,H,W)`，输出尺度`（N,C_out,H_out,W_out）`。 + +**主要参数解释** ++ `in_channels`(int) – 输入信号的通道 ++ `out_channels`(int) – 卷积产生的通道 ++ `kerner_size`(int or tuple) - 卷积核的尺寸 ++ `stride`(int or tuple, optional) - 卷积步长，默认值为1 ++ `padding`(int or tuple, optional) - 输入的每一条边补充0的层数，默认不填充 ++ `dilation`(int or tuple, optional) – 卷积核元素之间的间距 ++ `groups`(int, optional) – 从输入通道到输出通道的阻塞连接数 ++ `bias`(bool, optional) - 如果bias=True，添加偏置 + +**example** + +```python +######## Pytorch卷积层输出大小验证 +import torch +import torch.nn as nn +import torch.autograd as autograd +# With square kernels and equal stride +# output_shape: height = (50-3)/2+1 = 24.5，卷积向下取整，所以 height=24. +m = nn.Conv2d(16, 33, 3, stride=2) +# # non-square kernels and unequal stride and with padding +# m = nn.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2)) # 输出shape: torch.Size([20, 33, 28, 100]) +# # non-square kernels and unequal stride and with padding and dilation +# m = nn.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2), dilation=(3, 1)) # 输出shape: torch.Size([20, 33, 26, 100]) +input = autograd.Variable(torch.randn(20, 16, 50, 100)) +output = m(input) + +print(output.shape) # 输出shape: torch.Size([20, 16, 24, 49]) +``` + +### keras.layers.Conv2D + +```python +keras.layers.Conv2D(filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, dilation_rate=(1, 1), activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None) +``` + +2D 卷积层 (例如对图像的空间卷积)。输入输出尺寸格式要求和池化层函数一样。**输入尺寸：(N,H,W,C)**。 + +**当使用该层作为模型第一层时，需要提供 input_shape 参数**（整数元组，不包含 batch 轴），例如，input_shape=(128, 128, 3) 表示 128x128 RGB 图像，在 data_format="channels_last" 时。 + +**主要参数解释** ++ `filters`: 整数，输出空间的维度 （即卷积中滤波器的输出数量）。 ++ `kernel_size`: 一个整数，或者 2 个整数表示的元组或列表， 指明 2D 卷积窗口的宽度和高度。 可以是一个整数，为所有空间维度指定相同的值。 ++ `strides`: 一个整数，或者 2 个整数表示的元组或列表， 指明卷积沿宽度和高度方向的步长。 可以是一个整数，为所有空间维度指定相同的值。 指定任何 stride 值 != 1 与指定 dilation_rate 值 != 1 两者不兼容。 ++ `padding`: `"valid"` 或 `"same"` (大小写敏感)，默认`valid`。 ++ `data_format`: 字符串， `channels_last (默认)` 或 `channels_first` 之一，表示输入中维度的顺序。 `channels_last` 对应输入尺寸为 `(batch_size, height, width, channels)`， channels_first 对应输入尺寸为 `(batch_size, channels, height, width)`。 它默认为从 `Keras` 配置文件 `~/.keras/keras.json` 中 找到的 `image_data_format` 值。 如果你从未设置它，将使用 `channels_last`。 ## softmax回归 **分类问题**中，直接使用输出层的输出有两个问题： + 神经网络输出层的输出值的范围不确定，我们难以直观上判断这些值的意义 From eb0a58b5b31b745145aef5b47b74d15cfbe07e0e Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Sat, 8 Aug 2020 23:08:51 +0800 Subject: [PATCH 330/386] =?UTF-8?q?=E6=9B=B4=E6=96=B0pytorch=E5=92=8Ckeras?= =?UTF-8?q?=E6=A1=86=E6=9E=B6=E7=9A=84=E5=8D=B7=E7=A7=AF=E5=B1=82=E5=92=8C?= =?UTF-8?q?=E6=B1=A0=E5=8C=96=E5=B1=82=E8=BE=93=E5=87=BAshape=E8=AE=A1?= =?UTF-8?q?=E7=AE=97=E5=85=AC=E5=BC=8F=E5=8D=B3=E4=BB=A3=E7=A0=81=E5=AE=9E?= =?UTF-8?q?=E4=BE=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...207\346\240\207\350\256\241\347\256\227.md" | 18 +++++++++++++----- 1 file changed, 13 insertions(+), 5 deletions(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" index 637eec98..d0a810c5 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" @@ -588,9 +588,9 @@ def voc_eval(detpath, + `MACC`：multiply-accumulate，乘法累加次数。 ### 卷积层FLOPs计算 -+ $FLOPs=(2*C_i*K*K-1)*H*W*C_o$（不考虑bias） -+ $FLOPs=(2*C_i*K*K)*H*W*C_o$（考虑bias） -+ $MACC=(C_i*K*K)*H*W*C_o$（考虑bias） ++ $FLOPs=(2*C_i*K^2-1)*H*W*C_o$（不考虑bias） ++ $FLOPs=(2*C_i*K^2)*H*W*C_o$（考虑bias） ++ $MACC=(C_i*K^2)*H*W*C_o$（考虑bias） **$C_i$ 为输入特征图通道数，$K$ 为过滤器尺寸，$H,W,C_o$ 为输出特征图的高，宽和通道数**。`二维卷积过程`如下图所示： @@ -600,11 +600,19 @@ def voc_eval(detpath, 公式解释，参考[这里](https://zhuanlan.zhihu.com/p/70306015?utm_source=wechat_session&utm_medium=social&utm_oi=571954943427219456)，如下： -执行卷积操作后输出 $C_o$ 张输出特征图，每张特征图上有 HxW 个像素点，而这其中的每个像素点的值都是由过滤器与输入特征图卷积得到的，过滤器中有 $C_i*K*K$ 个点，每个点都要和输入特征图对应点作一次相乘操作（浮点操作数为 $C_i*K*K$），然后将这些过滤器和输入特征图对应点相乘所得的数相加起来（浮点操作数为 $C_i*K*K-1$，n个数相加，所需要的浮点操作数为 n-1），得到一个值，对应于一张输出特征图中的一个像素，输出特征图有 $C_o$ 张，故有 $C_o$ 个过滤器参与卷积运算。 +理解 FLOPs 的计算公式分两步，括号内是第一步，计算出output feature map的一个pixel，然后再乘以 $H\times W\times C_o$，从而拓展到整个 output feature map。括号内的部分又可以分为两步：$(2\times C_i\times K^2-1)=(C_i\times K^2) + (C_i\times K^2-1)$。第一项是乘法运算次数，第二项是加法运算次数，因为 n 个数相加，要加 n-1次，所以不考虑bias的情况下，会有一个-1，如果考虑bias，刚好中和掉，括号内变为$(2\times C_i\times K^2)$ -所以卷积层的 $FLOPs=(2*C_{i}*K*K-1)*H*W*C_o$ +所以卷积层的 $FLOPs=(2\times C_{i}\times K^2-1)\times H\times W\times C_o$ ($C_i$ 为输入特征图通道数，$K$ 为过滤器尺寸，$H, W, C_o$为输出特征图的高，宽和通道数)。 +### 全连接层的 FLOPs 计算 + +全连接层的 $FLOPs = (2I − 1)O$，$I$ 是输入层的维度，$O$ 是输出层的维度。 + +### 参考资料 +[PRUNING CONVOLUTIONAL NEURAL NETWORKS +FOR RESOURCE EFFICIENT INFERENCE +](https://arxiv.org/pdf/1611.06440.pdf) ## 目标检测度量标准汇总 ![目标检测指标汇总](../images/目标检测度量标准汇总.jpg) ## 参考资料 From dbdfc9a8f6578d24ec214686239a3dc23cc88b5b Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Sat, 8 Aug 2020 23:09:07 +0800 Subject: [PATCH 331/386] =?UTF-8?q?=E6=9B=B4=E6=96=B0pytorch=E5=92=8Ckeras?= =?UTF-8?q?=E6=A1=86=E6=9E=B6=E7=9A=84=E5=8D=B7=E7=A7=AF=E5=B1=82=E5=92=8C?= =?UTF-8?q?=E6=B1=A0=E5=8C=96=E5=B1=82=E8=BE=93=E5=87=BAshape=E8=AE=A1?= =?UTF-8?q?=E7=AE=97=E5=85=AC=E5=BC=8F=E5=8D=B3=E4=BB=A3=E7=A0=81=E5=AE=9E?= =?UTF-8?q?=E4=BE=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...4\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" | 1 + 1 file changed, 1 insertion(+) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" index d0a810c5..cdbeef43 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" @@ -613,6 +613,7 @@ def voc_eval(detpath, [PRUNING CONVOLUTIONAL NEURAL NETWORKS FOR RESOURCE EFFICIENT INFERENCE ](https://arxiv.org/pdf/1611.06440.pdf) + ## 目标检测度量标准汇总 ![目标检测指标汇总](../images/目标检测度量标准汇总.jpg) ## 参考资料 From fbc04afd80f867e0403f2b69c8cda304d412c3cb Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Sat, 8 Aug 2020 23:14:10 +0800 Subject: [PATCH 332/386] =?UTF-8?q?=E6=9B=B4=E6=96=B0pytorch=E5=92=8Ckeras?= =?UTF-8?q?=E6=A1=86=E6=9E=B6=E7=9A=84=E5=8D=B7=E7=A7=AF=E5=B1=82=E5=92=8C?= =?UTF-8?q?=E6=B1=A0=E5=8C=96=E5=B1=82=E8=BE=93=E5=87=BAshape=E8=AE=A1?= =?UTF-8?q?=E7=AE=97=E5=85=AC=E5=BC=8F=E5=8D=B3=E4=BB=A3=E7=A0=81=E5=AE=9E?= =?UTF-8?q?=E4=BE=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\267\346\214\207\346\240\207\350\256\241\347\256\227.md" | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" index cdbeef43..50a638bc 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" @@ -588,9 +588,9 @@ def voc_eval(detpath, + `MACC`：multiply-accumulate，乘法累加次数。 ### 卷积层FLOPs计算 -+ $FLOPs=(2*C_i*K^2-1)*H*W*C_o$（不考虑bias） -+ $FLOPs=(2*C_i*K^2)*H*W*C_o$（考虑bias） -+ $MACC=(C_i*K^2)*H*W*C_o$（考虑bias） ++ $FLOPs=(2\times C_i\times K^2-1)\times H\times W\times C_o$（不考虑bias） ++ $FLOPs=(2\times C_i\times K^2)\times H\times W\times C_o$（考虑bias） ++ $MACC=(C_i\times K^2)\times H\times W\times C_o$（考虑bias） **$C_i$ 为输入特征图通道数，$K$ 为过滤器尺寸，$H,W,C_o$ 为输出特征图的高，宽和通道数**。`二维卷积过程`如下图所示： From a88a675f482c0de2baafd56d3491c6a0326ca71f Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Sat, 8 Aug 2020 23:24:15 +0800 Subject: [PATCH 333/386] =?UTF-8?q?=E6=9B=B4=E6=96=B0pytorch=E5=92=8Ckeras?= =?UTF-8?q?=E6=A1=86=E6=9E=B6=E7=9A=84=E5=8D=B7=E7=A7=AF=E5=B1=82=E5=92=8C?= =?UTF-8?q?=E6=B1=A0=E5=8C=96=E5=B1=82=E8=BE=93=E5=87=BAshape=E8=AE=A1?= =?UTF-8?q?=E7=AE=97=E5=85=AC=E5=BC=8F=E5=8D=B3=E4=BB=A3=E7=A0=81=E5=AE=9E?= =?UTF-8?q?=E4=BE=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...55\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index e00106a0..03f961f0 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -1,4 +1,6 @@ # 深度学习 +## 滤波器与卷积核 +这两个术语之间有着本质的区别：仅在 1 通道的情况下，滤波器和内核这两个术语等价，在一般情况下，它们是不同的。**每个过滤器实际上是卷积核的集合**，图层的每个输入通道都有一个卷积核，并且是唯一的。 ## 卷积层和池化输出大小计算 > 不管是TensorFlow、Keras、Caffe还是Pytorch，其卷积层和池化层的参数默认值可能有所不同，但是最终的卷积输出大小计算公式是一样的 ### CNN中术语解释 @@ -31,7 +33,7 @@ $N = (W − F + 2P )/S+1$，**这里表示的是向下取整再加1**。 3，池化层如果设置为填充像素(`VALID`)，对于 Pytorch 即参数padding = 0 的条件下，池化得到的特征图大小计算方式：$N=(W-F)/S+1$，这里表示的是向下取整再加1。 -**卷积向下取整，池化向上取整**。**在实际设计的卷积神经网络中，卷积层一般填充像素(`same`)，池化层一般不填充像素(`valid`)**？注意：`stride`为1的时候，当`kernel`为 3 `padding`为 1 或者`kernel`为5 `padding`为2，这种情况可直接得出卷积前后尺寸不变。 +**valid 条件下卷积和池化一般都向下取整**。**在实际设计的卷积神经网络中，卷积层一般填充像素(`same`)，池化层一般不填充像素(`valid`)**？注意：`stride`为1的时候，当`kernel`为 3 `padding`为 1 或者`kernel`为5 `padding`为2，这种情况可直接得出卷积前后尺寸不变。 Keras 和 TensorFlow 设置卷积层的过程中可以设置 padding 参数值为 valid 或 same。“valid” 代表只进行有效的卷积，对边界数据不处理。‘same’ 代表 TensorFlow会自动对原图像进行补零（表示卷积核可以停留在图像边缘），也就是自动设置`padding` 值让输出与输入形状相同**。 From b8da8231c45fcf16d404de42e55a3bac0acf092a Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Sat, 8 Aug 2020 23:26:39 +0800 Subject: [PATCH 334/386] =?UTF-8?q?=E6=9B=B4=E6=96=B0pytorch=E5=92=8Ckeras?= =?UTF-8?q?=E6=A1=86=E6=9E=B6=E7=9A=84=E5=8D=B7=E7=A7=AF=E5=B1=82=E5=92=8C?= =?UTF-8?q?=E6=B1=A0=E5=8C=96=E5=B1=82=E8=BE=93=E5=87=BAshape=E8=AE=A1?= =?UTF-8?q?=E7=AE=97=E5=85=AC=E5=BC=8F=E5=8D=B3=E4=BB=A3=E7=A0=81=E5=AE=9E?= =?UTF-8?q?=E4=BE=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...55\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 03f961f0..32559791 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -33,7 +33,7 @@ $N = (W − F + 2P )/S+1$，**这里表示的是向下取整再加1**。 3，池化层如果设置为填充像素(`VALID`)，对于 Pytorch 即参数padding = 0 的条件下，池化得到的特征图大小计算方式：$N=(W-F)/S+1$，这里表示的是向下取整再加1。 -**valid 条件下卷积和池化一般都向下取整**。**在实际设计的卷积神经网络中，卷积层一般填充像素(`same`)，池化层一般不填充像素(`valid`)**？注意：`stride`为1的时候，当`kernel`为 3 `padding`为 1 或者`kernel`为5 `padding`为2，这种情况可直接得出卷积前后尺寸不变。 +**Pytorch 和 tensorflow 的卷积和池化函数，在valid 条件下卷积和池化都向下取整**。**在实际设计的卷积神经网络中，卷积层一般填充像素(`same`)，池化层一般不填充像素(`valid`)**？注意：`stride`为 1 的时候，当`kernel`为 3 `padding`为 1 或者`kernel`为 5 `padding`为2，这种情况可直接得出卷积前后尺寸不变。 Keras 和 TensorFlow 设置卷积层的过程中可以设置 padding 参数值为 valid 或 same。“valid” 代表只进行有效的卷积，对边界数据不处理。‘same’ 代表 TensorFlow会自动对原图像进行补零（表示卷积核可以停留在图像边缘），也就是自动设置`padding` 值让输出与输入形状相同**。 @@ -59,7 +59,7 @@ class torch.nn.MaxPool2d(kernel_size, stride=None, padding=0, dilation=1, return + `kernel_size`(int or tuple) - max pooling的窗口大小 + `stride`(int or tuple, optional) - max pooling的窗口移动的步长。**默认值是kernel_size** + `padding`(int or tuple, optional) - **默认值为0，即不填充像素**，输入的每一条边补充0的层数 -+ `ceil_mode` - **默认值为 False**，如果等于True，计算输出信号大小的时候，会使用向上取整，代替默认的向下取整的操作 ++ `ceil_mode` - **默认值为 False**，默认向下取整，如果等于True，计算输出信号大小的时候，会使用向上取整。 **example** From b7649e13224085dfa94c23b01deed59ee27a296c Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Mon, 10 Aug 2020 09:48:58 +0800 Subject: [PATCH 335/386] =?UTF-8?q?fix=E5=88=97=E8=A1=A8=E6=98=BE=E7=A4=BA?= =?UTF-8?q?=E8=AF=AD=E6=B3=95=E9=97=AE=E9=A2=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...40\351\235\242\350\257\225\351\242\230.md" | 21 ++++++++++++------- 1 file changed, 14 insertions(+), 7 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 32559791..f2ce449c 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -148,6 +148,10 @@ keras.layers.Conv2D(filters, kernel_size, strides=(1, 1), padding='valid', data_ + `strides`: 一个整数，或者 2 个整数表示的元组或列表， 指明卷积沿宽度和高度方向的步长。 可以是一个整数，为所有空间维度指定相同的值。 指定任何 stride 值 != 1 与指定 dilation_rate 值 != 1 两者不兼容。 + `padding`: `"valid"` 或 `"same"` (大小写敏感)，默认`valid`。 + `data_format`: 字符串， `channels_last (默认)` 或 `channels_first` 之一，表示输入中维度的顺序。 `channels_last` 对应输入尺寸为 `(batch_size, height, width, channels)`， channels_first 对应输入尺寸为 `(batch_size, channels, height, width)`。 它默认为从 `Keras` 配置文件 `~/.keras/keras.json` 中 找到的 `image_data_format` 值。 如果你从未设置它，将使用 `channels_last`。 + +### 总结 +`Pytorch` 的 Conv2d 函数不要求提供 输入数据的大小 `(H,W)`，但是要提供输入深度，`Keras` 的 Conv2d 函数第一层要求提供 `input_shape` 参数 `(H,W,C)`，其他层不需要。 + ## softmax回归 **分类问题**中，直接使用输出层的输出有两个问题： + 神经网络输出层的输出值的范围不确定，我们难以直观上判断这些值的意义 @@ -451,7 +455,7 @@ ReLU 的输出要么是 0, 要么是输入本身。虽然方程简单，但实 Python深度学习p127. ## 什么是dropout? -+ dropout可以防止过拟合，dropout简单来说就是：我们在前向传播的时候，**让某个神经元的激活值以一定的概率p停止工作**，这样可以使模型的泛化性更强，因为它不会依赖某些局部的特征。 ++ dropout可以防止过拟合，dropout简单来说就是：我们在前向传播的时候，**让某个神经元的激活值以一定的概率 p 停止工作**，这样可以使模型的泛化性更强，因为它不会依赖某些局部的特征。 + `dropout`效果跟`bagging`效果类似（bagging是减少方差variance，而boosting是减少偏差bias） + 加入dropout会使神经网络训练时间边长，模型预测时不需要dropout，记得关掉。 @@ -460,12 +464,15 @@ Python深度学习p127. ### dropout具体工作流程 以 标准神经网络为例，正常的流程是：我们首先把输入数据x通过网络前向传播，然后把误差反向传播一决定如何更新参数让网络进行学习。使用dropout之后，过程变成如下： -(1). 首先随机（临时）删掉网络中一半的隐藏神经元，输入输出神经元保持不变（图3中虚线为部分临时被删除的神经元）； -(2). 然后把输入x通过修改后的网络进行前向传播计算，然后把得到的损失结果通过修改的网络反向传播。一小批训练样本执行完这个过程后，在没有被删除的神经元上按照随机梯度下降法更新对应的参数（w，b）； -(3). 然后重复这一过程： -+ 恢复被删掉的神经元（此时被删除的神经元保持原样没有更新w参数，而没有被删除的神经元已经有所更新） -+ 从隐藏层神经元中随机选择一个一半大小的子集临时删除掉（同时备份被删除神经元的参数）。 -+ 对一小批训练样本，先前向传播然后反向传播损失并根据随机梯度下降法更新参数（w，b） （没有被删除的那一部分参数得到更新，删除的神经元参数保持被删除前的结果）。 +1. 首先随机（临时）删掉网络中一半的隐藏神经元，输入输出神经元保持不变（图3中虚线为部分临时被删除的神经元）； + +2. 然后把输入x通过修改后的网络进行前向传播计算，然后把得到的损失结果通过修改的网络反向传播。一小批训练样本执行完这个过程后，在没有被删除的神经元上按照随机梯度下降法更新对应的参数（w，b）； + +3. 然后重复这一过程： + + 恢复被删掉的神经元（此时被删除的神经元保持原样没有更新w参数，而没有被删除的神经元已经有所更新）。 + + 从隐藏层神经元中随机选择一个一半大小的子集临时删除掉（同时备份被删除神经元的参数）。 + + 对一小批训练样本，先前向传播然后反向传播损失并根据随机梯度下降法更新参数（w，b） （没有被删除的那一部分参数得到更新，删除的神经元参数保持被删除前的结果）。 + ### dropout在神经网络中的应用 (1). 在训练模型阶段 From cd1e4f4e3b0818f70483162e65fdfabd31202568 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Mon, 10 Aug 2020 09:50:35 +0800 Subject: [PATCH 336/386] =?UTF-8?q?fix=E5=88=97=E8=A1=A8=E6=98=BE=E7=A4=BA?= =?UTF-8?q?=E8=AF=AD=E6=B3=95=E9=97=AE=E9=A2=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...4\271\240\351\235\242\350\257\225\351\242\230.md" | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index f2ce449c..a3158b5f 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -464,14 +464,14 @@ Python深度学习p127. ### dropout具体工作流程 以 标准神经网络为例，正常的流程是：我们首先把输入数据x通过网络前向传播，然后把误差反向传播一决定如何更新参数让网络进行学习。使用dropout之后，过程变成如下： -1. 首先随机（临时）删掉网络中一半的隐藏神经元，输入输出神经元保持不变（图3中虚线为部分临时被删除的神经元）； +1，首先随机（临时）删掉网络中一半的隐藏神经元，输入输出神经元保持不变（图3中虚线为部分临时被删除的神经元）； -2. 然后把输入x通过修改后的网络进行前向传播计算，然后把得到的损失结果通过修改的网络反向传播。一小批训练样本执行完这个过程后，在没有被删除的神经元上按照随机梯度下降法更新对应的参数（w，b）； +2，然后把输入x通过修改后的网络进行前向传播计算，然后把得到的损失结果通过修改的网络反向传播。一小批训练样本执行完这个过程后，在没有被删除的神经元上按照随机梯度下降法更新对应的参数（w，b）； -3. 然后重复这一过程： - + 恢复被删掉的神经元（此时被删除的神经元保持原样没有更新w参数，而没有被删除的神经元已经有所更新）。 - + 从隐藏层神经元中随机选择一个一半大小的子集临时删除掉（同时备份被删除神经元的参数）。 - + 对一小批训练样本，先前向传播然后反向传播损失并根据随机梯度下降法更新参数（w，b） （没有被删除的那一部分参数得到更新，删除的神经元参数保持被删除前的结果）。 +3，然后重复这一过程： ++ 恢复被删掉的神经元（此时被删除的神经元保持原样没有更新w参数，而没有被删除的神经元已经有所更新）; ++ 从隐藏层神经元中随机选择一个一半大小的子集临时删除掉（同时备份被删除神经元的参数）; ++ 对一小批训练样本，先前向传播然后反向传播损失并根据随机梯度下降法更新参数（w，b） （没有被删除的那一部分参数得到更新，删除的神经元参数保持被删除前的结果）。 ### dropout在神经网络中的应用 (1). 在训练模型阶段 From 467d5a53c6410ae68cb162433a04d3df5e41d98d Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Mon, 10 Aug 2020 11:06:46 +0800 Subject: [PATCH 337/386] =?UTF-8?q?fix=E8=AF=AD=E6=B3=95=E9=97=AE=E9=A2=98?= =?UTF-8?q?=E5=92=8C=E5=86=85=E5=AE=B9=E9=94=99=E8=AF=AF?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...40\351\235\242\350\257\225\351\242\230.md" | 33 ++++++++++--------- ...07\346\240\207\350\256\241\347\256\227.md" | 2 +- 2 files changed, 19 insertions(+), 16 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index a3158b5f..265bdb24 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -15,11 +15,12 @@ ### 卷积输出大小计算 1，图片经卷积 `Conv2D` 后**输出大小计算公式**如下： $N = (W − F + 2P )/S+1$，**这里表示的是向下取整再加1**。 -+ 输入图片大小 W×W -+ Filter大小 F×F -+ 步长 S -+ padding的像素数 P -+ 输出特征图大小 N×N ++ 输入图片大小 `W×W`（默认输入尺寸为正方形） ++ Filter大小 `F×F` ++ 步长 `S` ++ padding的像素数 `P` ++ 输出特征图大小 `N×N` + 输出通道数 = 卷积核/池化核数量 @@ -31,11 +32,13 @@ $N = (W − F + 2P )/S+1$，**这里表示的是向下取整再加1**。 反卷积输出结果计算实例。例如，输入：2x2， 卷积核：4x4， 滑动步长：3，填充像素为0， 输出：7x7 ，其计算过程就是， (2 - 1) * 3 + 4 = 7。 -3，池化层如果设置为填充像素(`VALID`)，对于 Pytorch 即参数padding = 0 的条件下，池化得到的特征图大小计算方式：$N=(W-F)/S+1$，这里表示的是向下取整再加1。 +3，池化层如果设置为不填充像素（对于Pytorch，设置参数`padding = 0`，对于Keras/TensorFlow，设置参数`padding="valid"`），池化得到的特征图大小计算方式: $N=(W-F)/S+1$，这里表示的是向下取整再加1。 -**Pytorch 和 tensorflow 的卷积和池化函数，在valid 条件下卷积和池化都向下取整**。**在实际设计的卷积神经网络中，卷积层一般填充像素(`same`)，池化层一般不填充像素(`valid`)**？注意：`stride`为 1 的时候，当`kernel`为 3 `padding`为 1 或者`kernel`为 5 `padding`为2，这种情况可直接得出卷积前后尺寸不变。 +**Pytorch 和 tensorflow 的卷积和池化函数，在 `0/"valid"` 条件下卷积和池化都向下取整**。**在实际设计的卷积神经网络中，卷积层一般填充像素(`same`)，池化层一般不填充像素(`valid`)**？注意：`stride`为 1 的时候，当`kernel`为 3 `padding`为 1 或者`kernel`为 5 `padding`为2，这种情况可直接得出卷积前后尺寸不变。 -Keras 和 TensorFlow 设置卷积层的过程中可以设置 padding 参数值为 valid 或 same。“valid” 代表只进行有效的卷积，对边界数据不处理。‘same’ 代表 TensorFlow会自动对原图像进行补零（表示卷积核可以停留在图像边缘），也就是自动设置`padding` 值让输出与输入形状相同**。 +### 理解边界效应与填充padding +如果希望输出特征图的空间维度 +`Keras/TensorFlow` 设置卷积层的过程中可以设置 padding 参数值为 “valid” 或 “same”。“valid” 代表只进行有效的卷积，对边界数据不处理。“same” 代表 TensorFlow会自动对原图像进行补零（表示卷积核可以停留在图像边缘），也就是自动设置`padding` 值让输出与输入形状相同**。 ### 参考资料 + [CNN中的参数解释及计算](https://flat2010.github.io/2018/06/15/%E6%89%8B%E7%AE%97CNN%E4%B8%AD%E7%9A%84%E5%8F%82%E6%95%B0/) @@ -566,7 +569,7 @@ HOG 特征提取方法就是将一个image： ## 神经网络为什么用交叉熵损失函数 判断一个输出向量和期望的向量有多接近，交叉熵（cross entroy）是常用的评判方法之一。交叉熵刻画了两个概率分布之间的距离，是分类问题中使用比较广泛的一种损失函数。 -给定两个概率分布 `p` 和 `q` ，通过q来表示p的交叉熵公式为：$H(p,q)=−∑p(x)logq(x)$ +给定两个概率分布 `p` 和 `q` ，通过 `q` 来表示 `p` 的交叉熵公式为：$H(p,q)=−∑p(x)logq(x)$ ## softmax公式写一下 @@ -616,22 +619,22 @@ $$softmax(y)_{i} = \frac{e^{yi}}{\sum_{j=1}^{n}e^{yj}}$$ 有的朋友又问了：如何找到最好的阈值点呢？ 首先，需要说明的是我们对于这两个指标的要求：我们希望查准率和查全率同时都非常高。 但实际上这两个指标是一对矛盾体，无法做到双高。图中明显看到，如果其中一个非常高，另一个肯定会非常低。选取合适的阈值点要根据实际需求，比如我们想要高的查全率，那么我们就会牺牲一些查准率，在保证查全率最高的情况下，查准率也不那么低。 ### F1 分数 -如果想要找到 `P` 和 `R` 二者之间的一个平衡点，我们就需要一个新的指标：`F1` 分数。F1 分数同时考虑了查准率和查全率，让二者同时达到最高，取一个平衡。`F1` 计算公式如下： +如果想要找到 $P$ 和 $R$ 二者之间的一个平衡点，我们就需要一个新的指标：$F1$ 分数。$F1$ 分数同时考虑了查准率和查全率，让二者同时达到最高，取一个平衡。$F1$ 计算公式如下： $$F1 = \frac{2*P*R}{P+R} = \frac{2*TP}{样例总数+TP-TN}$$ -F1 度量的一般形式：$F_{\beta}$，能让我们表达出对查准率/查全率的偏见，$F_{\beta}$计算公式如下： +$F1$ 度量的一般形式：$F_{\beta}$，能让我们表达出对查准率/查全率的偏见，$F_{\beta}$计算公式如下： $$F_{\beta} = \frac{1+\beta^{2}*P*R}{(\beta ^{2}*P)+R}$$ -其中$\beta >1$对查全率有更大影响，$\beta < 1$对查准率有更大影响。 +其中 $\beta >1$ 对查全率有更大影响，$\beta < 1$ 对查准率有更大影响。 不同的计算机视觉问题，对两类错误有不同的偏好，常常在某一类错误不多于一定阈值的情况下，努力减少另一类错误。在目标检测中，**mAP**（mean Average Precision）作为一个统一的指标将这两种错误兼顾考虑。 ### mAP指标解释 -`AP` 衡量的是训练好的模型在每个类别上的好坏，`mAP` 衡量的是模型在所有类别上的好坏，得到 AP 后 mAP 的计算就变得很简单了，就是取所有 AP 的平均值。 +`AP` 衡量的是训练好的模型在每个类别上的好坏，`mAP` 衡量的是模型在所有类别上的好坏，得到 `AP` 后 `mAP` 的计算就变得很简单了，就是取所有 `AP` 的平均值。 -`mAP` 这个术语有不同的定义。此度量指标通常用于信息检索、图像分类和目标检测领域。然而这两个领域计算 mAP 的方式却不相同。这里我们只谈论目标检测中的 mAP 计算方法。 +`mAP` 这个术语有不同的定义。此度量指标通常用于信息检索、图像分类和目标检测领域。然而这两个领域计算 `mAP` 的方式却不相同。这里我们只谈论目标检测中的 `mAP` 计算方法。 -`mAP` 常作为目标检测算法的评价指标，具体来说就是，对于每张图片检测模型会输出多个预测框（远超真实框的个数），我们使用 IoU(Intersection Over Union，交并比)来标记预测框是否预测准确。标记完成后，随着预测框的增多，查全率 R 总会上升，**在不同查全率 R 水平下对准确率 P 做平均，即得到AP**，最后再对所有类别按其所占比例做平均，即得到 `mAP` 指标。 +`mAP` 常作为目标检测算法的评价指标，具体来说就是，对于每张图片检测模型会输出多个预测框（远超真实框的个数），我们使用 `IoU`(Intersection Over Union，交并比)来标记预测框是否预测准确。标记完成后，随着预测框的增多，查全率 `R` 总会上升，**在不同查全率 `R` 水平下对准确率 `P` 做平均，即得到AP**，最后再对所有类别按其所占比例做平均，即得到 `mAP` 指标。 ### 交并比IOU diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" index 50a638bc..abb326b7 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" @@ -289,7 +289,7 @@ def soft_nms(dets, thresh, type='gaussian'): return keep ``` ## 类别AP计算 -目标检测领域常用的评估标准是：`mAP(mean average precision)`，计算mAP需要先计算`AP`，计算`AP`需涉及到`precision`和`recall`的计算，而这两者的计算又需设计`TP`、`FP`、`FN`的计算。 +**目标检测领域常用的评估标准是**：`mAP(mean average precision)`，计算mAP需要先计算`AP`，计算`AP`需涉及到`precision`和`recall`的计算，而这两者的计算又需设计`TP`、`FP`、`FN`的计算。 `AP`的计算一般先涉及到`P-R`曲线（precision-recall curve）的绘制，P-R曲线下面与x轴围成的面积称为`average precison（AP）`。下图是一个二分类问题的P-R曲线： ![分类问题的PR曲线图](../images/分类PR曲线图.png) From 338cb72b0f3612d61b6d4341d7caba8a12275f8c Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Mon, 10 Aug 2020 11:42:32 +0800 Subject: [PATCH 338/386] =?UTF-8?q?fix=20=E6=95=B0=E6=8D=AE=E7=BB=93?= =?UTF-8?q?=E6=9E=84=E4=B8=8E=E7=AE=97=E6=B3=95=E5=AD=A6=E4=B9=A0=E7=AC=94?= =?UTF-8?q?=E8=AE=B0.md=E6=96=87=E6=A1=A3=E7=9A=84=E5=86=85=E5=AE=B9?= =?UTF-8?q?=E6=98=BE=E7=A4=BA=E8=AF=AD=E6=B3=95=E9=97=AE=E9=A2=98=EF=BC=8C?= =?UTF-8?q?=E6=9B=B4=E6=96=B0=E7=AC=AC9=E7=AB=A0=E5=86=85=E5=AE=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...4\345\214\205\351\227\256\351\242\230.jpg" | Bin 0 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zh_H7hJ7%}dmw&^b%#0j0A8YUDpHc&>;h)(EX_x;0^wNB-_U?auOstw8XXMH$uL`Ed LGmT;mf8GASOMz%O literal 0 HcmV?d00001 diff --git "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" index 76193c98..695d32f8 100644 --- "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" +++ "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" @@ -2,10 +2,12 @@ > [十大经典排序算法动画与解析，看我就够了！（配代码完全版）](https://mp.weixin.qq.com/s/vn3KiV-ez79FmbZ36SX9lg) 10大排序算法时间复杂度及空间复杂度如下图： -![10大排序算法时间复杂度及空间复杂度](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E6%8E%92%E5%BA%8F%E7%AE%97%E6%B3%95%E6%97%B6%E9%97%B4%E5%A4%8D%E6%9D%82%E5%BA%A6.jpg) +![10大排序算法时间复杂度及空间复杂度](../images/排序算法时间复杂度.jpg) # 第一章，算法简介 ## 1.2，二分法查找元素 +二分查找是一种算法，其输入是一个有序的元素列表（必须有序的原因稍后解释）。如果要查找的元素包含在列表中，二分查找返回其位置；否则返回 `null`，使用`二分查找`时，每次猜测的是中间的数字，从而将余下的数字排除一半。（**仅仅当列表是有序的时候，二分查找才管用**） + 一般而言，对于包含n个元素的列表查找某个元素，使用二分法最多需要$log_{2}n$步(**时间复杂度为$log_{2}n$**)，简单查找最多需要n步。大O表示法指出了算法最糟糕情况下的运行时间。二分法实例代码如下： ```python def binary_search(list, item): @@ -63,7 +65,8 @@ if __name__ == "__main__": ### 2.2.1，链表 链表中的元素可存储在内存的任何地方。 链表的每个元素都存储了下一个元素的地址，从而使一系列随机的内存地址串在一起。链表结构直观显示如下图所示： -![2.1链表示意图](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/2.1%E9%93%BE%E8%A1%A8.png) + +![2.1链表示意图](../images/2.1链表.png) 链表的优势在插入元素方面，那数组的优势又是什么呢？ ### 2.2.2，数组 @@ -135,8 +138,7 @@ C语言标准库中的函数qsort实现的就是快速排序。快速排序也 2. 将数组分成两个子数组：小于基准值的元素和大于基准值额元素； 3. 对这两个子数组进行排序。 -**在平均情况下，快速排序时间复杂度$O(nlogn)$** -快速排序代码如下： +**在平均情况下，快速排序时间复杂度$O(nlogn)$**。快速排序代码如下： ```python def quicksort(array): if len(array) < 2: @@ -150,7 +152,7 @@ def quicksort(array): return quicksort(less) + [pivot] + quicksort(greater) print(quicksort([4, 90, 0, 2, 17, 79, 12])) # [0, 2, 4, 12, 17, 79, 90] ``` -上面的代码空间复杂度很大，真正的快排是原地排序，空间复杂度为O(1),代码如下： +上面的代码空间复杂度很大，真正的快排是原地排序，空间复杂度为O(1)，代码如下： ```python # _*_ coding:utf-8 _*_ @@ -186,11 +188,14 @@ if __name__ == "__main__": ## 4.3 再谈大Ｏ表示法 快速排序的独特之处在于，其速度取决于选择的基准值。在讨论快速排序的运行时间前，我 们再来看看最常见的大O运行时间。 -![4.3 再谈大O表示法](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/4.3%E5%86%8D%E8%B0%88%E5%A4%A7O%E8%A1%A8%E7%A4%BA%E6%B3%95.png) -选择排序，其运行时间为O(n2)，速度非常慢。 -还有一种名为合并排序（ merge sort） 的排序算法，其运行时间为O(n log n)，比选择排序快得多！快速排序的情况比较棘手，在最糟情况下，其运行时间为O(n2)。与选择排序一样慢！但这是最糟情况。**在平均情况下，快速排序的运行时间为O(n log n)。** -由对数的换底公式，loga n和 logb n只有一个常数因子不同，这个因子在大O记法中被丢弃。因此记作O(log n)，**而不论对数的底是多少，是对数时间算法的标准记法**。 +![4.3 再谈大O表示法](../images/4.3再谈大O表示法.png) + ++ **选择排序**，其运行时间为 $O(n^2)$，速度非常慢。 ++ 还有一种名为**合并排序**（merge sort）的排序算法，其运行时间为 $O(nlogn)$，比选择排序快得多！ ++ **快速排序**的情况比较棘手，在最糟情况下，其运行时间为 $O(n^2)$。与选择排序一样慢！但这是最糟情况。**在平均情况下，快速排序的运行时间为 $O(nlogn)$。** + +由对数的换底公式，$log_a n$ 和 $log_b n$ 只有一个常数因子不同，这个因子在大O记法中被丢弃。因此记作$O(log n)$，**而不论对数的底是多少，是对数时间算法的标准记法**。 ## 4.4，小结 + D&C将问题逐步分解。使用D&C处理列表时，基线条件很可能是空数组或只包含一个元 素的数组。 @@ -199,24 +204,23 @@ if __name__ == "__main__": + 比较简单查找和二分查找时，常量几乎无关紧要，因为列表很长时， O(log n)的速度比O(n) 快得多。 # 第五章，散列表 -数组和链表结构可以用以查找，栈不行。 -散列表也叫哈希表(Hash table)，散列表有些类似Python中的字典dict结构。 -散列表可用以： +数组和链表结构可以用以查找，栈不行。**散列表也叫哈希表(Hash table)，散列表有些类似 Python 中的字典 `dict` 结构**。散列表可用以： + 模拟映射关系； + 防止重复； + 缓冲/记住数据，以免服务器再通过处理来生成它们。 ## 5.3，冲突 给两个键分配的位置相同，这被称为冲突（collision）。处理冲突最简单的办法就是：如果两个键映射到了同一个位置，就在这个位置存储一个链表。 ## 5.4，性能 -散列表，数组，链表的查找、插入、删除元素的时间复杂度，如下表所示： -![散列表查找、插入删除元素操作时间复杂度分析](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/5.4%E6%80%A7%E8%83%BD.png) +散列表，数组，链表的查找、插入删除元素的时间复杂度，如下表所示： + +![散列表查找、插入删除元素操作时间复杂度分析](../images/5.4性能.png) 在平均情况下，散列表的查找（获取给定索引处的值）速度与数组一样快，而插入和删除速 度与链表一样快，因此它兼具两者的优点！但在最糟情况下，散列表的各种操作的速度都很慢。 因此，在使用散列表时，避开最糟情况至关重要。为此，需要避免冲突。而要避免冲突，需要有： + 较低的填装因子； + 良好的散列函数。 ## 5.5，小结 -散列表是一种功能强大的数据结构，其操作速度快，还能让你以不同的方式建立数据模型。 -你可能很快会发现自己经常在使用它。 +散列表是一种功能强大的数据结构，其操作速度快，还能让你以不同的方式建立数据模型。 你可能很快会发现自己经常在使用它。 + + 你可以结合散列函数和数组来创建散列表。 + 冲突很糟糕，你应使用可以最大限度减少冲突的散列函数。 + 散列表的查找、插入和删除速度都非常快。 @@ -239,21 +243,25 @@ if __name__ == "__main__": 图由节点（node）和边（edge）组成。 ## 6.2，广度优先搜索 在广度优先搜索的执行过程中，搜索范围从起点开始逐渐向外延伸，即先检查一度关系，再检查二度关系。 -## 6.3，队列 -栈：后进先出（Last In First Out，LIFO）的数据结构 -队列：先进先出（First In First Out，FIFO）的数据结构，支持入队和出对操作。 +## 6.3，栈与队列 + ++ **栈**：后进先出（Last In First Out，LIFO）的数据结构 ++ **队列**：先进先出（First In First Out，FIFO）的数据结构，支持入队和出对操作。 + ## 6.4，代码实现图结构 图中每个节点都与相邻节点相连，**散列表**结构可以表示这种关系。 图分为有向图（directed graph）和无向图（undirected graph），有向图关系是单向的，无向图没有箭头，直接相连的节点互为邻居。对从自己出发有指向他人的箭头，则有邻居。 ## 6.4.1 运行时间 -如果你在你的整个人际关系网中搜索芒果销售商，就意味着你将沿每条边前行（记住，边是从一个人到另一个人的箭头或连接），因此运行时间至少为O(边数)。你还使用了一个队列，其中包含要检查的每个人。将一个人添加到队列需要的时间是固定的，即为O(1)，因此对每个人都这样做需要的总时间为O(人数)。所以，广度优先搜索的运行时间为O(人数 + 边数)，这通常写作O(V + E)，其中V为顶点（ vertice）数， E为边数。 +如果你在你的整个人际关系网中搜索芒果销售商，就意味着你将沿每条边前行（记住，边是从一个人到另一个人的箭头或连接），因此运行时间至少为 $O(边数)$。你还使用了一个队列，其中包含要检查的每个人。将一个人添加到队列需要的时间是固定的，即为 $O(1)$，因此对每个人都这样做需要的总时间为 $O(人数)$。所以，广度优先搜索的运行时间为 $O(人数 + 边数)$，这通常写作 $O(V + E)$，其中 $V$ 为顶点（ vertice）数， $E$ 为边数。 # 第七章，迪克斯特拉算法 ## 7.1，使用迪克斯特拉算法 迪克斯特拉算法能够找出加权图中前往X的最短路径。对于寻找耗时最少的路径，迪克斯特拉算法包含４个步骤： -(1) 找出“最便宜”的节点，即可在最短时间内到达的节点。 -(2) 更新该节点的邻居的开销，其含义将稍后介绍。 -(3) 重复这个过程，直到对图中的每个节点都这样做了。 -(4) 计算最终路径。 + +1. 找出“最便宜”的节点，即可在最短时间内到达的节点。 +2. 更新该节点的邻居的开销，其含义将稍后介绍。 +3. 重复这个过程，直到对图中的每个节点都这样做了。 +4. 计算最终路径。 + 每个节点都有开销。开销指的是从起点前往该节点需要多长时间。 ## 7.2，术语 + 带权重的图称为加权图（ weighted graph），不带权重的图称为非加权图（ unweighted graph）。 @@ -261,13 +269,16 @@ if __name__ == "__main__": + 在无向图中，每条边都是一个环。狄克斯特拉算法只适用于有向无环图（ directed acyclic graph， DAG）。 图可能有环，所谓环，是指由一个节点出发，走一圈后可以又回到原节点，如下图所示： -![环示意图](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/7.2%E7%8E%AF%E7%A4%BA%E6%84%8F%E5%9B%BE.png) + +![环示意图](../images/7.2环示意图.png) ## 7.3，负权边 因此， **不能将狄克斯特拉算法用于包含负权边的图**。在包含负权边的图中，要找出最短路径，可使用另一种算法——贝尔曼福德算法（ Bellman-Ford algorithm）。 ## 7.4，编程实现狄克斯特拉算法 + 以下图为例，编程实现耗时最短的路径。 -![图算法问题](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/7.4%E5%9B%BE%E7%AE%97%E6%B3%95%E9%97%AE%E9%A2%98.png) + +![图算法问题](../images/7.4图算法问题.png) 代码如下： ```python @@ -351,14 +362,17 @@ print(costs) ## 8.3，集合覆盖问题 每个广播台都覆盖特定区域的州，找出覆盖全美50个州的最小广播集合。 贪婪算法解决这个问题，当广播台数量过多，算法所耗费的时间将激增。 -## 8.3.1，近似算法 -贪婪算法可化解危机！使用下面的贪婪算法可得到非常接近的解。 -(1) 选出这样一个广播台，即它**覆盖了最多的未覆盖州**。即便这个广播台覆盖了一些已覆盖的州，也没有关系。 -(2) 重复第一步，直到覆盖了所有的州。 +### 8.3.1，近似算法 +集合覆盖问题举例：每个广播台都覆盖特定区域的州，找出覆盖全美50个州的最小广播集合。贪婪算法可以解决这个问题，当广播台数量过多，算法所耗费的时间将激增。 + +1. 选出这样一个广播台，即它**覆盖了最多的未覆盖州**。即便这个广播台覆盖了一些已覆盖的州，也没有关系。 +2. 重复第一步，直到覆盖了所有的州。 + 这是一种近似算法（ approximation algorithm） 。在获得精确解需要的时间太长时，可使用近似算法。判断近似算法优劣的标准如下： + 速度有多快； + 得到的近似解与最优解的接近程度。 -代码实例： + +**代码实例：** ```python """ 准备工作 @@ -396,23 +410,52 @@ print(final_stations) 贪心算法的实质是每次选出当前的最优解，不管整体，是基于一定假设下的最优解。 ## 8.4，NP完全问题 旅行商问题和集合覆盖问题有一些共同之处：**你需要计算所有的解，并从中选出最小/最短的那个**。这两个问题都属于NP完全问题。NP完全问题的简单定义是，以难解著称的问题，如**旅行商问题和集合覆盖**问题。很多非常聪明的人都认为，根本不可能编写出可快速解决这些问题的算法。 -## 8.4.1，如何识别NP完全问题 -NP完全问题无处不在！如果能够判断出要解决的问题属于NP完全问题就好了，这样就不用 去寻找完美的解决方案，而是使用近似算法即可。但要判断问题是不是NP完全问题很难，易于解决的问题和NP完全问题的差别通常很小。 -但如果要找出经由指定几个点的的最短路径，就是旅行商问题——NP完全问题。简言之，没办法判断问题是不是NP完全问题，但还是有一些蛛丝马迹可循的。 +### 8.4.1，如何识别NP完全问题 +`NP` 完全问题无处不在！如果能够判断出要解决的问题属于 `NP` 完全问题就好了，这样就不用 去寻找完美的解决方案，而是使用近似算法即可。但要判断问题是不是NP完全问题很难，易于解决的问题和 `NP` 完全问题的差别通常很小。 + +但如果要找出经由指定几个点的的最短路径，就是旅行商问题——`NP完全问题`。简言之，没办法判断问题是不是 `NP` 完全问题，但还是有一些蛛丝马迹可循的。 + + 元素较少时算法的运行速度非常快，但随着元素数量的增加，速度会变得非常慢。 + 涉及“所有组合”的问题通常是NP完全问题。 + 不能将问题分成小问题，必须考虑各种可能的情况。这可能是NP完全问题。 + 如果问题涉及序列（如旅行商问题中的城市序列）且难以解决，它可能就是NP完全问题。 + 如果问题涉及集合（如广播台集合）且难以解决，它可能就是NP完全问题。 + 如果问题可转换为集合覆盖问题或旅行商问题，那它肯定是NP完全问题。 -# 8.5，小结 +## 8.5，小结 + 贪婪算法寻找局部最优解，企图以这种方式获得全局最优解。 + 对于NP完全问题，还没有找到快速解决方案。 + 面临NP完全问题时，最佳的做法是使用近似算法。 + 贪婪算法易于实现、运行速度快，是不错的近似算法。 # 第九章，动态规划 -学习动态规划，这是一种解决棘手问题的方法，**它将问题分成小问题**，并先着手解决这些小问题。 -工作原理：动态规划先解决子问题，再逐步解决大问题。 -# 第十章，K最近邻算法 +## 9.1，概念 +**动态规划算法是通过拆分问题，定义问题状态和状态之间的关系，使得问题能够以递推（或者说分治）的方式去解决**。在学习动态规划之前需要明确掌握几个重要概念，如下： + ++ **阶段**：对于一个完整的问题过程，适当的切分为若干个相互联系的子问题，每次在求解一个子问题，则对应一个阶段，整个问题的求解转化为按照阶段次序去求解。 ++ **状态**：状态表示每个阶段开始时所处的客观条件，即在求解子问题时的已知条件。状态描述了研究的问题过程中的状况。 ++ **决策**：决策表示当求解过程处于某一阶段的某一状态时，可以根据当前条件作出不同的选择，从而确定下一个阶段的状态，这种选择称为决策。 ++ **策略**：由所有阶段的决策组成的决策序列称为全过程策略，简称策略。 ++ **最优策略**：在所有的策略中，找到代价最小，性能最优的策略，此策略称为最优策略。 ++ **状态转移方程**：状态转移方程是确定两个相邻阶段状态的演变过程，描述了状态之间是如何演变的。 +## 9.2，背包问题 +学习动态规划，这是一种解决棘手问题的方法，**它将问题分成小问题**，并先着手解决这些小问题，每个动态规划问题都是从一个网格入手，背包问题的网格如下： +![9.2背包问题网格图](../images/9.2-背包问题.jpg) + +**工作原理**：`动态规划先解决子问题，再逐步解决大问题`。从背包问题的网格计算入手，可明白为何计算小背包可装入的商品的最大价值。余下了空间时，你可根据这些子问题的答案来确定余下的空间可装入哪些商品。计算每个单元格的价值时，使用的公式都相同。 这个公式如下： + +![背包问题公式图](../images/9.2-背包问题公示图.png) + +**网格的行顺序发生变化时，最终答案没有变化**。各行的排列顺序对最终结果无关紧要。 + +动态规划功能强大，它能够解决子问题并使用这些答案来解决大问题。 **但仅当每个子问题都是离散的，即不依赖于其他子问题时，动态规划才管用**。 这意味着使用动态规划算 法解决不了去巴黎玩的问题。 +## 9.1，最长公共子串 +通过动态规划问题，得到以下启示： + ++ 动态规划可帮助你在给定约束条件下找到最优解在背包问题中，你必须在背包容量给定的情况下，偷到价值最高的商品。 ++ 在问题可分解为彼此独立且离散的子问题时，就可使用动态规划来解决。 +要设计出动态规划解决方案可能很难，这正是本节要介绍的。下面是一些通用的小贴士： ++ 每种动态规划解决方案都涉及网格。 ++ 单元格中的值通常就是你要优化的值。在前面的背包问题中，单元格的值为商品的价值。 ++ 每个单元格都是一个子问题，因此你应考虑如何将问题分成子问题，这有助于你找出网格的坐标轴。 +# 第十章，K最近邻算法 # 第十一章　接下来如何做 From 8bb9c13923aef1f1c94e2bf8d4cceb7907aee50b Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Mon, 10 Aug 2020 12:24:36 +0800 Subject: [PATCH 339/386] =?UTF-8?q?fix=20=E6=95=B0=E5=AD=97=E5=9B=BE?= =?UTF-8?q?=E5=83=8F=E5=A4=84=E7=90=86=E5=9F=BA=E7=A1=80.md=E8=AF=AD?= =?UTF-8?q?=E6=B3=95=E9=94=99=E8=AF=AF?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...04\347\220\206\345\237\272\347\241\200.md" | 44 ++++++++++--------- ...ll\351\235\242\350\257\225\351\242\230.md" | 4 +- 2 files changed, 25 insertions(+), 23 deletions(-) diff --git "a/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206\345\237\272\347\241\200.md" "b/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206\345\237\272\347\241\200.md" index 11730f1a..1e14cbda 100644 --- "a/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206\345\237\272\347\241\200.md" +++ "b/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206\345\237\272\347\241\200.md" @@ -1,17 +1,4 @@ -## 3D目标检测笔记 -1. EfficientNet is a backbone. In the original paper, they used Hourglass backbone, which has a structure similar to UNet. -CenterNet is only a type of decoder, not the whole network. -2. 8 outputs, not exactly classes. -1 for binary mask -7 for regression variables x, y, z, yaw, pitch_sin, pitch_cos, roll -3. Regression predicts raw 3d coordinates of a car x, y, z. And binary mask predicts the 2d position on the image row, col. 2d map is much more precise but contains incomplete information. Function optimize_xy is used to shift x, y (on the road) so that they correspond to row, col (in image) -4. CenterNet模型：作者在构建模型时将目标作为一个点——即目标BBox的中心点。检测器采用关键点估计来找到中心点，并回归到其他目标属性，例如尺寸，3D位置，方向，甚至姿态。 -5. I use this idea to predict x, y, z coordinates of the vehicle and also yaw, pitch_cos, pitch_sin, roll angles. -For pitch I predict sin and cos, because, as we will see, this angle can be both near 0 and near 3.14. -These 7 parameters are my regression target variables instead of shift_x, shift_y, size_x, size_y -6. In this competition the world coordinate (Xw, Yw, Zw) is same with camera coordinate (Xc, Yc, Zc), the camera is origin. - -# 数字图像处理 +# 数字图像处理基础 ## 相机成像的原理 相机成像的原理：针孔相机(Pinhole Camera)通过投影变换，可以将三维Camera坐标转换为二维的图像坐标。这个变换矩阵解释相机的内在属性，称为`相机内参（Camera Intrinsic） K`。 ## yaw航向角，pitch俯仰角，roll翻滾角 @@ -19,21 +6,24 @@ These 7 parameters are my regression target variables instead of shift_x, shift_ ## 均值模糊与高斯模糊 ### 均值模糊（不常用） 所谓"模糊"，可以理解成每一个像素都取周边像素的平均值(**这种叫均值模糊**)，在图形上，就相当于产生"模糊"效果，但是在数值上，这是一种"平滑化"，"中间点"会失去细节。（高斯模糊效果如下图所示：） -![高斯模糊效果图](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E9%AB%98%E6%96%AF%E6%A8%A1%E7%B3%8A.jpg) -注意这里，计算平均值时，取值范围越大，"模糊效果"越强烈，也就是模糊半径越大，图像就越模糊。从数值角度看，就是数值越平滑。下图是分别是原图、模糊半径3像素、模糊半径10像素的效果： +![高斯模糊效果图](../images/高斯模糊.jpg) + +注意这里，计算平均值时，取值范围越大，"模糊效果"越强烈，也就是模糊半径越大，图像就越模糊。从数值角度看，就是数值越平滑。下图是分别是原图、模糊半径 3 像素、模糊半径 10 像素的效果： +![不同模糊半径效果图.jpg](../images/不同模糊半径效果图.jpg) ### 高斯模糊 从数学的角度来看，图像的高斯模糊过程就是图像像素与像素的正态分布做卷积, 同时高斯模糊对图像来说就是一个低通滤波器。简单来说高斯模糊就是一种图像模糊滤波器，它用正态分布计算图像中每个像素的变换。其中N维空间正态分布方程为： -![N维空间的正太分布方程](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/N%E7%BB%B4%E7%A9%BA%E9%97%B4%E6%AD%A3%E5%A4%AA%E5%88%86%E5%B8%83%E6%96%B9%E7%A8%8B.png) +![N维空间的正太分布方程](../images/N维空间正太分布方程.png) 在二维空间定义为： -![二维空间的正太分布方程](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E4%BA%8C%E7%BB%B4%E7%A9%BA%E9%97%B4%E7%9A%84%E6%AD%A3%E5%A4%AA%E5%88%86%E5%B8%83%E6%96%B9%E7%A8%8B.png) -其中r是模糊半径 ($r^2 = u^2 + v^2$），`σ` 是正态分布的标准偏差。在二维空间中，这个公式生成的曲面的等高线是从中心开始呈正态分布的同心圆。分布不为零的像素组成的卷积矩阵与原始图像做变换。每个像素的值都是周围相邻像素值的加权平均。原始像素的值有最大的高斯分布值，所以有最大的权重，相邻像素随着距离原始像素越来越远，其权重也越来越小。这样进行模糊处理比其它的均衡模糊滤波器更高地保留了边缘效果，效果如下图所示： +![二维空间的正太分布方程](../images/二维空间的正太分布方程.png) -![不同标准偏差大小(σ)效果图](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E4%B8%8D%E5%90%8C%E6%A8%A1%E7%B3%8A%E5%8D%8A%E5%BE%84%E6%95%88%E6%9E%9C%E5%9B%BE.jpg) +其中 $r$ 是模糊半径 $(r^2 = u^2 + v^2）$，$σ$ 是正态分布的标准偏差。在二维空间中，这个公式生成的曲面的等高线是从中心开始呈正态分布的同心圆。分布不为零的像素组成的卷积矩阵与原始图像做变换。每个像素的值都是周围相邻像素值的加权平均。原始像素的值有最大的高斯分布值，所以有最大的权重，相邻像素随着距离原始像素越来越远，其权重也越来越小。这样进行模糊处理比其它的均衡模糊滤波器更高地保留了边缘效果，效果如下图所示： + +![不同标准偏差大小(σ)效果图](../images/不同模糊半径效果图.jpg) ## 图像处理中平滑和锐化操作是什么？ @@ -61,4 +51,16 @@ These 7 parameters are my regression target variables instead of shift_x, shift_ + [高斯模糊算法](http://www.ruanyifeng.com/blog/2012/11/gaussian_blur.html) + [高斯模糊](https://zh.wikipedia.org/wiki/%E9%AB%98%E6%96%AF%E6%A8%A1%E7%B3%8A) ## numpy手撕代码 -人脸识别的场景下，输入105*12的feature map，用这个在1000512的特征库当中用欧氏距离去匹配105*12的feature map，用这个在1000*512的特征库当中用欧氏距离去匹配10*1000的特征，得到这个output。 +人脸识别的场景下，输入$105*times 12$的feature map，用这个在1000512的特征库当中用欧氏距离去匹配105*12的feature map，用这个在$1000\times 512$的特征库当中用欧氏距离去匹配10*1000的特征，得到这个output。 +## 3D目标检测笔记 +1. EfficientNet is a backbone. In the original paper, they used Hourglass backbone, which has a structure similar to UNet. +CenterNet is only a type of decoder, not the whole network. +2. 8 outputs, not exactly classes. +1 for binary mask +7 for regression variables x, y, z, yaw, pitch_sin, pitch_cos, roll +3. Regression predicts raw 3d coordinates of a car x, y, z. And binary mask predicts the 2d position on the image row, col. 2d map is much more precise but contains incomplete information. Function optimize_xy is used to shift x, y (on the road) so that they correspond to row, col (in image) +4. CenterNet模型：作者在构建模型时将目标作为一个点——即目标BBox的中心点。检测器采用关键点估计来找到中心点，并回归到其他目标属性，例如尺寸，3D位置，方向，甚至姿态。 +5. I use this idea to predict x, y, z coordinates of the vehicle and also yaw, pitch_cos, pitch_sin, roll angles. +For pitch I predict sin and cos, because, as we will see, this angle can be both near 0 and near 3.14. +These 7 parameters are my regression target variables instead of shift_x, shift_y, size_x, size_y +6. In this competition the world coordinate (Xw, Yw, Zw) is same with camera coordinate (Xc, Yc, Zc), the camera is origin. \ No newline at end of file diff --git "a/\347\274\226\347\250\213\350\257\255\350\250\200/cpp-python-shell\351\235\242\350\257\225\351\242\230.md" "b/\347\274\226\347\250\213\350\257\255\350\250\200/cpp-python-shell\351\235\242\350\257\225\351\242\230.md" index bb7dacd3..9287964c 100644 --- "a/\347\274\226\347\250\213\350\257\255\350\250\200/cpp-python-shell\351\235\242\350\257\225\351\242\230.md" +++ "b/\347\274\226\347\250\213\350\257\255\350\250\200/cpp-python-shell\351\235\242\350\257\225\351\242\230.md" @@ -42,8 +42,8 @@ print(res) # ['中国', '美国', '法国', '填充值', '填充值', '英国'] ``` ## Python 错误处理 和其他高级语言一样，`Python` 也内置了一套`try...except...finally...` 的错误处理机制。 -当我们认为某些代码可能会出错时，就可以用 `try` 来运行这段代码，如果执行出错，则后续代码不会继续执行，而是直接跳转至跳转至错误处理代码，即 `except` 语句块，执行完 `except` 后，如果有 `finally` 语句块，则执行。 -finally 语句块，至此，执行完毕。跳转至错误处理代码， + +当我们认为某些代码可能会出错时，就可以用 `try` 来运行这段代码，如果执行出错，则后续代码不会继续执行，而是直接跳转至跳转至错误处理代码，即 `except` 语句块，执行完 `except` 后，如果有 `finally` 语句块，则执行。至此，执行完毕。跳转至错误处理代码， ## Python 内置错误类型 + `IOError`：输入输出异常 + `AttributeError`：试图访问一个对象没有的属性 From ace886b78ea93f729c04c7d13374a549f3ecbfaf Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Mon, 10 Aug 2020 12:28:29 +0800 Subject: [PATCH 340/386] =?UTF-8?q?fix=20=E6=95=B0=E5=AD=97=E5=9B=BE?= =?UTF-8?q?=E5=83=8F=E5=A4=84=E7=90=86=E5=9F=BA=E7=A1=80.md=E8=AF=AD?= =?UTF-8?q?=E6=B3=95=E9=94=99=E8=AF=AF?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\204\347\220\206\345\237\272\347\241\200.md" | 2 +- ...346\214\207offer\351\242\230\350\247\243.md" | 17 +++++++++-------- ...\272\347\241\200\347\237\245\350\257\206.md" | 2 +- 3 files changed, 11 insertions(+), 10 deletions(-) diff --git "a/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206\345\237\272\347\241\200.md" "b/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206\345\237\272\347\241\200.md" index 1e14cbda..7c15e4cd 100644 --- "a/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206\345\237\272\347\241\200.md" +++ "b/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206\345\237\272\347\241\200.md" @@ -51,7 +51,7 @@ + [高斯模糊算法](http://www.ruanyifeng.com/blog/2012/11/gaussian_blur.html) + [高斯模糊](https://zh.wikipedia.org/wiki/%E9%AB%98%E6%96%AF%E6%A8%A1%E7%B3%8A) ## numpy手撕代码 -人脸识别的场景下，输入$105*times 12$的feature map，用这个在1000512的特征库当中用欧氏距离去匹配105*12的feature map，用这个在$1000\times 512$的特征库当中用欧氏距离去匹配10*1000的特征，得到这个output。 +人脸识别的场景下，输入 $105\times 12$的feature map，用这个在$1000\times 512$的特征库当中用欧氏距离去匹配105*12的feature map，用这个在$1000\times 512$的特征库当中用欧氏距离去匹配$10*\times 1000$的特征，得到这个output。 ## 3D目标检测笔记 1. EfficientNet is a backbone. In the original paper, they used Hourglass backbone, which has a structure similar to UNet. CenterNet is only a type of decoder, not the whole network. diff --git "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\345\211\221\346\214\207offer\351\242\230\350\247\243.md" "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\345\211\221\346\214\207offer\351\242\230\350\247\243.md" index c6fc4cbf..114c8dac 100644 --- "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\345\211\221\346\214\207offer\351\242\230\350\247\243.md" +++ "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\345\211\221\346\214\207offer\351\242\230\350\247\243.md" @@ -1,19 +1,20 @@ -## 030 最小的K个数 +## Leetcode +### 030-最小的K个数 > [华为OJ2051-最小的K个数（Top K问题）](https://songlee24.github.io/2015/03/21/hua-wei-OJ2051/) **题目描述**： 输入 `n` 个数，输出其中最小的 `k` 个 -### 方法１部分排序 -### 方法2 快速划分 -### 方法3 构建大根堆 +#### 方法１部分排序 +#### 方法2 快速划分 +#### 方法3 构建大根堆 + 遍历前 `1-k` 个数，创建大小为 `k` 的大根堆用来存储最小的 k 个数，大根堆堆顶元素是最小 k 个数中最大的一个； + 遍历 `k-n` 个数，得到元素　`X`，如果　`X`　比堆顶元素 `Y` 大，则不改变原来的堆，因为这个元素比大根堆中所有元素都大； + 如果　`X`　比堆顶元素 `Y` 小，那么用 `X` 替换堆顶的元素 `Y`。在 X 替换堆顶元素 Y 之后，大根堆的结构可能被破坏，需要进行向下调整(重新排序)。调整过程的时间复杂度是 $O(log_2k)$ 。 -## 最长公共子序列问题 +### 1143-最长公共子序列问题 -# 手撕代码题 -## 瓜子二手车 +## 手撕代码题 +### 瓜子二手车 1. 给出二叉树中的两个结点，找出它们的最低公共父节点. 2. [牛顿迭代求sqrt](https://leetcode.com/problems/sqrtx/). ✓ 3. 通过均匀硬币生成1到N随机数 @@ -29,6 +30,6 @@ + 一米长的绳子，任意分成三份，能构成三角形的概率 + 两个人约定在七点到八点之间见面，先到的人等20分钟就可以走，问两人见面的概率 -## 58同城 +### 58同城 1. 手撕硬nms ✓ 2. 判断链表是否对称 ✓ \ No newline at end of file diff --git "a/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200\347\237\245\350\257\206.md" "b/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200\347\237\245\350\257\206.md" index b8a4408a..0edac058 100644 --- "a/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200\347\237\245\350\257\206.md" +++ "b/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200\347\237\245\350\257\206.md" @@ -67,7 +67,7 @@ def run_thread(n): + 总线程数<= CPU数量：并行运行 + 总线程数 > CPU数量：并发运行 -# 手撕代码题 +## 手撕代码题 1. 十个数平分两组，差最小. 2. 给出五个会议起始和终止的时间，固定时间内最多安排几场会议. 3. 合并k个链表. 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"b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -1,6 +1,6 @@ # 深度学习 ## 滤波器与卷积核 -这两个术语之间有着本质的区别：仅在 1 通道的情况下，滤波器和内核这两个术语等价，在一般情况下，它们是不同的。**每个过滤器实际上是卷积核的集合**，图层的每个输入通道都有一个卷积核，并且是唯一的。 +这两个术语之间有着本质的区别：仅在 `1` 通道的情况下，滤波器和内核这两个术语等价，在一般情况下，它们是不同的。**每个过滤器实际上是卷积核的集合**，图层的每个输入通道都有一个卷积核，并且是唯一的。 ## 卷积层和池化输出大小计算 > 不管是TensorFlow、Keras、Caffe还是Pytorch，其卷积层和池化层的参数默认值可能有所不同，但是最终的卷积输出大小计算公式是一样的 ### CNN中术语解释 @@ -691,18 +691,56 @@ def calculateIoU(candidateBound, groundTruthBound): 1. 离线增强 ： 直接对硬盘上的数据集进行处理，并保存增强后的数据集文件。数据的数目会变成增强因子 x 原数据集的数目 ，这种方法常常用于数据集很小的时候 2. 在线增强 ： 这种增强的方法用于，获得 batch 数据之后，然后对这个batch的数据进行增强，如旋转、平移、翻折等相应的变化，由于有些数据集不能接受线性级别的增长，这种方法长用于大的数据集，很多机器学习框架已经支持了这种数据增强方式，并且可以使用GPU优化计算。 +### Reference +[深度学习中的数据增强](https://blog.csdn.net/zhelong3205/article/details/81810743 ) + +## 为什么`Faster RCNN/Mask RCNN` 需要使用 `ROI Pooling/ROI Align`? +为了使得最后面的两个全连接层能够共享 `conv layers(VGG/ResNet)` 权重。在所有的 `RoIs` 都被 `pooling` 成（512×7×7）的`feature map`后，将它 `reshape` 成一个一维的向量，就可以利用 `VGG16` 的预训练的权重来初始化前两层全连接。 ## ROI Pooling替换为ROI Align，及各自原理 -faster rcnn将roi pooling替换为roi align效果有所提升。 +`faster rcnn` 将 `roi pooling` 替换为 `roi align` 效果有所提升。 -#### ROI Pooling原理 +### ROI Pooling原理 +`RPN`　生成的 `ROI` 区域大小是对应与输入图像大小（每个roi区域大小各不相同），为了能够共享权重，所以需要将这些 `ROI`　映射回特征图上，并固定大小。ROI Pooling操作过程如下图： -#### ROI Align原理 +![roi pooling](../images/roi%20pooling.png) -## Reference +**`ROI Pooling` 具体操作如下：** + +1. `Conv layers` 使用的是 `VGG16`，`feat_stride=32`(即表示，经过网络层后图片缩小为原图的 1/32),原图$800\times 800$,最后一层特征图 `feature map` 大小: $25\times 25$ +2. 假定原图中有一 `region proposal`，大小为$665\times 665$，这样，映射到特征图中的大小：$665/32=20.78$，即大小为 $20.78\times 20.78$，源码中，在计算的时候会进行取整操作，于是，进行所谓的第一次量化，即映射的特征图大小为20*20； +3. 假定 `pooled_w=7、pooled_h=7`，即 `pooling` 后固定成$7\times 7$ 大小的特征图，所以，将上面在 `feature map`上映射的 $20\times 20$ 的 `region proposal` 划分成 `49`个同等大小的小区域，每个小区域的大小 $20/7=2.86$,，即 $2.86\times 2.86$，此时，进行第二次量化，故小区域大小变成 $2\times 2$； +4. 每个$2\times 2$的小区域里，取出其中最大的像素值，作为这一个区域的‘代表’，这样，49 个小区域就输出 49 个像素值，组成 $7\times 7$ 大小的 `feature map`。 + +所以，通过上面可以看出，经过**两次量化，即将浮点数取整**，原本在特征图上映射的 $20\times 20$ 大小的 region proposal，偏差成大小为 $7\times 7$ 的，这样的像素偏差势必会对后层的回归定位产生影响。所以，产生了后面的替代方案: `RoiAlign`。 +### ROI Align原理 +ROI Align的工作原理如下图。 + +![roi align](../images/roi%20align.png) + +`ROI Align` 是在 `Mask RCNN` 中使用以便使生成的候选框`region proposal` 映射产生固定大小的 `feature map` 时提出的，根据上图，有着类似的映射： + +1. `Conv layers` 使用的是 `VGG16`，`feat_stride=32` (即表示，经过网络层后图片缩小为原图的1/32),原图 $800\times 800$，最后一层特征图feature map大小: $25*\times 25$; +2. 假定原图中有一region proposal，大小为 $665*\times 665$，这样，映射到特征图中的大小：$665/32=20.78$，即大小为 $20.78\times 20.78$，此时，没有像 `RoiPooling` 那样就行取整操作，保留浮点数; +3. 假定 `pooled_w=7,pooled_h=7`，即pooling后固定成 $7\times 7$ 大小的特征图，所以，将在 feature map上映射的 $20.78\times 20.78$ 的 `region proposal` 划分成 49 个同等大小的小区域，每个小区域的大小 $20.78/7=2.97$,即 $2.97\times 2.97$; +4. 假定采样点数为 4，即表示，对于每个 $2.97\times 2.97$ 的小区域，平分四份，每一份取其中心点位置，而中心点位置的像素，采用双线性插值法进行计算，这样，就会得到四个点的像素值，如下图: + +![roi align双线性插值](../images/roi%20align线性插值.png) -1.[深度学习中的数据增强](https://blog.csdn.net/zhelong3205/article/details/81810743 ) +上图中，四个红色叉叉`‘×’`的像素值是通过双线性插值算法计算得到的。 + +最后，取四个像素值中最大值作为这个小区域(即：$2.97\times 2.97$ 大小的区域)的像素值，如此类推，同样是 `49` 个小区域得到 `49` 个像素值，组成 $7\times 7$ 大小的 `feature map`。 + +### RoiPooling 和 RoiAlign 总结 + +总结：知道了 `RoiPooling` 和 `RoiAlign` 实现原理，在以后的项目中可以根据实际情况进行方案的选择；对于检测图片中大目标物体时，两种方案的差别不大，而如果是图片中有较多小目标物体需要检测，则优先选择 `RoiAlign`，更精准些。 +### Reference +[RoIPooling、RoIAlign笔记](https://www.cnblogs.com/wangyong/p/8523814.html) + +## CNN的反向传播算法推导 + + [四张图彻底搞懂CNN反向传播算法（通俗易懂）](https://zhuanlan.zhihu.com/p/81675803) ++ [反向传播算法推导过程（非常详细）](https://zhuanlan.zhihu.com/p/79657669) ## Reference @@ -712,4 +750,8 @@ faster rcnn将roi pooling替换为roi align效果有所提升。 ## Focal Loss 介绍一下 +## Reference +1. [《Batch Normalization Accelerating Deep Network Training by Reducing Internal Covariate Shift》阅读笔记与实现](https://blog.csdn.net/happynear/article/details/44238541) +2. [深度学习中 Batch Normalization为什么效果好](https://blog.csdn.net/happynear/article/details/44238541) +3. [详解机器学习中的梯度消失、爆炸原因及其解决方法](https://blog.csdn.net/qq_25737169/article/details/78847691) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" index abb326b7..aeb6d8b4 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" @@ -93,7 +93,7 @@ if __name__ == "__main__": bbox的数据结构为(xmin,ymin,xmax,ymax)--(x1,y1,x2,y2), 每个bounding box的左上角和右下角的坐标 输入： - bbox1, bbox2 + bbox1, bbox2: Single numpy bounding box, Shape: [4] 输出： iou值 """ @@ -288,7 +288,7 @@ def soft_nms(dets, thresh, type='gaussian'): scores = scores[tmp] return keep ``` -## 类别AP计算 +## AP计算 **目标检测领域常用的评估标准是**：`mAP(mean average precision)`，计算mAP需要先计算`AP`，计算`AP`需涉及到`precision`和`recall`的计算，而这两者的计算又需设计`TP`、`FP`、`FN`的计算。 `AP`的计算一般先涉及到`P-R`曲线（precision-recall curve）的绘制，P-R曲线下面与x轴围成的面积称为`average precison（AP）`。下图是一个二分类问题的P-R曲线： From b58d191e489eeea49e239b9750dc7f907d92fa3c Mon Sep 17 00:00:00 2001 From: HerleysZhang Date: Mon, 10 Aug 2020 15:40:57 +0800 Subject: [PATCH 342/386] =?UTF-8?q?fix=20=E7=9B=AE=E6=A0=87=E6=A3=80?= =?UTF-8?q?=E6=B5=8B=E5=9F=BA=E6=9C=AC=E6=A6=82=E5=BF=B5=E4=B8=8E=E8=AF=84?= =?UTF-8?q?=E4=BB=B7=E6=8C=87=E6=A0=87=E8=AE=A1=E7=AE=97.md=E8=AF=AD?= =?UTF-8?q?=E6=B3=95=E5=8F=8A=E5=86=85=E5=AE=B9=E6=98=BE=E7=A4=BA=E9=97=AE?= =?UTF-8?q?=E9=A2=98=EF=BC=8Cadd=20=E5=8F=A6=E4=B8=80=E4=B8=AA=E7=89=88?= =?UTF-8?q?=E6=9C=AC=E7=9A=84nms=20python=E4=BB=A3=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...40\351\235\242\350\257\225\351\242\230.md" | 12 +-- ...07\346\240\207\350\256\241\347\256\227.md" | 74 ++++++++++++++++--- 2 files changed, 70 insertions(+), 16 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index be2a4ee3..262faa75 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -1,4 +1,4 @@ -# 深度学习 +# 深度学习基础 ## 滤波器与卷积核 这两个术语之间有着本质的区别：仅在 `1` 通道的情况下，滤波器和内核这两个术语等价，在一般情况下，它们是不同的。**每个过滤器实际上是卷积核的集合**，图层的每个输入通道都有一个卷积核，并且是唯一的。 ## 卷积层和池化输出大小计算 @@ -6,11 +6,11 @@ ### CNN中术语解释 `CNN`网络的主要参数有下面这么几个： -+ 卷积核Kernal（在Tensorflow中称为filter）； -+ 填充Padding； ++ 卷积核 `Kernal` 大小（在 `Tensorflow/keras` 中称为`filter`）； ++ 填充 `Padding` ； + 滑动步长Strides； -+ 池化核Kernal（在Tensorflow中称为filter）； -+ 通道数Channels。 ++ 池化核 `Kernal`（在 `Tensorflow/keras` 中称为 `filter`）； ++ 输入/输出通道数 `Channels`。 ### 卷积输出大小计算 1，图片经卷积 `Conv2D` 后**输出大小计算公式**如下： @@ -713,7 +713,7 @@ def calculateIoU(candidateBound, groundTruthBound): 3. 假定 `pooled_w=7、pooled_h=7`，即 `pooling` 后固定成$7\times 7$ 大小的特征图，所以，将上面在 `feature map`上映射的 $20\times 20$ 的 `region proposal` 划分成 `49`个同等大小的小区域，每个小区域的大小 $20/7=2.86$,，即 $2.86\times 2.86$，此时，进行第二次量化，故小区域大小变成 $2\times 2$； 4. 每个$2\times 2$的小区域里，取出其中最大的像素值，作为这一个区域的‘代表’，这样，49 个小区域就输出 49 个像素值，组成 $7\times 7$ 大小的 `feature map`。 -所以，通过上面可以看出，经过**两次量化，即将浮点数取整**，原本在特征图上映射的 $20\times 20$ 大小的 region proposal，偏差成大小为 $7\times 7$ 的，这样的像素偏差势必会对后层的回归定位产生影响。所以，产生了后面的替代方案: `RoiAlign`。 +所以，通过上面可以看出，经过**两次量化，即将浮点数取整**，原本在特征图上映射的 $20\times 20$ 大小的 `region proposal`，偏差成大小为 $7\times 7$ 的，这样的像素偏差势必会对后层的回归定位产生影响。所以，产生了后面的替代方案: `RoiAlign`。 ### ROI Align原理 ROI Align的工作原理如下图。 diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" index aeb6d8b4..640f70ef 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" @@ -160,12 +160,13 @@ if __name__=='__main__': 以上图为例，每个选出来的Bounding Box检测框（既BBox）用（x,y,h,w, confidence score，Pdog,Pcat）表示，confidence score表示background和foreground的置信度得分，取值范围[0,1]。Pdog,Pcat分布代表类别是狗和猫的概率。如果是100类的目标检测模型，BBox输出向量为5+100=105。 ### [NMS算法](https://juejin.im/entry/5bdbc26151882516da0ddd25) -NMS主要就是通过迭代的形式，不断地以最大得分的框去与其他框做IoU操作，并过滤那些IoU较大的框。 -其实现的思想主要是将各个框的置信度进行排序，然后选择其中置信度最高的框A，将其作为标准选择其他框，同时设置一个阈值，[当其他框B与A的重合程度超过阈值就将B舍弃掉](https://blog.csdn.net/williamyi96/article/details/77996167 )，然后在剩余的框中选择置信度最大的框，重复上述操作。算法过程如下： -1. 根据候选框类别分类概率排序：F>E>D>C>B>A，并标记最大概率的矩形框F作为标准框。 -2. 分别判断A~E与F的重叠度IOU(两框的交并比)是否大于某个设定的阈值，假设B、D与F的重叠度超过阈值，那么就扔掉B、D； -3. 从剩下的矩形框A、C、E中，选择概率最大的E，标记为要保留下来的，然后判读E与A、C的重叠度，扔掉重叠度超过设定阈值的矩形框； -4. 对剩下的bbx，循环执行(2)和(3)直到所有的bbx均满足要求（即不能再移除bbx） +NMS主要就是通过迭代的形式，不断地以最大得分的框去与其他框做 `IoU` 操作，并过滤那些 `IoU` 较大的框。 + +**其实现的思想主要是将各个框的置信度进行排序**，然后选择其中置信度最高的框 A，将其作为标准选择其他框，同时设置一个阈值，[当其他框B与A的重合程度超过阈值就将 B 舍弃掉](https://blog.csdn.net/williamyi96/article/details/77996167 )，然后在剩余的框中选择置信度最大的框，重复上述操作。算法过程如下： +1. 根据候选框类别分类概率排序：`F>E>D>C>B>A`，并标记最大概率的矩形框F作为标准框。 +2. 分别判断 `A~E` 与 `F` 的重叠度 `IOU` (两框的交并比)是否大于某个设定的阈值，假设 `B、D` 与 `F` 的重叠度超过阈值，那么就扔掉 `B、D`； +3. 从剩下的矩形框 `A、C、E`中，选择概率最大的 `E`，标记为要保留下来的，然后判读 `E` 与 `A、C` 的重叠度，扔掉重叠度超过设定阈值的矩形框； +4. 对剩下的 `bbox`，循环执行(2)和(3)直到所有的 `bbox` 均满足要求（即不能再移除 `bbox`） **nms的python代码如下**： ```Python @@ -228,6 +229,58 @@ if __name__ == "__main__": [210. 30. 420. 5. 0.8] [430. 280. 460. 360. 0.7]] +**另一个版本的 nms 的 python 代码如下：** +```python +from __future__ import print_function +import numpy as np +import time + +def intersect(box_a, box_b): + max_xy = np.minimum(box_a[:, 2:], box_b[2:]) + min_xy = np.maximum(box_a[:, :2], box_b[:2]) + inter = np.clip((max_xy - min_xy), a_min=0, a_max=np.inf) + return inter[:, 0] * inter[:, 1] + +def get_iou(box_a, box_b): + """Compute the jaccard overlap of two sets of boxes. The jaccard overlap + is simply the intersection over union of two boxes. + E.g.: + A ∩ B / A ∪ B = A ∩ B / (area(A) + area(B) - A ∩ B) + The box should be [x1,y1,x2,y2] + Args: + box_a: Single numpy bounding box, Shape: [4] or Multiple bounding boxes, Shape: [num_boxes,4] + box_b: Single numpy bounding box, Shape: [4] + Return: + jaccard overlap: Shape: [box_a.shape[0], box_a.shape[1]] + """ + if box_a.ndim==1: + box_a=box_a.reshape([1,-1]) + inter = intersect(box_a, box_b) + area_a = ((box_a[:, 2]-box_a[:, 0]) * + (box_a[:, 3]-box_a[:, 1])) # [A,B] + area_b = ((box_b[2]-box_b[0]) * + (box_b[3]-box_b[1])) # [A,B] + union = area_a + area_b - inter + return inter / union # [A,B] + +def nms(bboxs,scores,thresh): + """ + The box should be [x1,y1,x2,y2] + :param bboxs: multiple bounding boxes, Shape: [num_boxes,4] + :param scores: The score for the corresponding box + :return: keep inds + """ + if len(bboxs)==0: + return [] + order=scores.argsort()[::-1] + keep=[] + while order.size>0: + i=order[0] + keep.append(i) + ious=get_iou(bboxs[order],bboxs[i]) + order=order[ious<=thresh] + return keep +``` ## Soft NMS算法 Soft NMS算法是对NMS算法的改进，是发表在ICCV2017的文章中提出的。`NMS`算法存在一个问题是可能会把一些目标框给过滤掉，从而导致目标的`recall`指标比较低。原来的NMS可以描述如下：将IOU大于阈值的窗口的得分全部置为0，计算公式如下： @@ -296,11 +349,12 @@ def soft_nms(dets, thresh, type='gaussian'): ### 近似计算AP(approximated average precision) $$AP = \sum_{k=1}^{N}P(k)\Delta r(k)$$ + + 这个计算方式称为 approximated 形式的，而插值计算的方式里面这个是最精确的，每个样本点都参与了计算； + 很显然位于一条竖直线上的点对计算AP没有贡献； + 这里 `N` 为数据总量，`k` 为每个样本点的索引， $Δr(k)=r(k)−r(k−1)$。 -**近似计算**`AP`和绘制·PR`曲线代码如下： +**近似计算**`AP`和绘制`PR`曲线代码如下： ```Python import numpy as np import matplotlib.pyplot as plt @@ -352,7 +406,7 @@ def draw_PR_curve(predict_scores, eval_labels, name, cls_idx=1): ``` ### 插值计算(Interpolated average precision) -插值计算AP的公式的演变过程我不写了，详情可以参考这篇[文章](https://arleyzhang.github.io/articles/c521a01c/)，我这里的公式和图也是参考此文章的。`11点插值计算方式计算AP公式`如下： +插值计算 AP 的公式的演变过程这里不做讨论，详情可以参考这篇[文章](https://arleyzhang.github.io/articles/c521a01c/)，我这里的公式和图也是参考此文章的。`11点插值计算方式计算AP公式`如下： ![11点插值计算方式计算AP公式](../images/插值计算AP公式.png) @@ -363,7 +417,7 @@ def draw_PR_curve(predict_scores, eval_labels, name, cls_idx=1): ![插值计算方式计算AP的PR曲线图](../images/插值计算AP的PR曲线图.png) -从曲线上看，真实 `AP< approximated AP < Interpolated AP`，11-points Interpolated AP 可能大也可能小，当数据量很多的时候会接近于 Interpolated AP，与 Interpolated AP不同，前面的公式中计算AP时都是对PR曲线的面积估计，PASCAL的论文里给出的公式就更加简单粗暴了，直接计算11个阈值处的precision的平均值。如下： +从曲线上看，真实 `AP< approximated AP < Interpolated AP`，`11-points Interpolated AP` 可能大也可能小，当数据量很多的时候会接近于 `Interpolated AP`，与 `Interpolated AP` 不同，前面的公式中计算 `AP` 时都是对 `PR` 曲线的面积估计，PASCAL的论文里给出的公式就更加简单粗暴了，直接计算`11 个阈值处的 `precision` 的平均值。如下： ![PASCAL论文给出的11点计算AP公式](../images/11点计算AP公式.png) @@ -374,7 +428,7 @@ def draw_PR_curve(predict_scores, eval_labels, name, cls_idx=1): （1）在 `VOC2007`，只需要选取当Recall >= 0, 0.1, 0.2, ..., 1共11个点时的Precision最大值，然后AP就是这11个Precision的平均值，map就是所有类别AP值的平均。 -（2）在 `VOC2010` 及以后，需要针对每一个不同的Recall值（包括0和1），选取其大于等于这些Recall值时的Precision最大值，然后计算PR曲线下面积作为AP值，map就是所有类别AP值的平均。 +（2）在 `VOC2010` 及以后，需要针对每一个不同的 `Recall` 值（包括0和1），选取其大于等于这些 `Recall` 值时的 `Precision` 最大值，然后计算PR曲线下面积作为AP值，map就是所有类别AP值的平均。 （3）COCO 数据集，设定多个IOU阈值（0.5-0.95,0.05为步长），在每一个IOU阈值下都有某一类别的AP值，然后求不同IOU阈值下的AP平均，就是所求的最终的某类别的AP值。 From 6281ef1d3aaa34fc213b1b1c45ae9e389ef1ad27 Mon Sep 17 00:00:00 2001 From: HerleysZhang Date: Mon, 10 Aug 2020 16:45:30 +0800 Subject: [PATCH 343/386] =?UTF-8?q?fix=20=E7=9B=AE=E6=A0=87=E6=A3=80?= =?UTF-8?q?=E6=B5=8B=E5=9F=BA=E6=9C=AC=E6=A6=82=E5=BF=B5=E4=B8=8E=E8=AF=84?= =?UTF-8?q?=E4=BB=B7=E6=8C=87=E6=A0=87=E8=AE=A1=E7=AE=97.md=E8=AF=AD?= =?UTF-8?q?=E6=B3=95=E5=8F=8A=E5=86=85=E5=AE=B9=E6=98=BE=E7=A4=BA=E9=97=AE?= =?UTF-8?q?=E9=A2=98=EF=BC=8Cadd=20=E5=8F=A6=E4=B8=80=E4=B8=AA=E7=89=88?= =?UTF-8?q?=E6=9C=AC=E7=9A=84nms=20python=E4=BB=A3=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...14\207\346\240\207\350\256\241\347\256\227.md" | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" index 640f70ef..f19152da 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" @@ -79,8 +79,7 @@ if __name__ == "__main__": [ -82.50967 -173.01933 189.01933 98.50967 ] [-173.01933 -354.03867 370.03867 189.01933 ]] ## 交并比IOU -交并比（Intersection-over-Union，IoU），目标检测中使用的一个概念，是产生的候选框（candidate bound）与原标记框（ground truth bound）的交叠率，即它们的交集与并集的比值。最理想情况是完全重叠，即比值为1。 -计算公式如下： +交并比（Intersection-over-Union，IoU），目标检测中使用的一个概念，是产生的候选框（candidate bound）与原标记框（ground truth bound）的交叠率，即它们的交集与并集的比值。最理想情况是完全重叠，即比值为1。计算公式如下： ![IOU计算公式](../images/IOU计算公式.png) @@ -154,11 +153,11 @@ if __name__=='__main__': ## 非极大值抑制算法NMS ### [NMS介绍](https://juejin.im/entry/5bdbc26151882516da0ddd25) -在目标检测中，常会利用非极大值抑制算法(NMS，non maximum suppression)对生成的大量候选框进行后处理，去除冗余的候选框，得到最佳检测框，以加快目标检测的效率。其本质思想是其思想是搜素局部最大值，抑制非极大值。非极大值抑制，在计算机视觉任务中得到了广泛的应用，例如边缘检测、人脸检测、目标检测（DPM，YOLO，SSD，Faster R-CNN）等。即如图 2所示实现效果，消除多余的候选框，找到最佳的bbox。`NMS过程`如下图所示： +在目标检测中，常会利用非极大值抑制算法(NMS，non maximum suppression)对生成的大量候选框进行后处理，去除冗余的候选框，得到最佳检测框，以加快目标检测的效率。其本质思想是其思想是搜素局部最大值，抑制非极大值。非极大值抑制，在计算机视觉任务中得到了广泛的应用，例如边缘检测、人脸检测、目标检测（DPM，YOLO，SSD，Faster R-CNN）等。即如下图所示实现效果，消除多余的候选框，找到最佳的 `bbox`。`NMS过程`如下图所示： ![NMS过程](../images/NMS过程.png) -以上图为例，每个选出来的Bounding Box检测框（既BBox）用（x,y,h,w, confidence score，Pdog,Pcat）表示，confidence score表示background和foreground的置信度得分，取值范围[0,1]。Pdog,Pcat分布代表类别是狗和猫的概率。如果是100类的目标检测模型，BBox输出向量为5+100=105。 +以上图为例，每个选出来的 `Bounding Box` 检测框（既BBox）用`（x,y,h,w, confidence score，Pdog,Pcat）`表示，`confidence score` 表示 `background` 和 `foreground` 的置信度得分，取值范围`[0,1]`。Pdog, Pcat分布代表类别是狗和猫的概率。如果是 100 类的目标检测模型，`BBox` 输出向量为 `5+100=105`。 ### [NMS算法](https://juejin.im/entry/5bdbc26151882516da0ddd25) NMS主要就是通过迭代的形式，不断地以最大得分的框去与其他框做 `IoU` 操作，并过滤那些 `IoU` 较大的框。 @@ -343,6 +342,7 @@ def soft_nms(dets, thresh, type='gaussian'): ``` ## AP计算 **目标检测领域常用的评估标准是**：`mAP(mean average precision)`，计算mAP需要先计算`AP`，计算`AP`需涉及到`precision`和`recall`的计算，而这两者的计算又需设计`TP`、`FP`、`FN`的计算。 + `AP`的计算一般先涉及到`P-R`曲线（precision-recall curve）的绘制，P-R曲线下面与x轴围成的面积称为`average precison（AP）`。下图是一个二分类问题的P-R曲线： ![分类问题的PR曲线图](../images/分类PR曲线图.png) @@ -417,7 +417,7 @@ def draw_PR_curve(predict_scores, eval_labels, name, cls_idx=1): ![插值计算方式计算AP的PR曲线图](../images/插值计算AP的PR曲线图.png) -从曲线上看，真实 `AP< approximated AP < Interpolated AP`，`11-points Interpolated AP` 可能大也可能小，当数据量很多的时候会接近于 `Interpolated AP`，与 `Interpolated AP` 不同，前面的公式中计算 `AP` 时都是对 `PR` 曲线的面积估计，PASCAL的论文里给出的公式就更加简单粗暴了，直接计算`11 个阈值处的 `precision` 的平均值。如下： +从曲线上看，真实 `AP< approximated AP < Interpolated AP`，`11-points Interpolated AP` 可能大也可能小，当数据量很多的时候会接近于 `Interpolated AP`，与 `Interpolated AP` 不同，前面的公式中计算 `AP` 时都是对 `PR` 曲线的面积估计，PASCAL的论文里给出的公式就更加简单粗暴了，直接计算`11 个阈值处的 `precision` 的平均值。`PASCAL` 论文给出的11点计算AP公式如下： ![PASCAL论文给出的11点计算AP公式](../images/11点计算AP公式.png) @@ -654,10 +654,9 @@ def voc_eval(detpath, 公式解释，参考[这里](https://zhuanlan.zhihu.com/p/70306015?utm_source=wechat_session&utm_medium=social&utm_oi=571954943427219456)，如下： -理解 FLOPs 的计算公式分两步，括号内是第一步，计算出output feature map的一个pixel，然后再乘以 $H\times W\times C_o$，从而拓展到整个 output feature map。括号内的部分又可以分为两步：$(2\times C_i\times K^2-1)=(C_i\times K^2) + (C_i\times K^2-1)$。第一项是乘法运算次数，第二项是加法运算次数，因为 n 个数相加，要加 n-1次，所以不考虑bias的情况下，会有一个-1，如果考虑bias，刚好中和掉，括号内变为$(2\times C_i\times K^2)$ +**理解 `FLOPs` 的计算公式分两步**。括号内是第一步，计算出`output feature map` 的一个 `pixel`，然后再乘以 $H\times W\times C_o$，从而拓展到整个 output feature map。括号内的部分又可以分为两步：$(2\times C_i\times K^2-1)=(C_i\times K^2) + (C_i\times K^2-1)$。第一项是乘法运算次数，第二项是加法运算次数，因为 $n$ 个数相加，要加 $n-1$次，所以不考虑 `bias` 的情况下，会有一个 -1，如果考虑 `bias`，刚好中和掉，括号内变为$(2\times C_i\times K^2)$。 -所以卷积层的 $FLOPs=(2\times C_{i}\times K^2-1)\times H\times W\times C_o$ -($C_i$ 为输入特征图通道数，$K$ 为过滤器尺寸，$H, W, C_o$为输出特征图的高，宽和通道数)。 +所以卷积层的 $FLOPs=(2\times C_{i}\times K^2-1)\times H\times W\times C_o$ ($C_i$ 为输入特征图通道数，$K$ 为过滤器尺寸，$H, W, C_o$为输出特征图的高，宽和通道数)。 ### 全连接层的 FLOPs 计算 From 90d3cab62805e333f18f4022599b4c74df2b128c Mon Sep 17 00:00:00 2001 From: HerleysZhang Date: Mon, 10 Aug 2020 16:56:11 +0800 Subject: [PATCH 344/386] =?UTF-8?q?fix=20=E7=9B=AE=E6=A0=87=E6=A3=80?= =?UTF-8?q?=E6=B5=8B=E5=9F=BA=E6=9C=AC=E6=A6=82=E5=BF=B5=E4=B8=8E=E8=AF=84?= =?UTF-8?q?=E4=BB=B7=E6=8C=87=E6=A0=87=E8=AE=A1=E7=AE=97.md=E8=AF=AD?= =?UTF-8?q?=E6=B3=95=E5=8F=8A=E5=86=85=E5=AE=B9=E6=98=BE=E7=A4=BA=E9=97=AE?= =?UTF-8?q?=E9=A2=98=EF=BC=8Cadd=20=E5=8F=A6=E4=B8=80=E4=B8=AA=E7=89=88?= =?UTF-8?q?=E6=9C=AC=E7=9A=84nms=20python=E4=BB=A3=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" | 2 ++ 1 file changed, 2 insertions(+) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" index f19152da..dffdc62c 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" @@ -669,6 +669,8 @@ FOR RESOURCE EFFICIENT INFERENCE ## 目标检测度量标准汇总 ![目标检测指标汇总](../images/目标检测度量标准汇总.jpg) +## 目标检测领域中的数据不均衡问题 +参考此论文[Imbalance Problems in Object Detection](https://arxiv.org/abs/1909.00169.pdf)，我的理解之后补充。 ## 参考资料 + [目标检测评价标准-AP mAP](https://Harleyzhang.github.io/articles/c521a01c/) + [目标检测的性能评价指标](https://zhuanlan.zhihu.com/p/70306015?utm_source=wechat_session&utm_medium=social&utm_oi=571954943427219456) From 6d5e53b2242f9d9f9523423bed7f6a4626daacaa Mon Sep 17 00:00:00 2001 From: HerleysZhang Date: Mon, 10 Aug 2020 17:12:59 +0800 Subject: [PATCH 345/386] =?UTF-8?q?fix=20=E7=9B=AE=E6=A0=87=E6=A3=80?= =?UTF-8?q?=E6=B5=8B=E5=9F=BA=E6=9C=AC=E6=A6=82=E5=BF=B5=E4=B8=8E=E8=AF=84?= =?UTF-8?q?=E4=BB=B7=E6=8C=87=E6=A0=87=E8=AE=A1=E7=AE=97.md=E8=AF=AD?= =?UTF-8?q?=E6=B3=95=E5=8F=8A=E5=86=85=E5=AE=B9=E6=98=BE=E7=A4=BA=E9=97=AE?= =?UTF-8?q?=E9=A2=98=EF=BC=8Cadd=20=E5=8F=A6=E4=B8=80=E4=B8=AA=E7=89=88?= =?UTF-8?q?=E6=9C=AC=E7=9A=84nms=20python=E4=BB=A3=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...40\351\235\242\350\257\225\351\242\230.md" | 38 +++++++++++-------- 1 file changed, 23 insertions(+), 15 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 262faa75..74c5ba4c 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -13,32 +13,24 @@ + 输入/输出通道数 `Channels`。 ### 卷积输出大小计算 -1，图片经卷积 `Conv2D` 后**输出大小计算公式**如下： -$N = (W − F + 2P )/S+1$，**这里表示的是向下取整再加1**。 +1，图片经卷积 `Conv2D` 后**输出大小计算公式**如下：$N = (W − F + 2P )/S+1$，**这里表示的是向下取整再加1**。 + 输入图片大小 `W×W`（默认输入尺寸为正方形） + Filter大小 `F×F` + 步长 `S` + padding的像素数 `P` + 输出特征图大小 `N×N` -输出通道数 = 卷积核/池化核数量 - - 2，反卷积 `Conv2DTranspose` 得到的图片大小计算方式： -**反卷积的大小是由卷积核大小与滑动步长决定**， in是输入大小， k是卷积核大小， s是滑动步长， padding的像素数 P, out是输出大小。 -得到 $out = (in - 1) * s -2p + k$，还有另外一个写法：$W = (N - 1)*S - 2P + F$。 - -（`反卷积`也称为转置卷积，一般主要用来还原feature map的尺寸大小，在 cnn 可视化，fcn 中达到 pixel classification，以及 gan中从特征生成图像都需要用到反卷积的操作。） +**反卷积的大小是由卷积核大小与滑动步长决定**， $in$ 是输入大小， $k$ 是卷积核大小， $s$ 是滑动步长， `padding` 的像素数 $p$, $out$ 是输出大小。得到 $out = (in - 1) * s -2p + k$，还有另外一个写法：$W = (N - 1)*S - 2P + F$。 -反卷积输出结果计算实例。例如，输入：2x2， 卷积核：4x4， 滑动步长：3，填充像素为0， 输出：7x7 ，其计算过程就是， (2 - 1) * 3 + 4 = 7。 +`反卷积`也称为转置卷积，一般主要用来还原 `feature map` 的尺寸大小，在 `cnn` 可视化，`fcn` 中达到 `pixel classification`，以及 `gan` 中从特征生成图像都需要用到反卷积的操作。反卷积输出结果计算实例。例如，输入：`2x2`， 卷积核大小：`4x4`， 滑动步长：`3`，填充像素为 `0`， 输出：`7x7` ，其计算过程就是， `(2 - 1) * 3 + 4 = 7`。 -3，池化层如果设置为不填充像素（对于Pytorch，设置参数`padding = 0`，对于Keras/TensorFlow，设置参数`padding="valid"`），池化得到的特征图大小计算方式: $N=(W-F)/S+1$，这里表示的是向下取整再加1。 +3，池化层如果设置为不填充像素（对于 `Pytorch`，设置参数`padding = 0`，对于 `Keras/TensorFlow`，设置参数`padding="valid"`），池化得到的特征图大小计算方式: $N=(W-F)/S+1$，这里表示的是向下取整再加 `1`。 -**Pytorch 和 tensorflow 的卷积和池化函数，在 `0/"valid"` 条件下卷积和池化都向下取整**。**在实际设计的卷积神经网络中，卷积层一般填充像素(`same`)，池化层一般不填充像素(`valid`)**？注意：`stride`为 1 的时候，当`kernel`为 3 `padding`为 1 或者`kernel`为 5 `padding`为2，这种情况可直接得出卷积前后尺寸不变。 +**`Pytorch` 和 `tensorflow` 的卷积和池化函数，在 `0/"valid"` 条件下卷积和池化都向下取整**。**在实际设计的卷积神经网络中，卷积层一般填充像素(`same`)，池化层一般不填充像素(`valid`)**？注意：`stride`为 1 的时候，当`kernel`为 3 `padding`为 `1` 或者`kernel`为 `5` `padding`为2，这种情况可直接得出卷积前后尺寸不变。 ### 理解边界效应与填充padding -如果希望输出特征图的空间维度 -`Keras/TensorFlow` 设置卷积层的过程中可以设置 padding 参数值为 “valid” 或 “same”。“valid” 代表只进行有效的卷积，对边界数据不处理。“same” 代表 TensorFlow会自动对原图像进行补零（表示卷积核可以停留在图像边缘），也就是自动设置`padding` 值让输出与输入形状相同**。 +如果希望输出特征图的空间维度`Keras/TensorFlow` 设置卷积层的过程中可以设置 `padding` 参数值为 `“valid” 或 “same”`。`“valid”` 代表只进行有效的卷积，对边界数据不处理。`“same” 代表 TensorFlow` 会自动对原图像进行补零（表示卷积核可以停留在图像边缘），也就是自动设置`padding` 值让输出与输入形状相同**。 ### 参考资料 + [CNN中的参数解释及计算](https://flat2010.github.io/2018/06/15/%E6%89%8B%E7%AE%97CNN%E4%B8%AD%E7%9A%84%E5%8F%82%E6%95%B0/) @@ -584,9 +576,10 @@ $$softmax(y)_{i} = \frac{e^{yi}}{\sum_{j=1}^{n}e^{yj}}$$ ## 目标检测基本概念 为了了解模型的泛化能力，即判断模型的好坏，我们需要用某个指标来衡量，有了评价指标，就可以对比不同模型的优劣，并通过这个指标来进一步调参优化模型。**对于分类和回归两类监督模型，分别有各自的评判标准**。 + ### 准确率、精确率、召回率、F1 -**准确率 – Accuracy**，预测正确的结果占总样本的百分比，公式如下：准确率 = (TP+TN)/(TP+TN+FP+FN). +**准确率 – Accuracy**，预测正确的结果占总样本的百分比，公式如下：$准确率 = (TP+TN)/(TP+TN+FP+FN)$. > 虽然准确率可以判断总的正确率，但是在样本不平衡 的情况下，并不能作为很好的指标来衡量结果。举个简单的例子，比如在一个总样本中，正样本占 90%，负样本占 10%，样本是严重不平衡的。对于这种情况，我们只需要将全部样本预测为正样本即可得到 90% 的高准确率，但实际上我们并没有很用心的分类，只是随便无脑一分而已。这就说明了：由于样本不平衡的问题，导致了得到的高准确率结果含有很大的水分。即如果样本不平衡，准确率就会失效。 @@ -618,6 +611,13 @@ $$softmax(y)_{i} = \frac{e^{yi}}{\sum_{j=1}^{n}e^{yj}}$$ 但问题是：这个阈值是我们随便定义的，我们并不知道这个阈值是否符合我们的要求。 因此，为了找到一个最合适的阈值满足我们的要求，我们就必须遍历 0 到 1 之间所有的阈值，而每个阈值下都对应着一对查准率和查全率，从而我们就得到了这条曲线。 有的朋友又问了：如何找到最好的阈值点呢？ 首先，需要说明的是我们对于这两个指标的要求：我们希望查准率和查全率同时都非常高。 但实际上这两个指标是一对矛盾体，无法做到双高。图中明显看到，如果其中一个非常高，另一个肯定会非常低。选取合适的阈值点要根据实际需求，比如我们想要高的查全率，那么我们就会牺牲一些查准率，在保证查全率最高的情况下，查准率也不那么低。 + +### PR、ROC曲线与AUC面积 ++ **PR 曲线是以 Recall 为横轴，Precision 为纵轴；而 ROC曲线则是以 FPR 为横轴，TPR 为纵轴**。**P-R 曲线越靠近右上角性能越好**。 ++ `P、R 和 F1` 评价在信息检索中用的比较多，在机器学习中还有一种 `ROC` 曲线来评价性能。 ++ **`PR` 曲线展示的是`Precision vs Recall的`曲线，`ROC`曲线展示的是`FPR`（x 轴：False positive rate） vs `TPR`（True positive rate, TPR）曲线**。 ++ PR曲线与ROC曲线的相同点是都采用了`TPR (Recall)`，都可以用AUC来衡量分类器的效果。不同点是 `ROC` 曲线使用了 `FPR`，而 `PR` 曲线使用了 `Precision`，因此**PR曲线的两个指标都聚焦于正例**。类别不平衡问题中由于主要关心正例，所以在此情况下PR曲线被广泛认为优于ROC曲线。 + ### F1 分数 如果想要找到 $P$ 和 $R$ 二者之间的一个平衡点，我们就需要一个新的指标：$F1$ 分数。$F1$ 分数同时考虑了查准率和查全率，让二者同时达到最高，取一个平衡。$F1$ 计算公式如下： $$F1 = \frac{2*P*R}{P+R} = \frac{2*TP}{样例总数+TP-TN}$$ @@ -629,6 +629,14 @@ $$F_{\beta} = \frac{1+\beta^{2}*P*R}{(\beta ^{2}*P)+R}$$ 不同的计算机视觉问题，对两类错误有不同的偏好，常常在某一类错误不多于一定阈值的情况下，努力减少另一类错误。在目标检测中，**mAP**（mean Average Precision）作为一个统一的指标将这两种错误兼顾考虑。 +### 二分类、多分类指标计算 +#### 二分类 +$$F1\ score = \frac{2 \times P \times R}{(P + R)}$$。 +其中，准确率(precise) $P = TP / (TP + FP)$，召回率(recall) $R = TP / (TP + FN)$. +#### 多分类 ++ **Macro F1**: **将 `n` 分类的评价拆成 `n` 个二分类的评价**，计算每个二分类的 `F1 score`，`n` 个 `F1 score` 的平均值即为 `Macro F1`。 ++ **Micro F1**: 将n分类的评价拆成n个二分类的评价，将n个二分类评价的TP、FP、RN对应相加，计算评价准确率和召回率，由这2个准确率和召回率计算的F1 score即为Micro F1。 ++ 一般来讲，Macro F1、Micro F1高的分类效果好。Macro F1受样本数量少的类别影响大。 ### mAP指标解释 `AP` 衡量的是训练好的模型在每个类别上的好坏，`mAP` 衡量的是模型在所有类别上的好坏，得到 `AP` 后 `mAP` 的计算就变得很简单了，就是取所有 `AP` 的平均值。 From 8080c1664e76c9e739335b9bcb55ebd8d7924830 Mon Sep 17 00:00:00 2001 From: HerleysZhang Date: Mon, 10 Aug 2020 18:03:11 +0800 Subject: [PATCH 346/386] =?UTF-8?q?update=20=E7=9B=AE=E6=A0=87=E6=A3=80?= =?UTF-8?q?=E6=B5=8B=E5=9F=BA=E6=9C=AC=E6=A6=82=E5=BF=B5=E4=B8=8E=E8=AF=84?= =?UTF-8?q?=E4=BB=B7=E6=8C=87=E6=A0=87=E8=AE=A1=E7=AE=97.md=EF=BC=8Cadd=20?= =?UTF-8?q?=E7=A5=9E=E7=BB=8F=E7=BD=91=E7=BB=9C=E5=8F=82=E6=95=B0=E9=87=8F?= =?UTF-8?q?=E8=AE=A1=E7=AE=97=E6=96=B9=E6=B3=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...40\351\235\242\350\257\225\351\242\230.md" | 18 ++++++++--------- ...07\346\240\207\350\256\241\347\256\227.md" | 20 ++++++++++++++++++- 2 files changed, 28 insertions(+), 10 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 74c5ba4c..d661284f 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -1,19 +1,17 @@ # 深度学习基础 ## 滤波器与卷积核 -这两个术语之间有着本质的区别：仅在 `1` 通道的情况下，滤波器和内核这两个术语等价，在一般情况下，它们是不同的。**每个过滤器实际上是卷积核的集合**，图层的每个输入通道都有一个卷积核，并且是唯一的。 +在只有一个通道的情况下，“卷积核”就相当于 “filter”，这两个概念是可以互换的。但在一般情况下，它们是两个完全不同的概念。每个 “filter” 实际上恰好是“卷积核”的一个集合，在当前层，每个通道都对应一个卷积核，且这个卷积核是独一无二的。 ## 卷积层和池化输出大小计算 > 不管是TensorFlow、Keras、Caffe还是Pytorch，其卷积层和池化层的参数默认值可能有所不同，但是最终的卷积输出大小计算公式是一样的 ### CNN中术语解释 -`CNN`网络的主要参数有下面这么几个： - +卷积层主要参数有下面这么几个： + 卷积核 `Kernal` 大小（在 `Tensorflow/keras` 中称为`filter`）； + 填充 `Padding` ； -+ 滑动步长Strides； -+ 池化核 `Kernal`（在 `Tensorflow/keras` 中称为 `filter`）； -+ 输入/输出通道数 `Channels`。 ++ 滑动步长 `Stride`； ++ 输出通道数 `Channels`。 ### 卷积输出大小计算 -1，图片经卷积 `Conv2D` 后**输出大小计算公式**如下：$N = (W − F + 2P )/S+1$，**这里表示的是向下取整再加1**。 +1，图片经卷积 `Conv2D` 后**输出大小计算公式**如下：$\left \lfloor N = \frac{W-F+2P}{S}+1 \right \rfloor$，其中${\color{Red} \left \lfloor \right \rfloor}$"是向下取整符号，用于结果不是整数时进行向下取整。 + 输入图片大小 `W×W`（默认输入尺寸为正方形） + Filter大小 `F×F` + 步长 `S` @@ -34,7 +32,7 @@ ### 参考资料 + [CNN中的参数解释及计算](https://flat2010.github.io/2018/06/15/%E6%89%8B%E7%AE%97CNN%E4%B8%AD%E7%9A%84%E5%8F%82%E6%95%B0/) - ++ [CNN基础知识——卷积（Convolution）、填充（Padding）、步长(Stride)](https://zhuanlan.zhihu.com/p/77471866) ## Pytorch、Keras 的卷积层和池化层函数 + 注意：`Pytorc` h的卷积层和池化层的输入 `shape` 格式为 **(N,C,H,W)**，`Keras` 的卷积层和池化层的输入 `shape` 格式为 **(N,H,W,C)** @@ -547,7 +545,9 @@ HOG 特征提取方法就是将一个image： 1. **卷积层有参数，池化层没有参数**； 2. **经过卷积层节点矩阵深度会改变**。池化层不会改变节点矩阵的深度，但是它可以缩小节点矩阵的大小。 ## 卷积层参数数量计算方法 -假设输入层矩阵维度是96×96×3，第一层卷积层使用尺寸为5×5、深度为16的过滤器（卷积核尺寸为5×5、卷积核数量为16），那么这层卷积层的参数个数为５×5×3×16+16=1216个 +计算公式：**卷积层参数量 = (filter size \* 前一层特征图的通道数)\* 当前层filter数量 + 当前层filter数量**。 + +假设输入层矩阵维度是 96×96×3，第一层卷积层使用尺寸为 5×5、深度为 16 的过滤器（卷积核尺寸为 5×5、卷积核数量为 16），那么这层卷积层的参数个数为 ５×5×3×16+16=1216个。 ## 卷积层输出大小计算 diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" index dffdc62c..967eebdc 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" @@ -642,6 +642,8 @@ def voc_eval(detpath, + `MACC`：multiply-accumulate，乘法累加次数。 ### 卷积层FLOPs计算 +> 卷积操作本质上是个线性运算 + + $FLOPs=(2\times C_i\times K^2-1)\times H\times W\times C_o$（不考虑bias） + $FLOPs=(2\times C_i\times K^2)\times H\times W\times C_o$（考虑bias） + $MACC=(C_i\times K^2)\times H\times W\times C_o$（考虑bias） @@ -659,13 +661,29 @@ def voc_eval(detpath, 所以卷积层的 $FLOPs=(2\times C_{i}\times K^2-1)\times H\times W\times C_o$ ($C_i$ 为输入特征图通道数，$K$ 为过滤器尺寸，$H, W, C_o$为输出特征图的高，宽和通道数)。 ### 全连接层的 FLOPs 计算 - +> 默认卷积核 全连接层的 $FLOPs = (2I − 1)O$，$I$ 是输入层的维度，$O$ 是输出层的维度。 +## 神经网络参数量计算 +### 卷积层参数数量计算方法 +**卷积层参数量 = $C_i\times K^2\times C_o + C_o$** + +**$C_i$ 为输入特征图通道数，$K$ 为过滤器(卷积核)尺寸，$C_o$ 为输出的特征图的 channel 数(也是 filter 的数量)**，算式第二项是偏置项的参数量 。(一般不写偏置项，偏置项对总参数量的数量级的影响可以忽略不记，这里为了准确起见，把偏置项的参数量也考虑进来。） + +假设输入层矩阵维度是 96×96×3，第一层卷积层使用尺寸为 5×5、深度为 16 的过滤器（卷积核尺寸为 5×5、卷积核数量为 16），那么这层卷积层的参数个数为 ５×5×3×16+16=1216个。 +### BN层参数量计算方法 +**BN层参数量** = $2\times C_i$ + +其中 $C_i$ 为输入的channel数（BN层有两个需要学习的参数，平移因子和缩放因子） + +### 全连接层参数量计算方法 +**全连接层参数量** = $T_i\times T_o + T_O$ +$T_i$ 为输入向量的长度， $T_o$ 为输出向量的长度，其中第二项为偏置项参数量。 (目前全连接层已经逐渐被 Global Average Pooling 层取代了) ### 参考资料 [PRUNING CONVOLUTIONAL NEURAL NETWORKS FOR RESOURCE EFFICIENT INFERENCE ](https://arxiv.org/pdf/1611.06440.pdf) +[神经网络参数量的计算：以UNet为例](https://zhuanlan.zhihu.com/p/57437131) ## 目标检测度量标准汇总 ![目标检测指标汇总](../images/目标检测度量标准汇总.jpg) From 63b9d06aa86e07ca3714e7a1e72600d72901148d Mon Sep 17 00:00:00 2001 From: HerleysZhang Date: Mon, 10 Aug 2020 18:05:21 +0800 Subject: [PATCH 347/386] =?UTF-8?q?update=20=E7=9B=AE=E6=A0=87=E6=A3=80?= =?UTF-8?q?=E6=B5=8B=E5=9F=BA=E6=9C=AC=E6=A6=82=E5=BF=B5=E4=B8=8E=E8=AF=84?= =?UTF-8?q?=E4=BB=B7=E6=8C=87=E6=A0=87=E8=AE=A1=E7=AE=97.md=EF=BC=8Cadd=20?= =?UTF-8?q?=E7=A5=9E=E7=BB=8F=E7=BD=91=E7=BB=9C=E5=8F=82=E6=95=B0=E9=87=8F?= =?UTF-8?q?=E8=AE=A1=E7=AE=97=E6=96=B9=E6=B3=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...6\214\207\346\240\207\350\256\241\347\256\227.md" | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" index 967eebdc..cc0b55bc 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" @@ -663,22 +663,22 @@ def voc_eval(detpath, ### 全连接层的 FLOPs 计算 > 默认卷积核 全连接层的 $FLOPs = (2I − 1)O$，$I$ 是输入层的维度，$O$ 是输出层的维度。 -## 神经网络参数量计算 -### 卷积层参数数量计算方法 +## 神经网络参数量计算方法 +### 卷积层参数量 **卷积层参数量 = $C_i\times K^2\times C_o + C_o$** **$C_i$ 为输入特征图通道数，$K$ 为过滤器(卷积核)尺寸，$C_o$ 为输出的特征图的 channel 数(也是 filter 的数量)**，算式第二项是偏置项的参数量 。(一般不写偏置项，偏置项对总参数量的数量级的影响可以忽略不记，这里为了准确起见，把偏置项的参数量也考虑进来。） 假设输入层矩阵维度是 96×96×3，第一层卷积层使用尺寸为 5×5、深度为 16 的过滤器（卷积核尺寸为 5×5、卷积核数量为 16），那么这层卷积层的参数个数为 ５×5×3×16+16=1216个。 -### BN层参数量计算方法 +### BN层参数量 **BN层参数量** = $2\times C_i$ -其中 $C_i$ 为输入的channel数（BN层有两个需要学习的参数，平移因子和缩放因子） +其中 $C_i$ 为输入的 `channel` 数（BN层有两个需要学习的参数，平移因子和缩放因子） -### 全连接层参数量计算方法 +### 全连接层参数量 **全连接层参数量** = $T_i\times T_o + T_O$ -$T_i$ 为输入向量的长度， $T_o$ 为输出向量的长度，其中第二项为偏置项参数量。 (目前全连接层已经逐渐被 Global Average Pooling 层取代了) +$T_i$ 为输入向量的长度， $T_o$ 为输出向量的长度，其中第二项为偏置项参数量。 (目前全连接层已经逐渐被 `Global Average Pooling` 层取代了) ### 参考资料 [PRUNING CONVOLUTIONAL NEURAL NETWORKS FOR RESOURCE EFFICIENT INFERENCE From a75718c6517db514818c6f1be731d6e37b3a781e Mon Sep 17 00:00:00 2001 From: HerleysZhang Date: Mon, 10 Aug 2020 19:36:00 +0800 Subject: [PATCH 348/386] =?UTF-8?q?update=20=E7=9B=AE=E6=A0=87=E6=A3=80?= =?UTF-8?q?=E6=B5=8B=E5=9F=BA=E6=9C=AC=E6=A6=82=E5=BF=B5=E4=B8=8E=E8=AF=84?= =?UTF-8?q?=E4=BB=B7=E6=8C=87=E6=A0=87=E8=AE=A1=E7=AE=97.md=EF=BC=8Cadd=20?= =?UTF-8?q?=E7=A5=9E=E7=BB=8F=E7=BD=91=E7=BB=9C=E5=8F=82=E6=95=B0=E9=87=8F?= =?UTF-8?q?=E8=AE=A1=E7=AE=97=E6=96=B9=E6=B3=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...46\344\271\240\351\235\242\350\257\225\351\242\230.md" | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index d661284f..abdc2760 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -133,24 +133,24 @@ keras.layers.Conv2D(filters, kernel_size, strides=(1, 1), padding='valid', data_ 2D 卷积层 (例如对图像的空间卷积)。输入输出尺寸格式要求和池化层函数一样。**输入尺寸：(N,H,W,C)**。 -**当使用该层作为模型第一层时，需要提供 input_shape 参数**（整数元组，不包含 batch 轴），例如，input_shape=(128, 128, 3) 表示 128x128 RGB 图像，在 data_format="channels_last" 时。 +**当使用该层作为模型第一层时，需要提供 input_shape 参数**（整数元组，不包含 batch 轴），例如，`input_shape=(128, 128, 3)` 表示 `128x128` 的 `RGB` 图像，在 `data_format="channels_last"` 时。 **主要参数解释** -+ `filters`: 整数，输出空间的维度 （即卷积中滤波器的输出数量）。 ++ `filters`: 整数，输出空间的维度 （**即卷积中滤波器的输出数量**）。 + `kernel_size`: 一个整数，或者 2 个整数表示的元组或列表， 指明 2D 卷积窗口的宽度和高度。 可以是一个整数，为所有空间维度指定相同的值。 + `strides`: 一个整数，或者 2 个整数表示的元组或列表， 指明卷积沿宽度和高度方向的步长。 可以是一个整数，为所有空间维度指定相同的值。 指定任何 stride 值 != 1 与指定 dilation_rate 值 != 1 两者不兼容。 + `padding`: `"valid"` 或 `"same"` (大小写敏感)，默认`valid`。 + `data_format`: 字符串， `channels_last (默认)` 或 `channels_first` 之一，表示输入中维度的顺序。 `channels_last` 对应输入尺寸为 `(batch_size, height, width, channels)`， channels_first 对应输入尺寸为 `(batch_size, channels, height, width)`。 它默认为从 `Keras` 配置文件 `~/.keras/keras.json` 中 找到的 `image_data_format` 值。 如果你从未设置它，将使用 `channels_last`。 ### 总结 -`Pytorch` 的 Conv2d 函数不要求提供 输入数据的大小 `(H,W)`，但是要提供输入深度，`Keras` 的 Conv2d 函数第一层要求提供 `input_shape` 参数 `(H,W,C)`，其他层不需要。 +`Pytorch` 的 Conv2d 函数不要求提供 输入数据的大小 `(H,W)`，但是要提供输入深度，`Keras` 的 `Conv2d` 函数第一层要求提供 `input_shape` 参数 `(H,W,C)`，其他层不需要。 ## softmax回归 **分类问题**中，直接使用输出层的输出有两个问题： + 神经网络输出层的输出值的范围不确定，我们难以直观上判断这些值的意义 + 由于真实标签是离散值，这些离散值与不确定范围的输出值之间的误差难以衡量 -`softmax回归`解决了以上两个问题，它**将输出值变换为值为正且和为 1 的概率分布**，公式如下： +`softmax` 回归解决了以上两个问题，它**将输出值变换为值为正且和为 1 的概率分布**，公式如下： $$ softmax(y)_{i} = y_{i}^{'} = \frac{e^{yi}}{\sum_{j=1}^{n}e^{yj}} $$ From 6076ac7eccedc2074a1f7684f434fe5c4b816542 Mon Sep 17 00:00:00 2001 From: HerleysZhang Date: Wed, 12 Aug 2020 20:31:40 +0800 Subject: [PATCH 349/386] =?UTF-8?q?update=202020=E5=B9=B4=E6=A0=A1?= =?UTF-8?q?=E6=8B=9B=E5=8F=AF=E6=8A=95=E9=80=92=E5=85=AC=E5=8F=B8?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 18 +++++++++--------- ...240\351\235\242\350\257\225\351\242\230.md" | 4 +++- 2 files changed, 12 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index e34c3e25..9daada66 100644 --- a/README.md +++ b/README.md @@ -64,20 +64,20 @@ |------------|---------------|---------------|-------------|----------------| |百度|美团|地平线机器人|顺丰科技|否| |阿里巴巴|滴滴出行|图森未来|招银网络科技|否| -|腾讯|菜鸟网络|momenta|平安科技|，是，| +|腾讯|拼多多/菜鸟网络|momenta|平安科技|，是，| |字节跳动|京东|小马智行|cvte|是，，| |微软|网易|蔚来汽车|海康威视|，是，| |谷歌|快手|小鹏汽车|虹软科技|否| |商汤|爱奇艺|科大讯飞|华为|否| -|旷世|小米|寒武纪|大华|否| -|依图|陌陌|格灵深瞳|中兴|否| +|旷世|小米|寒武纪/依图|大华|否| +|博世|陌陌|格灵深瞳|中兴|否| |大疆无人机|美图MTlab|文行知远|联想|否| |蚂蚁金服|360安全|云天励飞|汇顶科技|否| -|拼多多|搜狗|松鼠AI|美的中央研究院|，是，| -|英伟达|猿辅导|思必驰|锐明技术|否| -|Intel|新浪/搜狐/金山|奥比中光|联发科|否| -|亚马逊|YY/虎牙/BIGO/斗鱼|优必选||否| -|博世中国|oppo/vivo/一加|度小满金融|戴尔|否| +|英伟达|搜狗|松鼠AI|美的中央研究院|，是，| +|Intel/亚马逊|猿辅导|思必驰|锐明技术|否| +|无|新浪/搜狐/金山|奥比中光|联发科|否| +|无|YY/虎牙/BIGO/斗鱼|优必选|联影医疗|否| +|无|oppo/vivo/一加|度小满金融|戴尔|否| |无|贝壳找房|深睿医疗|TP-LINK|否| |无|携程/去哪儿/途家|京东数科|ZOOM|否| |无|瓜子二手车|猎豹移动|广联达|否| @@ -88,7 +88,7 @@ |无|趣头条/一点资讯|数美科技|微众银行|是，否| |无|知乎|驭势科技|中国移动成研院|否| |无|蘑菇街|随手科技|远景智能|否| -|无|转转|智加科技|联影医疗|否| +|无|转转|智加科技|无|否| |无|同花顺/老虎证券|星环科技|中科创达|否| |无|乐信/有赞|趋势科技|恒润科技|否| |无|金蝶软件(中国)|暗物智能DMAI|航天二院706所|否| diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index abdc2760..fd3c4bcb 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -1,6 +1,8 @@ # 深度学习基础 ## 滤波器与卷积核 在只有一个通道的情况下，“卷积核”就相当于 “filter”，这两个概念是可以互换的。但在一般情况下，它们是两个完全不同的概念。每个 “filter” 实际上恰好是“卷积核”的一个集合，在当前层，每个通道都对应一个卷积核，且这个卷积核是独一无二的。 + +**滤波器的数量，卷积核的shape。** ## 卷积层和池化输出大小计算 > 不管是TensorFlow、Keras、Caffe还是Pytorch，其卷积层和池化层的参数默认值可能有所不同，但是最终的卷积输出大小计算公式是一样的 ### CNN中术语解释 @@ -18,7 +20,7 @@ + padding的像素数 `P` + 输出特征图大小 `N×N` -2，反卷积 `Conv2DTranspose` 得到的图片大小计算方式： +2，反卷积（也叫转置卷积） `keras-Conv2DTranspose`（`pytorch-ConvTranspose2d`） 得到的图片大小计算方式： **反卷积的大小是由卷积核大小与滑动步长决定**， $in$ 是输入大小， $k$ 是卷积核大小， $s$ 是滑动步长， `padding` 的像素数 $p$, $out$ 是输出大小。得到 $out = (in - 1) * s -2p + k$，还有另外一个写法：$W = (N - 1)*S - 2P + F$。 `反卷积`也称为转置卷积，一般主要用来还原 `feature map` 的尺寸大小，在 `cnn` 可视化，`fcn` 中达到 `pixel classification`，以及 `gan` 中从特征生成图像都需要用到反卷积的操作。反卷积输出结果计算实例。例如，输入：`2x2`， 卷积核大小：`4x4`， 滑动步长：`3`，填充像素为 `0`， 输出：`7x7` ，其计算过程就是， `(2 - 1) * 3 + 4 = 7`。 From 1a8dbf4a3588ea60af35d927e09044ad06226da5 Mon Sep 17 00:00:00 2001 From: HerleysZhang Date: Wed, 12 Aug 2020 20:38:55 +0800 Subject: [PATCH 350/386] =?UTF-8?q?update=202019=E7=A7=8B=E6=8B=9B?= =?UTF-8?q?=E5=AE=9E=E4=B9=A0=E6=80=BB=E7=BB=93=E5=86=85=E5=AE=B9=E6=98=BE?= =?UTF-8?q?=E7=A4=BA=E8=AF=AD=E6=B3=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...24\350\201\230\346\200\273\347\273\223.md" | 35 ++++++++++--------- 1 file changed, 18 insertions(+), 17 deletions(-) diff --git "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" index 588ca486..dfc225c5 100644 --- "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" +++ "b/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" @@ -5,9 +5,9 @@ 4. 实习平台很重要，**一定要尽量投大公司、知名企业或者行业独角兽**，不仅是面试正规和面试官温和亲切的态度，更重要的是自己本身面试之后经过反省总结，也能学到很多东西，小公司的面试大多都不是很正规。好的面试官，不管你技术实力是否满足他们要求，面试官一般都会很温和，让你放轻松，给你足够的尊重，有些面试官甚至会给你提些很有帮助和恳切的建议。这里，给我留下良好印象的是阿里和地平线机器人的面试官，面试官人真的超级友好和亲切。 ## 找实习建议 1. **心态第一，坚持为胜，找实习是一个持久战**。对于技术实力一般的人来说，真的要保持良好的心态，不抛弃、不放弃，这中间也许你会经历很多失败，但是真的只要坚持下来，我不保证你能拿到很好公司的offer，但是你自己本省一定能从面试中学到很多东西，尤其是很多大公司的面试官会给你一些很恳切的建议，可以让你受益匪浅。 -2. **实习要趁早**。建议有条件的研一或者大三就去实习，对于暑期实习来说，简历投递一定要趁早，最好2月份就开始，我很晚才投简历，很可能就会错过内推时间和岗位hc没有的情况，我4月份开始投简历已经算是很晚了，所以也直接导致我投了海康威视之后，一直是简历复选中，很有可能就是hc已经没了。 -3. **数据结构与算法题必须刷**。虽然这是老生常谈的建议，但是我们必须记住很多大公司一定会有笔试题，就算是内推免笔试的，面试过程中也很有可能会出数据结构与算法题。这里我建议去**leetcode**或者牛客网上刷**剑指offer**，一般把简单和中等难度题刷会就可以，笔试题一般都不会很难，除非是谷歌、MSRA那种公司可能会对笔试题有更高的要求。刷题的话，第一次刷不会可以去看参考解题思路和答案，**看完后要自己写出代码**。 -4. **常见面试题要掌握**。其实关于计算机基础和计算机视觉算法原理的面试题，可以提前准备下，有些题频繁的问到，可以提前准备下，比如：1*1卷积作用，链式求导公式，多线程与多进程区别，tcp/udp通信原理等。这些常见面试题，都可以在牛客网找的到，当然**不同岗位面试题不一样，甚至每个人的面试问题都有很大不一样**，不要因为别人面经上的题不会，你就有些失去信心，但是**对于同一个岗位的频繁出现的面试问题还是必须掌握**。机器学习、算法工程师面试考点汇总，参考[这里](https://www.nowcoder.com/discuss/165930)。 +2. **实习要趁早**。建议有条件的 `研一` 或者 `大三` 就去实习，对于暑期实习来说，简历投递一定要趁早，最好 `2` 月份就开始，我很晚才投简历，很可能就会错过内推时间和岗位 `hc` 没有的情况，我 `4` 月份开始投简历已经算是很晚了，所以也直接导致我投了海康威视之后，一直是简历复选中，很有可能就是 `hc` 已经没了。 +3. **数据结构与算法题必须刷**。虽然这是老生常谈的建议，但是我们必须记住很多大公司一定会有笔试题，就算是内推免笔试的，面试过程中也很有可能会出数据结构与算法题。这里我建议去 **leetcode** 或者牛客网上刷**剑指offer**，一般把简单和中等难度题刷会就可以，笔试题一般都不会很难，除非是谷歌、MSRA 那种公司可能会对笔试题有更高的要求。刷题的话，第一次刷不会可以去看参考解题思路和答案，**看完后要自己写出代码**。 +4. **常见面试题要掌握**。其实关于计算机基础和计算机视觉算法原理的面试题，可以提前准备下，有些题频繁的问到，可以提前准备下，比如：`1*1` 卷积作用，链式求导公式，多线程与多进程区别，`tcp/udp` 通信原理等。这些常见面试题，都可以在牛客网找的到，当然**不同岗位面试题不一样，甚至每个人的面试问题都有很大不一样**，不要因为别人面经上的题不会，你就有些失去信心，但是**对于同一个岗位的频繁出现的面试问题还是必须掌握**。机器学习、算法工程师面试考点汇总，参考[这里](https://www.nowcoder.com/discuss/165930)。 5. **简历要有亮点**。**paper、算法比赛、项目、实习必须要有一个啊，博客、github一定要有，这真的很给简历加分**！实验室没有paper和项目条件的，可以考虑去**[天池](https://tianchi.aliyun.com/home/)、[Datafountain](https://www.datafountain.cn/)、[kaggle](https://www.kaggle.com/)**上打比赛，真的可以学到很多东西。 ## 面试过程建议 1. **不要紧张，表达要清楚流利**，要记住，**绝大部分面试官都是很友好和亲切的**，尤其是大公司的面试官真的超级温和，**这里为阿里和地平线机器人面试官点赞**！对于那种不尊重人和看不起人的面试官，我个人觉得没必要去他们公司了，一般这种面试官会出现在小公司，一个面试官连对面试者基本的尊重都做不到，我难以想象这家公司的文化是什么样子。 @@ -16,28 +16,29 @@ ## 计算机视觉岗找实习心得 1. 首先，我的水平真的算是很一般的，真正的大佬都是很轻松的拿到数个满意offer。我自己是本科是双非大学自动化专业，研究生是中等211大学控制工程专业，本科主要搞嵌入式方向，研究生才转为计算机视觉方向和深度学习方向，这里也给后来者一个建议，**如果不是真心热爱、喜欢你所从事的计算机视觉方向，只是为了钱的话那就真的没必要了**，**CV岗竞争真的很激烈**，nlp和开发岗好很多，而且现在开发岗工资真的很高啊！ 2. 其次，最开始找实习的时候，我没有刷过数据结构和算法题，导致我华为(程序写出来了，但是输入输出格式没注意)、百度、腾讯笔试统统挂了，那段时间真的超级难过，后悔没有提前刷题。4月20号之后，我开始在boss直聘上投简历，这里陆陆续续收到2家小公司面试通知和offer，也算是给了我些信心。 -3. 最后，在这段时间一边把之前项目细节搞清楚，一边开始复习**栈、队列、链表、二叉树和经典数据结构算法原理**，并在[leetcode](https://leetcode-cn.com/)上刷题，因为时间关系刷的不多，然后就是把Faster RCNN、FPN、Mask RCNN、retinanet、ResNet、VGG、InceptionV3等网络结构原理和细节部分彻底搞清楚，并去看了部分检测框架源码，然后就是深度学习算法的一些基本原理：链式求导过程、BP反向传播、SGD优化器原理、激活函数公式及原理、常见图像处理算法等，这里涉及一些公式，还就是C/C++/Python编程基础，和计算机基础等。每经过一次面试，我都会自我总结，这使得我后期对面试也开始有了些自己的经验和心得。 -对于CV实习岗，面试涉及到的知识可参考下面的部分。 +3. 最后，在这段时间一边把之前项目细节搞清楚，一边开始复习**栈、队列、链表、二叉树和经典数据结构算法原理**，并在 [leetcode](https://leetcode-cn.com/) 上刷题，因为时间关系刷的不多，然后就是把 `Faster RCNN、FPN、Mask RCNN、retinanet、ResNet、VGG、InceptionV3` 等经典网络结构原理和细节部分彻底搞清楚，并去看了部分检测框架源码，然后就是深度学习算法的一些基本原理：链式求导过程、`BP` 反向传播、`SGD` 优化器原理、激活函数公式及原理、常见图像处理算法等，这里涉及一些公式，还就是 `C/C++/Python` 编程基础，和计算机基础等。每经过一次面试，我都会自我总结，这使得我后期对面试也开始有了些自己的经验和心得。 +对于 `CV` 实习岗，面试涉及到的知识可参考下面的部分。 ## 计算机视觉面试问题分类总结 1. 目标检测算法原理和网络结构细节: - + 经典的two-stage算法: Faster RCNN、FPN、Mask RCNN等 - + one-stage算法：SSD、yolo、retinanet等 -2. CNN的SOTA网络原理和细节: ResNet、VGG、InceptionV3、DenseNet等. -3. 深度学习算法公式理解：链式求导过程、BP反向传播、SGD优化器原理、激活函数公式及原理、常见图像处理算法等. -4. C/C++/Java/Python编程基础 + + 经典的two-stage算法: `Faster RCNN、FPN、Mask RCNN` 等 + + one-stage算法：`SSD、yolo、retinanet` 等 + + 无 anchor 算法: `centernet`、`FCOS`等 +2. CNN 的 `SOTA` 网络原理和细节: `ResNet、VGG、InceptionV3、DenseNet` 等. +3. 深度学习算法公式理解：链式求导过程、BP 反向传播、SGD 优化器原理、激活函数公式及原理、常见图像处理算法等. +4. C/C++/Java/Python 编程基础 + **C++构造函数与析构函数意义**、指针和引用区别、new，malloc区别、抽象类和接口的区别等 - + Python浅拷贝和深拷贝区别、装饰器使用、super()用法、高阶函数：**map/reduce/filter/sorted用法**、try...except使用等 + + Python 浅拷贝和深拷贝区别、装饰器使用、`super()` 用法、高阶函数：**map/reduce/filter/sorted用法**、try...except使用等 + 如何用C++/Java/Python**写多进程和多线程代码** 5. 计算机基础:计算机网络、操作系统、数据库 - + TCP/IP算法, IP寻址, socket通信流程 + + `TCP/IP` 算法, IP 寻址, socket 通信流程 + 大端小端存储，如何将小端存储模式转为大端存储模式 - + OSI七层模型解释 - + 数据库基本操作，及sql语句 + + `OSI` 七层模型解释 + + 数据库基本操作，及 `sql` 语句 + 多进程与多线程区别 6. 常用工程工具使用基础 - + cmake、git语法等和操作 - + linux系统开发，常见命令使用和shell语法 -7. 项目或者paper**细节**，根据项目细节延伸问如何做提升和改变 + + `cmake、git` 语法等和操作 + + linux系统开发，常见命令使用和 `shell` 语法 +7. 项目或者 paper **细节**，根据项目细节延伸问如何做提升和改变 ## 已面试的公司和进展 |投递公司|简历投递渠道|进展| |-------|----|------| From 178aef76eeb59e90168aa2c5f95ac87e5771ddfa Mon Sep 17 00:00:00 2001 From: HerleysZhang Date: Fri, 14 Aug 2020 14:59:03 +0800 Subject: [PATCH 351/386] =?UTF-8?q?add=20C++=20Primer=E5=AD=A6=E4=B9=A0?= =?UTF-8?q?=E7=AC=94=E8=AE=B0.md(xmind=E6=96=87=E4=BB=B6=E8=87=AA=E5=8A=A8?= =?UTF-8?q?=E8=BD=AC=E5=8C=96=E8=80=8C=E6=9D=A5)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...40\351\235\242\350\257\225\351\242\230.md" | 9 +- ...46\344\271\240\347\254\224\350\256\260.md" | 219 ++++++++++++++++++ ...07\346\240\207\350\256\241\347\256\227.md" | 2 +- 3 files changed, 221 insertions(+), 9 deletions(-) create mode 100644 "\347\274\226\347\250\213\350\257\255\350\250\200/C++ Primer\345\255\246\344\271\240\347\254\224\350\256\260.md" diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index fd3c4bcb..bcb478df 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -752,16 +752,9 @@ ROI Align的工作原理如下图。 + [四张图彻底搞懂CNN反向传播算法（通俗易懂）](https://zhuanlan.zhihu.com/p/81675803) + [反向传播算法推导过程（非常详细）](https://zhuanlan.zhihu.com/p/79657669) -## Reference - -1. [《Batch Normalization Accelerating Deep Network Training by Reducing Internal Covariate Shift》阅读笔记与实现](https://blog.csdn.net/happynear/article/details/44238541) -2. [深度学习中 Batch Normalization为什么效果好](https://blog.csdn.net/happynear/article/details/44238541) -3. [详解机器学习中的梯度消失、爆炸原因及其解决方法](https://blog.csdn.net/qq_25737169/article/details/78847691) - ## Focal Loss 介绍一下 ## Reference 1. [《Batch Normalization Accelerating Deep Network Training by Reducing Internal Covariate Shift》阅读笔记与实现](https://blog.csdn.net/happynear/article/details/44238541) -2. [深度学习中 Batch Normalization为什么效果好](https://blog.csdn.net/happynear/article/details/44238541) -3. [详解机器学习中的梯度消失、爆炸原因及其解决方法](https://blog.csdn.net/qq_25737169/article/details/78847691) +2. [详解机器学习中的梯度消失、爆炸原因及其解决方法](https://blog.csdn.net/qq_25737169/article/details/78847691) diff --git "a/\347\274\226\347\250\213\350\257\255\350\250\200/C++ Primer\345\255\246\344\271\240\347\254\224\350\256\260.md" "b/\347\274\226\347\250\213\350\257\255\350\250\200/C++ Primer\345\255\246\344\271\240\347\254\224\350\256\260.md" new file mode 100644 index 00000000..1b4e56ac --- /dev/null +++ "b/\347\274\226\347\250\213\350\257\255\350\250\200/C++ Primer\345\255\246\344\271\240\347\254\224\350\256\260.md" @@ -0,0 +1,219 @@ +# C++ Primer学习笔记 + +## 第一部分 C++基础 + +### 第2章 变量和基本类型 + +- 2.1 基本内置类型 +- 2.2 变量 + + - 变量定义 + - 变量声明和定义的关系 + - 标识符 + - 作用域 + +- 2.3 符合类型 +- 2.4 const限定符 +- 2.5 标准输入输出 + + - `int v1, v2 = 0; std::cin >> v1 >> v2;` + - `strd::cout << "The sum of" << v1 << "and" << v2 << "is" << v1+v2 << endl;` + +- 问题：string类的输入运算符和gtline函数分别是如何处理空白字符的 + + - 类似 is >> s 的读取：string对象会忽略开头的空白并从第一个字符开始，直到遇见下一空白为止。 + + - 读入一行用字符用 getline + + - 类似 getling(is, s) 的读取：string对象会从输入流中读取字符，直到遇见换行符 endl 为止。 + + - 一次读入一个词用 cin + +### 第3章 字符串、向量、数组 + +- 3.1 命名空间的using声明 +- 3.2 标准库类型 string + + - string表示可变长字符序列 + - 定义和初始化string + + - string s1 = "value"：一般初始化方式 + - string s2： 默认初始化 + - string s3(n, 'c')：把 s3 初始化为连续 n 个字符 c 组成的串 + + - string的操作 + + - 输入输出字符串 + + - string s1 = "hello world" +cout << s1 << endl; //输出s1字符串（打印hello world） + - string s1, s2; +cin >> s1 >> s2; //把第一个输入读取到s1中，第二个读取到s2中 + + - 返回 s 中字符的个数：s.size() + - 返回字符串 s1 和 s2连接后的结果 + - 返回字符串 s1 和 s2连接后的结果 + + - 处理对象中的字符 + + - isalnum(c) 当 c 是字母或数字时为真 + - isalpha(c) 当 c 是字母时为真 + - isdigit(c) 当 c 是数字时为真 + - islower(c) 当 c 是小写字母时为真 + - isupper(c) 当 c 是大写字母时为真 + - tolower(c) 当 c 是大写字母时，输出对应的小写字母；否则原样输出 c + - toupper(c) 当 c 是小写字母时，输出对应的大写字母；否则原样输出 c + +- 3.3 标准库类型vector +- 3.4 迭代器介绍 +- 3.5 数组 + +### 第4章 表达式 + +### 第5章 语句 + +### 第6章 函数 + +### 第7章 类 + +## 第二部分 C++标准库 + +### 第8章 IO库 + +- 8.1 IO类 +- 8.2 文件输入输出 +- 8.3 string 流 + +### 第9章 顺序容器 + +- 9.1 顺序容器概述 + + - 容器定义 + + - 一个容器就是一些特定类型对象的集合，顺序容器为程序员提供控制元素存储和访问顺序的能力 + + - 顺序容器类型 + + - vector + + - 可变大小数组 + + - deque + + - 双端队列 + + - list + + - 双向链表 + + - forward_list + + - 单向链表 + + - array + + - 固定大小数组 + + - string + + - 与 vector 相似的容器，但专门用于保存字符。 + + - 如何选择顺序容器类型 + + - 通常，vector是最好的选择，除非有很好的理由选择其他容器。 + - 如果程序要求随机访问元素，应使用 vector 或 deque。 + - 1，string 和 vector将元素保存在连续的内存空间中，支持快速随机访问（下标操作），尾部之外的位置插入或删除元素很慢，每次插入或删除操作后，需要移动插入/删除位置之后的所有元素 + - 2，list 和 forward_list 在容器任何位置添加和删除元素都很快速，但是不支持元素的随机访问，每访问一个元素都得遍历整个容器（链表的数据结构）。 + +- 9.2 容器库概览 + + - 容器操作 + - 迭代器 + - 容器类型成员 + - begin 和 end 成员 + - 容器定义和初始化 + +- 9.3 顺序容器操作 +- 9.4 vecror 对象是如何增长的 +- 9.5 额外的 string 操作 +- 9.6 容器适配器 + +### 第10章 泛型算法 + +### 第11章 关联容器 + +### 第12章 动态内存 + +## 第四部分高级主题 + +### 第17章 标准库特殊设施 + +### 第18章 用于大型程序的工具 + +### 第19章 特殊工具与技术 + +## 第三部分 类设计者工具 + +### 第14章 重载运算与类型转换 + +- 14.1 基本概念 + + - 重载运算符是具有特殊名字的函数，由关键字operator 和其后要定义的运算符号共同组成。 + - 和其他函数一样，它也包含返回类型、参数列表以及函数体 + - 当一个重载的运算符是成员函数时， this 绑定到左侧运算对象。动态运算符函数的参数量比运算对象的数量少一个。 + - 只能重载大多数的运算符，而不能发明新的运算符号。 + - 调用方式： +1，data1 + data2; +2，operator+(data1, data2); + +- 14.2 输入输出运算符 +- 14.3 算术关系运算符 +- 14.4 赋值运算符 +- 14.5 下标运算符 + +### 第13章 拷贝控制 + +### 第15章 面向对象程序设计 + +- 15.1 OOP概述 + + - 面向对象程序设计（object-oriented programming）的核心思想是数据抽象、继承和动态绑定。 + - 继承 + + - 通过继承联系在一起的类构成一种层次关系。 + - 派生类定义实例： +class Bulk_quote: public Quote{...} + - 每个基类前都可以有访问说明符，其作用是控制派生类从基类继承而来的成员是否对派生类可见。 + + - 动态绑定： +1，使用同一段代码可以分别处理基类和派生类对象。 +2，函数的运行版本由实参决定，即在运行时选择函数的版本。 + +- 15.2 定义基类和派生类 + + - 定义基类 + - 定义派生类 + - 类型转换与继承 + +- 15.3 虚函数 + + - 使用虚函数可以执行动态绑定 + - 对于某些函数，基类希望它的派生类各自定义适合自身的版本，基类将这些函数声明成虚函数，这就是虚函数的由来和定义。 + - OOP的核心思想是多态性（polymorphism） + - 当且仅当通过指针或引用调用虚函数时，才会在运行时解析该调用，也只有在这种情况下对象的动态类型才有可能与静态类型不同。 + - 通常，只有成员函数（或友元）中的代码才需要使用作用域运算符（::）来回避虚函数的机制。 + +- 15.4 抽象基类 +- 15.5 访问控制与继承 +- 15.6 继承中的类作用域 +- 15.7 构造函数与拷贝控制 + + - 虚析构函数（不是析构函数） + - 合成拷贝控制与继承 + - 派生类的拷贝控制成员 + - 继承的构造函数 + +- 15.8 容器与继承 + +### 第16章 模板与泛型编程 + diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" index cc0b55bc..f5376b1c 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" @@ -159,7 +159,7 @@ if __name__=='__main__': 以上图为例，每个选出来的 `Bounding Box` 检测框（既BBox）用`（x,y,h,w, confidence score，Pdog,Pcat）`表示，`confidence score` 表示 `background` 和 `foreground` 的置信度得分，取值范围`[0,1]`。Pdog, Pcat分布代表类别是狗和猫的概率。如果是 100 类的目标检测模型，`BBox` 输出向量为 `5+100=105`。 ### [NMS算法](https://juejin.im/entry/5bdbc26151882516da0ddd25) -NMS主要就是通过迭代的形式，不断地以最大得分的框去与其他框做 `IoU` 操作，并过滤那些 `IoU` 较大的框。 +`NMS` 主要就是通过迭代的形式，不断地以最大得分的框去与其他框做 `IoU` 操作，并过滤那些 `IoU` 较大的框。 **其实现的思想主要是将各个框的置信度进行排序**，然后选择其中置信度最高的框 A，将其作为标准选择其他框，同时设置一个阈值，[当其他框B与A的重合程度超过阈值就将 B 舍弃掉](https://blog.csdn.net/williamyi96/article/details/77996167 )，然后在剩余的框中选择置信度最大的框，重复上述操作。算法过程如下： 1. 根据候选框类别分类概率排序：`F>E>D>C>B>A`，并标记最大概率的矩形框F作为标准框。 From 0255d3b5f90d218d587a989a4fe30f6d59925c50 Mon Sep 17 00:00:00 2001 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zii>O)7`^$d?ggjb!ZHQrw@Tm0>+b}gFVi80-mWeS>qtiyT1R-r@9Yn>lK)l@C*ee%})HRxvxgNLOM5eyK0?2i*#yfQTIMPF4bgURk~( z29AT-)4q0eiVOkw?JX~+$=5`J&LL*bI=hSxOBAj`k~pMJSBel#baZi8rOmWYg^hbd zuGCFSSmvGs4bG!Mj@PJNadbdBH!**5N30?}^i?34Cr|LWds#r9+!Dp3610NYgrXk> zEAtoRKH$4;GluyGNlt!C3$0iD7vj=NmPdqMH&N!7fy#+h_?9!}O>Sc5&YE8P2RT$MBh7p1c#a<-;=hhLY>&6#jU z(GcERl(_ujaVMp2vLQlY7kHr2tu_tj&;U2Jvk&ib zsb*0o7AQ4=`Ze5)IWfJZZ?L4`jO+aPvkd_Y6$$fx4bBNijBCXzO8Fnz(YGueanz<1 zeS(0Sw6|dUVK5W?1MU9*2YVx8{lGjfS3r2BI>v>#-B!I>zQ&8^|0W0wW0I7h-Kci%qGLw}uf}Mr{;NQO}?5-m+{B13mw+U~<(+uP*g~bRqLj7L(G_?t2*-aI2^DObfHtxUz!++NG9gh)6x?+IO1=ht zx{^O9xxbnI`N{~@@&)@S^<1Zxj6aZV6^BQJ$oyj(G2%+6+I%@DbT~FR9S*YwB-2 ClN&Vv literal 0 HcmV?d00001 diff --git "a/\345\234\260\345\271\263\347\272\277\346\234\272\345\231\250\344\272\272\345\256\236\344\271\240\346\200\273\347\273\223.md" "b/\345\234\260\345\271\263\347\272\277\346\234\272\345\231\250\344\272\272\345\256\236\344\271\240\346\200\273\347\273\223.md" index cabcc74e..dcaeee9c 100644 --- "a/\345\234\260\345\271\263\347\272\277\346\234\272\345\231\250\344\272\272\345\256\236\344\271\240\346\200\273\347\273\223.md" +++ "b/\345\234\260\345\271\263\347\272\277\346\234\272\345\231\250\344\272\272\345\256\236\344\271\240\346\200\273\347\273\223.md" @@ -7,7 +7,7 @@ + 还有一些其他工作：比如数据送标、训练集/测试集制作、日常标注答疑等。 关于工作内容上，自己做的抽烟检测问题，是属于多分类问题，在学术界多分类问题不是什么大问题，早已经有可行的解决方案了，但是在实际工业中发现，即使是三分类的这样简单的问题，在实际项目中也会碰到各种各样的问题，这让我认识到 AI 要想真正落地，是真的需要一批真心喜欢技术，又踏实的人去把 AI 技术落地，这中间也许会碰到一些 `dirty work` 吧，对于实习生的成长也可能是不利的，但是对于业务项目来说，这些工作可能又是必须做的。 -## 关于工作环境 +## 关于工作环境\\\ 地平线是 `to B` 型公司，因此底层技术和业务项目显得尤为重要，需要每一个地平线研发人员扎实做底层技术和踏实做好业务产品。在这边整体工作环境还不错，虽然会加班，但是公司为每个员工配备了升降桌和电竞椅，以考虑员工的身体健康，每天也有零食来补充能量。身边的同事很友好，让我`感动`的在于有些同事虽然平时很忙，但是有问题找他的时候，他也会很耐心的帮你解决疑惑。最后，感谢周围每一个帮助过我的同事：轩爷、丽娜、方神、根利、辉哥、弈霖、翔哥、华哥等，还有一起实习的小伙伴：圣琦、家康、成武。 ## 关于个人成长 有些遗憾，在这边实习个人成长方面没有达到自己的要求，虽然刚来第一个月自己成长很快，但是后期成长有限，`自己的成长跟付出不成正比吧`。这一点就不细说了，希望地平线以后能完善对于实习生的培养规范，不能只是一味的要求干活。值得让我注意的是，我发现光干活真的并不一定能让你得到很多成长，但是学习别人的优秀工程代码和学习新知识，然后再应用到实际项目中，那样不仅自己能得到成长，工作效率也能大大提升。自己后期就是稍微看了一个代码优秀的系统性的项目代码，短时间内就让自己收获很多，可惜后期时间有限，留给自己学习的时间不多，否则自己的成长能更多些。 diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" index 212396d5..af41d335 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" @@ -17,9 +17,9 @@ > 一般地，行阶梯型矩阵的秩等于其“台阶数”-非零行的行数。 **低秩近似算法能减小计算开销的原理**如下： -给定权重矩阵 $W \epsilon R^{m*n}$ ,若能将其表示为若干个低秩矩阵的组合，即 $W=\sum_{i=1}^{n}\alpha_{i}M_{i}$ ,其中 $W\epsilon R^{m*n}$ 为低秩矩阵，其秩为 $r_{i}$ ,并满足 $r_{i}<批标准化(BNorm)->激活(Activ)->池化(Pool)这样的顺序操作组成的。对于异或神经网络，设计出的模块是由批标准化(BNorm)->**二值化激活(BinActiv)**->二值化卷积(BinConv)->池化(Pool)的顺序操作完成。这样做的原因是批标准化以后，保证了输入均值为0，然后进行二值化激活，保证了数据为-1或者+1，然后进行二值化卷积，这样能最大程度上减少特征信息的损失。二值化残差网络结构定义实例代码如下： +2. 二值化神经网络以其高的模型压缩率和在前传中计算速度上的优势，近几年格外受到重视和发展，成为神经网络模型研究中的非常热门的一个研究方向。但是，第一篇真正意义上将神经网络中的权重值和激活函数值同时做到二值化的是 `Courbariaux` 等人 2016 年发表的名为《Binarynet: Training deep neural networks with weights and activations constrained to +1 or -1》的一篇论文。**这篇论文第一次给出了关于如何对网络进行二值化和如何训练二值化神经网络的方法**。 +3. CNN 网络一个典型的模块是由卷积(`Conv`)->批标准化(`BNorm`)->激活(`Activ`)->池化(`Pool`)这样的顺序操作组成的。对于异或神经网络，设计出的模块是由批标准化(`BNorm`)->**二值化激活(BinActiv)**->二值化卷积(`BinConv`)->池化(`Pool`)的顺序操作完成。这样做的原因是批标准化以后，保证了输入均值为 `0`，然后进行二值化激活，保证了数据为 `-1` 或者 `+1`，然后进行二值化卷积，这样能最大程度上减少特征信息的损失。二值化残差网络结构定义实例代码如下： -```Pyhon -# 残差块Residual Block定义 +```pyhon def residual_unit(data, num_filter, stride, dim_match, num_bits=1): + """残差块 Residual Block 定义 + """ bnAct1 = bnn.BatchNorm(data=data, num_bits=num_bits) conv1 = bnn.Convolution(data=bnAct1, num_filter=num_filter, kernel=(3, 3), stride=stride, pad=(1, 1)) convBn1 = bnn.BatchNorm(data=conv1, num_bits=num_bits) @@ -74,17 +75,19 @@ def residual_unit(data, num_filter, stride, dim_match, num_bits=1): return conv2 + shortcut ``` ### 二值网络的梯度下降 -现在的神经网络几乎都是基于梯度下降算法来训练的，但是二值网络的权重只有 $\pm 1$，无法直接计算梯度信息，也无法进行权重更新。为解决这个问题，[Courbariaux](https://arxiv.org/pdf/1602.02830v1.pdf)等人提出二值连接（binary connect）算法，该算法采取单精度与二值结合的方式来训练二值神经网络（），这是第一次给出了关于如何对网络进行二值化和如何训练二值化神经网络的方法。过程如下： -1. 权重 weight 初始化为浮点 -2. 前向传播 Forward Pass: +现在的神经网络几乎都是基于梯度下降算法来训练的，但是二值网络的权重只有 $\pm 1$，无法直接计算梯度信息，也无法进行权重更新。为解决这个问题，[Courbariaux](https://arxiv.org/pdf/1602.02830v1.pdf) 等人提出二值连接（binary connect）算法，该算法采取单精度与二值结合的方式来训练二值神经网络（），这是第一次给出了关于如何对网络进行二值化和如何训练二值化神经网络的方法。过程如下： +1. 权重 `weight` 初始化为浮点 +2. 前向传播 `Forward Pass`: + 利用决定化方式（`sign(x)函数`）把 Weight 量化为 `+1/-1`, 以0为阈值 + 利用量化后的 Weight (只有+1/-1)来计算前向传播，由二值权重与输入进行卷积运算（实际上只涉及加法），获得卷积层输出。 -3. 反向传播 Backward Pass: +3. 反向传播 `Backward Pass`: + 把梯度更新到浮点的 Weight 上（根据放松后的符号函数，计算相应梯度值，并根据该梯度的值对单精度的权重进行参数更新） - + 训练结束： 把 Weight 永久性转化为+1/-1, 以便 inference 使用 + + 训练结束： 把 Weight 永久性转化为 `+1/-1`, 以便 `inference` 使用 ### 两个问题 网络二值化需要解决两个问题：如何对权重进行二值化和如何计算二值权重的梯度。 + **1，如何对权重进行二值化？** + 权重二值化一般有两种选择： + 直接根据权重的正负进行二值化：$x^{b}=sign(x)$。符号函数 `sign(x)` 定义如下： $$ @@ -98,16 +101,18 @@ $$ + 进行随机的二值化，即对每一个权重，以一定概率取 $\pm 1$ **2，如何计算二值权重的梯度？** -二值权重的梯度为0，无法进行参数更新。为解决这个问题，需要**对符号函数进行放松**，即用 $Htanh(x) = max(-1, min(1,x))$ 来代替 $sinx(x)$。当 x 在区间 [-1,1] 时，存在梯度值1，否则梯度为 0 。 + +二值权重的梯度为0，无法进行参数更新。为解决这个问题，需要**对符号函数进行放松**，即用 $Htanh(x) = max(-1, min(1,x))$ 来代替 $sinx(x)$。当 x 在区间 [-1,1] 时，存在梯度值 1，否则梯度为 0 。 ### 二值连接算法改进 之前的二值连接算法只对权重进行了二值化，但是网络的中间输出值依然是单精度的，于是 Rastegari 等人对此进行了改进，提出用**单精度对角阵与二值矩阵之积来近似表示原矩阵的算法**，以提升二值网络的分类性能，弥补二值网络在精度上弱势。该算法将原卷积运算分解为如下过程： $$I*W\approx (I*B)\alpha$$ 其中 $I\epsilon \mathbb{R}^{c\times w_{in}\times h_{in}}$ 为该层的输入张量，$I \epsilon \mathbb{R}^{c\times w\times h}$ 为该层的一个滤波器，$B=sign(W)\epsilon \{+1, -1\}^{c \times w\times h}$为该滤波器所对应的二值权重。 + 这里，Rastegari 等人认为单靠二值运算，很难达到原单精度卷积元素的结果，于是他们使用了一个单精度放缩因子 $\alpha \epsilon \mathbb{R}^{+}$ 来对二值滤波器卷积后的结果进行放缩。而$\alpha$ 的取值，则可根据优化目标： $$min \left \|| W -\alpha B \right \||^{2}$$ -得到$\alpha = \frac{1}{n}\left \|| W \right \||_{\iota _{1}}$。二值连接改进的算法训练过程与之前的算法大致相同，不同的地方在于梯度的计算过程还考虑了 $\alpha$ 的影响。由于 $\alpha$ 这个单精度的缩放因子的存在，有效降低了重构误差，并首次在 ImageNet 数据集上取得了与 Alex-Net相当的精度。如下图所示： +得到$\alpha = \frac{1}{n}\left \|| W \right \||_{\iota _{1}}$。二值连接改进的算法训练过程与之前的算法大致相同，不同的地方在于梯度的计算过程还考虑了 $\alpha$ 的影响。由于 $\alpha$ 这个单精度的缩放因子的存在，有效降低了重构误差，并首次在 ImageNet 数据集上取得了与 Alex-Net 相当的精度。如下图所示： ![二值化网络精度对比](../images/二值化算法精度.png) 可以看到的是`权重二值化神经网络（BWN）`和全精度神经网络的精确度几乎一样，但是与异或神经网络（XNOR-Net）相比而言，Top-1 和 Top-5 都有 10+% 的损失。 @@ -117,9 +122,9 @@ $$min \left \|| W -\alpha B \right \||^{2}$$ + [https://github.com/Ewenwan/MVision/tree/master/CNN/Deep_Compression/quantization/BNN](https://github.com/Ewenwan/MVision/tree/master/CNN/Deep_Compression/quantization/BNN) + [二值神经网络（Binary Neural Network，BNN）](https://blog.csdn.net/stdcoutzyx/article/details/50926174) ### 二值网络设计注意事项 -+ 不要使用 kernel = (1, 1) 的 Convolution （包括 resnet 的 bottleneck）：二值网络中的 weight 都为 1bit， 如果再是 1x1 大小， 会极大地降低表达能力 -+ 增大 Channel 数目 + 增大 activation bit 数 要协同配合：如果一味增大 channel 数， 最终 feature map 因为 bit 数过低， 还是浪费了模型容量。 同理反过来也是。 -+ 建议使用 4bit 及以下的 activation bit， 过高带来的精度收益变小， 而会显著提高 inference 计算量 ++ 不要使用 kernel = (1, 1) 的 `Convolution` （包括 resnet 的 bottleneck）：二值网络中的 weight 都为 1bit， 如果再是 1x1 大小， 会极大地降低表达能力 ++ 增大 `Channel` 数目 + 增大 activation bit 数 要协同配合：如果一味增大 channel 数， 最终 feature map 因为 bit 数过低， 还是浪费了模型容量。 同理反过来也是。 ++ 建议使用 4bit 及以下的 `activation bit`， 过高带来的精度收益变小， 而会显著提高 inference 计算量 ## 知识蒸馏 知识蒸馏（knowledge distillation），是迁移学习（transfer learning）的一种，简单来说就是训练一个大模型（teacher）和一个小模型（student），将庞大而复杂的大模型学习到的知识，通过一定技术手段迁移到精简的小模型上，从而使小模型能够获得与大模型相近的性能。 student模型最终的损失函数由两部分组成： @@ -129,8 +134,10 @@ student模型最终的损失函数由两部分组成： 软标签计算公式：$$q_{i} = \frac{z_{i}/T}{\sum_{j}z_{j}/T}$$ 这两个损失函数的重要程度可通过一定的权重进行调节，在实际应用中， `T` 的取值会影响最终的结果，一般而言，较大的 T 能够获得较高的准确度，T（蒸馏温度参数） 属于知识蒸馏模型训练超参数的一种。**T是一个可调节的超参数、T值越大、概率分布越软（论文中的描述），曲线便越平滑**，相当于在迁移学习的过程中添加了扰动，从而使得学生网络在借鉴学习的时候更有效、泛化能力更强，这其实就是一种抑制过拟合的策略。知识蒸馏的整个过程如下图： + ![知识蒸馏模型训练过程](../images/知识蒸馏模型过程.png) -student模型的实际模型结构和小模型一样，但是损失函数包含了两部分，`mxnet` 代码示例如下： + +`student` 模型的实际模型结构和小模型一样，但是损失函数包含了两部分，`mxnet` 代码示例如下： ```Pyhon def get_symbol(data, class_labels, resnet_layer_num,Temperature,mimic_weight,num_classes=2): conv1 = ConvFactory(data, 48, kernel=(3, 3), stride=(2, 2), pad=(1, 1), name="conv1_3x3") @@ -166,9 +173,9 @@ objective2 = tf.scalar_mul(0.5, tf.square(student_tau-teacher_tau)) # 第二项为预测结果与普通类别标签的交叉熵。 tf_loss = (args.lamda*tf.reduce_sum(objective1) + (1-args.lamda)*tf.reduce_sum(objective2))/batch_size ``` -`tf.scalar_mul` 函数为对 tf 张量进行固定倍率 scalar 缩放函数。**一般T的取值在 1 - 20 之间，这里我参考了开源代码，取值为 3**。我发现在开源代码中 `student` 模型的训练，有些是和 `teacher` 模型一起训练的，有些是 teacher 模型训练好后直接指导 student 模型训练。 +`tf.scalar_mul` 函数为对 `tf` 张量进行固定倍率 `scalar` 缩放函数。**一般 T 的取值在 1 - 20 之间，这里我参考了开源代码，取值为 3**。我发现在开源代码中 `student` 模型的训练，有些是和 `teacher` 模型一起训练的，有些是 teacher 模型训练好后直接指导 student 模型训练。 ## 浅层网络 -浅层网络：通过设计一个更浅（层数较少）结构更紧凑的网络来实现对复杂模型效果的逼近, 但是浅层网络的表达能力很难与深层网络相匹敌。因此，这种设计方法的局限性在于只能应用解决在较为简单问题上。如分类问题中类别数较少的task。 +浅层网络：通过设计一个更浅（层数较少）结构更紧凑的网络来实现对复杂模型效果的逼近, 但是浅层网络的表达能力很难与深层网络相匹敌。因此，这种设计方法的局限性在于只能应用解决在较为简单问题上。如分类问题中类别数较少的 task。 ## 参考资料 1. [神经网络模型压缩和加速之知识蒸馏](https://www.cnblogs.com/dyl222/p/11079489.html) 2. [https://github.com/chengshengchan/model_compression/blob/master/teacher-student.py](https://github.com/chengshengchan/model_compression/blob/master/teacher-student.py) diff --git "a/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200\347\237\245\350\257\206.md" "b/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200\347\237\245\350\257\206.md" index 0edac058..9616c9ad 100644 --- "a/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200\347\237\245\350\257\206.md" +++ "b/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200\347\237\245\350\257\206.md" @@ -9,6 +9,70 @@ + 常用工具（Cmake/Git/Docker/正则表达式） ## 操作系统 +### Linux查看cpu和cache信息 +1，`Linux` 查看 `cpu` 信息命令：`cat /proc/cpuinfo`。 +```shell script +(base) harley@harley-pc:/sys/devices/system/cpu/cpu0/cache/index3$ cat /proc/cpuinfo +processor : 0 +vendor_id : GenuineIntel +cpu family : 6 +model : 158 +model name : Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz +stepping : 10 +microcode : 0xb4 +cpu MHz : 3192.005 +cache size : 12288 KB +physical id : 0 +siblings : 1 +core id : 0 +cpu cores : 1 +apicid : 0 +initial apicid : 0 +fpu : yes +fpu_exception : yes +cpuid level : 22 +wp : yes +flags : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon nopl xtopology tsc_reliable nonstop_tsc cpuid pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 avx2 smep bmi2 invpcid rdseed adx smap clflushopt xsaveopt xsavec xsaves arat md_clear flush_l1d arch_capabilities +bugs : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs itlb_multihit srbds +bogomips : 6384.01 +clflush size : 64 +cache_alignment : 64 +address sizes : 43 bits physical, 48 bits virtual +power management: +... +processor : 3 +... +``` +2，`Linux` 查询 `L1/L2/L3 cache`大小：`cat /sys/devices/system/cpu/cpu0/cache/index*/size`(`*`为 `0/1/2/3`) +```shell +(base) harley@harley-pc:~$ cat /sys/devices/system/cpu/cpu0/cache/index0/size +32K +(base) harley@harley-pc:~$ cat /sys/devices/system/cpu/cpu0/cache/index0/type +Data +(base) harley@harley-pc:~$ cat /sys/devices/system/cpu/cpu0/cache/index1/type +Instruction +(base) harley@harley-pc:~$ cat /sys/devices/system/cpu/cpu0/cache/index1/size +32K +(base) harley@harley-pc:~$ cat /sys/devices/system/cpu/cpu0/cache/index2/size +256K +(base) harley@harley-pc:~$ cat /sys/devices/system/cpu/cpu0/cache/index3/size +12288K +``` +现代 `CPU`的 `L1 cache` 是逻辑核私有的，L1 cache分指令L1 cache和数据L1 cache，大小相等都为 `32 KB`；目前，L2 cache也是片内私有，所以每个核只有`256 KB`；而对于L3 cache，一个物理核CPU的所有逻辑核共享，所以在每个逻辑核来看，L3 cache都为`12288 KB`。本机的虚拟机总共有 `1` 个物理核，而本机共有 `6` 核 `12` 线程，所以可推算得到**本机的 `Cache` 信息： ++ L1 cache： + + L1 Data: `192 KB = 32 x 6 KB` + + L1 Instruction: `192 KB = 32 x 6 KB` ++ L2 cache：`1536 KB = 256 X 6 KB` ++ L3 cache：`12288 KB` + +### Windows查看cpu和cache信息 +任务管理器--->性能，即可查看 `cpu` 和 `L1/L2/L3 Cache` 大小，如下图所示： + +![windows查看cpu信息](../images/cpu信息和Cache大小.png) + +或者下载安装 [`cpuz`](https://www.cpuid.com/softwares/cpu-z.html) 软件，打开即可查看，如下图所示。 + +![windows-zpuz查看cpu信息](../images/cpuz-查询cpu信息.png) ### 并发与并行区别 + **并发**是指宏观上在一段时间内能同时运行多个程序，而**并行**则指在同一时刻能运行多个指令。 + 并行需要硬件支持，如多流水线、多核处理器或者分布式计算系统。 From c2d3481618620ccf9188c102263cd73479c351f1 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Thu, 17 Sep 2020 11:59:14 +0800 Subject: [PATCH 353/386] =?UTF-8?q?fix=20some=20show=20error=20in=20?= =?UTF-8?q?=E6=95=B0=E5=AD=97=E5=9B=BE=E5=83=8F=E5=A4=84=E7=90=86=E5=9F=BA?= =?UTF-8?q?=E7=A1=80.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...17\345\244\204\347\220\206\345\237\272\347\241\200.md" | 8 +++++--- ...51\347\256\227\346\263\225\346\200\273\347\273\223.md" | 3 +++ 2 files changed, 8 insertions(+), 3 deletions(-) diff --git "a/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206\345\237\272\347\241\200.md" "b/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206\345\237\272\347\241\200.md" index 7c15e4cd..aa12bfa9 100644 --- "a/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206\345\237\272\347\241\200.md" +++ "b/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206\345\237\272\347\241\200.md" @@ -45,13 +45,15 @@ 1. 微分法 2. 高通滤波法 -一般来说，图像的能量主要集中在其低频部分，噪声所在的频段主要在高频段，同时图像边缘信息也主要集中在其高频部分。这将导致原始图像在平滑处理之后，图像边缘和图像轮廓模糊的情况出现。为了减少这类不利效果的影响，就需要利用图像锐化技术，使图像的边缘变得清晰。图像锐化处理的目的是为了使图像的边缘、轮廓线以及图像的细节变得清晰，经过平滑的图像变得模糊的根本原因是因为图像受到了平均或积分运算，因此可以对其进行逆运算(如微分运算)就可以使图像变得清晰。微分运算是求信号的变化率，由傅立叶变换的微分性质可知，微分运算具有较强高频分量作用。从频率域来考虑，图像模糊的实质是因为其高频分量被衰减，因此可以用高通滤波器来使图像清晰。但要注意能够进行锐化处理的图像必须有较高的性噪比，否则锐化后图像性噪比反而更低，从而使得噪声增加的比信号还要多，因此一般是先去除或减轻噪声后再进行锐化处理。 +一般来说，图像的能量主要集中在其低频部分，噪声所在的频段主要在高频段，同时图像边缘信息也主要集中在其高频部分。这将导致原始图像在平滑处理之后，图像边缘和图像轮廓模糊的情况出现。为了减少这类不利效果的影响，就需要利用图像锐化技术，使图像的边缘变得清晰。**图像锐化处理的目的是为了使图像的边缘、轮廓线以及图像的细节变得清晰**. + +经过平滑的图像变得模糊的根本原因是因为图像受到了平均或积分运算，因此可以对其进行逆运算(如微分运算)就可以使图像变得清晰。微分运算是求信号的变化率，由傅立叶变换的微分性质可知，微分运算具有较强高频分量作用。从频率域来考虑，图像模糊的实质是因为其高频分量被衰减，因此可以用高通滤波器来使图像清晰。但要注意能够进行锐化处理的图像必须有较高的性噪比，否则锐化后图像性噪比反而更低，从而使得噪声增加的比信号还要多，因此一般是先去除或减轻噪声后再进行锐化处理。 ### Reference + [图像增强－图像锐化](https://www.cnblogs.com/BYTEMAN/archive/2012/07/21/2603021.html) + [高斯模糊算法](http://www.ruanyifeng.com/blog/2012/11/gaussian_blur.html) + [高斯模糊](https://zh.wikipedia.org/wiki/%E9%AB%98%E6%96%AF%E6%A8%A1%E7%B3%8A) ## numpy手撕代码 -人脸识别的场景下，输入 $105\times 12$的feature map，用这个在$1000\times 512$的特征库当中用欧氏距离去匹配105*12的feature map，用这个在$1000\times 512$的特征库当中用欧氏距离去匹配$10*\times 1000$的特征，得到这个output。 +人脸识别的场景下，输入 $105\times 12$ 的 `feature map`，用这个在$1000 \times 512$ 的特征库当中用欧氏距离去匹配 $105 \times 12$ 的 feature map，用这个在 $1000\times 512$ 的特征库当中用欧氏距离去匹配 $10*\times 1000$ 的特征，得到这个 `output`。 ## 3D目标检测笔记 1. EfficientNet is a backbone. In the original paper, they used Hourglass backbone, which has a structure similar to UNet. CenterNet is only a type of decoder, not the whole network. @@ -59,7 +61,7 @@ CenterNet is only a type of decoder, not the whole network. 1 for binary mask 7 for regression variables x, y, z, yaw, pitch_sin, pitch_cos, roll 3. Regression predicts raw 3d coordinates of a car x, y, z. And binary mask predicts the 2d position on the image row, col. 2d map is much more precise but contains incomplete information. Function optimize_xy is used to shift x, y (on the road) so that they correspond to row, col (in image) -4. CenterNet模型：作者在构建模型时将目标作为一个点——即目标BBox的中心点。检测器采用关键点估计来找到中心点，并回归到其他目标属性，例如尺寸，3D位置，方向，甚至姿态。 +4. `CenterNet` 模型：作者在构建模型时将目标作为一个点——即目标BBox的中心点。检测器采用关键点估计来找到中心点，并回归到其他目标属性，例如尺寸，3D位置，方向，甚至姿态。 5. I use this idea to predict x, y, z coordinates of the vehicle and also yaw, pitch_cos, pitch_sin, roll angles. For pitch I predict sin and cos, because, as we will see, this angle can be both near 0 and near 3.14. These 7 parameters are my regression target variables instead of shift_x, shift_y, size_x, size_y diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" index af41d335..5704625e 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" @@ -139,6 +139,9 @@ student模型最终的损失函数由两部分组成： `student` 模型的实际模型结构和小模型一样，但是损失函数包含了两部分，`mxnet` 代码示例如下： ```Pyhon +# -*-coding-*- : utf-8 +import mxnet as mx + def get_symbol(data, class_labels, resnet_layer_num,Temperature,mimic_weight,num_classes=2): conv1 = ConvFactory(data, 48, kernel=(3, 3), stride=(2, 2), pad=(1, 1), name="conv1_3x3") conv2 = InceptionFactoryB(conv1, 10, 12, 10, 12, 16, (1,1),'conv2_block') From 701b5ed628244b5f8a6a0f35d68ebb4d06aab4b9 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Mon, 21 Sep 2020 11:24:08 +0800 Subject: [PATCH 354/386] improve document specification --- ...40\351\235\242\350\257\225\351\242\230.md" | 26 ++++++++++++++----- 1 file changed, 19 insertions(+), 7 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index bcb478df..537aa848 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -3,10 +3,15 @@ 在只有一个通道的情况下，“卷积核”就相当于 “filter”，这两个概念是可以互换的。但在一般情况下，它们是两个完全不同的概念。每个 “filter” 实际上恰好是“卷积核”的一个集合，在当前层，每个通道都对应一个卷积核，且这个卷积核是独一无二的。 **滤波器的数量，卷积核的shape。** + ## 卷积层和池化输出大小计算 + > 不管是TensorFlow、Keras、Caffe还是Pytorch，其卷积层和池化层的参数默认值可能有所不同，但是最终的卷积输出大小计算公式是一样的 + ### CNN中术语解释 + 卷积层主要参数有下面这么几个： + + 卷积核 `Kernal` 大小（在 `Tensorflow/keras` 中称为`filter`）； + 填充 `Padding` ； + 滑动步长 `Stride`； @@ -176,7 +181,8 @@ $$L = -\sum_{c=1}^{M}y_{c}log(p_{c})或者H(p,q)=-\sum p(x)logq(x)$$ 后一层神经元在前一层神经元的感受空间，也可以定义为卷积神经网络中每层的特征图（`Feature Map`）上的像素点在原始图像中映射的区域大小，即如下图所示： ![感受野大小](../images/感受野空间.jpg) -注意：感受野在 `CNN` 中是呈指数级增加的。小卷积核（如3*3）通过多层叠加可取得与大卷积核（如7*7）同等规模的感受野，此外采用小卷积核有两个优势： +注意：感受野在 `CNN` 中是呈指数级增加的。小卷积核（如3*3）通过多层叠加可取得与大卷积核（如7\*7）同等规模的感受野，此外采用小卷积核有两个优势： + 1. 小卷积核需多层叠加，加深了网络深度进而增强了网络容量(model capacity)和复杂度（model complexity） 2. 增强了网络容量的同时减少了参数个数。 @@ -248,7 +254,9 @@ if __name__ == '__main__': rf = inFromOut(net_struct[net]['net'], i+1) print "Layer Name = %s, Output size = %3d, Stride = % 3d, RF size = %3d" % (net_struct[net]['name'][i], p[0], p[1], rf) ``` + **程序输出结果如下**： + ```shell layer output sizes given image = 224x224 ************net structrue name is vgg16************** @@ -318,8 +326,10 @@ Layer Name = pool5, Output size = 5, Stride = 32, RF size = 195 卷积操作可获取图像区域不同类型特征，而汇合等操作可对这些特征进行融合和抽象，随着若干卷积、汇合等操作的堆叠，各层得到的深度特征逐渐从泛化特征（如边缘、纹理等）过渡到高层语义表示（躯干、头部等模式）。 ## 什么样的数据集不适合深度学习 + + 数据集太小，数据样本不足时，深度学习相对其它机器学习算法，没有明显优势。 + 数据集没有局部相关特性，目前深度学习表现比较好的领域主要是图像／语音／自然语言处理等领域，这些领域的一个共性是局部相关性。图像中像素组成物体，语音信号中音位组合成单词，文本数据中单词组合成句子，这些特征元素的组合一旦被打乱，表示的含义同时也被改变。对于没有这样的局部相关性的数据集，不适于使用深度学习算法进行处理。举个例子：预测一个人的健康状况，相关的参数会有年龄、职业、收入、家庭状况等各种元素，将这些元素打乱，并不会影响相关的结果。 + ## 什么造成梯度消失问题 + 神经网络的训练中，通过改变神经元的权重，使网络的输出值尽可能逼近标签以降低误差值，训练普遍使用BP算法，核心思想是，计算出输出与标签间的损失函数值，然后计算其相对于每个神经元的梯度，进行权值的迭代。 + 梯度消失会造成权值更新缓慢，模型训练难度增加。造成梯度消失的一个原因是，许多激活函数将输出值挤压在很小的区间内，在激活函数两端较大范围的定义域内梯度为0，造成学习停止。 @@ -423,10 +433,10 @@ x, & \text{if $x$ $\geq$ 0} \\ ReLU 的输出要么是 0, 要么是输入本身。虽然方程简单，但实际上效果更好。在网上看了很多版本的解释，有从程序实例分析也有从数学上分析，我找了个相对比较直白的回答，如下： -1. ReLU函数计算简单，可以减少很多计算量。反向传播求误差梯度时，涉及除法，计算量相对较大，采用ReLU激活函数，可以节省很多计算量； -2. **避免梯度消失问题**。对于深层网络，sigmoid函数反向传播时，很容易就会出现梯度消失问题（在sigmoid接近饱和区时，变换太缓慢，导数趋于0，这种情况会造成信息丢失），从而无法完成深层网络的训练。 -3. 可以缓解过拟合问题的发生。Relu会使一部分神经元的输出为0，这样就造成了网络的稀疏性，并且减少了参数的相互依存关系，缓解了过拟合问题的发生。 -4. 相比`sigmoid`型函数，`ReLU`函数有助于随机梯度下降方法收敛。 +1. `ReLU` 函数计算简单，可以减少很多计算量。反向传播求误差梯度时，涉及除法，计算量相对较大，采用 `ReLU` 激活函数，可以节省很多计算量； +2. **避免梯度消失问题**。对于深层网络，`sigmoid` 函数反向传播时，很容易就会出现梯度消失问题（在sigmoid接近饱和区时，变换太缓慢，导数趋于 0，这种情况会造成信息丢失），从而无法完成深层网络的训练。 +3. 可以缓解过拟合问题的发生。`ReLU` 会使一部分神经元的输出为 0，这样就造成了网络的稀疏性，并且减少了参数的相互依存关系，缓解了过拟合问题的发生。 +4. 相比 `sigmoid` 型函数，`ReLU` 函数有助于随机梯度下降方法收敛。 #### 参考链接 [ReLU为什么比Sigmoid效果好](https://www.twblogs.net/a/5c2dd30fbd9eee35b21c4337/zh-cn) @@ -635,18 +645,20 @@ $$F_{\beta} = \frac{1+\beta^{2}*P*R}{(\beta ^{2}*P)+R}$$ #### 二分类 $$F1\ score = \frac{2 \times P \times R}{(P + R)}$$。 其中，准确率(precise) $P = TP / (TP + FP)$，召回率(recall) $R = TP / (TP + FN)$. + #### 多分类 + **Macro F1**: **将 `n` 分类的评价拆成 `n` 个二分类的评价**，计算每个二分类的 `F1 score`，`n` 个 `F1 score` 的平均值即为 `Macro F1`。 + **Micro F1**: 将n分类的评价拆成n个二分类的评价，将n个二分类评价的TP、FP、RN对应相加，计算评价准确率和召回率，由这2个准确率和召回率计算的F1 score即为Micro F1。 + 一般来讲，Macro F1、Micro F1高的分类效果好。Macro F1受样本数量少的类别影响大。 -### mAP指标解释 + +#### mAP指标解释 `AP` 衡量的是训练好的模型在每个类别上的好坏，`mAP` 衡量的是模型在所有类别上的好坏，得到 `AP` 后 `mAP` 的计算就变得很简单了，就是取所有 `AP` 的平均值。 `mAP` 这个术语有不同的定义。此度量指标通常用于信息检索、图像分类和目标检测领域。然而这两个领域计算 `mAP` 的方式却不相同。这里我们只谈论目标检测中的 `mAP` 计算方法。 `mAP` 常作为目标检测算法的评价指标，具体来说就是，对于每张图片检测模型会输出多个预测框（远超真实框的个数），我们使用 `IoU`(Intersection Over Union，交并比)来标记预测框是否预测准确。标记完成后，随着预测框的增多，查全率 `R` 总会上升，**在不同查全率 `R` 水平下对准确率 `P` 做平均，即得到AP**，最后再对所有类别按其所占比例做平均，即得到 `mAP` 指标。 -### 交并比IOU +#### 交并比IOU 交并比（Intersection-over-Union，IoU），目标检测中使用的一个概念，是产生的候选框面积（candidate bound）与原标记框面积（ground truth bound）的交叠率，即它们的交集与并集的比值。最理想情况是完全重叠，即比值为1。 计算公式如下：$$IOU = \frac{A \cap B}{A \cup B}$$ From 73a61446264958bd8a38833a241f7e85a85e4d10 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Mon, 21 Sep 2020 11:25:13 +0800 Subject: [PATCH 355/386] improve document specification --- ...45\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename "\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" => "\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" (100%) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" similarity index 100% rename from "\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" rename to "\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" From 6ad02a306d7fc31135cf178722c77e7c641c042c Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Mon, 21 Sep 2020 11:59:51 +0800 Subject: [PATCH 356/386] improve document specification --- ...55\246\344\271\240\345\237\272\347\241\200.md" | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" index 537aa848..c5bd65be 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" @@ -625,6 +625,7 @@ $$softmax(y)_{i} = \frac{e^{yi}}{\sum_{j=1}^{n}e^{yj}}$$ 有的朋友又问了：如何找到最好的阈值点呢？ 首先，需要说明的是我们对于这两个指标的要求：我们希望查准率和查全率同时都非常高。 但实际上这两个指标是一对矛盾体，无法做到双高。图中明显看到，如果其中一个非常高，另一个肯定会非常低。选取合适的阈值点要根据实际需求，比如我们想要高的查全率，那么我们就会牺牲一些查准率，在保证查全率最高的情况下，查准率也不那么低。 ### PR、ROC曲线与AUC面积 + + **PR 曲线是以 Recall 为横轴，Precision 为纵轴；而 ROC曲线则是以 FPR 为横轴，TPR 为纵轴**。**P-R 曲线越靠近右上角性能越好**。 + `P、R 和 F1` 评价在信息检索中用的比较多，在机器学习中还有一种 `ROC` 曲线来评价性能。 + **`PR` 曲线展示的是`Precision vs Recall的`曲线，`ROC`曲线展示的是`FPR`（x 轴：False positive rate） vs `TPR`（True positive rate, TPR）曲线**。 @@ -643,20 +644,20 @@ $$F_{\beta} = \frac{1+\beta^{2}*P*R}{(\beta ^{2}*P)+R}$$ ### 二分类、多分类指标计算 #### 二分类 -$$F1\ score = \frac{2 \times P \times R}{(P + R)}$$。 +$$F1\ score = \frac{2 \times P \times R}{(P + R)}$$ 其中，准确率(precise) $P = TP / (TP + FP)$，召回率(recall) $R = TP / (TP + FN)$. #### 多分类 -+ **Macro F1**: **将 `n` 分类的评价拆成 `n` 个二分类的评价**，计算每个二分类的 `F1 score`，`n` 个 `F1 score` 的平均值即为 `Macro F1`。 -+ **Micro F1**: 将n分类的评价拆成n个二分类的评价，将n个二分类评价的TP、FP、RN对应相加，计算评价准确率和召回率，由这2个准确率和召回率计算的F1 score即为Micro F1。 -+ 一般来讲，Macro F1、Micro F1高的分类效果好。Macro F1受样本数量少的类别影响大。 ++ **Macro F1**: 将 `n` 分类的评价拆成 `n` 个二分类的评价，计算每个二分类的 `F1 score`，`n` 个 `F1 score` 的平均值即为 `Macro F1`。 ++ **Micro F1**: 将 n 分类的评价拆成 n 个二分类的评价，将 n 个二分类评价的TP、FP、RN对应相加，计算评价准确率和召回率，由这 `2` 个准确率和召回率计算的F1 score 即为 Micro F1。 ++ 一般来讲，Macro F1、Micro F1 值高的分类效果好。`Macro F1`受样本数量少的类别影响大。 #### mAP指标解释 -`AP` 衡量的是训练好的模型在每个类别上的好坏，`mAP` 衡量的是模型在所有类别上的好坏，得到 `AP` 后 `mAP` 的计算就变得很简单了，就是取所有 `AP` 的平均值。 +`AP` 衡量的是训练好的模型在每个类别上的好坏，`mAP` 衡量的是模型在所有类别上的好坏，得到 AP 后 mAP 的计算就变得很简单了，就是取所有 `AP` 的平均值。 `mAP` 这个术语有不同的定义。此度量指标通常用于信息检索、图像分类和目标检测领域。然而这两个领域计算 `mAP` 的方式却不相同。这里我们只谈论目标检测中的 `mAP` 计算方法。 -`mAP` 常作为目标检测算法的评价指标，具体来说就是，对于每张图片检测模型会输出多个预测框（远超真实框的个数），我们使用 `IoU`(Intersection Over Union，交并比)来标记预测框是否预测准确。标记完成后，随着预测框的增多，查全率 `R` 总会上升，**在不同查全率 `R` 水平下对准确率 `P` 做平均，即得到AP**，最后再对所有类别按其所占比例做平均，即得到 `mAP` 指标。 +`mAP` 常作为目标检测算法的评价指标，具体来说就是，对于每张图片检测模型会输出多个预测框（远超真实框的个数），我们使用 `IoU` (Intersection Over Union，交并比)来标记预测框是否预测准确。标记完成后，随着预测框的增多，查全率 `R` 总会上升，**在不同查全率 `R` 水平下对准确率 `P` 做平均，即得到AP**，最后再对所有类别按其所占比例做平均，即得到 `mAP` 指标。 #### 交并比IOU @@ -716,7 +717,7 @@ def calculateIoU(candidateBound, groundTruthBound): ### Reference [深度学习中的数据增强](https://blog.csdn.net/zhelong3205/article/details/81810743 ) -## 为什么`Faster RCNN/Mask RCNN` 需要使用 `ROI Pooling/ROI Align`? +## 为什么 Faster RCNN/Mask RCNN 需要使用 ROI Pooling/ROI Align? 为了使得最后面的两个全连接层能够共享 `conv layers(VGG/ResNet)` 权重。在所有的 `RoIs` 都被 `pooling` 成（512×7×7）的`feature map`后，将它 `reshape` 成一个一维的向量，就可以利用 `VGG16` 的预训练的权重来初始化前两层全连接。 ## ROI Pooling替换为ROI Align，及各自原理 From 2349c7c655c95f8d7dd281f1eb76edb1583b93c5 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Mon, 21 Sep 2020 12:00:37 +0800 Subject: [PATCH 357/386] improve document specification --- ...72\246\345\255\246\344\271\240\345\237\272\347\241\200.md" | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" index c5bd65be..b54bb70a 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" @@ -633,10 +633,12 @@ $$softmax(y)_{i} = \frac{e^{yi}}{\sum_{j=1}^{n}e^{yj}}$$ ### F1 分数 如果想要找到 $P$ 和 $R$ 二者之间的一个平衡点，我们就需要一个新的指标：$F1$ 分数。$F1$ 分数同时考虑了查准率和查全率，让二者同时达到最高，取一个平衡。$F1$ 计算公式如下： + $$F1 = \frac{2*P*R}{P+R} = \frac{2*TP}{样例总数+TP-TN}$$ $F1$ 度量的一般形式：$F_{\beta}$，能让我们表达出对查准率/查全率的偏见，$F_{\beta}$计算公式如下： -$$F_{\beta} = \frac{1+\beta^{2}*P*R}{(\beta ^{2}*P)+R}$$ + +$$F_{\beta} = \frac{1+\beta^{2}*P*R}{(\beta^{2}*P)+R}$$ 其中 $\beta >1$ 对查全率有更大影响，$\beta < 1$ 对查准率有更大影响。 From e626ad28fb81cc68fa62094dbfb54ea7c9d81f57 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Mon, 21 Sep 2020 13:57:33 +0800 Subject: [PATCH 358/386] =?UTF-8?q?improve=20document=20specification=20on?= =?UTF-8?q?=20=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0=E5=9F=BA=E7=A1=80.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...45\255\246\344\271\240\345\237\272\347\241\200.md" | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" index b54bb70a..24a51222 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" @@ -553,6 +553,7 @@ HOG 特征提取方法就是将一个image： 3. `ReLU` 处理了sigmoid、tanh中常见的梯度消失问题，同时也是计算梯度最快的激励函数。但是，ReLU函数也有自身缺陷，即在 x < 0 时，梯度便为 y。换句话说，对于小于 y 的这部分卷积结果响应，它们一旦变为负值将再无法影响网络训练——这种现象被称作“死区"。 ![Relu函数及梯度图](../images/relu函数及其梯度.jpg) + ## 卷积层和池化层有什么区别 1. **卷积层有参数，池化层没有参数**； 2. **经过卷积层节点矩阵深度会改变**。池化层不会改变节点矩阵的深度，但是它可以缩小节点矩阵的大小。 @@ -575,13 +576,13 @@ HOG 特征提取方法就是将一个image： 判断一个输出向量和期望的向量有多接近，交叉熵（cross entroy）是常用的评判方法之一。交叉熵刻画了两个概率分布之间的距离，是分类问题中使用比较广泛的一种损失函数。 给定两个概率分布 `p` 和 `q` ，通过 `q` 来表示 `p` 的交叉熵公式为：$H(p,q)=−∑p(x)logq(x)$ -## softmax公式写一下 +## softmax公式 $$softmax(y)_{i} = \frac{e^{yi}}{\sum_{j=1}^{n}e^{yj}}$$ ![softmax公式](../images/softmax公式.png) -## 1*1卷积的主要作用有以下几点 +## 1*1卷积的主要作用 + **降维（ dimension reductionality ）**。比如，一张500 \* 500且厚度depth为100 的图片在20个filter上做1\*1的卷积，那么结果的大小为500\*500\*20。 + **加入非线性**。卷积层之后经过激励层，1\*1的卷积在前一层的学习表示上添加了非线性激励（ non-linear activation ），提升网络的表达能力； @@ -627,9 +628,9 @@ $$softmax(y)_{i} = \frac{e^{yi}}{\sum_{j=1}^{n}e^{yj}}$$ ### PR、ROC曲线与AUC面积 + **PR 曲线是以 Recall 为横轴，Precision 为纵轴；而 ROC曲线则是以 FPR 为横轴，TPR 为纵轴**。**P-R 曲线越靠近右上角性能越好**。 -+ `P、R 和 F1` 评价在信息检索中用的比较多，在机器学习中还有一种 `ROC` 曲线来评价性能。 -+ **`PR` 曲线展示的是`Precision vs Recall的`曲线，`ROC`曲线展示的是`FPR`（x 轴：False positive rate） vs `TPR`（True positive rate, TPR）曲线**。 -+ PR曲线与ROC曲线的相同点是都采用了`TPR (Recall)`，都可以用AUC来衡量分类器的效果。不同点是 `ROC` 曲线使用了 `FPR`，而 `PR` 曲线使用了 `Precision`，因此**PR曲线的两个指标都聚焦于正例**。类别不平衡问题中由于主要关心正例，所以在此情况下PR曲线被广泛认为优于ROC曲线。 ++ **PR 曲线展示的是Precision vs Recall 的曲线，ROC 曲线展示的是 FPR（x 轴：False positive rate） vs TPR（True positive rate, TPR）曲线**。 ++ `P、R` 和 `F1` 指标评价在信息检索中用的比较多，在机器学习中还有一种 `ROC` 曲线来评价性能。 ++ PR 曲线与 ROC 曲线的相同点是都采用了 `TPR (Recall)`，都可以用 AUC 来衡量分类器的效果。不同点是 `ROC` 曲线使用了 `FPR`，而 `PR` 曲线使用了 `Precision`，因此**PR曲线的两个指标都聚焦于正例**。类别不平衡问题中由于主要关心正例，所以在此情况下PR曲线被广泛认为优于ROC曲线。 ### F1 分数 如果想要找到 $P$ 和 $R$ 二者之间的一个平衡点，我们就需要一个新的指标：$F1$ 分数。$F1$ 分数同时考虑了查准率和查全率，让二者同时达到最高，取一个平衡。$F1$ 计算公式如下： From a1940bac6f67e543b082cbeb0a9aad25ca28513c Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Mon, 21 Sep 2020 14:12:43 +0800 Subject: [PATCH 359/386] =?UTF-8?q?improve=20document=20specification=20on?= =?UTF-8?q?=20=E7=9B=AE=E6=A0=87=E6=A3=80=E6=B5=8B=E5=9F=BA=E6=9C=AC?= =?UTF-8?q?=E6=A6=82=E5=BF=B5=E4=B8=8E=E8=AF=84=E4=BB=B7=E6=8C=87=E6=A0=87?= =?UTF-8?q?.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...07\346\240\207\350\256\241\347\256\227.md" | 24 +++++++++---------- 1 file changed, 12 insertions(+), 12 deletions(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" index f5376b1c..a0a2523f 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" @@ -1,7 +1,7 @@ ## 前言 -**不同的问题和不同的数据集都会有不同的模型评价指标，比如分类问题，数据集类别平衡的情况下可以使用准确率作为评价指标，但是现实中的数据集几乎都是类别不平衡的，所以一般都是采用 `AP` 作为评价指标，分别计算每个类别的 `AP`，再计算`mAP`**。 +**不同的问题和不同的数据集都会有不同的模型评价指标，比如分类问题，数据集类别平衡的情况下可以使用准确率作为评价指标，但是现实中的数据集几乎都是类别不平衡的，所以一般都是采用 `AP` 作为分类的评价指标，分别计算每个类别的 `AP`，再计算`mAP`**。 ## anchors -所谓 `anchors`，实际上就是一组由 generate_anchors.py 生成的矩形框。其中每行的4个值 `(x1,y1,x2,y2)` 表矩形左上和右下角点坐标。9个矩形共有3种形状，长宽比为大约为 `{1:1, 1:2, 2:1}` 三种, 实际上通过anchors就引入了检测中常用到的多尺度方法。generate_anchors.py的代码如下： +所谓 `anchors`，实际上就是一组由 generate_anchors.py 生成的矩形框。其中每行的4个值 `(x1,y1,x2,y2)` 表矩形左上和右下角点坐标。9 个矩形共有 3 种形状，长宽比为大约为 `{1:1, 1:2, 2:1}` 三种, 实际上通过anchors就引入了检测中常用到的多尺度方法。generate_anchors.py的代码如下： ```Python import numpy as np import six @@ -294,7 +294,7 @@ Soft NMS算法是对NMS算法的改进，是发表在ICCV2017的文章中提出 ![高斯加权形式的soft NMS算法](../images/高斯加权形式的NMS.jpg) 注意，这两种形式，思想都是M为当前得分最高框，$b_{i}$为待处理框， $b_{i}$和M的IOU越大，bbox的得分$s_{i}$就下降的越厉害($N_{t}$为给定阈值)。更多细节可以参考原[论文](https://arxiv.org/pdf/1704.04503.pdf) -soft nms的python代码如下： +soft nms 的 python 代码如下： ```Python def soft_nms(dets, thresh, type='gaussian'): x1 = dets[:, 0] @@ -341,9 +341,9 @@ def soft_nms(dets, thresh, type='gaussian'): return keep ``` ## AP计算 -**目标检测领域常用的评估标准是**：`mAP(mean average precision)`，计算mAP需要先计算`AP`，计算`AP`需涉及到`precision`和`recall`的计算，而这两者的计算又需设计`TP`、`FP`、`FN`的计算。 +**目标检测领域常用的评估标准是**：`mAP`(mean average precision)，计算 mAP 需要先计算 `AP`，计算 `AP` 需涉及到 `precision` 和 `recall` 的计算，而这两者的计算又需设计 `TP`、`FP`、`FN` 的计算。 -`AP`的计算一般先涉及到`P-R`曲线（precision-recall curve）的绘制，P-R曲线下面与x轴围成的面积称为`average precison（AP）`。下图是一个二分类问题的P-R曲线： +`AP` 的计算一般先涉及到 `P-R` 曲线（precision-recall curve）的绘制，P-R曲线下面与x轴围成的面积称为`average precison（AP）`。下图是一个二分类问题的P-R曲线： ![分类问题的PR曲线图](../images/分类PR曲线图.png) @@ -410,27 +410,27 @@ def draw_PR_curve(predict_scores, eval_labels, name, cls_idx=1): ![11点插值计算方式计算AP公式](../images/插值计算AP公式.png) -+ 这是通常意义上的 11points_Interpolated 形式的 AP，选取固定的 {0,0.1,0.2,…,1.0} 11个阈值，这个在PASCAL2007中有使用 ++ 这是通常意义上的 11 points_Interpolated 形式的 AP，选取固定的 {0,0.1,0.2,…,1.0} 11个阈值，这个在PASCAL2007中有使用 + 这里因为参与计算的只有11个点，所以 K=11，称为11points_Interpolated，k为阈值索引 + $P_{interp}(k)$ 取第 k 个阈值所对应的样本点之后的样本中的最大值，只不过这里的阈值被限定在了 {0,0.1,0.2,…,1.0} 范围内。 ![插值计算方式计算AP的PR曲线图](../images/插值计算AP的PR曲线图.png) -从曲线上看，真实 `AP< approximated AP < Interpolated AP`，`11-points Interpolated AP` 可能大也可能小，当数据量很多的时候会接近于 `Interpolated AP`，与 `Interpolated AP` 不同，前面的公式中计算 `AP` 时都是对 `PR` 曲线的面积估计，PASCAL的论文里给出的公式就更加简单粗暴了，直接计算`11 个阈值处的 `precision` 的平均值。`PASCAL` 论文给出的11点计算AP公式如下： +从曲线上看，真实 `AP< approximated AP < Interpolated AP`，`11-points Interpolated AP` 可能大也可能小，当数据量很多的时候会接近于 `Interpolated AP`，与 `Interpolated AP` 不同，前面的公式中计算 `AP` 时都是对 `PR` 曲线的面积估计，PASCAL的论文里给出的公式就更加简单粗暴了，直接计算`11` 个阈值处的 `precision` 的平均值。`PASCAL` 论文给出的 11 点计算 AP 公式如下： ![PASCAL论文给出的11点计算AP公式](../images/11点计算AP公式.png) -### AP计算代码实现 +## 项目中的AP计算代码 ### 不同数据集map计算方法 -由于 mAP 是数据集中所有类别 AP 值得平均，所以我们要计算mAP，首先得知道某一类别的AP值怎么求。不同数据集的某类别的AP计算方法大同小异，主要分为三种： +由于 mAP 是数据集中所有类别 AP 值得平均，所以我们要计算mAP，首先得知道某一类别的 AP 值怎么求。不同数据集的某类别的 AP 计算方法大同小异，主要分为三种： （1）在 `VOC2007`，只需要选取当Recall >= 0, 0.1, 0.2, ..., 1共11个点时的Precision最大值，然后AP就是这11个Precision的平均值，map就是所有类别AP值的平均。 -（2）在 `VOC2010` 及以后，需要针对每一个不同的 `Recall` 值（包括0和1），选取其大于等于这些 `Recall` 值时的 `Precision` 最大值，然后计算PR曲线下面积作为AP值，map就是所有类别AP值的平均。 +（2）在 `VOC2010` 及以后，需要针对每一个不同的 `Recall` 值（包括0和1），选取其大于等于这些 `Recall` 值时的 `Precision` 最大值，然后计算PR曲线下面积作为 AP 值，map 就是所有类别 AP 值的平均。 -（3）COCO 数据集，设定多个IOU阈值（0.5-0.95,0.05为步长），在每一个IOU阈值下都有某一类别的AP值，然后求不同IOU阈值下的AP平均，就是所求的最终的某类别的AP值。 +（3）`COCO` 数据集，设定多个 IOU 阈值（0.5-0.95,0.05为步长），在每一个IOU阈值下都有某一类别的 AP 值，然后求不同 IOU 阈值下的 AP 平均，就是所求的最终的某类别的AP值。 `VOC` 数据集中计算 `AP` 的代码（用的是插值计算方法，代码出自[py-faster-rcnn仓库](https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/datasets/voc_eval.py)）如下： @@ -470,7 +470,7 @@ def voc_ap(rec, prec, use_07_metric=False): ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) return ap ``` -**给定目标检测结果文件和测试集标签文件xml等计算AP** +**给定目标检测结果文件和测试集标签文件 xml 等计算AP** ```Python def parse_rec(filename): """ Parse a PASCAL VOC xml file From fe0ffadd0d5da775eb8af74acb758b2959bc7c6c Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Wed, 23 Sep 2020 09:49:38 +0800 Subject: [PATCH 360/386] =?UTF-8?q?=E8=BF=81=E7=A7=BB=E6=96=87=E6=A1=A3?= =?UTF-8?q?=E5=88=B0=E9=9D=A2=E7=BB=8F=E5=8F=8A=E9=9D=A2=E8=AF=95=E6=80=BB?= =?UTF-8?q?=E7=BB=93=E7=9B=AE=E5=BD=95=E4=B8=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" | 2 +- ...\272\272\345\256\236\344\271\240\346\200\273\347\273\223.md" | 0 ...\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" | 0 ...\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" | 0 ...\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" | 0 5 files changed, 1 insertion(+), 1 deletion(-) rename "\345\234\260\345\271\263\347\272\277\346\234\272\345\231\250\344\272\272\345\256\236\344\271\240\346\200\273\347\273\223.md" => "\351\235\242\347\273\217\345\217\212\351\235\242\350\257\225\346\200\273\347\273\223/\345\234\260\345\271\263\347\272\277\346\234\272\345\231\250\344\272\272\345\256\236\344\271\240\346\200\273\347\273\223.md" (100%) rename "\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" => "\351\235\242\347\273\217\345\217\212\351\235\242\350\257\225\346\200\273\347\273\223/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" (100%) rename "\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" => "\351\235\242\347\273\217\345\217\212\351\235\242\350\257\225\346\200\273\347\273\223/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" (100%) rename "\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" => "\351\235\242\347\273\217\345\217\212\351\235\242\350\257\225\346\200\273\347\273\223/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" (100%) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" index 24a51222..6dc1cd3e 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" @@ -655,7 +655,7 @@ $$F1\ score = \frac{2 \times P \times R}{(P + R)}$$ + **Micro F1**: 将 n 分类的评价拆成 n 个二分类的评价，将 n 个二分类评价的TP、FP、RN对应相加，计算评价准确率和召回率，由这 `2` 个准确率和召回率计算的F1 score 即为 Micro F1。 + 一般来讲，Macro F1、Micro F1 值高的分类效果好。`Macro F1`受样本数量少的类别影响大。 -#### mAP指标解释 +#### mAP指标理解 `AP` 衡量的是训练好的模型在每个类别上的好坏，`mAP` 衡量的是模型在所有类别上的好坏，得到 AP 后 mAP 的计算就变得很简单了，就是取所有 `AP` 的平均值。 `mAP` 这个术语有不同的定义。此度量指标通常用于信息检索、图像分类和目标检测领域。然而这两个领域计算 `mAP` 的方式却不相同。这里我们只谈论目标检测中的 `mAP` 计算方法。 diff --git "a/\345\234\260\345\271\263\347\272\277\346\234\272\345\231\250\344\272\272\345\256\236\344\271\240\346\200\273\347\273\223.md" "b/\351\235\242\347\273\217\345\217\212\351\235\242\350\257\225\346\200\273\347\273\223/\345\234\260\345\271\263\347\272\277\346\234\272\345\231\250\344\272\272\345\256\236\344\271\240\346\200\273\347\273\223.md" similarity index 100% rename from "\345\234\260\345\271\263\347\272\277\346\234\272\345\231\250\344\272\272\345\256\236\344\271\240\346\200\273\347\273\223.md" rename to "\351\235\242\347\273\217\345\217\212\351\235\242\350\257\225\346\200\273\347\273\223/\345\234\260\345\271\263\347\272\277\346\234\272\345\231\250\344\272\272\345\256\236\344\271\240\346\200\273\347\273\223.md" diff --git "a/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" "b/\351\235\242\347\273\217\345\217\212\351\235\242\350\257\225\346\200\273\347\273\223/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" similarity index 100% rename from "\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" rename to "\351\235\242\347\273\217\345\217\212\351\235\242\350\257\225\346\200\273\347\273\223/\346\232\221\346\234\237\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\256\236\344\271\240\345\272\224\350\201\230\346\200\273\347\273\223.md" diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" "b/\351\235\242\347\273\217\345\217\212\351\235\242\350\257\225\346\200\273\347\273\223/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" similarity index 100% rename from "\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" rename to "\351\235\242\347\273\217\345\217\212\351\235\242\350\257\225\346\200\273\347\273\223/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\345\256\236\344\271\240\351\235\242\347\273\217.md" diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" "b/\351\235\242\347\273\217\345\217\212\351\235\242\350\257\225\346\200\273\347\273\223/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" similarity index 100% rename from "\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" rename to "\351\235\242\347\273\217\345\217\212\351\235\242\350\257\225\346\200\273\347\273\223/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" From dbe27cec6bbe9a14e2d5ab878b16d59450c3cc3c Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Wed, 30 Sep 2020 15:08:36 +0800 Subject: [PATCH 361/386] Update document show syntax. --- ...46\344\271\240\345\237\272\347\241\200.md" | 2 +- ...07\346\240\207\350\256\241\347\256\227.md" | 27 ++++++++++++------- 2 files changed, 19 insertions(+), 10 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" index 6dc1cd3e..68b2bc5b 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" @@ -578,7 +578,7 @@ HOG 特征提取方法就是将一个image： ## softmax公式 -$$softmax(y)_{i} = \frac{e^{yi}}{\sum_{j=1}^{n}e^{yj}}$$ +$$softmax(y)_{i} = \frac{e^{y_{i}}}{\sum_{j=1}^{n}e^{y_j}}$$ ![softmax公式](../images/softmax公式.png) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" index a0a2523f..5dd2a759 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" @@ -665,26 +665,35 @@ def voc_eval(detpath, 全连接层的 $FLOPs = (2I − 1)O$，$I$ 是输入层的维度，$O$ 是输出层的维度。 ## 神经网络参数量计算方法 ### 卷积层参数量 -**卷积层参数量 = $C_i\times K^2\times C_o + C_o$** - -**$C_i$ 为输入特征图通道数，$K$ 为过滤器(卷积核)尺寸，$C_o$ 为输出的特征图的 channel 数(也是 filter 的数量)**，算式第二项是偏置项的参数量 。(一般不写偏置项，偏置项对总参数量的数量级的影响可以忽略不记，这里为了准确起见，把偏置项的参数量也考虑进来。） +**卷积层参数量 = $C_i\times K^2\times C_o + C_o$**。**$C_i$ 为输入特征图通道数，$K$ 为过滤器(卷积核)尺寸，$C_o$ 为输出的特征图的 channel 数(也是 filter 的数量)**，算式第二项是偏置项的参数量 。(一般不写偏置项，偏置项对总参数量的数量级的影响可以忽略不记，这里为了准确起见，把偏置项的参数量也考虑进来。） 假设输入层矩阵维度是 96×96×3，第一层卷积层使用尺寸为 5×5、深度为 16 的过滤器（卷积核尺寸为 5×5、卷积核数量为 16），那么这层卷积层的参数个数为 ５×5×3×16+16=1216个。 ### BN层参数量 -**BN层参数量** = $2\times C_i$ - -其中 $C_i$ 为输入的 `channel` 数（BN层有两个需要学习的参数，平移因子和缩放因子） +**BN层参数量** = $2\times C_i$。其中 $C_i$ 为输入的 `channel` 数（BN层有两个需要学习的参数，平移因子和缩放因子） ### 全连接层参数量 -**全连接层参数量** = $T_i\times T_o + T_O$ - -$T_i$ 为输入向量的长度， $T_o$ 为输出向量的长度，其中第二项为偏置项参数量。 (目前全连接层已经逐渐被 `Global Average Pooling` 层取代了) +**全连接层参数量** = $T_i\times T_o + T_O$。$T_i$ 为输入向量的长度， $T_o$ 为输出向量的长度，公式的第二项为偏置项参数量。 (目前全连接层已经逐渐被 `Global Average Pooling` 层取代了) ### 参考资料 [PRUNING CONVOLUTIONAL NEURAL NETWORKS FOR RESOURCE EFFICIENT INFERENCE ](https://arxiv.org/pdf/1611.06440.pdf) [神经网络参数量的计算：以UNet为例](https://zhuanlan.zhihu.com/p/57437131) +## 双精度、单精度和半精度 +`CPU/GPU` 的浮点计算能力得区分不同精度的浮点数，分为双精度 `FP64`、单精度 `FP32` 和半精度 `FP16`。因为采用不同位数的浮点数的表达精度不一样，所以造成的计算误差也不一样，对于需要处理的数字范围大而且需要精确计算的科学计算来说，就要求采用双精度浮点数，而对于常见的多媒体和图形处理计算，`32` 位的单精度浮点计算已经足够了，对于要求精度更低的机器学习等一些应用来说，半精度 `16` 位浮点数就可以甚至 `8` 位浮点数就已经够用了。 +对于浮点计算来说， `CPU` 可以同时支持不同精度的浮点运算，但在 `GPU` 里针对单精度和双精度就需要各自独立的计算单元。 +## 浮点计算能力 +`FLOPS`：每秒浮点运算次数，每秒所执行的浮点运算次数，浮点运算包括了所有涉及小数的运算，比整数运算更费时间。下面几个是表示浮点运算能力的单位。 + ++ `MFLOPS`（megaFLOPS）：等于每秒一佰万（=10^6）次的浮点运算。 ++ `GFLOPS`（gigaFLOPS）：等于每秒拾亿（=10^9）次的浮点运算。 ++ `TFLOPS`（teraFLOPS）：等于每秒万亿（=10^12）次的浮点运算。 ++ `PFLOPS`（petaFLOPS）：等于每秒千万亿（=10^15）次的浮点运算。 ++ `EFLOPS`（exaFLOPS）：等于每秒百亿亿（=10^18）次的浮点运算。 + +### 参考资料 +[双精度，单精度和半精度](https://blog.csdn.net/sinat_24143931/article/details/78557852?utm_medium=distribute.pc_relevant_t0.none-task-blog-BlogCommendFromMachineLearnPai2-1.nonecase&depth_1-utm_source=distribute.pc_relevant_t0.none-task-blog-BlogCommendFromMachineLearnPai2-1.nonecase) +## 欧式距离 ## 目标检测度量标准汇总 ![目标检测指标汇总](../images/目标检测度量标准汇总.jpg) ## 目标检测领域中的数据不均衡问题 From adf4206691f991da846c9858c58ca1b26c329084 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Wed, 30 Sep 2020 15:11:53 +0800 Subject: [PATCH 362/386] Update document show syntax. --- ...\267\346\214\207\346\240\207\350\256\241\347\256\227.md" | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" index 5dd2a759..89611107 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" @@ -665,14 +665,14 @@ def voc_eval(detpath, 全连接层的 $FLOPs = (2I − 1)O$，$I$ 是输入层的维度，$O$ 是输出层的维度。 ## 神经网络参数量计算方法 ### 卷积层参数量 -**卷积层参数量 = $C_i\times K^2\times C_o + C_o$**。**$C_i$ 为输入特征图通道数，$K$ 为过滤器(卷积核)尺寸，$C_o$ 为输出的特征图的 channel 数(也是 filter 的数量)**，算式第二项是偏置项的参数量 。(一般不写偏置项，偏置项对总参数量的数量级的影响可以忽略不记，这里为了准确起见，把偏置项的参数量也考虑进来。） +卷积层参数量 = $C_i\times K^2\times C_o + C_o$。$C_i$ 为输入特征图通道数，$K$ 为过滤器(卷积核)尺寸，$C_o$ 为输出的特征图的 `channel` 数(也是 `filter` 的数量)，算式第二项是偏置项的参数量 。(一般不写偏置项，偏置项对总参数量的数量级的影响可以忽略不记，这里为了准确起见，把偏置项的参数量也考虑进来。） 假设输入层矩阵维度是 96×96×3，第一层卷积层使用尺寸为 5×5、深度为 16 的过滤器（卷积核尺寸为 5×5、卷积核数量为 16），那么这层卷积层的参数个数为 ５×5×3×16+16=1216个。 ### BN层参数量 -**BN层参数量** = $2\times C_i$。其中 $C_i$ 为输入的 `channel` 数（BN层有两个需要学习的参数，平移因子和缩放因子） +`BN` 层参数量 = $2\times C_i$。其中 $C_i$ 为输入的 `channel` 数（BN层有两个需要学习的参数，平移因子和缩放因子） ### 全连接层参数量 -**全连接层参数量** = $T_i\times T_o + T_O$。$T_i$ 为输入向量的长度， $T_o$ 为输出向量的长度，公式的第二项为偏置项参数量。 (目前全连接层已经逐渐被 `Global Average Pooling` 层取代了) +全连接层参数量 = $T_i\times T_o + T_O$。$T_i$ 为输入向量的长度， $T_o$ 为输出向量的长度，公式的第二项为偏置项参数量。 (目前全连接层已经逐渐被 `Global Average Pooling` 层取代了) ### 参考资料 [PRUNING CONVOLUTIONAL NEURAL NETWORKS FOR RESOURCE EFFICIENT INFERENCE From 3064903f63b6fdf51cf739c33eab201a1a8d43b8 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Wed, 30 Sep 2020 16:14:14 +0800 Subject: [PATCH 363/386] Correct the define of MACCs. --- ...07\346\240\207\350\256\241\347\256\227.md" | 32 +++++++++++++------ 1 file changed, 22 insertions(+), 10 deletions(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" index 89611107..80478df6 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" @@ -636,10 +636,13 @@ def voc_eval(detpath, return rec, prec, ap ``` -## FLOPs计算 -+ `FLOPs`：floating point operations 指的是浮点运算次数,**理解为计算量**，可以用来衡量算法/模型的复杂度。 -+ `FLOPS`：（全部大写）,Floating-point Operations Per Second，每秒所执行的浮点运算次数，理解为计算速度,是一个衡量硬件性能的指标。 -+ `MACC`：multiply-accumulate，乘法累加次数。 +## 模型计算量分析 +> 终端设备上运行深度学习算法需要考虑内存和算力的需求，因此需要进行模型复杂度分析，涉及到模型计算量（时间复杂度）和模型参数量（空间复杂度）分析。模型理论计算量：指模型推断时需要多少计算次数。 + ++ `FLOPs`：floating point operations 指的是浮点运算次数,**理解为计算量**，可以用来衡量算法/模型时间的复杂度。 ++ `FLOPS`：（全部大写）,Floating-point Operations Per Second，每秒所执行的浮点运算次数，理解为计算速度,是一个衡量硬件性能/模型速度的指标。 ++ `MACCs`：multiply-accumulate operations，乘-加操作次数，`MACCs` 大约是 FLOPs 的一半。将 $w[0]*x[0] + ...$ 视为一个乘法累加或 `1` 个 `MACC`。 +> 注意，网上很多文章将 MACCs 与 MACC 概念搞混，我猜测可能是机器翻译英文文章不准确的缘故，可以参考此[链接](http://machinethink.net/blog/how-fast-is-my-model/)了解更多。 ### 卷积层FLOPs计算 > 卷积操作本质上是个线性运算 @@ -663,22 +666,31 @@ def voc_eval(detpath, ### 全连接层的 FLOPs 计算 > 默认卷积核 全连接层的 $FLOPs = (2I − 1)O$，$I$ 是输入层的维度，$O$ 是输出层的维度。 -## 神经网络参数量计算方法 +## 模型参数量分析 +> 模型参数数量（params）：指模型含有多少参数，直接决定模型的大小，也影响推断时对内存的占用量。 + +模型参数量的分析是为了了解内存占用情况，内存带宽其实比 `FLOPs` 更重要。目前的计算机结构下，单次内存访问比单次运算慢得多的多。对每一层网络，端侧设备需要： ++ 从主内存中读取输入向量 / `feature map`； ++ 从主内存中读取权重并计算点积； ++ 将输出向量或 `feature map` 写回主内存。 + +`MAes`：memory accesse，内存访问次数 ### 卷积层参数量 -卷积层参数量 = $C_i\times K^2\times C_o + C_o$。$C_i$ 为输入特征图通道数，$K$ 为过滤器(卷积核)尺寸，$C_o$ 为输出的特征图的 `channel` 数(也是 `filter` 的数量)，算式第二项是偏置项的参数量 。(一般不写偏置项，偏置项对总参数量的数量级的影响可以忽略不记，这里为了准确起见，把偏置项的参数量也考虑进来。） +卷积层权重参数量 = $C_i\times K^2\times C_o + C_o$。$C_i$ 为输入特征图通道数，$K$ 为过滤器(卷积核)尺寸，$C_o$ 为输出的特征图的 `channel` 数(也是 `filter` 的数量)，算式第二项是偏置项的参数量 。(一般不写偏置项，偏置项对总参数量的数量级的影响可以忽略不记，这里为了准确起见，把偏置项的参数量也考虑进来。） 假设输入层矩阵维度是 96×96×3，第一层卷积层使用尺寸为 5×5、深度为 16 的过滤器（卷积核尺寸为 5×5、卷积核数量为 16），那么这层卷积层的参数个数为 ５×5×3×16+16=1216个。 ### BN层参数量 `BN` 层参数量 = $2\times C_i$。其中 $C_i$ 为输入的 `channel` 数（BN层有两个需要学习的参数，平移因子和缩放因子） ### 全连接层参数量 -全连接层参数量 = $T_i\times T_o + T_O$。$T_i$ 为输入向量的长度， $T_o$ 为输出向量的长度，公式的第二项为偏置项参数量。 (目前全连接层已经逐渐被 `Global Average Pooling` 层取代了) +全连接层参数量 = $T_i\times T_o + T_O$。$T_i$ 为输入向量的长度， $T_o$ 为输出向量的长度，公式的第二项为偏置项参数量。(目前全连接层已经逐渐被 `Global Average Pooling` 层取代了。) 注意，全连接层的权重参数量（内存占用）远远大于卷积层。 ### 参考资料 -[PRUNING CONVOLUTIONAL NEURAL NETWORKS ++ [PRUNING CONVOLUTIONAL NEURAL NETWORKS FOR RESOURCE EFFICIENT INFERENCE ](https://arxiv.org/pdf/1611.06440.pdf) -[神经网络参数量的计算：以UNet为例](https://zhuanlan.zhihu.com/p/57437131) - ++ [神经网络参数量的计算：以UNet为例](https://zhuanlan.zhihu.com/p/57437131) ++ [How fast is my model?](http://machinethink.net/blog/how-fast-is-my-model/) ++ [MobileNetV1 & MobileNetV2 简介](https://blog.csdn.net/mzpmzk/article/details/82976871) ## 双精度、单精度和半精度 `CPU/GPU` 的浮点计算能力得区分不同精度的浮点数，分为双精度 `FP64`、单精度 `FP32` 和半精度 `FP16`。因为采用不同位数的浮点数的表达精度不一样，所以造成的计算误差也不一样，对于需要处理的数字范围大而且需要精确计算的科学计算来说，就要求采用双精度浮点数，而对于常见的多媒体和图形处理计算，`32` 位的单精度浮点计算已经足够了，对于要求精度更低的机器学习等一些应用来说，半精度 `16` 位浮点数就可以甚至 `8` 位浮点数就已经够用了。 对于浮点计算来说， `CPU` 可以同时支持不同精度的浮点运算，但在 `GPU` 里针对单精度和双精度就需要各自独立的计算单元。 From 20d0659ffb9dd9666b5310b2f18c9d42fff178d6 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Wed, 30 Sep 2020 16:14:40 +0800 Subject: [PATCH 364/386] Correct the define of MACCs. --- ...\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" index 80478df6..7def2567 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" @@ -674,7 +674,7 @@ def voc_eval(detpath, + 从主内存中读取权重并计算点积； + 将输出向量或 `feature map` 写回主内存。 -`MAes`：memory accesse，内存访问次数 +`MAes`：memory accesse，内存访问次数。 ### 卷积层参数量 卷积层权重参数量 = $C_i\times K^2\times C_o + C_o$。$C_i$ 为输入特征图通道数，$K$ 为过滤器(卷积核)尺寸，$C_o$ 为输出的特征图的 `channel` 数(也是 `filter` 的数量)，算式第二项是偏置项的参数量 。(一般不写偏置项，偏置项对总参数量的数量级的影响可以忽略不记，这里为了准确起见，把偏置项的参数量也考虑进来。） From 824f14c46772d00bd0cc4eb601b8129552bb888e Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Wed, 30 Sep 2020 16:22:19 +0800 Subject: [PATCH 365/386] Correct the define of MACCs. --- ...4\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" | 1 - 1 file changed, 1 deletion(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" index 7def2567..3a85fcd1 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" @@ -664,7 +664,6 @@ def voc_eval(detpath, 所以卷积层的 $FLOPs=(2\times C_{i}\times K^2-1)\times H\times W\times C_o$ ($C_i$ 为输入特征图通道数，$K$ 为过滤器尺寸，$H, W, C_o$为输出特征图的高，宽和通道数)。 ### 全连接层的 FLOPs 计算 -> 默认卷积核 全连接层的 $FLOPs = (2I − 1)O$，$I$ 是输入层的维度，$O$ 是输出层的维度。 ## 模型参数量分析 > 模型参数数量（params）：指模型含有多少参数，直接决定模型的大小，也影响推断时对内存的占用量。 From a4f62d4308049da728ee459f86a8c1aa429e7286 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Wed, 30 Sep 2020 16:29:07 +0800 Subject: [PATCH 366/386] =?UTF-8?q?Add=20=E7=A5=9E=E7=BB=8F=E7=BD=91?= =?UTF-8?q?=E7=BB=9C=E6=A8=A1=E5=9E=8B=E5=A4=8D=E6=9D=82=E5=BA=A6=E5=88=86?= =?UTF-8?q?=E6=9E=90.md=20file?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...02\345\272\246\345\210\206\346\236\220.md" | 67 +++++++++++++++++++ 1 file changed, 67 insertions(+) create mode 100644 "\346\267\261\345\272\246\345\255\246\344\271\240/\347\245\236\347\273\217\347\275\221\347\273\234\346\250\241\345\236\213\345\244\215\346\235\202\345\272\246\345\210\206\346\236\220.md" diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\347\245\236\347\273\217\347\275\221\347\273\234\346\250\241\345\236\213\345\244\215\346\235\202\345\272\246\345\210\206\346\236\220.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\347\245\236\347\273\217\347\275\221\347\273\234\346\250\241\345\236\213\345\244\215\346\235\202\345\272\246\345\210\206\346\236\220.md" new file mode 100644 index 00000000..c803891b --- /dev/null +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\347\245\236\347\273\217\347\275\221\347\273\234\346\250\241\345\236\213\345\244\215\346\235\202\345\272\246\345\210\206\346\236\220.md" @@ -0,0 +1,67 @@ +## 模型计算量分析 +> 终端设备上运行深度学习算法需要考虑内存和算力的需求，因此需要进行模型复杂度分析，涉及到模型计算量（时间复杂度）和模型参数量（空间复杂度）分析。模型理论计算量：指模型推断时需要多少计算次数。 + ++ `FLOPs`：floating point operations 指的是浮点运算次数,**理解为计算量**，可以用来衡量算法/模型时间的复杂度。 ++ `FLOPS`：（全部大写）,Floating-point Operations Per Second，每秒所执行的浮点运算次数，理解为计算速度,是一个衡量硬件性能/模型速度的指标。 ++ `MACCs`：multiply-accumulate operations，乘-加操作次数，`MACCs` 大约是 FLOPs 的一半。将 $w[0]*x[0] + ...$ 视为一个乘法累加或 `1` 个 `MACC`。 +> 注意，网上很多文章将 MACCs 与 MACC 概念搞混，我猜测可能是机器翻译英文文章不准确的缘故，可以参考此[链接](http://machinethink.net/blog/how-fast-is-my-model/)了解更多。 + +### 卷积层FLOPs计算 +> 卷积操作本质上是个线性运算 + ++ $FLOPs=(2\times C_i\times K^2-1)\times H\times W\times C_o$（不考虑bias） ++ $FLOPs=(2\times C_i\times K^2)\times H\times W\times C_o$（考虑bias） ++ $MACC=(C_i\times K^2)\times H\times W\times C_o$（考虑bias） + +**$C_i$ 为输入特征图通道数，$K$ 为过滤器尺寸，$H,W,C_o$ 为输出特征图的高，宽和通道数**。`二维卷积过程`如下图所示： + +> 二维卷积是一个相当简单的操作：从卷积核开始，这是一个小的权值矩阵。这个卷积核在 2 维输入数据上「滑动」，对当前输入的部分元素进行矩阵乘法，然后将结果汇为单个输出像素。 + +![卷积过程](../images/二维卷积操作动态图.gif) + +公式解释，参考[这里](https://zhuanlan.zhihu.com/p/70306015?utm_source=wechat_session&utm_medium=social&utm_oi=571954943427219456)，如下： + +**理解 `FLOPs` 的计算公式分两步**。括号内是第一步，计算出`output feature map` 的一个 `pixel`，然后再乘以 $H\times W\times C_o$，从而拓展到整个 output feature map。括号内的部分又可以分为两步：$(2\times C_i\times K^2-1)=(C_i\times K^2) + (C_i\times K^2-1)$。第一项是乘法运算次数，第二项是加法运算次数，因为 $n$ 个数相加，要加 $n-1$次，所以不考虑 `bias` 的情况下，会有一个 -1，如果考虑 `bias`，刚好中和掉，括号内变为$(2\times C_i\times K^2)$。 + +所以卷积层的 $FLOPs=(2\times C_{i}\times K^2-1)\times H\times W\times C_o$ ($C_i$ 为输入特征图通道数，$K$ 为过滤器尺寸，$H, W, C_o$为输出特征图的高，宽和通道数)。 + +### 全连接层的 FLOPs 计算 +全连接层的 $FLOPs = (2I − 1)O$，$I$ 是输入层的维度，$O$ 是输出层的维度。 +## 模型参数量分析 +> 模型参数数量（params）：指模型含有多少参数，直接决定模型的大小，也影响推断时对内存的占用量。 + +模型参数量的分析是为了了解内存占用情况，内存带宽其实比 `FLOPs` 更重要。目前的计算机结构下，单次内存访问比单次运算慢得多的多。对每一层网络，端侧设备需要： ++ 从主内存中读取输入向量 / `feature map`； ++ 从主内存中读取权重并计算点积； ++ 将输出向量或 `feature map` 写回主内存。 + +`MAes`：memory accesse，内存访问次数。 +### 卷积层参数量 +卷积层权重参数量 = $C_i\times K^2\times C_o + C_o$。$C_i$ 为输入特征图通道数，$K$ 为过滤器(卷积核)尺寸，$C_o$ 为输出的特征图的 `channel` 数(也是 `filter` 的数量)，算式第二项是偏置项的参数量 。(一般不写偏置项，偏置项对总参数量的数量级的影响可以忽略不记，这里为了准确起见，把偏置项的参数量也考虑进来。） + +假设输入层矩阵维度是 96×96×3，第一层卷积层使用尺寸为 5×5、深度为 16 的过滤器（卷积核尺寸为 5×5、卷积核数量为 16），那么这层卷积层的参数个数为 ５×5×3×16+16=1216个。 +### BN层参数量 +`BN` 层参数量 = $2\times C_i$。其中 $C_i$ 为输入的 `channel` 数（BN层有两个需要学习的参数，平移因子和缩放因子） + +### 全连接层参数量 +全连接层参数量 = $T_i\times T_o + T_O$。$T_i$ 为输入向量的长度， $T_o$ 为输出向量的长度，公式的第二项为偏置项参数量。(目前全连接层已经逐渐被 `Global Average Pooling` 层取代了。) 注意，全连接层的权重参数量（内存占用）远远大于卷积层。 +### 参考资料 ++ [PRUNING CONVOLUTIONAL NEURAL NETWORKS +FOR RESOURCE EFFICIENT INFERENCE +](https://arxiv.org/pdf/1611.06440.pdf) ++ [神经网络参数量的计算：以UNet为例](https://zhuanlan.zhihu.com/p/57437131) ++ [How fast is my model?](http://machinethink.net/blog/how-fast-is-my-model/) ++ [MobileNetV1 & MobileNetV2 简介](https://blog.csdn.net/mzpmzk/article/details/82976871) +## 双精度、单精度和半精度 +`CPU/GPU` 的浮点计算能力得区分不同精度的浮点数，分为双精度 `FP64`、单精度 `FP32` 和半精度 `FP16`。因为采用不同位数的浮点数的表达精度不一样，所以造成的计算误差也不一样，对于需要处理的数字范围大而且需要精确计算的科学计算来说，就要求采用双精度浮点数，而对于常见的多媒体和图形处理计算，`32` 位的单精度浮点计算已经足够了，对于要求精度更低的机器学习等一些应用来说，半精度 `16` 位浮点数就可以甚至 `8` 位浮点数就已经够用了。 +对于浮点计算来说， `CPU` 可以同时支持不同精度的浮点运算，但在 `GPU` 里针对单精度和双精度就需要各自独立的计算单元。 +### 参考资料 +[双精度，单精度和半精度](https://blog.csdn.net/sinat_24143931/article/details/78557852?utm_medium=distribute.pc_relevant_t0.none-task-blog-BlogCommendFromMachineLearnPai2-1.nonecase&depth_1-utm_source=distribute.pc_relevant_t0.none-task-blog-BlogCommendFromMachineLearnPai2-1.nonecase) +## 浮点计算能力 +`FLOPS`：每秒浮点运算次数，每秒所执行的浮点运算次数，浮点运算包括了所有涉及小数的运算，比整数运算更费时间。下面几个是表示浮点运算能力的单位。 + ++ `MFLOPS`（megaFLOPS）：等于每秒一佰万（=10^6）次的浮点运算。 ++ `GFLOPS`（gigaFLOPS）：等于每秒拾亿（=10^9）次的浮点运算。 ++ `TFLOPS`（teraFLOPS）：等于每秒万亿（=10^12）次的浮点运算。 ++ `PFLOPS`（petaFLOPS）：等于每秒千万亿（=10^15）次的浮点运算。 ++ `EFLOPS`（exaFLOPS）：等于每秒百亿亿（=10^18）次的浮点运算。 \ No newline at end of file From 564ad236d3b9fb451f13d0220e8679bd8b336217 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Wed, 30 Sep 2020 17:01:54 +0800 Subject: [PATCH 367/386] Update the table of contents and titles to make the article content structure clearer --- ...07\346\240\207\350\256\241\347\256\227.md" | 123 ++++-------------- 1 file changed, 27 insertions(+), 96 deletions(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" index 3a85fcd1..7d33dd9c 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" @@ -1,6 +1,6 @@ ## 前言 **不同的问题和不同的数据集都会有不同的模型评价指标，比如分类问题，数据集类别平衡的情况下可以使用准确率作为评价指标，但是现实中的数据集几乎都是类别不平衡的，所以一般都是采用 `AP` 作为分类的评价指标，分别计算每个类别的 `AP`，再计算`mAP`**。 -## anchors +## 一，anchors 理解 所谓 `anchors`，实际上就是一组由 generate_anchors.py 生成的矩形框。其中每行的4个值 `(x1,y1,x2,y2)` 表矩形左上和右下角点坐标。9 个矩形共有 3 种形状，长宽比为大约为 `{1:1, 1:2, 2:1}` 三种, 实际上通过anchors就引入了检测中常用到的多尺度方法。generate_anchors.py的代码如下： ```Python import numpy as np @@ -78,7 +78,7 @@ if __name__ == "__main__": [ -37.254833 -82.50967 98.50967 53.254833] [ -82.50967 -173.01933 189.01933 98.50967 ] [-173.01933 -354.03867 370.03867 189.01933 ]] -## 交并比IOU +## 二，交并比IOU 交并比（Intersection-over-Union，IoU），目标检测中使用的一个概念，是产生的候选框（candidate bound）与原标记框（ground truth bound）的交叠率，即它们的交集与并集的比值。最理想情况是完全重叠，即比值为1。计算公式如下： ![IOU计算公式](../images/IOU计算公式.png) @@ -151,7 +151,7 @@ if __name__=='__main__': ![程序运行结果](../images/iou代码输出结果.png) -## 非极大值抑制算法NMS +## 三，NMS 算法 ### [NMS介绍](https://juejin.im/entry/5bdbc26151882516da0ddd25) 在目标检测中，常会利用非极大值抑制算法(NMS，non maximum suppression)对生成的大量候选框进行后处理，去除冗余的候选框，得到最佳检测框，以加快目标检测的效率。其本质思想是其思想是搜素局部最大值，抑制非极大值。非极大值抑制，在计算机视觉任务中得到了广泛的应用，例如边缘检测、人脸检测、目标检测（DPM，YOLO，SSD，Faster R-CNN）等。即如下图所示实现效果，消除多余的候选框，找到最佳的 `bbox`。`NMS过程`如下图所示： @@ -280,7 +280,7 @@ def nms(bboxs,scores,thresh): order=order[ious<=thresh] return keep ``` -## Soft NMS算法 +## 四，Soft NMS算法 Soft NMS算法是对NMS算法的改进，是发表在ICCV2017的文章中提出的。`NMS`算法存在一个问题是可能会把一些目标框给过滤掉，从而导致目标的`recall`指标比较低。原来的NMS可以描述如下：将IOU大于阈值的窗口的得分全部置为0，计算公式如下： ![硬NMS算法](../images/硬NMS算法.jpg) @@ -340,17 +340,21 @@ def soft_nms(dets, thresh, type='gaussian'): scores = scores[tmp] return keep ``` -## AP计算 +## 五，AP计算概述 **目标检测领域常用的评估标准是**：`mAP`(mean average precision)，计算 mAP 需要先计算 `AP`，计算 `AP` 需涉及到 `precision` 和 `recall` 的计算，而这两者的计算又需设计 `TP`、`FP`、`FN` 的计算。 `AP` 的计算一般先涉及到 `P-R` 曲线（precision-recall curve）的绘制，P-R曲线下面与x轴围成的面积称为`average precison（AP）`。下图是一个二分类问题的P-R曲线： ![分类问题的PR曲线图](../images/分类PR曲线图.png) -### 近似计算AP(approximated average precision) +知道了`AP` 的定义，下一步就是理解`AP`计算的实现，理论上可以通过积分来计算`AP`，公式如下： +$$AP=\int_0^1 P(r) dr$$ +但通常情况下都是使用近似或者插值的方法来计算 AP。 +### 5.1，近似计算AP + $$AP = \sum_{k=1}^{N}P(k)\Delta r(k)$$ -+ 这个计算方式称为 approximated 形式的，而插值计算的方式里面这个是最精确的，每个样本点都参与了计算； ++ 近似计算 AP(`approximated average precision`)，这种计算方式是 approximated 形式的； + 很显然位于一条竖直线上的点对计算AP没有贡献； + 这里 `N` 为数据总量，`k` 为每个样本点的索引， $Δr(k)=r(k)−r(k−1)$。 @@ -405,8 +409,8 @@ def draw_PR_curve(predict_scores, eval_labels, name, cls_idx=1): plt.close(fig) ``` -### 插值计算(Interpolated average precision) -插值计算 AP 的公式的演变过程这里不做讨论，详情可以参考这篇[文章](https://arleyzhang.github.io/articles/c521a01c/)，我这里的公式和图也是参考此文章的。`11点插值计算方式计算AP公式`如下： +### 5.2，插值计算AP +插值计算(`Interpolated average precision`) AP 的公式的演变过程这里不做讨论，详情可以参考这篇[文章](https://arleyzhang.github.io/articles/c521a01c/)，我这里的公式和图也是参考此文章的。`11点插值计算方式计算AP公式`如下： ![11点插值计算方式计算AP公式](../images/插值计算AP公式.png) @@ -421,20 +425,8 @@ def draw_PR_curve(predict_scores, eval_labels, name, cls_idx=1): ![PASCAL论文给出的11点计算AP公式](../images/11点计算AP公式.png) -## 项目中的AP计算代码 - -### 不同数据集map计算方法 -由于 mAP 是数据集中所有类别 AP 值得平均，所以我们要计算mAP，首先得知道某一类别的 AP 值怎么求。不同数据集的某类别的 AP 计算方法大同小异，主要分为三种： - -（1）在 `VOC2007`，只需要选取当Recall >= 0, 0.1, 0.2, ..., 1共11个点时的Precision最大值，然后AP就是这11个Precision的平均值，map就是所有类别AP值的平均。 - -（2）在 `VOC2010` 及以后，需要针对每一个不同的 `Recall` 值（包括0和1），选取其大于等于这些 `Recall` 值时的 `Precision` 最大值，然后计算PR曲线下面积作为 AP 值，map 就是所有类别 AP 值的平均。 - -（3）`COCO` 数据集，设定多个 IOU 阈值（0.5-0.95,0.05为步长），在每一个IOU阈值下都有某一类别的 AP 值，然后求不同 IOU 阈值下的 AP 平均，就是所求的最终的某类别的AP值。 - -`VOC` 数据集中计算 `AP` 的代码（用的是插值计算方法，代码出自[py-faster-rcnn仓库](https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/datasets/voc_eval.py)）如下： - -1, 在给定 `recal` 和 `precision` 的条件下计算AP： +## 六，AP计算代码 +1, 在给定 `recal` 和 `precision` 的条件下计算 `AP`： ```Python def voc_ap(rec, prec, use_07_metric=False): """ @@ -470,7 +462,7 @@ def voc_ap(rec, prec, use_07_metric=False): ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) return ap ``` -**给定目标检测结果文件和测试集标签文件 xml 等计算AP** +2，给定目标检测结果文件和测试集标签文件 `xml` 等计算 `AP`： ```Python def parse_rec(filename): """ Parse a PASCAL VOC xml file @@ -493,7 +485,6 @@ def parse_rec(filename): return objects - def voc_eval(detpath, annopath, imagesetfile, @@ -636,78 +627,18 @@ def voc_eval(detpath, return rec, prec, ap ``` -## 模型计算量分析 -> 终端设备上运行深度学习算法需要考虑内存和算力的需求，因此需要进行模型复杂度分析，涉及到模型计算量（时间复杂度）和模型参数量（空间复杂度）分析。模型理论计算量：指模型推断时需要多少计算次数。 - -+ `FLOPs`：floating point operations 指的是浮点运算次数,**理解为计算量**，可以用来衡量算法/模型时间的复杂度。 -+ `FLOPS`：（全部大写）,Floating-point Operations Per Second，每秒所执行的浮点运算次数，理解为计算速度,是一个衡量硬件性能/模型速度的指标。 -+ `MACCs`：multiply-accumulate operations，乘-加操作次数，`MACCs` 大约是 FLOPs 的一半。将 $w[0]*x[0] + ...$ 视为一个乘法累加或 `1` 个 `MACC`。 -> 注意，网上很多文章将 MACCs 与 MACC 概念搞混，我猜测可能是机器翻译英文文章不准确的缘故，可以参考此[链接](http://machinethink.net/blog/how-fast-is-my-model/)了解更多。 - -### 卷积层FLOPs计算 -> 卷积操作本质上是个线性运算 - -+ $FLOPs=(2\times C_i\times K^2-1)\times H\times W\times C_o$（不考虑bias） -+ $FLOPs=(2\times C_i\times K^2)\times H\times W\times C_o$（考虑bias） -+ $MACC=(C_i\times K^2)\times H\times W\times C_o$（考虑bias） - -**$C_i$ 为输入特征图通道数，$K$ 为过滤器尺寸，$H,W,C_o$ 为输出特征图的高，宽和通道数**。`二维卷积过程`如下图所示： - -> 二维卷积是一个相当简单的操作：从卷积核开始，这是一个小的权值矩阵。这个卷积核在 2 维输入数据上「滑动」，对当前输入的部分元素进行矩阵乘法，然后将结果汇为单个输出像素。 - -![卷积过程](../images/二维卷积操作动态图.gif) - -公式解释，参考[这里](https://zhuanlan.zhihu.com/p/70306015?utm_source=wechat_session&utm_medium=social&utm_oi=571954943427219456)，如下： - -**理解 `FLOPs` 的计算公式分两步**。括号内是第一步，计算出`output feature map` 的一个 `pixel`，然后再乘以 $H\times W\times C_o$，从而拓展到整个 output feature map。括号内的部分又可以分为两步：$(2\times C_i\times K^2-1)=(C_i\times K^2) + (C_i\times K^2-1)$。第一项是乘法运算次数，第二项是加法运算次数，因为 $n$ 个数相加，要加 $n-1$次，所以不考虑 `bias` 的情况下，会有一个 -1，如果考虑 `bias`，刚好中和掉，括号内变为$(2\times C_i\times K^2)$。 - -所以卷积层的 $FLOPs=(2\times C_{i}\times K^2-1)\times H\times W\times C_o$ ($C_i$ 为输入特征图通道数，$K$ 为过滤器尺寸，$H, W, C_o$为输出特征图的高，宽和通道数)。 - -### 全连接层的 FLOPs 计算 -全连接层的 $FLOPs = (2I − 1)O$，$I$ 是输入层的维度，$O$ 是输出层的维度。 -## 模型参数量分析 -> 模型参数数量（params）：指模型含有多少参数，直接决定模型的大小，也影响推断时对内存的占用量。 - -模型参数量的分析是为了了解内存占用情况，内存带宽其实比 `FLOPs` 更重要。目前的计算机结构下，单次内存访问比单次运算慢得多的多。对每一层网络，端侧设备需要： -+ 从主内存中读取输入向量 / `feature map`； -+ 从主内存中读取权重并计算点积； -+ 将输出向量或 `feature map` 写回主内存。 - -`MAes`：memory accesse，内存访问次数。 -### 卷积层参数量 -卷积层权重参数量 = $C_i\times K^2\times C_o + C_o$。$C_i$ 为输入特征图通道数，$K$ 为过滤器(卷积核)尺寸，$C_o$ 为输出的特征图的 `channel` 数(也是 `filter` 的数量)，算式第二项是偏置项的参数量 。(一般不写偏置项，偏置项对总参数量的数量级的影响可以忽略不记，这里为了准确起见，把偏置项的参数量也考虑进来。） - -假设输入层矩阵维度是 96×96×3，第一层卷积层使用尺寸为 5×5、深度为 16 的过滤器（卷积核尺寸为 5×5、卷积核数量为 16），那么这层卷积层的参数个数为 ５×5×3×16+16=1216个。 -### BN层参数量 -`BN` 层参数量 = $2\times C_i$。其中 $C_i$ 为输入的 `channel` 数（BN层有两个需要学习的参数，平移因子和缩放因子） - -### 全连接层参数量 -全连接层参数量 = $T_i\times T_o + T_O$。$T_i$ 为输入向量的长度， $T_o$ 为输出向量的长度，公式的第二项为偏置项参数量。(目前全连接层已经逐渐被 `Global Average Pooling` 层取代了。) 注意，全连接层的权重参数量（内存占用）远远大于卷积层。 -### 参考资料 -+ [PRUNING CONVOLUTIONAL NEURAL NETWORKS -FOR RESOURCE EFFICIENT INFERENCE -](https://arxiv.org/pdf/1611.06440.pdf) -+ [神经网络参数量的计算：以UNet为例](https://zhuanlan.zhihu.com/p/57437131) -+ [How fast is my model?](http://machinethink.net/blog/how-fast-is-my-model/) -+ [MobileNetV1 & MobileNetV2 简介](https://blog.csdn.net/mzpmzk/article/details/82976871) -## 双精度、单精度和半精度 -`CPU/GPU` 的浮点计算能力得区分不同精度的浮点数，分为双精度 `FP64`、单精度 `FP32` 和半精度 `FP16`。因为采用不同位数的浮点数的表达精度不一样，所以造成的计算误差也不一样，对于需要处理的数字范围大而且需要精确计算的科学计算来说，就要求采用双精度浮点数，而对于常见的多媒体和图形处理计算，`32` 位的单精度浮点计算已经足够了，对于要求精度更低的机器学习等一些应用来说，半精度 `16` 位浮点数就可以甚至 `8` 位浮点数就已经够用了。 -对于浮点计算来说， `CPU` 可以同时支持不同精度的浮点运算，但在 `GPU` 里针对单精度和双精度就需要各自独立的计算单元。 -## 浮点计算能力 -`FLOPS`：每秒浮点运算次数，每秒所执行的浮点运算次数，浮点运算包括了所有涉及小数的运算，比整数运算更费时间。下面几个是表示浮点运算能力的单位。 - -+ `MFLOPS`（megaFLOPS）：等于每秒一佰万（=10^6）次的浮点运算。 -+ `GFLOPS`（gigaFLOPS）：等于每秒拾亿（=10^9）次的浮点运算。 -+ `TFLOPS`（teraFLOPS）：等于每秒万亿（=10^12）次的浮点运算。 -+ `PFLOPS`（petaFLOPS）：等于每秒千万亿（=10^15）次的浮点运算。 -+ `EFLOPS`（exaFLOPS）：等于每秒百亿亿（=10^18）次的浮点运算。 - -### 参考资料 -[双精度，单精度和半精度](https://blog.csdn.net/sinat_24143931/article/details/78557852?utm_medium=distribute.pc_relevant_t0.none-task-blog-BlogCommendFromMachineLearnPai2-1.nonecase&depth_1-utm_source=distribute.pc_relevant_t0.none-task-blog-BlogCommendFromMachineLearnPai2-1.nonecase) -## 欧式距离 -## 目标检测度量标准汇总 +## 七，map计算方法 +因为 mAP 值的计算是对数据集中所有类别的 AP 值求平均，所以我们要计算 mAP，首先得知道某一类别的 AP 值怎么求。不同数据集的某类别的 AP 计算方法大同小异，主要分为三种： + +（1）在 `VOC2007`，只需要选取当Recall >= 0, 0.1, 0.2, ..., 1共11个点时的Precision最大值，然后AP就是这11个Precision的平均值，map就是所有类别AP值的平均。`VOC` 数据集中计算 `AP` 的代码（用的是插值计算方法，代码出自[py-faster-rcnn仓库](https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/datasets/voc_eval.py)） + +（2）在 `VOC2010` 及以后，需要针对每一个不同的 `Recall` 值（包括0和1），选取其大于等于这些 `Recall` 值时的 `Precision` 最大值，然后计算PR曲线下面积作为 AP 值，map 就是所有类别 AP 值的平均。 + +（3）`COCO` 数据集，设定多个 IOU 阈值（0.5-0.95,0.05为步长），在每一个IOU阈值下都有某一类别的 AP 值，然后求不同 IOU 阈值下的 AP 平均，就是所求的最终的某类别的AP值。 + +## 八，目标检测度量标准汇总 ![目标检测指标汇总](../images/目标检测度量标准汇总.jpg) -## 目标检测领域中的数据不均衡问题 +## 九，目标检测领域中的数据不均衡问题 参考此论文[Imbalance Problems in Object Detection](https://arxiv.org/abs/1909.00169.pdf)，我的理解之后补充。 ## 参考资料 + [目标检测评价标准-AP mAP](https://Harleyzhang.github.io/articles/c521a01c/) From 26927cbb0a833e245ecbae3411200c144a811513 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Wed, 30 Sep 2020 17:21:01 +0800 Subject: [PATCH 368/386] Update the table of contents and titles to make the article content structure clearer --- ...46\344\271\240\345\237\272\347\241\200.md" | 28 +++++++++---------- ...27\346\263\225\346\200\273\347\273\223.md" | 28 +++++++++---------- 2 files changed, 28 insertions(+), 28 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" index 68b2bc5b..f3033eff 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" @@ -1,10 +1,10 @@ # 深度学习基础 -## 滤波器与卷积核 +## 1，滤波器与卷积核 在只有一个通道的情况下，“卷积核”就相当于 “filter”，这两个概念是可以互换的。但在一般情况下，它们是两个完全不同的概念。每个 “filter” 实际上恰好是“卷积核”的一个集合，在当前层，每个通道都对应一个卷积核，且这个卷积核是独一无二的。 **滤波器的数量，卷积核的shape。** -## 卷积层和池化输出大小计算 +## 2，卷积层和池化输出大小计算 > 不管是TensorFlow、Keras、Caffe还是Pytorch，其卷积层和池化层的参数默认值可能有所不同，但是最终的卷积输出大小计算公式是一样的 @@ -40,12 +40,12 @@ ### 参考资料 + [CNN中的参数解释及计算](https://flat2010.github.io/2018/06/15/%E6%89%8B%E7%AE%97CNN%E4%B8%AD%E7%9A%84%E5%8F%82%E6%95%B0/) + [CNN基础知识——卷积（Convolution）、填充（Padding）、步长(Stride)](https://zhuanlan.zhihu.com/p/77471866) -## Pytorch、Keras 的卷积层和池化层函数 +## 3，Pytorch、Keras 的卷积层和池化层函数 + 注意：`Pytorc` h的卷积层和池化层的输入 `shape` 格式为 **(N,C,H,W)**，`Keras` 的卷积层和池化层的输入 `shape` 格式为 **(N,H,W,C)** -+ 注意：`Pytorch、Keras` 的卷积层和池化层函数，**padding参数值都默认为不填充像素**，默认值为 `0`和 `valid`。 ++ 注意：对于 `Pytorch、Keras` 的卷积层和池化层函数，其**padding参数值都默认为不填充像素**，默认值为 `0`和 `valid`。 -## Pytorch 和 Keras 的池化层函数 +## 4，Pytorch 、Keras 的池化层函数理解 ### torch.nn.MaxPool2d @@ -93,9 +93,9 @@ keras.layers.MaxPooling2D(pool_size=(2, 2), strides=None, padding='valid', data_ + `padding`: "valid" 或者 "same" （区分大小写）。 + `data_format`: 字符串，`channels_last` (默认)或 `channels_first` 之一。 表示输入各维度的顺序。 `channels_last` 代表尺寸是 `(batch, height, width, channels)` 的输入张量， 而 `channels_first` 代表尺寸是 `(batch, channels, height, width)` 的输入张量。 默认值根据 `Keras` 配置文件 `~/.keras/keras.json` 中的 `image_data_format` 值来设置。如果还没有设置过，那么**默认值就是 `"channels_last"`**。 -## Pytorch 和 Keras 的卷积层函数 +## 5，Pytorch 和 Keras 的卷积层函数理解 -### torch.nn.Conv2d +### 5.1，torch.nn.Conv2d ```python class torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True) ``` @@ -132,7 +132,7 @@ output = m(input) print(output.shape) # 输出shape: torch.Size([20, 16, 24, 49]) ``` -### keras.layers.Conv2D +### 5.2，keras.layers.Conv2D ```python keras.layers.Conv2D(filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, dilation_rate=(1, 1), activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None) @@ -149,10 +149,10 @@ keras.layers.Conv2D(filters, kernel_size, strides=(1, 1), padding='valid', data_ + `padding`: `"valid"` 或 `"same"` (大小写敏感)，默认`valid`。 + `data_format`: 字符串， `channels_last (默认)` 或 `channels_first` 之一，表示输入中维度的顺序。 `channels_last` 对应输入尺寸为 `(batch_size, height, width, channels)`， channels_first 对应输入尺寸为 `(batch_size, channels, height, width)`。 它默认为从 `Keras` 配置文件 `~/.keras/keras.json` 中 找到的 `image_data_format` 值。 如果你从未设置它，将使用 `channels_last`。 -### 总结 +### 5.3，总结 `Pytorch` 的 Conv2d 函数不要求提供 输入数据的大小 `(H,W)`，但是要提供输入深度，`Keras` 的 `Conv2d` 函数第一层要求提供 `input_shape` 参数 `(H,W,C)`，其他层不需要。 -## softmax回归 +## 6，softmax回归 **分类问题**中，直接使用输出层的输出有两个问题： + 神经网络输出层的输出值的范围不确定，我们难以直观上判断这些值的意义 + 由于真实标签是离散值，这些离散值与不确定范围的输出值之间的误差难以衡量 @@ -162,7 +162,7 @@ $$ softmax(y)_{i} = y_{i}^{'} = \frac{e^{yi}}{\sum_{j=1}^{n}e^{yj}} $$ -## 交叉熵损失函数 +## 7，交叉熵损失函数 交叉熵刻画了两个概率分布之间的距离，它是分类问题中使用比较广泛的一种损失函数，交叉熵一般会与softmax回归一起使用，公式如下： $$L = -\sum_{c=1}^{M}y_{c}log(p_{c})或者H(p,q)=-\sum p(x)logq(x)$$ @@ -173,11 +173,11 @@ $$L = -\sum_{c=1}^{M}y_{c}log(p_{c})或者H(p,q)=-\sum p(x)logq(x)$$ - $y_{c}$ ——指示变量（0或1）,如果该类别和样本的类别相同就是1，否则是0； - $p_{c}$ ——对于观测样本属于类别 $c$ 的预测概率。 -## 为什么交叉熵可以用作代价函数 +### 7.1，为什么交叉熵可以用作代价函数 从数学上来理解就是，为了让学到的模型分布更接近真实数据的分布，我们需要最小化模型数据分布与训练数据之间的 `KL 散度`，而因为训练数据的分布是固定的，因此最小化 `KL 散度`等价于最小化交叉熵，而且交叉熵计算更简单，所以机器/深度学习中常用交叉熵 `cross-entroy` 作为分类问题的损失函数。 -## 优化算法理解 +## 8，优化算法理解 `Adam`、`AdaGrad`、`RMSProp`优化算法具有自适应性 -## 感受野(Receptive Field) +## 9，感受野(Receptive Field) 后一层神经元在前一层神经元的感受空间，也可以定义为卷积神经网络中每层的特征图（`Feature Map`）上的像素点在原始图像中映射的区域大小，即如下图所示： ![感受野大小](../images/感受野空间.jpg) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" index 5704625e..a5c30cc2 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\347\245\236\347\273\217\347\275\221\347\273\234\345\216\213\347\274\251\347\256\227\346\263\225\346\200\273\347\273\223.md" @@ -12,7 +12,7 @@ + 后端压缩，是指包括`低秩近似`、未加限制的剪枝、`参数量化`以及二值网络等，目标在于尽可能减少模型大小，会对原始网络结构造成极大程度的改造。 总结：前端压缩几乎不改变原有网络结构（仅仅只是在原模型基础上减少了网络的层数或者滤波器个数），后端压缩对网络结构有不可逆的大幅度改变，造成原有深度学习库、甚至硬件设备不兼容改变之后的网络。其维护成本很高。 -## 低秩近似 +## 一，低秩近似 简单理解就是，卷积神经网络的权重矩阵往往稠密且巨大，从而计算开销大，有一种办法是**采用低秩近似的技术**将该稠密矩阵由若干个小规模矩阵近似重构出来，这种方法归类为低秩近似算法。 > 一般地，行阶梯型矩阵的秩等于其“台阶数”-非零行的行数。 @@ -20,9 +20,9 @@ 给定权重矩阵 $W\epsilon R^{m*n}$ , 若能将其表示为若干个低秩矩阵的组合，即 $W=\sum_{i=1}^{n}\alpha_{i}M_{i}$ , 其中 $W\epsilon R^{m*n}$ 为低秩矩阵，其秩为 $r_{i}$ , 并满足 $r_{i}<批标准化(`BNorm`)->激活(`Activ`)->池化(`Pool`)这样的顺序操作组成的。对于异或神经网络，设计出的模块是由批标准化(`BNorm`)->**二值化激活(BinActiv)**->二值化卷积(`BinConv`)->池化(`Pool`)的顺序操作完成。这样做的原因是批标准化以后，保证了输入均值为 `0`，然后进行二值化激活，保证了数据为 `-1` 或者 `+1`，然后进行二值化卷积，这样能最大程度上减少特征信息的损失。二值化残差网络结构定义实例代码如下： @@ -74,7 +74,7 @@ def residual_unit(data, num_filter, stride, dim_match, num_bits=1): shortcut = bnn.Convolution(data=bnAct1, num_filter=num_filter, kernel=(3, 3), stride=stride, pad=(1, 1)) return conv2 + shortcut ``` -### 二值网络的梯度下降 +### 4.1，二值网络的梯度下降 现在的神经网络几乎都是基于梯度下降算法来训练的，但是二值网络的权重只有 $\pm 1$，无法直接计算梯度信息，也无法进行权重更新。为解决这个问题，[Courbariaux](https://arxiv.org/pdf/1602.02830v1.pdf) 等人提出二值连接（binary connect）算法，该算法采取单精度与二值结合的方式来训练二值神经网络（），这是第一次给出了关于如何对网络进行二值化和如何训练二值化神经网络的方法。过程如下： 1. 权重 `weight` 初始化为浮点 2. 前向传播 `Forward Pass`: @@ -83,7 +83,7 @@ def residual_unit(data, num_filter, stride, dim_match, num_bits=1): 3. 反向传播 `Backward Pass`: + 把梯度更新到浮点的 Weight 上（根据放松后的符号函数，计算相应梯度值，并根据该梯度的值对单精度的权重进行参数更新） + 训练结束： 把 Weight 永久性转化为 `+1/-1`, 以便 `inference` 使用 -### 两个问题 +### 4.1，两个问题 网络二值化需要解决两个问题：如何对权重进行二值化和如何计算二值权重的梯度。 **1，如何对权重进行二值化？** @@ -103,7 +103,7 @@ $$ **2，如何计算二值权重的梯度？** 二值权重的梯度为0，无法进行参数更新。为解决这个问题，需要**对符号函数进行放松**，即用 $Htanh(x) = max(-1, min(1,x))$ 来代替 $sinx(x)$。当 x 在区间 [-1,1] 时，存在梯度值 1，否则梯度为 0 。 -### 二值连接算法改进 +### 4.3，二值连接算法改进 之前的二值连接算法只对权重进行了二值化，但是网络的中间输出值依然是单精度的，于是 Rastegari 等人对此进行了改进，提出用**单精度对角阵与二值矩阵之积来近似表示原矩阵的算法**，以提升二值网络的分类性能，弥补二值网络在精度上弱势。该算法将原卷积运算分解为如下过程： $$I*W\approx (I*B)\alpha$$ @@ -121,11 +121,11 @@ $$min \left \|| W -\alpha B \right \||^{2}$$ **更多内容，可以看这两篇文章：** + [https://github.com/Ewenwan/MVision/tree/master/CNN/Deep_Compression/quantization/BNN](https://github.com/Ewenwan/MVision/tree/master/CNN/Deep_Compression/quantization/BNN) + [二值神经网络（Binary Neural Network，BNN）](https://blog.csdn.net/stdcoutzyx/article/details/50926174) -### 二值网络设计注意事项 +### 4.4，二值网络设计注意事项 + 不要使用 kernel = (1, 1) 的 `Convolution` （包括 resnet 的 bottleneck）：二值网络中的 weight 都为 1bit， 如果再是 1x1 大小， 会极大地降低表达能力 + 增大 `Channel` 数目 + 增大 activation bit 数 要协同配合：如果一味增大 channel 数， 最终 feature map 因为 bit 数过低， 还是浪费了模型容量。 同理反过来也是。 + 建议使用 4bit 及以下的 `activation bit`， 过高带来的精度收益变小， 而会显著提高 inference 计算量 -## 知识蒸馏 +## 五，知识蒸馏 知识蒸馏（knowledge distillation），是迁移学习（transfer learning）的一种，简单来说就是训练一个大模型（teacher）和一个小模型（student），将庞大而复杂的大模型学习到的知识，通过一定技术手段迁移到精简的小模型上，从而使小模型能够获得与大模型相近的性能。 student模型最终的损失函数由两部分组成： + 第一项是由小模型的预测结果与大模型的“软标签”所构成的交叉熵（cross entroy）; @@ -177,8 +177,8 @@ objective2 = tf.scalar_mul(0.5, tf.square(student_tau-teacher_tau)) tf_loss = (args.lamda*tf.reduce_sum(objective1) + (1-args.lamda)*tf.reduce_sum(objective2))/batch_size ``` `tf.scalar_mul` 函数为对 `tf` 张量进行固定倍率 `scalar` 缩放函数。**一般 T 的取值在 1 - 20 之间，这里我参考了开源代码，取值为 3**。我发现在开源代码中 `student` 模型的训练，有些是和 `teacher` 模型一起训练的，有些是 teacher 模型训练好后直接指导 student 模型训练。 -## 浅层网络 -浅层网络：通过设计一个更浅（层数较少）结构更紧凑的网络来实现对复杂模型效果的逼近, 但是浅层网络的表达能力很难与深层网络相匹敌。因此，这种设计方法的局限性在于只能应用解决在较为简单问题上。如分类问题中类别数较少的 task。 +## 六，浅层网络 +浅层网络：通过设计一个更浅（层数较少）结构更紧凑的网络来实现对复杂模型效果的逼近, 但是浅层网络的表达能力很难与深层网络相匹敌。因此，这种设计方法的局限性在于只能应用解决在较为简单问题上。如分类问题中类别数较少的 `task`。 ## 参考资料 1. [神经网络模型压缩和加速之知识蒸馏](https://www.cnblogs.com/dyl222/p/11079489.html) 2. [https://github.com/chengshengchan/model_compression/blob/master/teacher-student.py](https://github.com/chengshengchan/model_compression/blob/master/teacher-student.py) From 3c780fb36daa4c1ad4b0b0095eaacb3e407f7272 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Wed, 30 Sep 2020 21:33:33 +0800 Subject: [PATCH 369/386] Add files! --- "images/KNN\347\256\227\346\263\225.png" | Bin 0 -> 14647 bytes .../cv2.imread\345\207\275\346\225\260.png" | Bin 0 -> 40140 bytes ...6\344\270\216\346\226\271\345\267\256.png" | Bin 0 -> 21130 bytes ...7\345\274\217\350\267\235\347\246\273.png" | Bin 0 -> 43532 bytes ...04\347\220\206\347\254\224\350\256\260.md" | 60 +++++++++++++ ...46\344\271\240\345\237\272\347\241\200.md" | 65 ++++++++++++++ ...46\344\271\240\347\256\227\346\263\225.md" | 84 ++++++++++++++++++ ...46\344\271\240\345\237\272\347\241\200.md" | 70 ++++++++------- ...02\345\272\246\345\210\206\346\236\220.md" | 6 +- 9 files changed, 252 insertions(+), 33 deletions(-) create mode 100644 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zuAvc|DNm_~9odDD)OP?9K-r9UparmBObKXC>vfNc!UhpuwkfQ2&>qYYM?4|;n@TsV zw z>dKN9kR`8>1MIcAT+oP|>+qClGkM}&NQ1|;jpGcIf6~Qhz#;CLpM%k_&B;~AB7P!!%dv$y7hI-p!KKY_mmx0c zXrgk-2uqAw%%uNrH*dAuob17Zl?y9|x8&Xhpj?r($~)!`CfT@@4{%+<*<++7p62zc zg}Ak%ZVGdV*aIt|h_rR%5T^1PSRuWuskt~IOaU(o{v}%7SrDvn6g#<55kKIh(G*8> zUy*YbiUU{m1$va?lgaf8uj zriI3jJEe;!-tIuTXQ{hF%n5Y?L;s)c#dd;3$Cx$Y)T8PFpeB!9JFLTpj9RQtlcyRG(fqh&x7;4 z`^Vq@PIdfSxaMzhh~W2m|1Tx~H?aW2a7YR%e3qnG1=UZAj(=$Ge`I1EsvS~Q*vI|Q z|NlijBpLm-620Y^e`hNG&kz4CpZ~M$|0AaV`&}uBP2#D@|1tQ#7eSDh1jq}@stL&7 Tle0w}4)#%$Rg 此笔记针对 Python 版本的 opencv3，c++ 版本的函数和 python 版本的函数参数几乎一样，只是矩阵格式从 ndarray 类型变成适合 c++ 的 mat 模板类型。注意，因为 python 版本的opncv只提供接口没有实现，故函数原型还是来自 c++版本的opencv，但是参数解释中的数据类型还是和 python 保持一致。 +### 图像的载入：imread() 函数 +**函数原型：** +```cpp +Mat imread(const sting& filename, int flags=None) +``` +**参数解释：** ++ `filename`：图像的文件路径，`sting` 字符串类型 ++ `flags`：载入标识，以何种方式读取图片，`int` 类型的 `flags`。常用取值解释如下： + + `flags = 0`：始终将图像转成灰度图再返回 + + `flags = 1`：始终将图像转换成彩色图再返回，如果读取的是灰度图，则其返回的矩阵 `shape` 将变为 `(height, width, 3)` + + `flags = 2`：如果载入的图像深度为 `16` 位或者 `32` 位，就返回对应深度的图像，否则，就转换为 `8` 位图像再返回。 + +**总结：**读取文件中的图片到 `OpenCV` 中，返回 `Mat` 或者 `ndarray` 类型的矩阵，以彩色模式载入图像时，解码后的图像会默认以 `BGR` 的通道顺序进行存储。 + +**cv2.imread()函数：** +`python-opencv` 库的 `imread` 函数的 `flags` 参数取值方式与 `C++` 版有所区别。使用函数 `cv2.imread()` 读入图像，图像要么在此程序的工作路径，要么函数参数指定了完整路径，第二个参数是要告诉函数应该如何读取这幅图片，取值如下： ++ `cv2.IMREAD_COLOR` : 取值 `1`，读入一副彩色图像。图像的透明度会被忽略，这是默认参数。 ++ `cv2.IMREAD_GRAYSCALE` : 取值 `0`，以灰度模式读入图像。 ++ `cv2.IMREAD_UNCHANGED` : 取值 `-1`，读入一幅图像，并且包括图像的 alpha 通道。 + +> Instead of these three flags, you can simply pass integers 1, 0 or -1 respectively. + +```python +import numpy as np +import cv2 +# Load an color image in grayscale +img = cv2.imread('messi5.jpg',0) +``` +`opencv-python` 库的读取图像函数 `cv2.imread()` 官方定义如下图。 + +![opencv-python库的读取图像函数官方定义](../images/cv2.imread函数.png) + +### 图像的显示：imshow()函数 +**函数原型：** +```cpp +void imshow(const string &winname, InputArray mat) +``` +**参数解释：** ++ `winname`：需要显示的窗口标识名称，`string` 字符串类型 ++ `mat`：需要显示的图像矩阵，`ndarray` numpy 矩阵类型 + +**总结：**`imshow` 函数用于在指定的窗口显示图像，窗口会自动调整为图像大小。 +### minMaxLoc 函数 +函数 `cv :: minMaxLoc` 查找最小和最大元素值及其位置，返回的位置坐标是**先列号，后行号**（列号，行号） 。在整个数组中搜索极值，或者如果mask不是空数组，则在指定的数组区域中搜索极值。（**只适合单通道矩阵**）。函数原型： +```CPP +CV_EXPORTS_W void minMaxLoc(InputArray src, CV_OUT double* minVal, + CV_OUT double* maxVal = 0, CV_OUT Point* minLoc = 0, + CV_OUT Point* maxLoc = 0, InputArray mask = noArray()); +``` +函数参数解释： ++ `src`：input single-channel array. ++ `minVal`：pointer to the returned minimum value; NULL is used if not required. ++ `maxVal`：pointer to the returned maximum value; NULL is used if not required. ++ `minLoc`：pointer to the returned minimum location (in 2D case); NULL is used if not required. ++ `maxLoc`：pointer to the returned maximum location (in 2D case); NULL is used if not required. +### 位深度的概念 ++ 灰度图的位深度是 `16`，则其矩阵的元素类型为 `uint16` ，彩色图其位深度一般是 `24` ，红色占 `8` 个位、蓝色占 `8` 个位、绿色占 `8` 个位，其矩阵的元素类型为 `uint8`。 ++ 位分辨率（ `Bit Resolution` ）又称色彩深度、色深或位深度，在位图图像或视频视频缓冲区，指一个像素中，每个颜色分量（`Red、Green、Blue、Alpha` 通道）的比特数。 ++ `matplotlib.image.imsave` 将灰度图的矩阵保存为图像格式时，其默认保存的图像通道数为 `4`：`RGBA`，其中 `RGB` 三个通道对应的二维矩阵数值完全一样。 diff --git "a/\346\234\272\345\231\250\345\255\246\344\271\240/\346\234\272\345\231\250\345\255\246\344\271\240\345\237\272\347\241\200.md" "b/\346\234\272\345\231\250\345\255\246\344\271\240/\346\234\272\345\231\250\345\255\246\344\271\240\345\237\272\347\241\200.md" new file mode 100644 index 00000000..e511219d --- /dev/null +++ "b/\346\234\272\345\231\250\345\255\246\344\271\240/\346\234\272\345\231\250\345\255\246\344\271\240\345\237\272\347\241\200.md" @@ -0,0 +1,65 @@ +## 一，Bias(偏差)，Error(误差)和Varience(方差) +> [机器学习中的Bias(偏差)，Error(误差)，和Variance(方差)有什么区别和联系？](https://www.zhihu.com/question/27068705) + +偏差-方差指标方法是试图对学习算法(模型)的期望泛化错误率进行拆解。`Error = Bias + Varience`： ++ `Error`: 反映的是整个模型的准确度 ++ `Bias`: 反映的是模型在**样本上的输出与真实值之间的误差**，即模型的准确性，以打靶事件为例，`low bias`，一般就得复杂化模型，表现出来就是点都打在靶心中间，但这样容易过拟合 (overfitting)，过拟合对应下图是 high variance，点很分散。 ++ `Varience`: 反映的是模型每一次输出的结果与模型输出期望之间的误差，即模型的稳定性，是训练集上训练出来的模型在测试集上的表现，同样以打靶事件为例，`low variance` 对应就是点都打的很集中，但不一定是靶心附近，手很稳，但是瞄的不准。 + +![偏差误差与方差](../images/偏差误差与方差.png) +### 1.1，偏差与方差公式 +对测试样本 `x`, 令 $y_{D}$ 为 x 在数据集中的标记，y 为 x 的真实标记， `f(x;D)` 为在训练集 D 上学习到的模型 f 在 x 上的预测输出。 ++ 训练过程中期望输出与真实标记（标签）的差别称为偏差（`bias`）：$bias^{2}(x) = (\bar{f} - y)^{2}$ ++ （交叉验证训练模型）使用样本数相同不同训练集训练出来的模型在测试集上产生的`方差`为： $var(x) = E_{D}[(f(x;D) - \bar{f})^{2}] $ +### 1.2，导致偏差和方差的原因 +> [机器学习中的Bias(偏差)，Error(误差)，和Variance(方差)有什么区别和联系？](https://www.zhihu.com/question/27068705) + ++ 偏差通常是由于我们对学习算法做了错误的假设，或者模型的复杂度不够； + + 比如真实模型是一个二次函数，而我们假设模型为一次函数，这就会导致偏差的增大（欠拟合）； + + 由偏差引起的误差通常在训练误差上就能体现，或者说训练误差主要是由偏差造成的 ++ 方差通常是由于模型的复杂度相对于训练集过高导致的； + + 比如真实模型是一个简单的二次函数，而我们假设模型是一个高次函数，这就会导致方差的增大（过拟合）； + + 由方差引起的误差通常体现在测试误差相对训练误差的增量上。 +### 1.3，深度学习中的偏差与方差 ++ 神经网络的拟合能力非常强，因此它的训练误差（偏差）通常较小； ++ 但是过强的拟合能力会导致较大的方差，使模型的测试误差（泛化误差）增大； ++ 深度学习的核心工作之一就是**研究如何降低模型的泛化误差**，这类方法统称为`正则化方法`。 +### 1.4，交叉验证 +在训练数据上面，我们可以进行·交叉验证(Cross-Validation)·。一种方法叫做 ·`K-fold Cross Validation` ( K 折交叉验证), K 折交叉验证，初始采样分割成 K 个子样本，一个单独的子样本被保留作为验证模型的数据，其他 K-1 个样本用来训练。交叉验证重复 K 次，每个子样本验证一次，平均 `K` 次的结果或者使用其它结合方式，最终得到一个单一估测。 ++ 当 `K` 值大的时候， 我们会有更少的 `Bias`(偏差), 更多的 `Variance`。 ++ 当 `K` 值小的时候， 我们会有更多的 `Bias`(偏差), 更少的 `Variance`。 +## 二，先验概率与后验概率 +> 这章知识的公式、定理、概念比较多，会难以理解，建议手推公式。 +### 2.1，条件概率 +一个事件发生后另一个事件发生的概率。设 A 与 B 为样本空间 Ω 中的两个事件，其中 P(B)>0。那么在事件 B 发生的条件下，事件 A 发生的条件概率为： +$$P(A|B) = \frac {P(A\cap B)} {P(B)}$$ +### 2.2，先验概率 +事件发生前的概率，可以是基于以往经验/分析，也可以是基于历史数据的统计，甚至可以是人的主观观点给出。一般是**单独**事件概率，如 P(x), P(y)。 +### 2.3，后验概率 ++ 事情已经发生，要求这件事情发生的原因是由某个因素引起的可能性的大小（**由果推因**：就是在知道“果”之后，去推测“因”的概率） ++ 后验概率和和先验概率的关系可以通过`贝叶斯`公式求得，公式如下： +$$P(B_{i}|A) = \frac {P(B_{i}\cdot P(A|B_{i}}{P(B_{1})\cdot P(A|B_{1}) + P(B_{2})\cdot P(A|B_{2}) }$$ +### 2.4，贝叶斯公式 +贝叶斯公式是建立在条件概率的基础上寻找事件发生的原因（即大事件 `A` 已经发生的条件下，分割中的小事件 `Bi` 的概率），设 `B1,B2,...` 是样本空间 `Ω` 的一个划分，则对任一事件 `A（P(A)>0)`, 有：$$P(B_{i}|A) = \frac {P(A|B_{i})P(B_{i})}{\sum_{j=1}^{n}P(B_{j})P(A|B_{j})}$$ + ++ `Bi` 常被视为导致试验结果A发生的”原因“； ++ `P(Bi)(i=1,2,...)` 表示各种原因发生的可能性大小，故称先验概率； ++ `P(Bi|A)(i=1,2...)` 则反映当试验产生了结果A之后，再对各种原因概率的新认识，故称后验概率。 +### 2.5，后验概率实例 +假设一个学校里有 `60％` 男生和 `40%` 女生。女生穿裤子的人数和穿裙子的人数相等，所有男生穿裤子。一个人在远处随机看到了一个穿裤子的学生。那么这个学生是女生的概率是多少？ ++ 使用贝叶斯定理，事件A是看到女生，事件B是看到一个穿裤子的学生。我们所要计算的是 $P(A|B)$。 ++ $P(A)$ 是忽略其它因素，看到女生的概率，在这里是 40%； ++ $P(A')$ 是忽略其它因素，看到不是女生（即看到男生）的概率，在这里是 60%； ++ $P(B|A)$ 是女生穿裤子的概率，在这里是 50%； ++ $P(B|A')$ 是男生穿裤子的概率，在这里是 100%； ++ $P(B)$ 是忽略其它因素，学生穿裤子的概率，$P(B) = P(B|A)P(A) + P(B|A')P(A')$，在这里是 0.5×0.4 + 1×0.6 = 0.8。 + +根据贝叶斯定理，我们计算出后验概率P(A|B): +$$P(A|B) = \frac {P(B|A)P(A)}{P(B)} = \frac {0.5\times 0.4} {0.8}$$ +## 三，似然函数 +## 四，最大似然估计 +## 参考资料 +1. [机器学习基础](https://github.com/imhuay/Algorithm_Interview_Notes-Chinese/blob/master/A-%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/A-%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E7%A1%80.md) +2. [机器学习中的Bias(偏差)，Error(误差)，和Variance(方差)有什么区别和联系？](https://www.zhihu.com/question/27068705) +3. [先验概率，后验概率，似然概率，条件概率，贝叶斯，最大似然](https://blog.csdn.net/suranxu007/article/details/50326873) +4. [你对贝叶斯统计有何理解](https://www.zhihu.com/question/21134457/answer/169523403) \ No newline at end of file diff --git "a/\346\234\272\345\231\250\345\255\246\344\271\240/\346\234\272\345\231\250\345\255\246\344\271\240\347\256\227\346\263\225.md" "b/\346\234\272\345\231\250\345\255\246\344\271\240/\346\234\272\345\231\250\345\255\246\344\271\240\347\256\227\346\263\225.md" new file mode 100644 index 00000000..3da06993 --- /dev/null +++ "b/\346\234\272\345\231\250\345\255\246\344\271\240/\346\234\272\345\231\250\345\255\246\344\271\240\347\256\227\346\263\225.md" @@ -0,0 +1,84 @@ +## 信息熵与交叉熵 +> [【直观详解】信息熵、交叉熵和相对熵](https://charlesliuyx.github.io/2017/09/11/%E4%BB%80%E4%B9%88%E6%98%AF%E4%BF%A1%E6%81%AF%E7%86%B5%E3%80%81%E4%BA%A4%E5%8F%89%E7%86%B5%E5%92%8C%E7%9B%B8%E5%AF%B9%E7%86%B5/) +[为什么交叉熵（cross-entropy）可以用于计算代价](https://www.zhihu.com/question/65288314) + +### 信息熵 +**其实信息熵是香农信息量（$log\frac{1}{p}$）的期望（均值），它不是针对每条信息，而是针对整个不确定性结果集而言，信息熵越大，事件不确定性就越大。单条信息只能从某种程度上影响结果集概率的分布**。信息熵定义： +$$H(P) = \sum_{i} P(i)log_{a} \frac{1}{P(i)} = -\sum_{i}P(i)log_{a} P(i)$$ +$P_{i}$ 表示第 i 个事件发生得概率，总的来说信息熵其实从某种意义上反映了**信息量存储下来需要多少存储空间**。 +总结为：根据真实分布，我们能够找到一个最优策略，以**最小的代价消除系统的不确定性**（比如编码），而这个代价的大小就是**信息熵**。 +### 相对熵/KL散度 +KL散度，有时候也叫KL距离，一般被用于计算两个分布之间的不同，记为 $D(P||Q) = H(P,Q) - H(P)$，也称之为 `KL散度`，离散事件数学公式如下： +$$D_{KL}(P \| Q) = \sum_i P(i)log_{a} \frac{P(i)}{Q(i)} = \sum_i P(i)[logP(x) - log Q(x)]$$ +当 `P(i) = Q(i)` 的时候，该值为0，深度学习过程也是一个降低该值的过程，**该值越低，训练出来的概率 Q 越接近样本集概率 P，即越准确**，或者可以理解为相对熵一把标尺，用来衡量两个函数是否相似，相似就是 0。即，**相对熵 = 某个策略的交叉熵 - 信息熵**（根据系统真实分布计算而得的信息熵，为最优策略），**当熵为常量时，交叉熵与KL散度相等**（推导公式可参考第二个链接）。 +### 交叉熵 cross-entroy +交叉熵是由信息熵而得来的，和 KL 散度关系密切，拓展用在机器学习/深度学习中作损失函数。假定在确定性更大的概率分布情况下，用更不确定的存储策略来计算，比如使用 `P` 的概率乘上 `Q` 的存储因子，套用信息熵公式： +$$H(P) = \sum_{i} P(i)log_{a} \frac{1}{Q(i)} = -\sum_{i}P(i)log_{a} Q(i)$$ +用预测概率 p 分布,去编码真实标签 q 的分布,得到的信息量。**交叉熵，用来衡量在给定的真实分布下，使用非真实分布指定的策略消除系统的不确定性所需要付出努力的大小**。总的来说，我们的目的是：让熵尽可能小，即存储空间小（消除系统的不确定的努力小）。**交叉熵的一些性质：** ++ 非负。 ++ 和 KL 散度相同，交叉熵也不具备对称性，即 `H(P,Q)≠H(Q,P)`。 ++ 交叉熵主要用于描述两个事件之间的相互关系，对同一个分布求交叉熵等于对其求熵 + +### 为什么交叉熵可以用作代价 +从数学上来理解就是，为了让学到的模型分布更接近真实数据的分布，我们需要最小化模型数据分布与训练数据之间的 `KL 散度`，而因为训练数据的分布是固定的，因此最小化 `KL 散度`等价于最小化交叉熵，而且交叉熵计算更简单，所以机器/深度学习中常用交叉熵 `cross-entroy` 作为分类问题的损失函数。 + +### KL散度与cross-entropy的关系 ++ `KL` 散度和交叉熵在特定条件下等价 ++ $D_{KL}(P||Q) = H(P,Q) - H(P)$ +## KNN算法 +`K` 近邻算法（KNN）是一种基本分类和回归方法。`KNN` 算法的核心思想是如果一个样本在特征空间中的 `k` 个最相邻的样本中的大多数属于一个类别，那该样本也属于这个类别，并具有这个类别上样本的特性。该方法在确定分类决策上只依据最邻近的一个或者几个样本的类别来决定待分类样本所属的类别。 如下图： + +![KNN算法](../images/KNN算法.png) + +在 `KNN` 中，通过计算对象间距离来作为各个对象之间的非相似性指标，避免了对象之间的匹配问题，在这里距离一般使用欧氏距离或曼哈顿距离，公式如下： + +![欧式距离计算](../images/欧式距离.png) + +同时，KNN通过依据 k 个对象中占优的类别进行决策，而不是单一的对象类别决策，这两点就是KNN算法的优势。 +### k 值的选取 ++ `k` 值较小，模型会变得复杂，容易发生过拟合 ++ `k` 值较大，模型比较简单，容易欠拟合 + +所以 `k` 值得选取也是一种调参？ +### KNN算法思路 +`KNN` 思想就是在训练集中数据和标签已知的情况下，输入测试数据，将测试数据的特征与训练集中对应的特征进行相互比较，找到训练集中与之最为相似的前 `K` 个数据，则该测试数据对应的类别就是K个数据中出现次数最多的那个分类，其算法的描述为： +1. 计算测试数据与各个训练数据之间的距离； +2. 按照距离的递增关系进行排序； +3. 选取距离最小的 `K` 个点； +4. 确定前 `K` 个点所在类别的出现频率； +5. 返回前 `K` 个点中出现频率最高的类别作为测试数据的预测分类。 +## 支持向量机SVM +> [机器学习中的算法(2)-支持向量机(SVM)基础](https://www.cnblogs.com/leftnoteasy/archive/2011/05/02/basic-of-svm.html) +### 支持向量机简述 ++ 支持向量机（Support Vector Machines, SVM）是一种二分类模型。它的基本模型是定义在特征空间上的间隔最大的线性分类器，间隔最大使它有别于感知机；支持向量机还包括核技巧，这使其成为实质上的非线性分类器。 ++ `SVM` 的学习策略是找到最大间隔（两个异类支持向量到超平面的距离之和 $\gamma = \frac{2}{||w}$ 称为“间隔”），可形式化为一个求解凸二次规划的问题，也等价于正则化的合页损失函数的最小化问题。 ++ `SVM` 的最优化算法是求解凸二次规划的最优化算法。 + +### SVM 基本型 +$$min \frac{1}{2}||w||^2$$ +$$s.t. y_{i}(w^Tx_i + b) \geqslant1, i = 1,2,...,m$$ +SVM 的最优化算法是一个凸二次规划（convex quadratic programming）问题，对上式使用拉格朗日乘子法可转化为对偶问题，并优化求解。 + +### 对偶问题求解 +对 `SVM` 基本型公式的每条约束添加拉格朗日乘子 $\alpha_i \geqslant 0$，则式子的拉格朗日函数如下： +$$L(w,b,a) = \frac 1 2||w||^2 - \sum{i=1}{n} \alpha_i (y_i(w^Tx_i+b) - 1)$$ +经过推导（参考机器学习西瓜书），可得 SVM 基本型的对偶问题： +$$\underset{\alpha}{max} = \sum_{i=1}^{m} = \frac 1 2 \sum_{i=1}^{m}\sum_{j=1}^{m}\alpha_i \alpha_j y_i y_j x^{T}_{i} x_j$$$$s.t. \sum_{i=1}^{m} = \alpha_{i}y_{i} = 0,$$$$\alpha_{i}\geqslant 0, i=1,2,...,m$$ +继续优化该问题，有 `SMO` 方法，SMO 的基本思路是先固定 $\alpha_i$ 之外的的所有参数，然后求 $\alpha_i$ 上的极值。 +## K-maens聚类算法 +### 分类与聚类算法 ++ 分类简单来说，就是根据文本的特征或属性，划分到已有的类别中。也就是说，这些类别是已知的，通过对已知分类的数据进行训练和学习，找到这些不同类的特征，再对未分类的数据进行分类。 ++ 聚类，就是你压根不知道数据会分为几类，通过聚类分析将数据或者说用户聚合成几个群体，那就是聚类了。聚类不需要对数据进行训练和学习。 ++ 分类属于监督学习，聚类属于无监督学习。常见的分类比如决策树分类算法、贝叶斯分类算法等聚类的算法最基本的有系统聚类，K-means 均值聚类。 +### K-mean聚类算法概述 +聚类的目的是找到每个样本 x 潜在的类别 y，并将同类别 y 的样本 x 放在一起。在聚类问题中，假定训练样本是${x^1,...,x^m}$，每个 $x^i \in R^n$，没有 `y`。`K-means` 算法是将样本聚类成 k 个簇（cluster），算法过程如下： +1. 随机选取 k 个聚类中心（cluster centroids）为 $\mu_1,\mu_1,...,\mu_k \in R^n$。 +2. 重复下面过程，直到质心不变或者变化很小： + + 对于每一个样例 `i` ，计算其所属类别：$$c^i = arg min \underset{j}{min}||x^i - \mu_j||^2$$ + + 对于每一个类 `j`，重新计算该类的质心：$$\mu_j = \frac {\sum_{i=1}^{m}1\{c^i = j\}x^i} { \sum_{i=1}^{m}1\{c^i = j\}}$$ + +`K` 是我们事先给定的聚类数，$c^i$ 代表样例 `i` 与 `k` 个类中距离最近的那个类，$c^i$ 的值是 `1` 到 `k` 中的一个。质心 $\mu_j$ 代表我们对属于同一个类的样本中心点的猜测。 + +## 参考资料 +[K-means聚类算法](https://www.cnblogs.com/jerrylead/archive/2011/04/06/2006910.html) + diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" index f3033eff..4a6d5c1a 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" @@ -1,14 +1,14 @@ # 深度学习基础 -## 1，滤波器与卷积核 +## 一，滤波器与卷积核 在只有一个通道的情况下，“卷积核”就相当于 “filter”，这两个概念是可以互换的。但在一般情况下，它们是两个完全不同的概念。每个 “filter” 实际上恰好是“卷积核”的一个集合，在当前层，每个通道都对应一个卷积核，且这个卷积核是独一无二的。 **滤波器的数量，卷积核的shape。** -## 2，卷积层和池化输出大小计算 +## 二，卷积层和池化输出大小计算 > 不管是TensorFlow、Keras、Caffe还是Pytorch，其卷积层和池化层的参数默认值可能有所不同，但是最终的卷积输出大小计算公式是一样的 -### CNN中术语解释 +### 2.1，CNN中术语解释 卷积层主要参数有下面这么几个： @@ -17,7 +17,7 @@ + 滑动步长 `Stride`； + 输出通道数 `Channels`。 -### 卷积输出大小计算 +### 2.2，卷积输出大小计算 1，图片经卷积 `Conv2D` 后**输出大小计算公式**如下：$\left \lfloor N = \frac{W-F+2P}{S}+1 \right \rfloor$，其中${\color{Red} \left \lfloor \right \rfloor}$"是向下取整符号，用于结果不是整数时进行向下取整。 + 输入图片大小 `W×W`（默认输入尺寸为正方形） + Filter大小 `F×F` @@ -34,20 +34,20 @@ **`Pytorch` 和 `tensorflow` 的卷积和池化函数，在 `0/"valid"` 条件下卷积和池化都向下取整**。**在实际设计的卷积神经网络中，卷积层一般填充像素(`same`)，池化层一般不填充像素(`valid`)**？注意：`stride`为 1 的时候，当`kernel`为 3 `padding`为 `1` 或者`kernel`为 `5` `padding`为2，这种情况可直接得出卷积前后尺寸不变。 -### 理解边界效应与填充padding +### 2.3，理解边界效应与填充padding 如果希望输出特征图的空间维度`Keras/TensorFlow` 设置卷积层的过程中可以设置 `padding` 参数值为 `“valid” 或 “same”`。`“valid”` 代表只进行有效的卷积，对边界数据不处理。`“same” 代表 TensorFlow` 会自动对原图像进行补零（表示卷积核可以停留在图像边缘），也就是自动设置`padding` 值让输出与输入形状相同**。 ### 参考资料 + [CNN中的参数解释及计算](https://flat2010.github.io/2018/06/15/%E6%89%8B%E7%AE%97CNN%E4%B8%AD%E7%9A%84%E5%8F%82%E6%95%B0/) + [CNN基础知识——卷积（Convolution）、填充（Padding）、步长(Stride)](https://zhuanlan.zhihu.com/p/77471866) -## 3，Pytorch、Keras 的卷积层和池化层函数 +## 三，Pytorch、Keras 的卷积层和池化层函数 + 注意：`Pytorc` h的卷积层和池化层的输入 `shape` 格式为 **(N,C,H,W)**，`Keras` 的卷积层和池化层的输入 `shape` 格式为 **(N,H,W,C)** + 注意：对于 `Pytorch、Keras` 的卷积层和池化层函数，其**padding参数值都默认为不填充像素**，默认值为 `0`和 `valid`。 -## 4，Pytorch 、Keras 的池化层函数理解 +## 四，Pytorch 、Keras 的池化层函数理解 -### torch.nn.MaxPool2d +### 4.1，torch.nn.MaxPool2d ```python class torch.nn.MaxPool2d(kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False) @@ -75,7 +75,7 @@ output = m(input) print(output.shape) # torch.Size([20, 16, 25, 16]) ``` -### keras.layers.MaxPooling2D +### 4.2，keras.layers.MaxPooling2D ```python keras.layers.MaxPooling2D(pool_size=(2, 2), strides=None, padding='valid', data_format=None) @@ -93,7 +93,7 @@ keras.layers.MaxPooling2D(pool_size=(2, 2), strides=None, padding='valid', data_ + `padding`: "valid" 或者 "same" （区分大小写）。 + `data_format`: 字符串，`channels_last` (默认)或 `channels_first` 之一。 表示输入各维度的顺序。 `channels_last` 代表尺寸是 `(batch, height, width, channels)` 的输入张量， 而 `channels_first` 代表尺寸是 `(batch, channels, height, width)` 的输入张量。 默认值根据 `Keras` 配置文件 `~/.keras/keras.json` 中的 `image_data_format` 值来设置。如果还没有设置过，那么**默认值就是 `"channels_last"`**。 -## 5，Pytorch 和 Keras 的卷积层函数理解 +## 五，Pytorch 和 Keras 的卷积层函数理解 ### 5.1，torch.nn.Conv2d ```python @@ -152,7 +152,7 @@ keras.layers.Conv2D(filters, kernel_size, strides=(1, 1), padding='valid', data_ ### 5.3，总结 `Pytorch` 的 Conv2d 函数不要求提供 输入数据的大小 `(H,W)`，但是要提供输入深度，`Keras` 的 `Conv2d` 函数第一层要求提供 `input_shape` 参数 `(H,W,C)`，其他层不需要。 -## 6，softmax回归 +## 六，softmax回归 **分类问题**中，直接使用输出层的输出有两个问题： + 神经网络输出层的输出值的范围不确定，我们难以直观上判断这些值的意义 + 由于真实标签是离散值，这些离散值与不确定范围的输出值之间的误差难以衡量 @@ -162,7 +162,7 @@ $$ softmax(y)_{i} = y_{i}^{'} = \frac{e^{yi}}{\sum_{j=1}^{n}e^{yj}} $$ -## 7，交叉熵损失函数 +## 七，交叉熵损失函数 交叉熵刻画了两个概率分布之间的距离，它是分类问题中使用比较广泛的一种损失函数，交叉熵一般会与softmax回归一起使用，公式如下： $$L = -\sum_{c=1}^{M}y_{c}log(p_{c})或者H(p,q)=-\sum p(x)logq(x)$$ @@ -175,10 +175,11 @@ $$L = -\sum_{c=1}^{M}y_{c}log(p_{c})或者H(p,q)=-\sum p(x)logq(x)$$ ### 7.1，为什么交叉熵可以用作代价函数 从数学上来理解就是，为了让学到的模型分布更接近真实数据的分布，我们需要最小化模型数据分布与训练数据之间的 `KL 散度`，而因为训练数据的分布是固定的，因此最小化 `KL 散度`等价于最小化交叉熵，而且交叉熵计算更简单，所以机器/深度学习中常用交叉熵 `cross-entroy` 作为分类问题的损失函数。 -## 8，优化算法理解 -`Adam`、`AdaGrad`、`RMSProp`优化算法具有自适应性 -## 9，感受野(Receptive Field) -后一层神经元在前一层神经元的感受空间，也可以定义为卷积神经网络中每层的特征图（`Feature Map`）上的像素点在原始图像中映射的区域大小，即如下图所示： +### 7.2，优化算法理解 +`Adam`、`AdaGrad`、`RMSProp`优化算法具有自适应性。 +## 八，感受野理解 +感受野理解(`Receptive Field`)是指后一层神经元在前一层神经元的感受空间，也可以定义为卷积神经网络中每层的特征图（`Feature Map`）上的像素点在原始图像中映射的区域大小，即如下图所示： + ![感受野大小](../images/感受野空间.jpg) 注意：感受野在 `CNN` 中是呈指数级增加的。小卷积核（如3*3）通过多层叠加可取得与大卷积核（如7\*7）同等规模的感受野，此外采用小卷积核有两个优势： @@ -186,7 +187,7 @@ $$L = -\sum_{c=1}^{M}y_{c}log(p_{c})或者H(p,q)=-\sum p(x)logq(x)$$ 1. 小卷积核需多层叠加，加深了网络深度进而增强了网络容量(model capacity)和复杂度（model complexity） 2. 增强了网络容量的同时减少了参数个数。 -### 感受野大小计算 +### 8.1，感受野大小计算 计算感受野时，我们需要知道： > 参考[感受野（receptive file）计算](https://www.starlg.cn/blog/2017/06/13/Receptive-File/) @@ -297,28 +298,35 @@ Layer Name = conv5, Output size = 12, Stride = 16, RF size = 163 Layer Name = pool5, Output size = 5, Stride = 32, RF size = 195 ``` -## 卷积操作作用 -+ 卷积网络中的卷积核参数是通过网络训练出来的 -+ 通过卷积核的组合以及随着网络后续操作的进行，卷积操作可获取图像区域不同类型特征；基本而一般的模式会逐渐被抽象为具有高层语义的“概念”表示，也就是自动学习到图像的高层特征 -+ 卷积神经网络学到的模式具有平移不变性（translation invariant），卷积神经网络可以学到模式的空间层次结构。 +## 九，卷积和池化操作的作用 +卷积层和池化层的理解可参考魏秀参的《解析卷积神经网络》书籍，卷积（`convolution` ）操作的作用如下： +1. 卷积网络中的卷积核参数是通过网络训练出来的。 +2. 通过卷积核的组合以及随着网络后续操作的进行，卷积操作可获取图像不同区域的不同类型特征；模型靠近底部的层提取的是局部的、高度通用的特征图，而更靠近顶部的层提取的是更加抽象的语义特征。 +3. 卷积神经网络学到的模式具有平移不变性（translation invariant），卷积神经网络可以学到模式的空间层次结构。 + +池化/汇合（`pooling` ）操作作用如下： +1. **特征不变性**（feature invariant）。汇合操作使模型更关注是否存在某些特征而不是特征具体的位置可看作是一种很强的先验，使特征学习包含某种程度自由度，能容忍一些特征微小的位移。 +2. **特征降维**。由于汇合操作的降采样作用，汇合结果中的一个元素对应于原输入数据的一个子区域（sub-region），因此汇合相当于在空间范围内做了维度约减（spatially dimension reduction），从而使模型可以抽取更广范围的特征。同时减小了下一层输入大小，进而减小计算量和参数个数。 +3. 在一定程度上能**防止过拟合（overfitting）**，更方便优化。 -## 卷积层conv与全连接层dense的区别 +### 参考资料 +魏秀参-《解析卷积神经网络》 +## 十，卷积层conv与全连接层dense的区别 + 卷积层学习到的是局部模式（对于图像，学到的就是在输入图像的二维小窗口中发现的模式） + 全连接层学习到的是全局模式（全局模式就算设计所有像素） -## CNN权值共享问题 +## 十一，CNN权值共享问题 首先**权值共享就是滤波器共享**，滤波器的参数是固定的，即是用相同的滤波器去扫一遍图像，提取一次特征特征，得到feature map。在卷积网络中，学好了一个滤波器，就相当于掌握了一种特征，这个滤波器在图像中滑动，进行特征提取，然后所有进行这样操作的区域都会被采集到这种特征，就好比上面的水平线。 -## CNN结构特点 -局部连接，权值共享，池化操作，多层次结构。 +## 十二，CNN结构特点 +典型的用于分类的CNN主要由**卷积层+激活函数+池化层**组成，最后用全连接层输出。卷积层负责提取图像中的局部特征；池化层用来大幅降低参数量级(降维)；全连接层类似传统神经网络的部分，用来输出想要的结果。 + +`CNN` 具有局部连接、权值共享、池化操作(简单说就是下采样)和多层次结构的特点。 -+ 局部连接使网络可以提取数据的局部特征 -+ 权值共享大大降低了网络的训练难度，一个Filter只提取一个特征，在整个图片（或者语音／文本） 中进行卷积 ++ 局部连接使网络可以提取数据的局部特征。 ++ 权值共享大大降低了网络的训练难度，一个Filter只提取一个特征，在整个图片（或者语音／文本） 中进行卷积。 + 池化操作与多层次结构一起，实现了数据的降维，将低层次的局部特征组合成为较高层次的特征，从而对整个图片进行表示。 -## pooling层作用 -1. 增加特征平移不变性。汇合可以提高网络对微小位移的容忍能力。 -2. 减小特征图大小。汇合层对空间局部区域进行下采样，使下一层需要的参数量和计算量减少，并降低过拟合风险。 -3. 最大汇合可以带来非线性。这是目前最大汇合更常用的原因之一。 +## ### Reference [(二)计算机视觉四大基本任务(分类、定位、检测、分割](https://zhuanlan.zhihu.com/p/31727402) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\347\245\236\347\273\217\347\275\221\347\273\234\346\250\241\345\236\213\345\244\215\346\235\202\345\272\246\345\210\206\346\236\220.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\347\245\236\347\273\217\347\275\221\347\273\234\346\250\241\345\236\213\345\244\215\346\235\202\345\272\246\345\210\206\346\236\220.md" index c803891b..5f9e6b30 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\347\245\236\347\273\217\347\275\221\347\273\234\346\250\241\345\236\213\345\244\215\346\235\202\345\272\246\345\210\206\346\236\220.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\347\245\236\347\273\217\347\275\221\347\273\234\346\250\241\345\236\213\345\244\215\346\235\202\345\272\246\345\210\206\346\236\220.md" @@ -1,4 +1,6 @@ -## 模型计算量分析 +## 目录结构 + +## 一，模型计算量分析 > 终端设备上运行深度学习算法需要考虑内存和算力的需求，因此需要进行模型复杂度分析，涉及到模型计算量（时间复杂度）和模型参数量（空间复杂度）分析。模型理论计算量：指模型推断时需要多少计算次数。 + `FLOPs`：floating point operations 指的是浮点运算次数,**理解为计算量**，可以用来衡量算法/模型时间的复杂度。 @@ -27,7 +29,7 @@ ### 全连接层的 FLOPs 计算 全连接层的 $FLOPs = (2I − 1)O$，$I$ 是输入层的维度，$O$ 是输出层的维度。 -## 模型参数量分析 +## 二，模型参数量分析 > 模型参数数量（params）：指模型含有多少参数，直接决定模型的大小，也影响推断时对内存的占用量。 模型参数量的分析是为了了解内存占用情况，内存带宽其实比 `FLOPs` 更重要。目前的计算机结构下，单次内存访问比单次运算慢得多的多。对每一层网络，端侧设备需要： From e7af5cc4e7b162e64c2c56f0eb2dbd66d0ba2995 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Wed, 30 Sep 2020 22:18:56 +0800 Subject: [PATCH 370/386] Add files! --- ...46\344\271\240\346\246\202\345\277\265.md" | 88 ++++ ...\240\207\345\207\206-AP\344\270\216mAP.md" | 395 ++++++++++++++++++ ...72\347\241\200\346\246\202\345\277\265.md" | 311 +------------- 3 files changed, 487 insertions(+), 307 deletions(-) create mode 100644 "\346\234\272\345\231\250\345\255\246\344\271\240/\346\234\272\345\231\250\345\255\246\344\271\240\346\246\202\345\277\265.md" create mode 100644 "\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\346\200\247\350\257\204\344\273\267\346\240\207\345\207\206-AP\344\270\216mAP.md" rename "\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" => "\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\346\250\241\345\236\213\347\232\204\345\237\272\347\241\200\346\246\202\345\277\265.md" (50%) diff --git "a/\346\234\272\345\231\250\345\255\246\344\271\240/\346\234\272\345\231\250\345\255\246\344\271\240\346\246\202\345\277\265.md" "b/\346\234\272\345\231\250\345\255\246\344\271\240/\346\234\272\345\231\250\345\255\246\344\271\240\346\246\202\345\277\265.md" new file mode 100644 index 00000000..156053ae --- /dev/null +++ "b/\346\234\272\345\231\250\345\255\246\344\271\240/\346\234\272\345\231\250\345\255\246\344\271\240\346\246\202\345\277\265.md" @@ -0,0 +1,88 @@ +## 一，欧氏距离与余弦相似度（cos距离） +> [专题-机器学习实践](https://github.com/imhuay/Algorithm_Interview_Notes-Chinese/blob/master/A-%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/A-%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%AE%9E%E8%B7%B5.md) +[余弦相似度 | 文本分析：基础](https://zhuanlan.zhihu.com/p/41420179) +### 1.1，余弦相似度 +通过对两个文本分词，`TF-IDF` 算法向量化，利用空间中两个向量的夹角，来判断这两个向量的相似程度：(`计算夹角的余弦，取值 0-1`) ++ 当两个向量夹角越大，距离越远，最大距离就是两个向量夹角 180°； ++ 夹角越小，距离越近，最小距离就是两个向量夹角 0°，完全重合。 ++ 夹角越小相似度越高，但由于有可能一个文章的特征向量词特别多导致整个向量维度很高，使得计算的代价太大不适合大数据量的计算。 + +**计算两个向量a、b的夹角余弦：** +我们知道，余弦定理：$cos(\theta) = \frac {a^2+b^2+c^2}{2ab}$ ，由此推得两个向量夹角余弦的计算公式如下： +$$cos(\theta) = \frac {ab}{||a|| \times ||b||} = \frac {x_{1}x_{2}+y_1y_2}{\sqrt{x^2_1+y^2_1}\sqrt{x^2_2+y^2_2}}$$ +（分子就是两个向量的内积，分母是两个向量的模长乘积） +### 1.2，欧式距离 +在欧几里得空间中，欧式距离其实就是向量空间中两点之间的距离。点 $x = (x_{1}, ..., x_{n})$ 和 $y = (y_{1}, ..., y_{n})$ 之间得欧氏距离计算公式如下： +$$d(x,y) = \sqrt {((x_{1}-y_{1})^{2} + (x_{2}-y_{2})^{2} + ... + (x_{n}-y_{n})^{2})}$$ +### 1.3，余弦相似度和欧氏距离的区别 ++ 欧式距离和余弦相似度都能度量 `2` 个向量之间的相似度 ++ 放到向量空间中看，欧式距离衡量`两点之间`的直线距离，而余弦相似度计算的是`两个向量`之间的夹角 ++ 没有归一化时，欧式距离的范围是 `[0, +∞]`，而余弦相似度的范围是 `[-1, 1]`；余弦距离是计算相似程度，而欧氏距离计算的是相同程度（对应值的相同程度） ++ 归一化的情况下，可以将空间想象成一个超球面（三维），欧氏距离就是球面上两点的直线距离，而向量余弦值等价于两点的球面距离，本质是一样。 +## 二，混淆矩阵与 PR +混淆矩阵表格如下： +|名称|定义| +|---|---| +|True Positive(真正例, `TP`)|将正类预测为正类数| +|True Negative(真负例, `TN`)|将负类预测为负类数| +|False Positive(假正例, `FP`)|将负类预测为正类数 → 误报 (Type I error)| +|False Negative(假负例子, `FN`)|将正类预测为负类数 → 漏报 (Type II error)| + +查准率 `P` 与查全率 `R` 的计算公式如下： ++ 查准率（准确率）`P = TP/(TP+FP)` ++ 查全率（召回率）`R = TP/(TP+FN)` + +**准确率描述了模型有多准**，即在预测为正例的结果中，有多少是真正例；**召回率则描述了模型有多全**，即在为真的样本中，有多少被我们的模型预测为正例。 +## 三，如何处理数据中的缺失值 +可以分为以下 2 种情况： ++ **缺失值较多**：直接舍弃该列特征，否则可能会带来较大噪声，从而对结果造成不良影响。 ++ **缺失值较少**：当缺失值较少（`< 10%`）时，可以考虑对缺失值进行填充，有几下几种填充策略： + + 用一个**异常值**填充（比如 0 ），缺失值作为一个特征处理：`data.fillna(0)` + + 用**均值|条件均值**填充：`data.fillna(data.mean())` + + 用**相邻数据**填充：`data.fillna(method='pad')`，`data.fillna(method='bfill')` + + **插值**：`data.interpolate()` + + **拟合**：简单来说，就是将缺失值也作为一个预测问题来处理：将数据分为正常数据和缺失数据，对有值的数据采用`随机森林`等方法拟合，然后对有缺失值的数据进行预测，用预测的值来填充 +## 四，机器学习项目流程 +1. 数学抽象 +2. 数据获取 +3. 预处理与特征选择 +4. 模型训练与调优 +5. 模型诊断 +6. 模型融合/集成 +7. 上线运行 +## 五，数据清洗与特征处理 +> [机器学习中的数据清洗与特征处理综述](https://tech.meituan.com/2015/02/10/machinelearning-data-feature-process.html) +美团的这篇综述文章总结得不错，虽然缺少实例不容易直观理解，但是对于我这个初学者来说也足够了，本章内容几乎都来自于参考文章。 +### 5.1，清洗标注数据 +清洗标注数据的方法，主要是是数据采样和样本过滤。 ++ `数据采样`：对于分类问题：选取正例，负例。对于回归问题，需要采集数据。对于采样得到的文本，根据需要设定样本权重，当模型不能使用全部的数据来训练时，需要对数据进行采样，设定一定的采样率。采样的方法包括随机采样，固定比例采样等方法。 ++ 样本过滤：1.结合业务情况进行数据的过滤，例如去除crawler抓取，spam，作弊等数据。 - 2.异常点检测，采用异常点检测算法对样本进行分析，常用的异常点检测算法包括 - 偏差检测，例如聚类，最近邻等。 +### 5.2，特征分类 +根据不同的分类方法，可以将特征分为： ++ `Low level` 特征和 `High level` 特征 ++ 稳定特征与动态特征。 ++ 二值特征、连续特征、枚举特征 + +`Low level` 特征是较低级别的特征，主要是原始特征，不需要或者需要很少的人工处理和干预，例如文本中的词向量特征，图像特征中的像素点大小，用户 `id`，商品 id等。High level 特征是经过比较复杂的处理，结合部分业务逻辑或者规则、模型得到的特征，例如人工打分，模型打分等特征，可以用于较复杂的非线性模型。Low level 比较针对性，覆盖面小。长尾样本的预测值主要受 high level 特征影响。 高频样本的预测值主要受 low level 特征影响。 + +`稳定特征` 是变化频率较少的特征，例如评价平均分，团购单价价格等，在较长时间段内数值都不会发生变化。动态特征是更新变化比较频繁的特征，有些甚至是实时计算得到的特征，例如距离特征，2 小时销量等特征。或者叫做实时特征和非实时特征。针对两类特征的不同可以针对性地设计特征存储和更新方式，例如对于稳定特征，可以建入索引，较长时间更新一次，如果做缓存的话，缓存的时间可以较长。对于动态特征，需要实时计算或者准实时地更新数据，如果做缓存的话，缓存过期时间需要设置的较短。 + +`二值特征主要是 0/1 特征`，即特征只取两种值：`0 或者 1`，例如`用户 id 特征`：目前的 id 是否是某个特定的 id，`词向量特征`：某个特定的词是否在文章中出现等等。连续值特征是取值为有理数的特征，特征取值个数不定，例如距离特征，特征取值为是0~正无穷。枚举值特征主要是特征有固定个数个可能值，例如今天周几，只有7个可能值：周1，周2，…，周日。在实际的使用中，我们可能对不同类型的特征进行转换，例如将枚举特征或者连续特征处理为二值特征。枚举特征处理为二值特征技巧：将枚举特征映射为多个特征，每个特征对应一个特定枚举值，例如今天周几，可以把它转换成7个二元特征：今天是否是周一，今天是否是周二，…，今天是否是周日。连续值处理为二值特征方法：先将连续值离散化（后面会介绍如何离散化)，再将离散化后的特征切分为N个二元特征，每个特征代表是否在这个区间内。 +### 5.3，特征处理与分析 +对特征进行分类后，需要对特征进行处理，`常用的特征处理方法`如下： ++ 特征归一化，离散化，缺省值处理 ++ 特征降维方法 ++ 特征选择方法 + +**特征归一化**。在有些算法中，例如线性模型或者距离相关的模型（聚类模型、knn 模型等），特征值的取值范围会对最终的结果产生较大影响，例如输入数据有两种不同的特征，其中的二元特征取值范围 `[0, 1]`，而距离特征取值可能是 [0，正无穷]，两种特征取值范围不一致，导致模型可能会偏向于取值范围较大额特征，为了平衡取值范围不一致的特征，需要对特征进行归一化处理，将特征值取值归一化到 [0,1] 区间，常用的归一化方法包括： +1. `函数归一化`，通过映射函数将特征取值映射到［0，1］区间，例如最大最小值归一化方法，是一种线性的映射。还有通过非线性函数的映射，例如 `log` 函数等。 +2. `分维度归一化`，可以使用最大最小归一化方法，但是最大最小值选取的是所属类别的最大最小值，即使用的是局部最大最小值，不是全局的最大最小值。 +3. `排序归一化`，不管原来的特征取值是什么样的，将特征按大小排序，根据特征所对应的序给予一个新的值。 + +**离散化**。在上面介绍过连续值的取值空间可能是无穷的，为了便于表示和在模型中处理，需要对连续值特征进行离散化处理。常用的离散化方法包括等值划分和等量划分。 +1. `等值划分`，是将特征按照值域进行均分，每一段内的取值等同处理。例如某个特征的取值范围为 [0，10]，我们可以将其划分为10段，[0，1)，[1，2)，…，[9，10)。 +2. `等量划分`，是根据样本总数进行均分，每段等量个样本划分为 1 段。例如距离特征，取值范围［0，3000000］，现在需要切分成 10 段，如果按照等比例划分的话，会发现绝大部分样本都在第 1 段中。使用等量划分就会避免这种问题，最终可能的切分是[0，100)，[100，300)，[300，500)，..，[10000，3000000]，前面的区间划分比较密，后面的比较稀疏。 + +**缺省值处理**。有些特征可能因为无法采样或者没有观测值而缺失，例如距离特征，用户可能禁止获取地理位置或者获取地理位置失败，此时需要对这些特征做特殊的处理，赋予一个缺省值。缺省值如何赋予，也有很多种方法。例如`单独表示，众数，平均值等`。 +## 参考资料 +[机器学习中的数据清洗与特征处理综述](https://tech.meituan.com/2015/02/10/machinelearning-data-feature-process.html) \ No newline at end of file diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\346\200\247\350\257\204\344\273\267\346\240\207\345\207\206-AP\344\270\216mAP.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\346\200\247\350\257\204\344\273\267\346\240\207\345\207\206-AP\344\270\216mAP.md" new file mode 100644 index 00000000..b2a5977c --- /dev/null +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\346\200\247\350\257\204\344\273\267\346\240\207\345\207\206-AP\344\270\216mAP.md" @@ -0,0 +1,395 @@ +## 前言 +**不同的问题和不同的数据集都会有不同的模型评价指标，比如分类问题，数据集类别平衡的情况下可以使用准确率作为评价指标，但是现实中的数据集几乎都是类别不平衡的，所以一般都是采用 `AP` 作为分类的评价指标，分别计算每个类别的 `AP`，再计算`mAP`**。 +## mAP定义及相关概念 +|评价指标|定义及理解| +|-------|--------| +|`mAP`|mean Average Precision, 即各类别AP的平均值| +|`AP`|PR曲线下面积，后文会详细讲解| +|`PR曲线`|Precision-Recall曲线| +|`Precision`|TP / (TP + FP)| +|`Recall`|TP / (TP + FN)| +|`TP`|IoU>0.5的检测框数量（同一Ground Truth只计算一次）| +|`FP`|IoU<=0.5的检测框，或者是检测到同一个GT的多余检测框的数量| +|`FN`|没有检测到的GT的数量| +## 一，评价标准的基本概念 +为了了解模型的泛化能力，即判断模型的好坏，我们需要用某个指标来衡量，有了评价指标，就可以对比不同模型的优劣，并通过这个指标来进一步调参优化模型。**对于分类和回归两类监督模型，分别有各自的评判标准**。 + +### 1.1，准确率、精确率、召回率、F1 +#### 准确率 +**准确率 – Accuracy**，预测正确的结果占总样本的百分比，公式如下：$准确率 = (TP+TN)/(TP+TN+FP+FN)$. + +> 虽然准确率可以判断总的正确率，但是在样本不平衡 的情况下，并不能作为很好的指标来衡量结果。举个简单的例子，比如在一个总样本中，正样本占 90%，负样本占 10%，样本是严重不平衡的。对于这种情况，我们只需要将全部样本预测为正样本即可得到 90% 的高准确率，但实际上我们并没有很用心的分类，只是随便无脑一分而已。这就说明了：由于样本不平衡的问题，导致了得到的高准确率结果含有很大的水分。即如果样本不平衡，准确率就会失效。 + +#### 精确率、召回率 +精确率（查准率）`P`、召回率（查全率）`R` 的计算涉及到混淆矩阵的定义，混淆矩阵表格如下： +|名称|定义| +|---|---| +|True Positive(真正例, `TP`)|将正类预测为正类数| +|True Negative(真负例, `TN`)|将负类预测为负类数| +|False Positive(假正例, `FP`)|将负类预测为正类数 → 误报 (Type I error)| +|False Negative(假负例子, `FN`)|将正类预测为负类数 → 漏报 (Type II error)| + +查准率与查全率计算公式： + ++ 查准率（精确率）P = TP/(TP+FP) ++ 查全率（召回率）R = TP/(TP+FN) + +> 精准率和准确率看上去有些类似，但是完全不同的两个概念。**精准率代表对正样本结果中的预测准确程度，而准确率则代表整体的预测准确程度**，既包括正样本，也包括负样本。 + +**精确率描述了模型有多准**，即在预测为正例的结果中，有多少是真正例；**召回率则描述了模型有多全**，即在为真的样本中，有多少被我们的模型预测为正例。精确率和召回率的区别在于**分母不同**，一个分母是预测为正的样本数，另一个是原来样本中所有的正样本数。 + +### 1.2，PR曲线图 + +精准率和召回率的关系可以用一个 `P-R` 图来展示，以查准率 `P` 为纵轴、查全率 `R` 为横轴作图，就得到了查准率－查全率曲线，简称**P-R**曲线，PR 曲线下的面积定义为 `AP`: + +![PR曲线图](../images/PR曲线图.jpeg) + +### 1.3，如何理解 P-R曲线 + +可以从排序型模型或者分类模型理解。以逻辑回归举例，逻辑回归的输出是一个 0 到 1 之间的概率数字，因此，如果我们想要根据这个概率判断用户好坏的话，我们就必须定义一个阈值 。通常来讲，逻辑回归的概率越大说明越接近 1，也就可以说他是坏用户的可能性更大。比如，我们定义了阈值为 0.5，即概率小于 0.5 的我们都认为是好用户，而大于 0.5 都认为是坏用户。因此，对于阈值为 0.5 的情况下，我们可以得到相应的一对查准率和查全率。 + +但问题是：这个阈值是我们随便定义的，我们并不知道这个阈值是否符合我们的要求。 因此，为了找到一个最合适的阈值满足我们的要求，我们就必须遍历 0 到 1 之间所有的阈值，而每个阈值下都对应着一对查准率和查全率，从而我们就得到了这条曲线。 + +有的朋友又问了：如何找到最好的阈值点呢？ 首先，需要说明的是我们对于这两个指标的要求：我们希望查准率和查全率同时都非常高。 但实际上这两个指标是一对矛盾体，无法做到双高。图中明显看到，如果其中一个非常高，另一个肯定会非常低。选取合适的阈值点要根据实际需求，比如我们想要高的查全率，那么我们就会牺牲一些查准率，在保证查全率最高的情况下，查准率也不那么低。 + +### 1.4，PR、ROC曲线与AUC面积 + ++ PR 曲线是以 Recall 为横轴，Precision 为纵轴；而 ROC曲线则是以 FPR 为横轴，TPR 为纵轴**。**P-R 曲线越靠近右上角性能越好**。 ++ PR 曲线展示的是Precision vs Recall 的曲线，ROC 曲线展示的是 FPR（x 轴：False positive rate） vs TPR（True positive rate, TPR）曲线。 ++ `P、R` 和 `F1` 指标评价在信息检索中用的比较多，在机器学习中还有一种 `ROC` 曲线来评价性能。 ++ PR 曲线与 ROC 曲线的相同点是都采用了 `TPR (Recall)`，都可以用 AUC 来衡量分类器的效果。不同点是 `ROC` 曲线使用了 `FPR`，而 `PR` 曲线使用了 `Precision`，因此**PR曲线的两个指标都聚焦于正例**。类别不平衡问题中由于主要关心正例，所以在此情况下PR曲线被广泛认为优于ROC曲线。 + +### 1.5，F1 分数 +如果想要找到 $P$ 和 $R$ 二者之间的一个平衡点，我们就需要一个新的指标：$F1$ 分数。$F1$ 分数同时考虑了查准率和查全率，让二者同时达到最高，取一个平衡。$F1$ 计算公式如下： + +$$F1 = \frac{2*P*R}{P+R} = \frac{2*TP}{样例总数+TP-TN}$$ + +$F1$ 度量的一般形式：$F_{\beta}$，能让我们表达出对查准率/查全率的偏见，$F_{\beta}$计算公式如下： + +$$F_{\beta} = \frac{1+\beta^{2}*P*R}{(\beta^{2}*P)+R}$$ + +其中 $\beta >1$ 对查全率有更大影响，$\beta < 1$ 对查准率有更大影响。 + +不同的计算机视觉问题，对两类错误有不同的偏好，常常在某一类错误不多于一定阈值的情况下，努力减少另一类错误。在目标检测中，**mAP**（mean Average Precision）作为一个统一的指标将这两种错误兼顾考虑。 + +### 1.6，二分类、多分类指标计算 +#### 二分类 +$$F1\ score = \frac{2 \times P \times R}{(P + R)}$$ +其中，准确率(precise) $P = TP / (TP + FP)$，召回率(recall) $R = TP / (TP + FN)$. + +#### 多分类 ++ **Macro F1**: 将 `n` 分类的评价拆成 `n` 个二分类的评价，计算每个二分类的 `F1 score`，`n` 个 `F1 score` 的平均值即为 `Macro F1`。 ++ **Micro F1**: 将 n 分类的评价拆成 n 个二分类的评价，将 n 个二分类评价的TP、FP、RN对应相加，计算评价准确率和召回率，由这 `2` 个准确率和召回率计算的F1 score 即为 Micro F1。 ++ 一般来讲，Macro F1、Micro F1 值高的分类效果好。`Macro F1`受样本数量少的类别影响大。 + +### 1.7，mAP指标理解 +`AP` 衡量的是训练好的模型在每个类别上的好坏，`mAP` 衡量的是模型在所有类别上的好坏，得到 AP 后 mAP 的计算就变得很简单了，就是取所有 `AP` 的平均值。`AP` 的计算公式比较复杂（所以单独作一章节内容），详细内容参考下文。 + +`mAP` 这个术语有不同的定义。此度量指标通常用于信息检索、图像分类和目标检测领域。然而这两个领域计算 `mAP` 的方式却不相同。这里我们只谈论目标检测中的 `mAP` 计算方法。 + +`mAP` 常作为目标检测算法的评价指标，具体来说就是，对于每张图片检测模型会输出多个预测框（远超真实框的个数），我们使用 `IoU` (Intersection Over Union，交并比)来标记预测框是否预测准确。标记完成后，随着预测框的增多，查全率 `R` 总会上升，**在不同查全率 `R` 水平下对准确率 `P` 做平均，即得到AP**，最后再对所有类别按其所占比例做平均，即得到 `mAP` 指标。 + +## 二，AP计算概述 +**目标检测领域常用的评估标准是**：`mAP`(mean average precision)，计算 mAP 需要先计算 `AP`，计算 `AP` 需涉及到 `precision` 和 `recall` 的计算，而这两者的计算又需设计 `TP`、`FP`、`FN` 的计算。 + +`AP` 的计算一般先涉及到 `P-R` 曲线（precision-recall curve）的绘制，P-R曲线下面与x轴围成的面积称为`average precison（AP）`。下图是一个二分类问题的P-R曲线： + +![分类问题的PR曲线图](../images/分类PR曲线图.png) + +知道了`AP` 的定义，下一步就是理解`AP`计算的实现，理论上可以通过积分来计算`AP`，公式如下： +$$AP=\int_0^1 P(r) dr$$ +但通常情况下都是使用近似或者插值的方法来计算 AP。 +### 2.1，近似计算AP + +$$AP = \sum_{k=1}^{N}P(k)\Delta r(k)$$ + ++ 近似计算 AP(`approximated average precision`)，这种计算方式是 approximated 形式的； ++ 很显然位于一条竖直线上的点对计算AP没有贡献； ++ 这里 `N` 为数据总量，`k` 为每个样本点的索引， $Δr(k)=r(k)−r(k−1)$。 + +**近似计算**`AP`和绘制`PR`曲线代码如下： +```Python +import numpy as np +import matplotlib.pyplot as plt + +class_names = ["car", "pedestrians", "bicycle"] + +def draw_PR_curve(predict_scores, eval_labels, name, cls_idx=1): + """calculate AP and draw PR curve, there are 3 types + Parameters: + @all_scores: single test dataset predict scores array, (-1, 3) + @all_labels: single test dataset predict label array, (-1, 3) + @cls_idx: the serial number of the AP to be calculated, example: 0,1,2,3... + """ + # print('sklearn Macro-F1-Score:', f1_score(predict_scores, eval_labels, average='macro')) + global class_names + fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(15, 10)) + # Rank the predicted scores from large to small, extract their corresponding index(index number), and generate an array + idx = predict_scores[:, cls_idx].argsort()[::-1] + eval_labels_descend = eval_labels[idx] + pos_gt_num = np.sum(eval_labels == cls_idx) # number of all gt + + predict_results = np.ones_like(eval_labels) + tp_arr = np.logical_and(predict_results == cls_idx, eval_labels_descend == cls_idx) # ndarray + fp_arr = np.logical_and(predict_results == cls_idx, eval_labels_descend != cls_idx) + + tp_cum = np.cumsum(tp_arr).astype(float) # ndarray, Cumulative sum of array elements. + fp_cum = np.cumsum(fp_arr).astype(float) + + precision_arr = tp_cum / (tp_cum + fp_cum) # ndarray + recall_arr = tp_cum / pos_gt_num + ap = 0.0 + prev_recall = 0 + for p, r in zip(precision_arr, recall_arr): + ap += p * (r - prev_recall) + # pdb.set_trace() + prev_recall = r + print("------%s, ap: %f-----" % (name, ap)) + + fig_label = '[%s, %s] ap=%f' % (name, class_names[cls_idx], ap) + ax.plot(recall_arr, precision_arr, label=fig_label) + + ax.legend(loc="lower left") + ax.set_title("PR curve about class: %s" % (class_names[cls_idx])) + ax.set(xticks=np.arange(0., 1, 0.05), yticks=np.arange(0., 1, 0.05)) + ax.set(xlabel="recall", ylabel="precision", xlim=[0, 1], ylim=[0, 1]) + + fig.savefig("./pr-curve-%s.png" % class_names[cls_idx]) + plt.close(fig) +``` + +### 2.2，插值计算AP +插值计算(`Interpolated average precision`) AP 的公式的演变过程这里不做讨论，详情可以参考这篇[文章](https://arleyzhang.github.io/articles/c521a01c/)，我这里的公式和图也是参考此文章的。`11点插值计算方式计算AP公式`如下： + +![11点插值计算方式计算AP公式](../images/插值计算AP公式.png) + ++ 这是通常意义上的 11 points_Interpolated 形式的 AP，选取固定的 {0,0.1,0.2,…,1.0} 11个阈值，这个在PASCAL2007中有使用 ++ 这里因为参与计算的只有11个点，所以 K=11，称为11points_Interpolated，k为阈值索引 ++ $P_{interp}(k)$ 取第 k 个阈值所对应的样本点之后的样本中的最大值，只不过这里的阈值被限定在了 {0,0.1,0.2,…,1.0} 范围内。 + +![插值计算方式计算AP的PR曲线图](../images/插值计算AP的PR曲线图.png) + +从曲线上看，真实 `AP< approximated AP < Interpolated AP`，`11-points Interpolated AP` 可能大也可能小，当数据量很多的时候会接近于 `Interpolated AP`，与 `Interpolated AP` 不同，前面的公式中计算 `AP` 时都是对 `PR` 曲线的面积估计，PASCAL的论文里给出的公式就更加简单粗暴了，直接计算`11` 个阈值处的 `precision` 的平均值。`PASCAL` 论文给出的 `11` 点计算 `AP` 的公式如下。 + +![PASCAL论文给出的11点计算AP公式](../images/11点计算AP公式.png) + +## 三，AP计算实现 +1, 在给定 `recal` 和 `precision` 的条件下计算 `AP`： +```Python +def voc_ap(rec, prec, use_07_metric=False): + """ + ap = voc_ap(rec, prec, [use_07_metric]) + Compute VOC AP given precision and recall. + If use_07_metric is true, uses the + VOC 07 11 point method (default:False). + """ + if use_07_metric: + # 11 point metric + ap = 0. + for t in np.arange(0., 1.1, 0.1): + if np.sum(rec >= t) == 0: + p = 0 + else: + p = np.max(prec[rec >= t]) + ap = ap + p / 11. + else: + # correct AP calculation + # first append sentinel values at the end + mrec = np.concatenate(([0.], rec, [1.])) + mpre = np.concatenate(([0.], prec, [0.])) + + # compute the precision envelope + for i in range(mpre.size - 1, 0, -1): + mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) + + # to calculate area under PR curve, look for points + # where X axis (recall) changes value + i = np.where(mrec[1:] != mrec[:-1])[0] + + # and sum (\Delta recall) * prec + ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) + return ap +``` +2，给定目标检测结果文件和测试集标签文件 `xml` 等计算 `AP`： +```Python +def parse_rec(filename): + """ Parse a PASCAL VOC xml file + Return : list, element is dict. + """ + tree = ET.parse(filename) + objects = [] + for obj in tree.findall('object'): + obj_struct = {} + obj_struct['name'] = obj.find('name').text + obj_struct['pose'] = obj.find('pose').text + obj_struct['truncated'] = int(obj.find('truncated').text) + obj_struct['difficult'] = int(obj.find('difficult').text) + bbox = obj.find('bndbox') + obj_struct['bbox'] = [int(bbox.find('xmin').text), + int(bbox.find('ymin').text), + int(bbox.find('xmax').text), + int(bbox.find('ymax').text)] + objects.append(obj_struct) + + return objects + +def voc_eval(detpath, + annopath, + imagesetfile, + classname, + cachedir, + ovthresh=0.5, + use_07_metric=False): + """rec, prec, ap = voc_eval(detpath, + annopath, + imagesetfile, + classname, + [ovthresh], + [use_07_metric]) + Top level function that does the PASCAL VOC evaluation. + detpath: Path to detections result file + detpath.format(classname) should produce the detection results file. + annopath: Path to annotations file + annopath.format(imagename) should be the xml annotations file. + imagesetfile: Text file containing the list of images, one image per line. + classname: Category name (duh) + cachedir: Directory for caching the annotations + [ovthresh]: Overlap threshold (default = 0.5) + [use_07_metric]: Whether to use VOC07's 11 point AP computation + (default False) + """ + # assumes detections are in detpath.format(classname) + # assumes annotations are in annopath.format(imagename) + # assumes imagesetfile is a text file with each line an image name + # cachedir caches the annotations in a pickle file + + # first load gt + if not os.path.isdir(cachedir): + os.mkdir(cachedir) + cachefile = os.path.join(cachedir, '%s_annots.pkl' % imagesetfile) + # read list of images + with open(imagesetfile, 'r') as f: + lines = f.readlines() + imagenames = [x.strip() for x in lines] + + if not os.path.isfile(cachefile): + # load annotations + recs = {} + for i, imagename in enumerate(imagenames): + recs[imagename] = parse_rec(annopath.format(imagename)) + if i % 100 == 0: + print('Reading annotation for {:d}/{:d}'.format( + i + 1, len(imagenames))) + # save + print('Saving cached annotations to {:s}'.format(cachefile)) + with open(cachefile, 'wb') as f: + pickle.dump(recs, f) + else: + # load + with open(cachefile, 'rb') as f: + try: + recs = pickle.load(f) + except: + recs = pickle.load(f, encoding='bytes') + + # extract gt objects for this class + class_recs = {} + npos = 0 + for imagename in imagenames: + R = [obj for obj in recs[imagename] if obj['name'] == classname] + bbox = np.array([x['bbox'] for x in R]) + difficult = np.array([x['difficult'] for x in R]).astype(np.bool) + det = [False] * len(R) + npos = npos + sum(~difficult) + class_recs[imagename] = {'bbox': bbox, + 'difficult': difficult, + 'det': det} + + # read dets + detfile = detpath.format(classname) + with open(detfile, 'r') as f: + lines = f.readlines() + + splitlines = [x.strip().split(' ') for x in lines] + image_ids = [x[0] for x in splitlines] + confidence = np.array([float(x[1]) for x in splitlines]) + BB = np.array([[float(z) for z in x[2:]] for x in splitlines]) + + nd = len(image_ids) + tp = np.zeros(nd) + fp = np.zeros(nd) + + if BB.shape[0] > 0: + # sort by confidence + sorted_ind = np.argsort(-confidence) + sorted_scores = np.sort(-confidence) + BB = BB[sorted_ind, :] + image_ids = [image_ids[x] for x in sorted_ind] + + # go down dets and mark TPs and FPs + for d in range(nd): + R = class_recs[image_ids[d]] + bb = BB[d, :].astype(float) + ovmax = -np.inf + BBGT = R['bbox'].astype(float) + + if BBGT.size > 0: + # compute overlaps + # intersection + ixmin = np.maximum(BBGT[:, 0], bb[0]) + iymin = np.maximum(BBGT[:, 1], bb[1]) + ixmax = np.minimum(BBGT[:, 2], bb[2]) + iymax = np.minimum(BBGT[:, 3], bb[3]) + iw = np.maximum(ixmax - ixmin + 1., 0.) + ih = np.maximum(iymax - iymin + 1., 0.) + inters = iw * ih + + # union + uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) + + (BBGT[:, 2] - BBGT[:, 0] + 1.) * + (BBGT[:, 3] - BBGT[:, 1] + 1.) - inters) + + overlaps = inters / uni + ovmax = np.max(overlaps) + jmax = np.argmax(overlaps) + + if ovmax > ovthresh: + if not R['difficult'][jmax]: + if not R['det'][jmax]: + tp[d] = 1. + R['det'][jmax] = 1 + else: + fp[d] = 1. + else: + fp[d] = 1. + + # compute precision recall + fp = np.cumsum(fp) + tp = np.cumsum(tp) + rec = tp / float(npos) + # avoid divide by zero in case the first detection matches a difficult + # ground truth + prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps) + ap = voc_ap(rec, prec, use_07_metric) + + return rec, prec, ap +``` + +## 四，map计算方法 +因为 mAP 值的计算是对数据集中所有类别的 AP 值求平均，所以我们要计算 mAP，首先得知道某一类别的 AP 值怎么求。不同数据集的某类别的 AP 计算方法大同小异，主要分为三种： + +（1）在 `VOC2007`，只需要选取当Recall >= 0, 0.1, 0.2, ..., 1共11个点时的Precision最大值，然后AP就是这11个Precision的平均值，map就是所有类别AP值的平均。`VOC` 数据集中计算 `AP` 的代码（用的是插值计算方法，代码出自[py-faster-rcnn仓库](https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/datasets/voc_eval.py)） + +（2）在 `VOC2010` 及以后，需要针对每一个不同的 `Recall` 值（包括0和1），选取其大于等于这些 `Recall` 值时的 `Precision` 最大值，然后计算PR曲线下面积作为 AP 值，map 就是所有类别 AP 值的平均。 + +（3）`COCO` 数据集，设定多个 IOU 阈值（0.5-0.95,0.05为步长），在每一个IOU阈值下都有某一类别的 AP 值，然后求不同 IOU 阈值下的 AP 平均，就是所求的最终的某类别的AP值。 + +## 五，目标检测度量标准汇总 +![目标检测指标汇总](../images/目标检测度量标准汇总.jpg) + +## 参考资料 ++ [目标检测评价标准-AP mAP](https://Harleyzhang.github.io/articles/c521a01c/) ++ [目标检测的性能评价指标](https://zhuanlan.zhihu.com/p/70306015?utm_source=wechat_session&utm_medium=social&utm_oi=571954943427219456) ++ [Soft-NMS](https://hellozhaozheng.github.io/z_post/%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89-SoftNMS-ICCV2017/) ++ [Recent Advances in Deep Learning for Object Detection](https://arxiv.org/abs/1908.03673v1) ++ [A Simple and Fast Implementation of Faster R-CNN](https://github.com/chenyuntc/simple-faster-rcnn-pytorch) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\346\250\241\345\236\213\347\232\204\345\237\272\347\241\200\346\246\202\345\277\265.md" similarity index 50% rename from "\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" rename to "\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\346\250\241\345\236\213\347\232\204\345\237\272\347\241\200\346\246\202\345\277\265.md" index 7d33dd9c..560fe97c 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\345\237\272\346\234\254\346\246\202\345\277\265\344\270\216\350\257\204\344\273\267\346\214\207\346\240\207\350\256\241\347\256\227.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\346\250\241\345\236\213\347\232\204\345\237\272\347\241\200\346\246\202\345\277\265.md" @@ -1,7 +1,5 @@ -## 前言 -**不同的问题和不同的数据集都会有不同的模型评价指标，比如分类问题，数据集类别平衡的情况下可以使用准确率作为评价指标，但是现实中的数据集几乎都是类别不平衡的，所以一般都是采用 `AP` 作为分类的评价指标，分别计算每个类别的 `AP`，再计算`mAP`**。 ## 一，anchors 理解 -所谓 `anchors`，实际上就是一组由 generate_anchors.py 生成的矩形框。其中每行的4个值 `(x1,y1,x2,y2)` 表矩形左上和右下角点坐标。9 个矩形共有 3 种形状，长宽比为大约为 `{1:1, 1:2, 2:1}` 三种, 实际上通过anchors就引入了检测中常用到的多尺度方法。generate_anchors.py的代码如下： +所谓 `anchors`，实际上就是一组由 generate_anchors.py 生成的矩形框。其中每行的4个值 `(x1,y1,x2,y2)` 表矩形左上和右下角点坐标。9 个矩形共有 3 种形状，长宽比为大约为 `{1:1, 1:2, 2:1}` 三种, 实际上通过anchors就引入了检测中常用到的多尺度方法。`generate_anchors.py` 的代码如下： ```Python import numpy as np import six @@ -340,309 +338,8 @@ def soft_nms(dets, thresh, type='gaussian'): scores = scores[tmp] return keep ``` -## 五，AP计算概述 -**目标检测领域常用的评估标准是**：`mAP`(mean average precision)，计算 mAP 需要先计算 `AP`，计算 `AP` 需涉及到 `precision` 和 `recall` 的计算，而这两者的计算又需设计 `TP`、`FP`、`FN` 的计算。 - -`AP` 的计算一般先涉及到 `P-R` 曲线（precision-recall curve）的绘制，P-R曲线下面与x轴围成的面积称为`average precison（AP）`。下图是一个二分类问题的P-R曲线： - -![分类问题的PR曲线图](../images/分类PR曲线图.png) - -知道了`AP` 的定义，下一步就是理解`AP`计算的实现，理论上可以通过积分来计算`AP`，公式如下： -$$AP=\int_0^1 P(r) dr$$ -但通常情况下都是使用近似或者插值的方法来计算 AP。 -### 5.1，近似计算AP - -$$AP = \sum_{k=1}^{N}P(k)\Delta r(k)$$ - -+ 近似计算 AP(`approximated average precision`)，这种计算方式是 approximated 形式的； -+ 很显然位于一条竖直线上的点对计算AP没有贡献； -+ 这里 `N` 为数据总量，`k` 为每个样本点的索引， $Δr(k)=r(k)−r(k−1)$。 - -**近似计算**`AP`和绘制`PR`曲线代码如下： -```Python -import numpy as np -import matplotlib.pyplot as plt - -class_names = ["car", "pedestrians", "bicycle"] - -def draw_PR_curve(predict_scores, eval_labels, name, cls_idx=1): - """calculate AP and draw PR curve, there are 3 types - Parameters: - @all_scores: single test dataset predict scores array, (-1, 3) - @all_labels: single test dataset predict label array, (-1, 3) - @cls_idx: the serial number of the AP to be calculated, example: 0,1,2,3... - """ - # print('sklearn Macro-F1-Score:', f1_score(predict_scores, eval_labels, average='macro')) - global class_names - fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(15, 10)) - # Rank the predicted scores from large to small, extract their corresponding index(index number), and generate an array - idx = predict_scores[:, cls_idx].argsort()[::-1] - eval_labels_descend = eval_labels[idx] - pos_gt_num = np.sum(eval_labels == cls_idx) # number of all gt - - predict_results = np.ones_like(eval_labels) - tp_arr = np.logical_and(predict_results == cls_idx, eval_labels_descend == cls_idx) # ndarray - fp_arr = np.logical_and(predict_results == cls_idx, eval_labels_descend != cls_idx) - - tp_cum = np.cumsum(tp_arr).astype(float) # ndarray, Cumulative sum of array elements. - fp_cum = np.cumsum(fp_arr).astype(float) - - precision_arr = tp_cum / (tp_cum + fp_cum) # ndarray - recall_arr = tp_cum / pos_gt_num - ap = 0.0 - prev_recall = 0 - for p, r in zip(precision_arr, recall_arr): - ap += p * (r - prev_recall) - # pdb.set_trace() - prev_recall = r - print("------%s, ap: %f-----" % (name, ap)) - - fig_label = '[%s, %s] ap=%f' % (name, class_names[cls_idx], ap) - ax.plot(recall_arr, precision_arr, label=fig_label) - - ax.legend(loc="lower left") - ax.set_title("PR curve about class: %s" % (class_names[cls_idx])) - ax.set(xticks=np.arange(0., 1, 0.05), yticks=np.arange(0., 1, 0.05)) - ax.set(xlabel="recall", ylabel="precision", xlim=[0, 1], ylim=[0, 1]) - - fig.savefig("./pr-curve-%s.png" % class_names[cls_idx]) - plt.close(fig) -``` - -### 5.2，插值计算AP -插值计算(`Interpolated average precision`) AP 的公式的演变过程这里不做讨论，详情可以参考这篇[文章](https://arleyzhang.github.io/articles/c521a01c/)，我这里的公式和图也是参考此文章的。`11点插值计算方式计算AP公式`如下： - -![11点插值计算方式计算AP公式](../images/插值计算AP公式.png) - -+ 这是通常意义上的 11 points_Interpolated 形式的 AP，选取固定的 {0,0.1,0.2,…,1.0} 11个阈值，这个在PASCAL2007中有使用 -+ 这里因为参与计算的只有11个点，所以 K=11，称为11points_Interpolated，k为阈值索引 -+ $P_{interp}(k)$ 取第 k 个阈值所对应的样本点之后的样本中的最大值，只不过这里的阈值被限定在了 {0,0.1,0.2,…,1.0} 范围内。 - - -![插值计算方式计算AP的PR曲线图](../images/插值计算AP的PR曲线图.png) - -从曲线上看，真实 `AP< approximated AP < Interpolated AP`，`11-points Interpolated AP` 可能大也可能小，当数据量很多的时候会接近于 `Interpolated AP`，与 `Interpolated AP` 不同，前面的公式中计算 `AP` 时都是对 `PR` 曲线的面积估计，PASCAL的论文里给出的公式就更加简单粗暴了，直接计算`11` 个阈值处的 `precision` 的平均值。`PASCAL` 论文给出的 11 点计算 AP 公式如下： - -![PASCAL论文给出的11点计算AP公式](../images/11点计算AP公式.png) - -## 六，AP计算代码 -1, 在给定 `recal` 和 `precision` 的条件下计算 `AP`： -```Python -def voc_ap(rec, prec, use_07_metric=False): - """ - ap = voc_ap(rec, prec, [use_07_metric]) - Compute VOC AP given precision and recall. - If use_07_metric is true, uses the - VOC 07 11 point method (default:False). - """ - if use_07_metric: - # 11 point metric - ap = 0. - for t in np.arange(0., 1.1, 0.1): - if np.sum(rec >= t) == 0: - p = 0 - else: - p = np.max(prec[rec >= t]) - ap = ap + p / 11. - else: - # correct AP calculation - # first append sentinel values at the end - mrec = np.concatenate(([0.], rec, [1.])) - mpre = np.concatenate(([0.], prec, [0.])) - - # compute the precision envelope - for i in range(mpre.size - 1, 0, -1): - mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) - - # to calculate area under PR curve, look for points - # where X axis (recall) changes value - i = np.where(mrec[1:] != mrec[:-1])[0] - - # and sum (\Delta recall) * prec - ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) - return ap -``` -2，给定目标检测结果文件和测试集标签文件 `xml` 等计算 `AP`： -```Python -def parse_rec(filename): - """ Parse a PASCAL VOC xml file - Return : list, element is dict. - """ - tree = ET.parse(filename) - objects = [] - for obj in tree.findall('object'): - obj_struct = {} - obj_struct['name'] = obj.find('name').text - obj_struct['pose'] = obj.find('pose').text - obj_struct['truncated'] = int(obj.find('truncated').text) - obj_struct['difficult'] = int(obj.find('difficult').text) - bbox = obj.find('bndbox') - obj_struct['bbox'] = [int(bbox.find('xmin').text), - int(bbox.find('ymin').text), - int(bbox.find('xmax').text), - int(bbox.find('ymax').text)] - objects.append(obj_struct) - - return objects - -def voc_eval(detpath, - annopath, - imagesetfile, - classname, - cachedir, - ovthresh=0.5, - use_07_metric=False): - """rec, prec, ap = voc_eval(detpath, - annopath, - imagesetfile, - classname, - [ovthresh], - [use_07_metric]) - Top level function that does the PASCAL VOC evaluation. - detpath: Path to detections result file - detpath.format(classname) should produce the detection results file. - annopath: Path to annotations file - annopath.format(imagename) should be the xml annotations file. - imagesetfile: Text file containing the list of images, one image per line. - classname: Category name (duh) - cachedir: Directory for caching the annotations - [ovthresh]: Overlap threshold (default = 0.5) - [use_07_metric]: Whether to use VOC07's 11 point AP computation - (default False) - """ - # assumes detections are in detpath.format(classname) - # assumes annotations are in annopath.format(imagename) - # assumes imagesetfile is a text file with each line an image name - # cachedir caches the annotations in a pickle file - - # first load gt - if not os.path.isdir(cachedir): - os.mkdir(cachedir) - cachefile = os.path.join(cachedir, '%s_annots.pkl' % imagesetfile) - # read list of images - with open(imagesetfile, 'r') as f: - lines = f.readlines() - imagenames = [x.strip() for x in lines] - - if not os.path.isfile(cachefile): - # load annotations - recs = {} - for i, imagename in enumerate(imagenames): - recs[imagename] = parse_rec(annopath.format(imagename)) - if i % 100 == 0: - print('Reading annotation for {:d}/{:d}'.format( - i + 1, len(imagenames))) - # save - print('Saving cached annotations to {:s}'.format(cachefile)) - with open(cachefile, 'wb') as f: - pickle.dump(recs, f) - else: - # load - with open(cachefile, 'rb') as f: - try: - recs = pickle.load(f) - except: - recs = pickle.load(f, encoding='bytes') - - # extract gt objects for this class - class_recs = {} - npos = 0 - for imagename in imagenames: - R = [obj for obj in recs[imagename] if obj['name'] == classname] - bbox = np.array([x['bbox'] for x in R]) - difficult = np.array([x['difficult'] for x in R]).astype(np.bool) - det = [False] * len(R) - npos = npos + sum(~difficult) - class_recs[imagename] = {'bbox': bbox, - 'difficult': difficult, - 'det': det} - - # read dets - detfile = detpath.format(classname) - with open(detfile, 'r') as f: - lines = f.readlines() - - splitlines = [x.strip().split(' ') for x in lines] - image_ids = [x[0] for x in splitlines] - confidence = np.array([float(x[1]) for x in splitlines]) - BB = np.array([[float(z) for z in x[2:]] for x in splitlines]) - - nd = len(image_ids) - tp = np.zeros(nd) - fp = np.zeros(nd) - - if BB.shape[0] > 0: - # sort by confidence - sorted_ind = np.argsort(-confidence) - sorted_scores = np.sort(-confidence) - BB = BB[sorted_ind, :] - image_ids = [image_ids[x] for x in sorted_ind] - - # go down dets and mark TPs and FPs - for d in range(nd): - R = class_recs[image_ids[d]] - bb = BB[d, :].astype(float) - ovmax = -np.inf - BBGT = R['bbox'].astype(float) - - if BBGT.size > 0: - # compute overlaps - # intersection - ixmin = np.maximum(BBGT[:, 0], bb[0]) - iymin = np.maximum(BBGT[:, 1], bb[1]) - ixmax = np.minimum(BBGT[:, 2], bb[2]) - iymax = np.minimum(BBGT[:, 3], bb[3]) - iw = np.maximum(ixmax - ixmin + 1., 0.) - ih = np.maximum(iymax - iymin + 1., 0.) - inters = iw * ih - - # union - uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) + - (BBGT[:, 2] - BBGT[:, 0] + 1.) * - (BBGT[:, 3] - BBGT[:, 1] + 1.) - inters) - - overlaps = inters / uni - ovmax = np.max(overlaps) - jmax = np.argmax(overlaps) - - if ovmax > ovthresh: - if not R['difficult'][jmax]: - if not R['det'][jmax]: - tp[d] = 1. - R['det'][jmax] = 1 - else: - fp[d] = 1. - else: - fp[d] = 1. - - # compute precision recall - fp = np.cumsum(fp) - tp = np.cumsum(tp) - rec = tp / float(npos) - # avoid divide by zero in case the first detection matches a difficult - # ground truth - prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps) - ap = voc_ap(rec, prec, use_07_metric) - - return rec, prec, ap -``` - -## 七，map计算方法 -因为 mAP 值的计算是对数据集中所有类别的 AP 值求平均，所以我们要计算 mAP，首先得知道某一类别的 AP 值怎么求。不同数据集的某类别的 AP 计算方法大同小异，主要分为三种： - -（1）在 `VOC2007`，只需要选取当Recall >= 0, 0.1, 0.2, ..., 1共11个点时的Precision最大值，然后AP就是这11个Precision的平均值，map就是所有类别AP值的平均。`VOC` 数据集中计算 `AP` 的代码（用的是插值计算方法，代码出自[py-faster-rcnn仓库](https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/datasets/voc_eval.py)） - -（2）在 `VOC2010` 及以后，需要针对每一个不同的 `Recall` 值（包括0和1），选取其大于等于这些 `Recall` 值时的 `Precision` 最大值，然后计算PR曲线下面积作为 AP 值，map 就是所有类别 AP 值的平均。 - -（3）`COCO` 数据集，设定多个 IOU 阈值（0.5-0.95,0.05为步长），在每一个IOU阈值下都有某一类别的 AP 值，然后求不同 IOU 阈值下的 AP 平均，就是所求的最终的某类别的AP值。 - -## 八，目标检测度量标准汇总 -![目标检测指标汇总](../images/目标检测度量标准汇总.jpg) -## 九，目标检测领域中的数据不均衡问题 +## 五，目标检测领域中的数据不均衡问题 参考此论文[Imbalance Problems in Object Detection](https://arxiv.org/abs/1909.00169.pdf)，我的理解之后补充。 ## 参考资料 -+ [目标检测评价标准-AP mAP](https://Harleyzhang.github.io/articles/c521a01c/) -+ [目标检测的性能评价指标](https://zhuanlan.zhihu.com/p/70306015?utm_source=wechat_session&utm_medium=social&utm_oi=571954943427219456) -+ [Soft-NMS](https://hellozhaozheng.github.io/z_post/%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89-SoftNMS-ICCV2017/) -+ [Recent Advances in Deep Learning for Object Detection](https://arxiv.org/abs/1908.03673v1) -+ [A Simple and Fast Implementation of Faster R-CNN](https://github.com/chenyuntc/simple-faster-rcnn-pytorch) ++ [NMS介绍](https://juejin.im/entry/5bdbc26151882516da0ddd25) ++ [Faster RCNN 源码解读(2) -- NMS(非极大抑制)](https://blog.csdn.net/williamyi96/article/details/77996167) \ No newline at end of file From f213074e4644d01a3c0a1747d0781456b5183bac Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Wed, 30 Sep 2020 22:54:52 +0800 Subject: [PATCH 371/386] Update title! --- ...46\344\271\240\345\237\272\347\241\200.md" | 227 +++++------------- ...72\347\241\200\347\237\245\350\257\206.md" | 0 2 files changed, 57 insertions(+), 170 deletions(-) rename "\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\346\250\241\345\236\213\347\232\204\345\237\272\347\241\200\346\246\202\345\277\265.md" => "\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\346\250\241\345\236\213\347\232\204\345\237\272\347\241\200\347\237\245\350\257\206.md" (100%) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" index 4a6d5c1a..409cadf8 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\345\237\272\347\241\200.md" @@ -299,10 +299,11 @@ Layer Name = pool5, Output size = 5, Stride = 32, RF size = 195 ``` ## 九，卷积和池化操作的作用 +> 卷积核池化的定义核过程理解是不难的，但是其作用却没有一个标准的答案，我在网上看了众多博客和魏秀参博士的书籍，总结了以下答案。 + 卷积层和池化层的理解可参考魏秀参的《解析卷积神经网络》书籍，卷积（`convolution` ）操作的作用如下： -1. 卷积网络中的卷积核参数是通过网络训练出来的。 +1. `局部感知，参数共享` 的特点大大降低了网络参数，保证了网络的稀疏性。 2. 通过卷积核的组合以及随着网络后续操作的进行，卷积操作可获取图像不同区域的不同类型特征；模型靠近底部的层提取的是局部的、高度通用的特征图，而更靠近顶部的层提取的是更加抽象的语义特征。 -3. 卷积神经网络学到的模式具有平移不变性（translation invariant），卷积神经网络可以学到模式的空间层次结构。 池化/汇合（`pooling` ）操作作用如下： 1. **特征不变性**（feature invariant）。汇合操作使模型更关注是否存在某些特征而不是特征具体的位置可看作是一种很强的先验，使特征学习包含某种程度自由度，能容忍一些特征微小的位移。 @@ -325,23 +326,24 @@ Layer Name = pool5, Output size = 5, Stride = 32, RF size = 195 + 局部连接使网络可以提取数据的局部特征。 + 权值共享大大降低了网络的训练难度，一个Filter只提取一个特征，在整个图片（或者语音／文本） 中进行卷积。 + 池化操作与多层次结构一起，实现了数据的降维，将低层次的局部特征组合成为较高层次的特征，从而对整个图片进行表示。 ++ 卷积神经网络学到的模式具有平移不变性（`translation invariant`），且可以学到模式的空间层次结构。 ## ### Reference [(二)计算机视觉四大基本任务(分类、定位、检测、分割](https://zhuanlan.zhihu.com/p/31727402) -## 深度特征的层次性 +## 十三，深度特征的层次性 卷积操作可获取图像区域不同类型特征，而汇合等操作可对这些特征进行融合和抽象，随着若干卷积、汇合等操作的堆叠，各层得到的深度特征逐渐从泛化特征（如边缘、纹理等）过渡到高层语义表示（躯干、头部等模式）。 -## 什么样的数据集不适合深度学习 +## 十四，什么样的数据集不适合深度学习 + 数据集太小，数据样本不足时，深度学习相对其它机器学习算法，没有明显优势。 + 数据集没有局部相关特性，目前深度学习表现比较好的领域主要是图像／语音／自然语言处理等领域，这些领域的一个共性是局部相关性。图像中像素组成物体，语音信号中音位组合成单词，文本数据中单词组合成句子，这些特征元素的组合一旦被打乱，表示的含义同时也被改变。对于没有这样的局部相关性的数据集，不适于使用深度学习算法进行处理。举个例子：预测一个人的健康状况，相关的参数会有年龄、职业、收入、家庭状况等各种元素，将这些元素打乱，并不会影响相关的结果。 -## 什么造成梯度消失问题 +## 十五，什么造成梯度消失问题 + 神经网络的训练中，通过改变神经元的权重，使网络的输出值尽可能逼近标签以降低误差值，训练普遍使用BP算法，核心思想是，计算出输出与标签间的损失函数值，然后计算其相对于每个神经元的梯度，进行权值的迭代。 + 梯度消失会造成权值更新缓慢，模型训练难度增加。造成梯度消失的一个原因是，许多激活函数将输出值挤压在很小的区间内，在激活函数两端较大范围的定义域内梯度为0，造成学习停止。 -## Overfitting怎么解决 +## 十六，Overfitting怎么解决 首先所谓过拟合，指的是一个模型过于复杂之后，它可以很好地“记忆”每一个训练数据中随机噪音的部分而忘记了去“训练”数据中的通用趋势。 **过拟合具体表现在：模型在训练数据上损失函数较小，预测准确率较高；但是在测试数据上损失函数比较大，预测准确率较低**。解决办法如下： + `数据增强`, 增加数据多样性; @@ -351,8 +353,8 @@ Layer Name = pool5, Output size = 5, Stride = 32, RF size = 195 + `BN` ,batch normalization; + Early Stopping(提前终止训练)。 -## L1和L2区别 -L1 范数（L1 norm）是指向量中各个元素绝对值之和，也有个美称叫“稀疏规则算子”（Lasso regularization）。 比如 向量 A=[1，-1，3]， 那么 A 的 L1 范数为 |1|+|-1|+|3|。简单总结一下就是： +## 十七，L1和L2区别 +L1 范数（`L1 norm`）是指向量中各个元素绝对值之和，也有个美称叫“稀疏规则算子”（Lasso regularization）。 比如 向量 A=[1，-1，3]， 那么 A 的 L1 范数为 |1|+|-1|+|3|。简单总结一下就是： + L1 范数: 为 x 向量各个元素绝对值之和。 + L2 范数: 为 x 向量各个元素平方和的 1/2 次方，L2 范数又称 Euclidean 范数或 Frobenius 范数 @@ -362,10 +364,10 @@ L1 范数（L1 norm）是指向量中各个元素绝对值之和，也有个美 `L1` 范数可以使权值参数稀疏，方便特征提取。 L2 范数可以防止过拟合，提升模型的泛化能力。 -## TensorFlow计算图 +## 十八，TensorFlow计算图概念 `Tensorflow` 是一个通过计算图的形式来表述计算的编程系统，计算图也叫数据流图，可以把计算图看做是一种有向图，Tensorflow 中的每一个计算都是计算图上的一个节点，而节点之间的边描述了计算之间的依赖关系。 -## BN（批归一化）的作用 +## 十九，BN（批归一化）的作用 在神经网络中间层也进行归一化处理，使训练效果更好的方法，就是批归一化Batch Normalization（BN） (1). **可以使用更高的学习率**。如果每层的scale不一致，实际上每层需要的学习率是不一样的，同一层不同维度的scale往往也需要不同大小的学习率，通常需要使用最小的那个学习率才能保证损失函数有效下降，Batch Normalization将每层、每维的scale保持一致，那么我们就可以直接使用较高的学习率进行优化。 @@ -378,17 +380,19 @@ L1 范数（L1 norm）是指向量中各个元素绝对值之和，也有个美 (5). **Batch Normalization调整了数据的分布，不考虑激活函数，它让每一层的输出归一化到了均值为0方差为1的分布**，这保证了梯度的有效性，可以解决反向传播过程中的梯度问题。目前大部分资料都这样解释，比如BN的原始论文认为的缓解了Internal Covariate Shift(ICS)问题。 -## 什么是梯度消失和爆炸，什么原因造成了梯度消失和梯度爆炸，怎么解决？ +## 二十，什么是梯度消失和爆炸 + 梯度消失是指在深度学习训练的过程中，梯度随着链式求导逐层传递逐层减小，最后趋近于0，导致对某些层的训练失效； + 梯度爆炸与梯度消失相反，梯度随着链式求导逐层传递逐层增大，最后趋于无穷，导致某些层无法收敛； > 在反向传播过程中需要对激活函数进行求导，如果导数大于 1，那么随着网络层数的增加，梯度更新将会朝着指数爆炸的方式增加这就是梯度爆炸。同样如果导数小于 1，那么随着网络层数的增加梯度更新信息会朝着指数衰减的方式减少这就是梯度消失。 +### 梯度消失和梯度爆炸产生的原因 **出现梯度消失和梯度爆炸的问题主要是因为参数初始化不当以及激活函数选择不当造成的**。其根本原因在于反向传播训练法则，属于先天不足。当训练较多层数的模型时，一般会出现梯度消失问题（gradient vanishing problem）和梯度爆炸问题（gradient exploding problem）。注意在反向传播中，当网络模型层数较多时，梯度消失和梯度爆炸是不可避免的。 **深度神经网络中的梯度不稳定性，根本原因在于前面层上的梯度是来自于后面层上梯度的乘积**。当存在过多的层次时，就出现了内在本质上的不稳定场景。前面的层比后面的层梯度变化更小，故变化更慢，故引起了梯度消失问题。前面层比后面层梯度变化更快，故引起梯度爆炸问题。 -解决梯度消失和梯度爆炸问题，常用的有以下几个方案： +### 如何解决梯度消失和梯度爆炸问题 +常用的有以下几个方案： + 预训练模型 + 微调 + 梯度剪切 + 正则化 @@ -396,13 +400,13 @@ L1 范数（L1 norm）是指向量中各个元素绝对值之和，也有个美 + BN批归一化 + CNN中的残差结构 + LSTM结构 -## RNN循环神经网络理解 +## 二十一，RNN循环神经网络理解 循环神经网络（recurrent neural network, RNN）, 主要应用在语音识别、语言模型、机器翻译以及时序分析等问题上。 **在经典应用中，卷积神经网络在不同的空间位置共享参数，循环神经网络是在不同的时间位置共享参数，从而能够使用有限的参数处理任意长度的序列。** `RNN` 可以看做作是同一神经网络结构在时间序列上被复制多次的结果，这个被复制多次的结构称为循环体，如何设计循环体的网络结构是RNN 解决实际问题的关键。RNN 的输入有两个部分，一部分为上一时刻的状态，另一部分为当前时刻的输入样本。 -## 训练过程中模型不收敛，是否说明这个模型无效，导致模型不收敛的原因有哪些? +## 二十二，训练过程中模型不收敛，是否说明这个模型无效，导致模型不收敛的原因有哪些? 不一定。导致模型不收敛的原因有很多种可能，常见的有以下几种： + 没有对数据做归一化。 @@ -417,7 +421,7 @@ L1 范数（L1 norm）是指向量中各个元素绝对值之和，也有个美 + 网络太深。隐藏层神经元数量错误。 + 更多回答，参考此[链接](http://theorangeduck.com/page/neural-network-not-working)。 -## VGG使用2个3*3卷积的优势在哪里？ +## 二十三，VGG使用2个3*3卷积的优势在哪里？ (1). **减少网络层参数**。用两个3\*3卷积比用1个5\*5卷积拥有更少的参数量，只有后者的2∗3∗3/5∗5=0.72。但是起到的效果是一样的，两个3*3的卷积层串联相当于一个5*5的卷积层，感受野的大小都是5×5，即1个像素会跟周围5*5的像素产生关联。把下图当成动态图看，很容易看到两个3×3卷积层堆叠（没有空间池化）有5×5的有效感受野。 ![2个３*3卷积层](../images/2个3_3卷积层.png) @@ -428,7 +432,11 @@ L1 范数（L1 norm）是指向量中各个元素绝对值之和，也有个美 **此回答可以参考TensorFlow实战p110，网上很多回答都说的不全**。 -## Relu比Sigmoid效果好在哪里？ +### 23.1，1*1卷积的主要作用 + ++ **降维（ dimension reductionality ）**。比如，一张500 \* 500且厚度depth为100 的图片在20个filter上做1\*1的卷积，那么结果的大小为500\*500\*20。 ++ **加入非线性**。卷积层之后经过激励层，1\*1的卷积在前一层的学习表示上添加了非线性激励（ non-linear activation ），提升网络的表达能力； +## 二十四，Relu比Sigmoid效果好在哪里？ Sigmoid函数公式如下： $\sigma (x)=\frac{1}{1+exp(-x)}$ @@ -446,17 +454,17 @@ ReLU 的输出要么是 0, 要么是输入本身。虽然方程简单，但实 3. 可以缓解过拟合问题的发生。`ReLU` 会使一部分神经元的输出为 0，这样就造成了网络的稀疏性，并且减少了参数的相互依存关系，缓解了过拟合问题的发生。 4. 相比 `sigmoid` 型函数，`ReLU` 函数有助于随机梯度下降方法收敛。 -#### 参考链接 +### 参考链接 [ReLU为什么比Sigmoid效果好](https://www.twblogs.net/a/5c2dd30fbd9eee35b21c4337/zh-cn) -## 神经网络中权值共享的理解 +## 二十五，神经网络中权值共享的理解 权值(权重)共享这个词是由LeNet5模型提出来的。以CNN为例，在对一张图偏进行卷积的过程中，使用的是同一个卷积核的参数。 比如一个3×3×1的卷积核，这个卷积核内9个的参数被整张图共享，而不会因为图像内位置的不同而改变卷积核内的权系数。说的再直白一些，就是用一个卷积核不改变其内权系数的情况下卷积处理整张图片（当然CNN中每一层不会只有一个卷积核的，这样说只是为了方便解释而已）。 #### 参考资料 [如何理解CNN中的权值共享](https://blog.csdn.net/chaipp0607/article/details/73650759) -## 对fine-tuning(微调模型的理解)，为什么要修改最后几层神经网络权值？ +## 二十六，对fine-tuning(微调模型的理解)，为什么要修改最后几层神经网络权值？ 使用预训练模型的好处，在于利用训练好的SOTA模型权重去做特征提取，可以节省我们训练模型和调参的时间。 至于为什么只微调最后几层神经网络权重，是因为： @@ -464,17 +472,17 @@ ReLU 的输出要么是 0, 要么是输入本身。虽然方程简单，但实 (1). CNN中更靠近底部的层（定义模型时先添加到模型中的层）编码的是更加通用的可复用特征，而更靠近顶部的层（最后添加到模型中的层）编码的是更专业业化的特征。微调这些更专业化的特征更加有用，它更代表了新数据集上的有用特征。 (2). 训练的参数越多，过拟合的风险越大。很多SOTA模型拥有超过千万的参数，在一个不大的数据集上训练这么多参数是有过拟合风险的，除非你的数据集像Imagenet那样大。 -#### 参考资料 +### 参考资料 Python深度学习p127. -## 什么是dropout? +## 二十七，什么是dropout? + dropout可以防止过拟合，dropout简单来说就是：我们在前向传播的时候，**让某个神经元的激活值以一定的概率 p 停止工作**，这样可以使模型的泛化性更强，因为它不会依赖某些局部的特征。 + `dropout`效果跟`bagging`效果类似（bagging是减少方差variance，而boosting是减少偏差bias） + 加入dropout会使神经网络训练时间边长，模型预测时不需要dropout，记得关掉。 ![dropou直观展示](../images/使用Dropout的神经网络模型.jpg) -### dropout具体工作流程 +### 27.1，dropout具体工作流程 以 标准神经网络为例，正常的流程是：我们首先把输入数据x通过网络前向传播，然后把误差反向传播一决定如何更新参数让网络进行学习。使用dropout之后，过程变成如下： 1，首先随机（临时）删掉网络中一半的隐藏神经元，输入输出神经元保持不变（图3中虚线为部分临时被删除的神经元）； @@ -486,7 +494,7 @@ Python深度学习p127. + 从隐藏层神经元中随机选择一个一半大小的子集临时删除掉（同时备份被删除神经元的参数）; + 对一小批训练样本，先前向传播然后反向传播损失并根据随机梯度下降法更新参数（w，b） （没有被删除的那一部分参数得到更新，删除的神经元参数保持被删除前的结果）。 -### dropout在神经网络中的应用 +### 27.2，dropout在神经网络中的应用 (1). 在训练模型阶段 不可避免的，在训练网络中的每个单元都要添加一道概率流程，标准网络和带有dropout网络的比较图如下所示： @@ -499,7 +507,7 @@ Python深度学习p127. ![dropout在测试模型时](../images/预测模型时Dropout的操作.jpg) -### 如何选择dropout 的概率 +### 27.3，如何选择dropout 的概率 input 的 dropout 概率推荐是 0.8， hidden layer 推荐是0.5， 但是也可以在一定的区间上取值。（All dropout nets use p = 0.5 for hidden units and p = 0.8 for input units.） ### 参考资料 @@ -507,7 +515,7 @@ input 的 dropout 概率推荐是 0.8， hidden layer 推荐是0.5， 但是也 1. [Dropout:A Simple Way to Prevent Neural Networks from Overfitting] 2. [深度学习中Dropout原理解析](https://zhuanlan.zhihu.com/p/38200980) -## HOG算法原理描述 +## 二十八，HOG算法原理描述 **方向梯度直方图（Histogram of Oriented Gradient, HOG）特征**是一种在计算机视觉和图像处理中用来进行物体检测的特征描述子。它通过计算和统计图像局部区域的梯度方向直方图来构成特征。在深度学习取得成功之前，Hog特征结合SVM分类器被广泛应用于图像识别中，在行人检测中获得了较大的成功。 ### HOG特征原理 @@ -528,20 +536,13 @@ HOG 特征提取方法就是将一个image： **总结**：颜色空间归一化——>梯度计算——>梯度方向直方图——>重叠块直方图归一化———>HOG特征 ### 参考资料 [HOG特征检测－简述](https://blog.csdn.net/liyuqian199695/article/details/53835989) -## 移动端深度学习框架知道哪些，用过哪些？ -知名的有TensorFlow Lite、小米MACE、腾讯的 ncnn 等，目前都没有用过。 -## 如何提升网络的泛化能力 -和防止模型过拟合的方法类似，另外还有模型融合方法。 -## BN算法，为什么要在后面加伽马和贝塔，不加可以吗？ -最后的 `scale and shift` 操作则是为了让因训练所需而“刻意”加入的BN能够有可能还原最初的输入。不加也可以。 -## 激活函数 - -### 激活函数的作用 +## 二十九，激活函数 +### 29.1，激活函数的作用 + **激活函数实现去线性化**。激活函数的引入是为了增加整个网络的表达能力（引入非线性因素），否则若干线形层的堆叠仍然只能起到线性映射的作用，无法形成复杂的函数。如果将每一个神经元（也就是神经网络的节点）的输出通过一个非线性函数，那么整个神经网络的模型也就不再是线性的了，这个非线性函数就是激活函数。 + 激活函数可以把**当前特征空间通过一定的线性映射转换到另一个空间**，让数据能够更好的被分类。 + 激活函数对模型学习、理解非常复杂和非线性的函数具有重要作用。 -### 常见的激活函数 +### 29.2，常见的激活函数 激活函数也称非线性映射函数，常见的激活函数有：`ReLU` 函数、`sigmoid` 函数、`tanh` 函数等，其计算公式如下： @@ -549,7 +550,7 @@ HOG 特征提取方法就是将一个image： + sigmoid函数：$f(x)=\frac{1}{1+e^{-x}}$ + tanh函数：$f(x)=\frac{1+e^{-2x}}{1+e^{-2x}}=\frac{2}{1+e^{-2x}}-1 = 2sigmoid(2x)-1$ -### 激活函数理解及函数梯度图 +### 29.3，激活函数理解及函数梯度图 1. `Sigmoid` 激活函数，可用作分类任务的输出层。可以看出经过sigmoid激活函数后，模型输出的值域被压缩到 `0到1` 区间（这和概率的取值范围一致），这正是分类任务中sigmoid很受欢迎的原因。 ![sigmoid激活函数及梯度图](../images/sigmoid激活函数.png) @@ -562,7 +563,7 @@ HOG 特征提取方法就是将一个image： ![Relu函数及梯度图](../images/relu函数及其梯度.jpg) -## 卷积层和池化层有什么区别 +## 三十，卷积层和池化层有什么区别 1. **卷积层有参数，池化层没有参数**； 2. **经过卷积层节点矩阵深度会改变**。池化层不会改变节点矩阵的深度，但是它可以缩小节点矩阵的大小。 ## 卷积层参数数量计算方法 @@ -570,7 +571,7 @@ HOG 特征提取方法就是将一个image： 假设输入层矩阵维度是 96×96×3，第一层卷积层使用尺寸为 5×5、深度为 16 的过滤器（卷积核尺寸为 5×5、卷积核数量为 16），那么这层卷积层的参数个数为 ５×5×3×16+16=1216个。 -## 卷积层输出大小计算 +## 三十一，卷积层输出大小计算 卷积中的特征图大小计算方式有两种，分别是`VALID`和`SAME`，卷积和池化都适用，除不尽的结果都向下取整。公式：`O = (W-F+2P)/S+1`，输入图片（Input）大小为 I=W×W，卷积核（Filter）大小为 F×F，步长（stride）为 S，填充（Padding）的像素数为 P。 @@ -579,135 +580,11 @@ HOG 特征提取方法就是将一个image： ![卷积填充方式](../images/卷积填充方式.png) -## 神经网络为什么用交叉熵损失函数 +## 三十二，神经网络为什么用交叉熵损失函数 判断一个输出向量和期望的向量有多接近，交叉熵（cross entroy）是常用的评判方法之一。交叉熵刻画了两个概率分布之间的距离，是分类问题中使用比较广泛的一种损失函数。 给定两个概率分布 `p` 和 `q` ，通过 `q` 来表示 `p` 的交叉熵公式为：$H(p,q)=−∑p(x)logq(x)$ - -## softmax公式 - -$$softmax(y)_{i} = \frac{e^{y_{i}}}{\sum_{j=1}^{n}e^{y_j}}$$ - -![softmax公式](../images/softmax公式.png) - -## 1*1卷积的主要作用 - -+ **降维（ dimension reductionality ）**。比如，一张500 \* 500且厚度depth为100 的图片在20个filter上做1\*1的卷积，那么结果的大小为500\*500\*20。 -+ **加入非线性**。卷积层之后经过激励层，1\*1的卷积在前一层的学习表示上添加了非线性激励（ non-linear activation ），提升网络的表达能力； - -## 目标检测基本概念 -为了了解模型的泛化能力，即判断模型的好坏，我们需要用某个指标来衡量，有了评价指标，就可以对比不同模型的优劣，并通过这个指标来进一步调参优化模型。**对于分类和回归两类监督模型，分别有各自的评判标准**。 - -### 准确率、精确率、召回率、F1 - -**准确率 – Accuracy**，预测正确的结果占总样本的百分比，公式如下：$准确率 = (TP+TN)/(TP+TN+FP+FN)$. - -> 虽然准确率可以判断总的正确率，但是在样本不平衡 的情况下，并不能作为很好的指标来衡量结果。举个简单的例子，比如在一个总样本中，正样本占 90%，负样本占 10%，样本是严重不平衡的。对于这种情况，我们只需要将全部样本预测为正样本即可得到 90% 的高准确率，但实际上我们并没有很用心的分类，只是随便无脑一分而已。这就说明了：由于样本不平衡的问题，导致了得到的高准确率结果含有很大的水分。即如果样本不平衡，准确率就会失效。 - -混淆矩阵： - -+ True Positive(真正例, TP)：将正类预测为正类数. -+ True Negative(真负例, TN)：将负类预测为负类数. -+ False Positive(假正例, FP)：将负类预测为正类数 → 误报 (Type I error). -+ False Negative(假负例子, FN)：将正类预测为负类数 → 漏报 (Type II error). - -查准率与查全率计算公式： - -+ 查准率（精确率）P = TP/(TP+FP) -+ 查全率（召回率）R = TP/(TP+FN) - -> 精准率和准确率看上去有些类似，但是完全不同的两个概念。**精准率代表对正样本结果中的预测准确程度，而准确率则代表整体的预测准确程度**，既包括正样本，也包括负样本。 - -**精确率描述了模型有多准**，即在预测为正例的结果中，有多少是真正例；**召回率则描述了模型有多全**，即在为真的样本中，有多少被我们的模型预测为正例。**精确率和召回率的区别在于分母不同，一个分母是预测为正的样本数，另一个是原来样本中所有的正样本数**。 -### 精准率和召回率的关系-PR曲线图 - -精准率和召回率的关系可以用一个 P-R 图来展示，以查准率 P 为纵轴、查全率 R 为横轴作图，就得到了查准率－查全率曲线，简称**P-R**曲线，PR 曲线下的面积定义为 `AP`: - -![PR曲线图](../images/PR曲线图.jpeg) - -**如何理解 P-R（查准率-查全率）这条曲线？** - -可以从排序型模型或者分类模型理解。以逻辑回归举例，逻辑回归的输出是一个 0 到 1 之间的概率数字，因此，如果我们想要根据这个概率判断用户好坏的话，我们就必须定义一个阈值 。通常来讲，逻辑回归的概率越大说明越接近 1，也就可以说他是坏用户的可能性更大。比如，我们定义了阈值为 0.5，即概率小于 0.5 的我们都认为是好用户，而大于 0.5 都认为是坏用户。因此，对于阈值为 0.5 的情况下，我们可以得到相应的一对查准率和查全率。 - -但问题是：这个阈值是我们随便定义的，我们并不知道这个阈值是否符合我们的要求。 因此，为了找到一个最合适的阈值满足我们的要求，我们就必须遍历 0 到 1 之间所有的阈值，而每个阈值下都对应着一对查准率和查全率，从而我们就得到了这条曲线。 - -有的朋友又问了：如何找到最好的阈值点呢？ 首先，需要说明的是我们对于这两个指标的要求：我们希望查准率和查全率同时都非常高。 但实际上这两个指标是一对矛盾体，无法做到双高。图中明显看到，如果其中一个非常高，另一个肯定会非常低。选取合适的阈值点要根据实际需求，比如我们想要高的查全率，那么我们就会牺牲一些查准率，在保证查全率最高的情况下，查准率也不那么低。 - -### PR、ROC曲线与AUC面积 - -+ **PR 曲线是以 Recall 为横轴，Precision 为纵轴；而 ROC曲线则是以 FPR 为横轴，TPR 为纵轴**。**P-R 曲线越靠近右上角性能越好**。 -+ **PR 曲线展示的是Precision vs Recall 的曲线，ROC 曲线展示的是 FPR（x 轴：False positive rate） vs TPR（True positive rate, TPR）曲线**。 -+ `P、R` 和 `F1` 指标评价在信息检索中用的比较多，在机器学习中还有一种 `ROC` 曲线来评价性能。 -+ PR 曲线与 ROC 曲线的相同点是都采用了 `TPR (Recall)`，都可以用 AUC 来衡量分类器的效果。不同点是 `ROC` 曲线使用了 `FPR`，而 `PR` 曲线使用了 `Precision`，因此**PR曲线的两个指标都聚焦于正例**。类别不平衡问题中由于主要关心正例，所以在此情况下PR曲线被广泛认为优于ROC曲线。 - -### F1 分数 -如果想要找到 $P$ 和 $R$ 二者之间的一个平衡点，我们就需要一个新的指标：$F1$ 分数。$F1$ 分数同时考虑了查准率和查全率，让二者同时达到最高，取一个平衡。$F1$ 计算公式如下： - -$$F1 = \frac{2*P*R}{P+R} = \frac{2*TP}{样例总数+TP-TN}$$ - -$F1$ 度量的一般形式：$F_{\beta}$，能让我们表达出对查准率/查全率的偏见，$F_{\beta}$计算公式如下： - -$$F_{\beta} = \frac{1+\beta^{2}*P*R}{(\beta^{2}*P)+R}$$ - -其中 $\beta >1$ 对查全率有更大影响，$\beta < 1$ 对查准率有更大影响。 - -不同的计算机视觉问题，对两类错误有不同的偏好，常常在某一类错误不多于一定阈值的情况下，努力减少另一类错误。在目标检测中，**mAP**（mean Average Precision）作为一个统一的指标将这两种错误兼顾考虑。 - -### 二分类、多分类指标计算 -#### 二分类 -$$F1\ score = \frac{2 \times P \times R}{(P + R)}$$ -其中，准确率(precise) $P = TP / (TP + FP)$，召回率(recall) $R = TP / (TP + FN)$. - -#### 多分类 -+ **Macro F1**: 将 `n` 分类的评价拆成 `n` 个二分类的评价，计算每个二分类的 `F1 score`，`n` 个 `F1 score` 的平均值即为 `Macro F1`。 -+ **Micro F1**: 将 n 分类的评价拆成 n 个二分类的评价，将 n 个二分类评价的TP、FP、RN对应相加，计算评价准确率和召回率，由这 `2` 个准确率和召回率计算的F1 score 即为 Micro F1。 -+ 一般来讲，Macro F1、Micro F1 值高的分类效果好。`Macro F1`受样本数量少的类别影响大。 - -#### mAP指标理解 -`AP` 衡量的是训练好的模型在每个类别上的好坏，`mAP` 衡量的是模型在所有类别上的好坏，得到 AP 后 mAP 的计算就变得很简单了，就是取所有 `AP` 的平均值。 - -`mAP` 这个术语有不同的定义。此度量指标通常用于信息检索、图像分类和目标检测领域。然而这两个领域计算 `mAP` 的方式却不相同。这里我们只谈论目标检测中的 `mAP` 计算方法。 - -`mAP` 常作为目标检测算法的评价指标，具体来说就是，对于每张图片检测模型会输出多个预测框（远超真实框的个数），我们使用 `IoU` (Intersection Over Union，交并比)来标记预测框是否预测准确。标记完成后，随着预测框的增多，查全率 `R` 总会上升，**在不同查全率 `R` 水平下对准确率 `P` 做平均，即得到AP**，最后再对所有类别按其所占比例做平均，即得到 `mAP` 指标。 - -#### 交并比IOU - -交并比（Intersection-over-Union，IoU），目标检测中使用的一个概念，是产生的候选框面积（candidate bound）与原标记框面积（ground truth bound）的交叠率，即它们的交集与并集的比值。最理想情况是完全重叠，即比值为1。 -计算公式如下：$$IOU = \frac{A \cap B}{A \cup B}$$ - -代码实现如下： - -```python -# candidateBound = [x1, y1, x2, y2] -def calculateIoU(candidateBound, groundTruthBound): - cx1 = candidateBound[0] - cy1 = candidateBound[1] - cx2 = candidateBound[2] - cy2 = candidateBound[3] - gx1 = groundTruthBound[0] - gy1 = groundTruthBound[1] - gx2 = groundTruthBound[2] - gy2 = groundTruthBound[3] - - carea = (cx2 - cx1) * (cy2 - cy1) #C的面积 - garea = (gx2 - gx1) * (gy2 - gy1) #G的面积 - x1 = max(cx1, gx1) - y1 = min(cy1, gy1) # 原点为(0, 0)，所以这里是min不是max - x2 = min(cx2, gx2) - y2 = max(cy2, gy2) - w = max(0, (x2 - x1)) - h = max(0, (y2 - y1)) - area = w * h #C∩G的面积 - - iou = area / (carea + garea - area) - return iou - -``` - -### 参考资料 -[分类模型评估指标——准确率、精准率、召回率、F1、ROC曲线、AUC曲线](https://easyai.tech/ai-definition/accuracy-precision-recall-f1-roc-auc/) -[ 一文让你彻底理解准确率，精准率，召回率，真正率，假正率，ROC/AUC](https://www.6aiq.com/article/1549986548173) -## 数据增强方法，离线数据增强和在线数据增强有什么区别？ +## 三十三，数据增强方法有哪些 常用数据增强方法： @@ -721,6 +598,7 @@ def calculateIoU(candidateBound, groundTruthBound): + 亮度、对比度增强：这是图像色彩进行增强的操作 + 锐化：`Sharpen`。imgaug库使用Sharpen函数。 +### 33.1，离线数据增强和在线数据增强有什么区别 数据增强分两类，一类是**离线增强**，一类是**在线增强**： 1. 离线增强 ： 直接对硬盘上的数据集进行处理，并保存增强后的数据集文件。数据的数目会变成增强因子 x 原数据集的数目 ，这种方法常常用于数据集很小的时候 @@ -728,10 +606,7 @@ def calculateIoU(candidateBound, groundTruthBound): ### Reference [深度学习中的数据增强](https://blog.csdn.net/zhelong3205/article/details/81810743 ) -## 为什么 Faster RCNN/Mask RCNN 需要使用 ROI Pooling/ROI Align? -为了使得最后面的两个全连接层能够共享 `conv layers(VGG/ResNet)` 权重。在所有的 `RoIs` 都被 `pooling` 成（512×7×7）的`feature map`后，将它 `reshape` 成一个一维的向量，就可以利用 `VGG16` 的预训练的权重来初始化前两层全连接。 - -## ROI Pooling替换为ROI Align，及各自原理 +## 三十四，ROI Pooling替换为ROI Align，及各自原理 `faster rcnn` 将 `roi pooling` 替换为 `roi align` 效果有所提升。 @@ -772,12 +647,24 @@ ROI Align的工作原理如下图。 ### Reference [RoIPooling、RoIAlign笔记](https://www.cnblogs.com/wangyong/p/8523814.html) -## CNN的反向传播算法推导 +## 三十五CNN的反向传播算法推导 + [四张图彻底搞懂CNN反向传播算法（通俗易懂）](https://zhuanlan.zhihu.com/p/81675803) + [反向传播算法推导过程（非常详细）](https://zhuanlan.zhihu.com/p/79657669) -## Focal Loss 介绍一下 +## 三十六，Focal Loss 公式 +## 快速回答 +### 为什么 Faster RCNN/Mask RCNN 需要使用 ROI Pooling/ROI Align? +为了使得最后面的两个全连接层能够共享 `conv layers(VGG/ResNet)` 权重。在所有的 `RoIs` 都被 `pooling` 成（512×7×7）的`feature map`后，将它 `reshape` 成一个一维的向量，就可以利用 `VGG16` 的预训练的权重来初始化前两层全连接。 +### softmax公式 +$$softmax(y)_{i} = \frac{e^{y_{i}}}{\sum_{j=1}^{n}e^{y_j}}$$ +![softmax公式](../images/softmax公式.png) +### 移动端深度学习框架知道哪些，用过哪些？ +知名的有TensorFlow Lite、小米MACE、腾讯的 ncnn 等，目前都没有用过。 +### 如何提升网络的泛化能力 +和防止模型过拟合的方法类似，另外还有模型融合方法。 +### BN算法，为什么要在后面加伽马和贝塔，不加可以吗？ +最后的 `scale and shift` 操作则是为了让因训练所需而“刻意”加入的BN能够有可能还原最初的输入。不加也可以。 ## Reference 1. [《Batch Normalization Accelerating Deep Network Training by Reducing Internal Covariate Shift》阅读笔记与实现](https://blog.csdn.net/happynear/article/details/44238541) 2. [详解机器学习中的梯度消失、爆炸原因及其解决方法](https://blog.csdn.net/qq_25737169/article/details/78847691) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\346\250\241\345\236\213\347\232\204\345\237\272\347\241\200\346\246\202\345\277\265.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\346\250\241\345\236\213\347\232\204\345\237\272\347\241\200\347\237\245\350\257\206.md" similarity index 100% rename from "\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\346\250\241\345\236\213\347\232\204\345\237\272\347\241\200\346\246\202\345\277\265.md" rename to "\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\346\250\241\345\236\213\347\232\204\345\237\272\347\241\200\347\237\245\350\257\206.md" From 5d4d627e11273fb5c354ce16f8306bca7ea9f8cc Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Fri, 16 Oct 2020 11:18:31 +0800 Subject: [PATCH 372/386] Add my blog site --- README.md | 8 ++++---- ...er\345\255\246\344\271\240\347\254\224\350\256\260.md" | 3 +-- ...\273\267\346\240\207\345\207\206-AP\344\270\216mAP.md" | 2 ++ 3 files changed, 7 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 9daada66..f457c72b 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@ ## 前言 -本仓库的知识点和文章对于我主要用于我个人复习和作知识点总结使用，部分内容参考自`github项目/网络资料/书籍`等，由于时间和能力有限，有些知识点其实掌握得还不够深和全面, 我推荐阅读以下 `github` 仓库项目（有些已经停止更新, 当作参考） +本仓库的知识点和文章主要用于个人复习和作知识点总结使用，部分内容参考自`github项目/网络资料/书籍`和[个人博客](https://www.armcv.cn/)等，由于时间和能力有限，有些知识点其实掌握得还不够深和全面, 我推荐额外阅读以下 `github` 仓库项目（有些已经停止更新, 当作参考） + [Algorithm_Interview_Notes-Chinese](https://github.com/imhuay/Algorithm_Interview_Notes-Chinese) + [关于Python的面试题](https://github.com/taizilongxu/interview_python) + [CS-Notes](https://github.com/CyC2018/CS-Notes) @@ -15,9 +15,9 @@ + [深度学习](https://github.com/HarleysZhang/2019_algorithm_intern_information/tree/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0) + [计算机视觉](https://github.com/HarleysZhang/2019_algorithm_intern_information/tree/master/%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89) -## 2019春招算法实习岗位表 -2019年春招近期实习岗位汇总，还有待完善，持续更新，也希望各位一起更新。 -注意：大部分岗位都是面向2020年毕业的学生。 +## 2020春招算法实习岗位表 +2020 年春招近期实习岗位汇总，还有待完善，持续更新，也希望各位一起更新。 +注意：大部分岗位都是面向 2021 年毕业的学生。 |公司|招聘岗位|工作地点|截止时间|投递方式|内推方式|是否投递|信息来源| |---|-------|-------|------|-------|-------|------|-------| diff --git "a/\347\274\226\347\250\213\350\257\255\350\250\200/C++ Primer\345\255\246\344\271\240\347\254\224\350\256\260.md" "b/\347\274\226\347\250\213\350\257\255\350\250\200/C++ Primer\345\255\246\344\271\240\347\254\224\350\256\260.md" index 1b4e56ac..cc0d05e3 100644 --- "a/\347\274\226\347\250\213\350\257\255\350\250\200/C++ Primer\345\255\246\344\271\240\347\254\224\350\256\260.md" +++ "b/\347\274\226\347\250\213\350\257\255\350\250\200/C++ Primer\345\255\246\344\271\240\347\254\224\350\256\260.md" @@ -215,5 +215,4 @@ class Bulk_quote: public Quote{...} - 15.8 容器与继承 -### 第16章 模板与泛型编程 - +### 第16章 模板与泛型编程 \ No newline at end of file diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\346\200\247\350\257\204\344\273\267\346\240\207\345\207\206-AP\344\270\216mAP.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\346\200\247\350\257\204\344\273\267\346\240\207\345\207\206-AP\344\270\216mAP.md" index b2a5977c..0f8a7ff1 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\346\200\247\350\257\204\344\273\267\346\240\207\345\207\206-AP\344\270\216mAP.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211/\347\233\256\346\240\207\346\243\200\346\265\213\346\200\247\350\257\204\344\273\267\346\240\207\345\207\206-AP\344\270\216mAP.md" @@ -393,3 +393,5 @@ def voc_eval(detpath, + [Soft-NMS](https://hellozhaozheng.github.io/z_post/%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89-SoftNMS-ICCV2017/) + [Recent Advances in Deep Learning for Object Detection](https://arxiv.org/abs/1908.03673v1) + [A Simple and Fast Implementation of Faster R-CNN](https://github.com/chenyuntc/simple-faster-rcnn-pytorch) ++ [分类模型评估指标——准确率、精准率、召回率、F1、ROC曲线、AUC曲线](https://easyai.tech/ai-definition/accuracy-precision-recall-f1-roc-auc/) ++ [ 一文让你彻底理解准确率，精准率，召回率，真正率，假正率，ROC/AUC](https://www.6aiq.com/article/1549986548173) \ No newline at end of file From f5def78aed4d3e458d8538ce9b0b610f5bf0ac8a Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Mon, 26 Oct 2020 20:48:40 +0800 Subject: [PATCH 373/386] Add MACC utilization calculation formula --- README.md | 6 +- ...6\344\271\240\347\254\224\350\256\260.png" | Bin 0 -> 291480 bytes ...1\347\256\227\345\205\254\345\274\217.png" | Bin 0 -> 4790 bytes ...02\345\272\246\345\210\206\346\236\220.md" | 53 +++++++++++++----- ...46\344\271\240\347\254\224\350\256\260.md" | 29 ++-------- ...\240\207\345\207\206-AP\344\270\216mAP.md" | 27 +++++++-- 6 files changed, 72 insertions(+), 43 deletions(-) create mode 100644 "images/C++ Primer\345\255\246\344\271\240\347\254\224\350\256\260.png" create mode 100644 "images/\347\256\227\345\212\233\345\210\251\347\224\250\347\216\207\350\256\241\347\256\227\345\205\254\345\274\217.png" diff --git a/README.md b/README.md index f457c72b..a15b0ba2 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,13 @@ ## 前言 本仓库的知识点和文章主要用于个人复习和作知识点总结使用，部分内容参考自`github项目/网络资料/书籍`和[个人博客](https://www.armcv.cn/)等，由于时间和能力有限，有些知识点其实掌握得还不够深和全面, 我推荐额外阅读以下 `github` 仓库项目（有些已经停止更新, 当作参考） + + [Algorithm_Interview_Notes-Chinese](https://github.com/imhuay/Algorithm_Interview_Notes-Chinese) + [关于Python的面试题](https://github.com/taizilongxu/interview_python) + [CS-Notes](https://github.com/CyC2018/CS-Notes) + [用动画的形式呈现解LeetCode题目的思路](https://github.com/MisterBooo/LeetCodeAnimation) - ## 算法/图像/CS基础/编程语言(`RoadMap`) + + [计算机基础](https://github.com/HarleysZhang/2019_algorithm_intern_information/tree/master/%E8%AE%A1%E7%AE%97%E6%9C%BA%E5%9F%BA%E7%A1%80) + [数据结构与算法](https://github.com/HarleysZhang/2019_algorithm_intern_information/tree/master/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E4%B8%8E%E7%AE%97%E6%B3%95) + [数字图像处理](https://github.com/HarleysZhang/2019_algorithm_intern_information/tree/master/%E6%95%B0%E5%AD%97%E5%9B%BE%E5%83%8F%E5%A4%84%E7%90%86) @@ -16,6 +17,7 @@ + [计算机视觉](https://github.com/HarleysZhang/2019_algorithm_intern_information/tree/master/%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89) ## 2020春招算法实习岗位表 + 2020 年春招近期实习岗位汇总，还有待完善，持续更新，也希望各位一起更新。 注意：大部分岗位都是面向 2021 年毕业的学生。 @@ -58,6 +60,7 @@ ## [深度学习面试题](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E9%9D%A2%E8%AF%95%E9%A2%98.md) ## 2020年校招可投递公司-计算机视觉/机器学习算法工程师 + 侧重于于计算机视觉/图像/机器学习算法工程师岗位,公司排名几乎部分先后。 |神级公司|互联网公司|AI独角兽公司|其他大公司|是否投递| @@ -97,4 +100,3 @@ |无|巨人网络/盛大游戏|酷家乐|华米/绿米|无| |无|最右/快看漫画|码隆科技||无| |无|猫眼娱乐/多牛传媒|||无| - diff --git "a/images/C++ Primer\345\255\246\344\271\240\347\254\224\350\256\260.png" "b/images/C++ Primer\345\255\246\344\271\240\347\254\224\350\256\260.png" new file mode 100644 index 0000000000000000000000000000000000000000..143cf5b37ddf4bb859c6ab634151359f8bad70ea GIT binary patch literal 291480 zcmdqJ2{@Gh-#MQLj6sc&jCrjDeglr-EzV8ZS-%3b@kc5!0kQnQ7BH7;_Vw+Uhn3IU7nqy zKHgbw-<(NzQ=;8=AO$!4B_oin*RXuOO!{UyeYS7f(Im0IGJKDU`4;qC&i+Lo?tCkG z!rsD=Yf15SquKb+0~+F*fmBnkRCpYVi!-HM>qo%2K;+hd6 z>9UnwUU*u-XE_h+)nr-MFwi^m)g3NE~@(O63efo|g{L?SZEtmwlYb zkAAmMbnrBGYS%vG$I-)b$kSRVY8O1!iSId#{8)1~2zmPN_^6b8g#4{gvu{F7z4q9= 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z%h)2&&J#!`?uO~e)4dX1z#FNYSZNXp6$dh!}U2GM?6V|sVOCNixpw5zM|D;rq4LicUlB48y& zkR2y0H$;tDU&x_Blf5O3Oy99Hro2c$tC+dulZttpc#Y%FG;jnqEJ5C@y=@lW;*;lq z?b4zS%Wy(*kF#w$Hl{1R>qDTF-orfIYLa9-M-p7POrU(7R^~04%z^ki>LmCwP%dC; z`c<@BYsAD^@tE7d6M;7!Wv>0S;;JWgmL!J8)}p9YFP42ujdJlSiT ztJom03&RT5=HJBH@=X{tK#LZ;u(n7|naA|iKO^;!7Bm9Gz%^U;SOVwg1zPgdyO$V5 zTG&{i@oukRbJ4-HOAA6aZ&Yd%YuVzH0B7M>r_s(G!rZl2#bKfy%{so5rAU?#n>OIu z1gYOmyfrVr_81`Md)XAAwC7(eEoLRr_ILv9)oCO(pGlzCKG=@u#oa^017aD`!+uDiC@M) z{rO*bt1Hu^9`)z}(%Ul|Xpef-qaHnA>VaX8deoyHp$CRN>QRq+B-8`L9`&e4Jwgu* zd(@*I^+>1}T5}*$P}vd+hmPp17|kkL>q`C68=rNa7-Op4JvBo4h5;hg%py-kdcOQa^A|vFGKYZD4Wrd$q9VkH(r&!yl$pMw-w;fSn|7Ctp=K#H`62S z=4jkb9gIDs{idIzBn_SW!F4BwZF*;UQ{o$)ERA8`AUuwWr^5Am70v20IMgOPa9H9G zB{R0)mM-_QS-p$Jp829ohL@1|sJe!By-aQFL!pl|@91zYo$E`Xa!C%RcG&{F&6D7JGI@+-mcC7)N8pr^MKDX19)PNZ`yuH8jdA zRHqiTcL=4vei~cZ27_v4jmEM<<#H%e{e|;s+;#F~4rB9dHa8O=Y0kv)8>pA*Ucb_{ a&i_9e%Im8qn!f1(0000 Date: Tue, 17 Sep 2019 23:32:11 +0800 Subject: [PATCH 258/386] =?UTF-8?q?=E6=B7=BB=E5=8A=A0roi=20pooling?= =?UTF-8?q?=E5=92=8Croi=20align=E5=8E=9F=E7=90=86=EF=BC=8C=E8=BF=9B?= =?UTF-8?q?=E7=A8=8B=E9=80=9A=E4=BF=A1?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...6\214\207offer\351\242\230\350\247\243.md" | 4 +- ...40\351\235\242\350\257\225\351\242\230.md" | 35 ++++++++++++++- ...ll\351\235\242\350\257\225\351\242\230.md" | 19 ++++---- ...72\347\241\200\347\237\245\350\257\206.md" | 43 ++++++++++++++++++- ...13\346\213\233\351\235\242\347\273\217.md" | 2 +- 5 files changed, 91 insertions(+), 12 deletions(-) diff --git "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\345\211\221\346\214\207offer\351\242\230\350\247\243.md" "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\345\211\221\346\214\207offer\351\242\230\350\247\243.md" index 790cb937..dd9064ae 100644 --- "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\345\211\221\346\214\207offer\351\242\230\350\247\243.md" +++ "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\345\211\221\346\214\207offer\351\242\230\350\247\243.md" @@ -8,4 +8,6 @@ ### 方法3 构建大根堆 + 遍历前 `1-k` 个数，创建大小为 `k` 的大根堆用来存储最小的 k 个数，大根堆堆顶元素是最小 k 个数中最大的一个； + 遍历 `k-n` 个数，得到元素　`X`，如果　`X`　比堆顶元素 `Y` 大，则不改变原来的堆，因为这个元素比大根堆中所有元素都大； -+ 如果　`X`　比堆顶元素 `Y` 小，那么用 `X` 替换堆顶的元素 `Y`。在 X 替换堆顶元素 Y 之后，大根堆的结构可能被破坏，需要进行向下调整(重新排序)。调整过程的时间复杂度是 $O(log_2k)$ 。 \ No newline at end of file ++ 如果　`X`　比堆顶元素 `Y` 小，那么用 `X` 替换堆顶的元素 `Y`。在 X 替换堆顶元素 Y 之后，大根堆的结构可能被破坏，需要进行向下调整(重新排序)。调整过程的时间复杂度是 $O(log_2k)$ 。 + +## 最长公共子序列问题 \ No newline at end of file diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 86075340..d8de9524 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -26,7 +26,7 @@ 例如 输入：2x2， 卷积核：4x4， 滑动步长：3，填充像素为0， 输出：7x7 ，其计算过程就是， (2 - 1) * 3 + 4 = 7 3.池化得到的特征图大小计算方式： -`W=(W-F)/S+1`，池化层一般不填充像素(`VALID`) +`N=(W-F)/S+1`，池化层一般不填充像素(`VALID`) **卷积向下取整，池化向上取整**。**卷积层一般填充像素(`SAME`)，池化层一般不填充像素(`VALID`)**？注意：`stride`为1的时候，当`kernel`为 3 `padding`为1或者`kernel`为5 `padding`为2，这种情况可直接得出卷积前后尺寸不变。 @@ -478,10 +478,41 @@ def calculateIoU(candidateBound, groundTruthBound): 数据增强分两类，一类是离线增强，一类是在线增强： 1. 离线增强 ： 直接对数据集进行处理，数据的数目会变成增强因子 x 原数据集的数目 ，这种方法常常用于数据集很小的时候 2. 在线增强 ： 这种增强的方法用于，获得 batch 数据之后，然后对这个batch的数据进行增强，如旋转、平移、翻折等相应的变化，由于有些数据集不能接受线性级别的增长，这种方法长用于大的数据集，很多机器学习框架已经支持了这种数据增强方式，并且可以使用GPU优化计算。 +## 为什么需要使用ROI Pooling/ROI Align? +为了使得最后面的两个全连接层能够共享 `conv layers`(VGG) 权重。在所有的 `RoIs` 都被 `pooling` 成（512×7×7）的`feature map`后，将它 `reshape` 成一个一维的向量，就可以利用 `VGG16` 的预训练的权重来初始化前两层全连接。 ## ROI Pooling替换为ROI Align，及各自原理 faster rcnn将roi pooling替换为roi align效果有所提升 #### ROI Pooling原理 +`RPN`　生成的 `ROI` 区域大小是对应与输入图像大小（每个roi区域大小各不相同），为了能够共享权重，所以需要将这些 `ROI`　映射回特征图上，并固定大小。`ROI Pooling` 具体操作如下： +> [RoIPooling、RoIAlign笔记](https://www.cnblogs.com/wangyong/p/8523814.html) + +ROI Align操作过程如下图： +![roi pooling](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/roi%20pooling.png) + +1. `Conv layers` 使用的是 `VGG16`，`feat_stride=32`(即表示，经过网络层后图片缩小为原图的1/32),原图800*800,最后一层特征图feature map大小:25\*25 +2. 假定原图中有一 `region proposal`，大小为665\*665，这样，映射到特征图中的大小：665/32=20.78,即20.78\*20.78，源码中，在计算的时候会进行取整操作，于是，进行所谓的第一次量化，即映射的特征图大小为20*20 +3. 假定pooled_w=7,pooled_h=7,即 `pooling` 后固定成7\*7大小的特征图，所以，将上面在 `feature map`上映射的20\*20的 region proposal划分成 `49`个同等大小的小区域，每个小区域的大小20/7=2.86,即2.86\*2.86，此时，进行第二次量化，故小区域大小变成2\*2 +4. 每个2\*2的小区域里，取出其中最大的像素值，作为这一个区域的‘代表’，这样，49个小区域就输出49个像素值，组成7\*7大小的feature map + +所以，通过上面可以看出，经过**两次量化，即将浮点数取整**，原本在特征图上映射的20\*20大小的region proposal，偏差成大小为14\*14的，这样的像素偏差势必会对后层的回归定位产生影响 + +所以，产生了替代方案: `RoiAlign`。 #### ROI Align原理 +> [RoIPooling、RoIAlign笔记](https://www.cnblogs.com/wangyong/p/8523814.html),这篇文章讲得很好。 + +ROI Align操作过程如下图： +![roi align](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/roi%20align.png) + +1. Conv layers使用的是VGG16，feat_stride=32(即表示，经过网络层后图片缩小为原图的1/32),原图800*800,最后一层特征图feature map大小:25*25 +2. 假定原图中有一region proposal，大小为665*665，这样，映射到特征图中的大小：665/32=20.78,即20.78*20.78，此时，没有像RoiPooling那样就行取整操作，保留浮点数 +3. 假定pooled_w=7,pooled_h=7,即pooling后固定成7*7大小的特征图，所以，将在 feature map上映射的20.78*20.78的region proposal 划分成49个同等大小的小区域，每个小区域的大小20.78/7=2.97,即2.97*2.97 +4. 假定采样点数为4，即表示，对于每个2.97*2.97的小区域，平分四份，每一份取其中心点位置，而中心点位置的像素，采用双线性插值法进行计算，这样，就会得到四个点的像素值，如下图: +![roi align双线性插值](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/roi%20align%E7%BA%BF%E6%80%A7%E6%8F%92%E5%80%BC.png) +上图中，四个红色叉叉`‘×’`的像素值是通过双线性插值算法计算得到的. + +最后，取四个像素值中最大值作为这个小区域(即：2.97*2.97大小的区域)的像素值，如此类推，同样是49个小区域得到49个像素值，组成7*7大小的feature map + +总结：知道了 `RoiPooling` 和 `RoiAlign` 实现原理，在以后的项目中可以根据实际情况进行方案的选择；对于检测图片中大目标物体时，两种方案的差别不大，而如果是图片中有较多小目标物体需要检测，则优先选择RoiAlign，更精准些。 ## Reference 1.[深度学习中的数据增强](https://blog.csdn.net/zhelong3205/article/details/81810743 ) ## Reference @@ -493,3 +524,5 @@ faster rcnn将roi pooling替换为roi align效果有所提升 + + diff --git "a/\347\274\226\347\250\213\350\257\255\350\250\200/cpp-python-shell\351\235\242\350\257\225\351\242\230.md" "b/\347\274\226\347\250\213\350\257\255\350\250\200/cpp-python-shell\351\235\242\350\257\225\351\242\230.md" index 3811e669..86a9eb1e 100644 --- "a/\347\274\226\347\250\213\350\257\255\350\250\200/cpp-python-shell\351\235\242\350\257\225\351\242\230.md" +++ "b/\347\274\226\347\250\213\350\257\255\350\250\200/cpp-python-shell\351\235\242\350\257\225\351\242\230.md" @@ -1,4 +1,10 @@ # Python/C/C++/计算机基础/图像处理基础 +# C/C++ 面试题 +## C++ 变量作用域 +作用域即是程序的一个区域，在程序中变量的作用域一般有三个地方： ++ 在函数或者一个代码块内部声明的变量，称为局部变量； ++ 在函数参数中定义的变量，称为形参； ++ 在所有函数外部声明的变量，比如在程序文件开头定义的变量，称为全局变量。 ## static关键字作用 + 在全局变量前加上关键字static，全局变量就定义为一个全局静态变量，全局静态变量在声明它的文件之外是不可见的，作用域范围为从定义之处开始，到文件结尾。 + 在函数返回类型前加`static`，函数就变为静态函数，静态函数只在声明它的文件中使用，不被其他文件所用。 @@ -16,9 +22,12 @@ ## const 关键字作用 `const`类型的对象在程序执行期间不能被修改改变。 +# Python 面试题 +## Python global语句的作用 +在编写程序的时候，如果想要**改变(重新赋值)**函数外部的变量，并且这个变量会作用于许多函数中，就需要告诉 Python 程序这个变量的作用域是全局变量，`global` 语句可以实现定义全局变量的作用。 ## lambda匿名函数好处 精简代码，`lambda`省去了定义函数，`map` 省去了写 `for` 循环过程: -```Python +```python str_1 = ["中国", "美国", "法国", "", "", "英国"] res = list(map(lambda x: "填充值" if x=="" else x, str_1)) print(res) # ['中国', '美国', '法国', '填充值', '填充值', '英国'] @@ -37,7 +46,6 @@ finally 语句块，至此，执行完毕。跳转至错误处理代码， + `SyntaxError`: Python 代码逻辑语法出错，不能执行 + `NameError`: 使用一个还未赋予对象的变量 - ## 简述any()和all()方法 + `any()`: 只要迭代器中有一个元素为真就为真; + `all()`: 迭代器中所有的判断项返回都是真，结果才为真. @@ -143,12 +151,7 @@ print(a) Pyhon 这种变量本身类型不固定，可以反复赋值不同类型的变量称为动态语言，与之对应的是静态语言。静态语言在定义变量时必须指定变量类型，如果赋值的时候类型不匹配，就会报错，Java/C++ 都是静态语言（`int a; a = 100`） ## Python装饰器解释 装饰器本质上是一个 Python 函数或类，**它可以让其他函数或类在不需要做任何代码修改的前提下增加额外功能**，装饰器的返回值也是一个函数/类对象。它经常用于有切面需求的场景，比如：插入日志、性能测试、事务处理、缓存、权限校验等场景，装饰器是解决这类问题的绝佳设计。有了装饰器，我们就可以**抽离出大量与函数功能本身无关的雷同代码到装饰器中并继续重用**。概括的讲，**装饰器的作用就是为已经存在的对象添加额外的功能**。 -## 多进程与多线程区别 -+ 多个进程之间相互独立，一个任务就是一个进程，进程内的“子任务”称为线程；线程是进程的一部分，一个进程至少有一个线程； -+ 进程是操作系统进行资源分配和调度的基本单位，线程是 `CPU` 进行资源分配和调度的基本单位； -+ 多线程和多进程最大的不同在于，多进程中，同一个变量，各自有一份拷贝存在于每个进程中，互不影响，多个进程之间相互独立；而**多线程中，所有变量都由所有线程共享**，所以，任何一个变量都可以被任何一个线程修改，因此，线程之间共享数据最大的危险在于多个线程同时改一个变量，把内容给改乱了。 -+ 进程间调用、通讯和切换开销均比多线程大，单个线程的崩溃会导致整个应用的退出。 -+ 存在大量IO，网络耗时或者需要和用户交互等操作时，使用多线程有利于提高系统的并发性和用户界面快速响应从而提高友好性。 + ## map与reduce函数用法解释下 1. map函数接收两个参数，一个是函数，一个是Iterable，map将传入的函数依次作用到序列的**每个元素**，并将结果作为新的Iterator返回，简单示例代码如下： ```Python diff --git "a/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200\347\237\245\350\257\206.md" "b/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200\347\237\245\350\257\206.md" index f9a32511..7e0ffb52 100644 --- "a/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200\347\237\245\350\257\206.md" +++ "b/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200\347\237\245\350\257\206.md" @@ -20,4 +20,45 @@ + 丢失修改 + 读脏数据 + 不可重复读 -+ 脏影读 \ No newline at end of file ++ 脏影读 +## 多进程与多线程区别 ++ **进程是资源分配的最小单位，线程是CPU调度（独立调度）的最小单位**。 ++ 多个进程之间相互独立，一个任务就是一个进程，进程内的“子任务”称为线程；线程是进程的一部分，一个进程至少有一个线程。 ++ 同一个进程中的所有线程的数据是共享的（进程通讯），线程之间可以直接通信；但是进程之间的数据是独立的，进程之间的交流需要借助中间代理（｀IPC`）来实现。 ++ 由于创建或撤销进程时，系统要为之分配资源或回收资源，如内存空间、I/O设备等，所以进程间调用、通讯和切换开销均比多线程大，单个线程的崩溃会导致整个应用的退出。 ++ 存在大量IO，网络耗时或者需要和用户交互等操作时，使用多线程（线程开销小、切换速度快）有利于提高系统的并发性和用户界面快速响应从而提高友好性。 + +## 进程/线程通信 +### 进程间通信 +进程间通信（IPC, InterProcess Communication）：是指在不同进程之间传播或交换信息。`IPC` 的方式有：管道、消息队列、信号量、共享存储、Socket等。`Socket`　支持不同主机的两个进程的　`IPC`。 +> python Process类参数：`target`表示调用的对象，就是子进程要执行的任务 + +`Python` 多线程通信可以通过导入`Process`（创建进程）　和`Queue`(创建队列)：｀from multiprocessing import Process, Queue｀ +### 线程通信 +一个进程所含的不同线程是共享内存的，所以线程之间共享数据有一个问题是多个线程共享一个变量，导致内容改乱了。解决办法是，如果不同线程间有共享的变量，其中一个方法就是在修改前给其上一把锁`lock`，确保一次只有一个线程能修改。`Python` 创建线程方式如下： +```python +t = threading.Thread(target=loop, name='LoopThread') # `target`表示调用的对象(自己定义的任务函数) +``` +`Python` 创建锁可以使用，`threading.lock()` 方法，实现对一个共享变量的锁定，修改完后 `release` 供其它线程使用，具体加锁代码如下： +```python +balance = 0 +lock = threading.Lock() # 创建锁 + +def run_thread(n): + for i in range(100000): + # 先要获取锁: + lock.acquire() + try: + # 放心地改吧: + change_it(n) + finally: + # 改完了一定要释放锁: + lock.release() +``` +## 并发与并行 +> [多进程与多线程的区别](https://www.cnblogs.com/kaituorensheng/p/3603057.html) +并行指物理上同时执行，并发指能够让多个任务在逻辑上交织执行的程序设计。 + +要让单核　`CPU` 运行多任务，就得用到**并发技术**，实现并发技术相当复杂，最容易理解的是“时间片轮转进程调度算法”，它的思想简单介绍如下：在操作系统的管理下，所有正在运行的进程轮流使用CPU，每个进程允许占用CPU的时间非常短(比如10毫秒)，这样用户根本感觉不出来CPU是在轮流为多个进程服务，就好象所有的进程都在不间断地运行一样。但实际上在任何一个时间内有且仅有一个进程占有CPU。如果一台计算机有多个CPU，情况就不同了，如果进程数小于CPU数，则不同的进程可以分配给不同的CPU来运行，这样，多个进程就是真正同时运行的，这便是并行。但如果进程数大于CPU数，则仍然需要使用并发技术。因此，我们可以得出以下结论： ++ 总线程数<= CPU数量：并行运行 ++ 总线程数 > CPU数量：并发运行 \ No newline at end of file diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" index 5924a3d8..d1b9fa6e 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" @@ -44,7 +44,7 @@ 4. `c++` 面向对象的三个特性：继承、多态、接口 5. 基类和派生类析构函数有什么区别 6. 讲下继承 -## 工商银行(15分钟)（软甲开发中心） +## 工商银行(15分钟)（软件开发中心） 1. 核心项目介绍(围绕项目提了一些问题) 2. 你的本科和研究所成绩，为什么投成都岗 3. 你的本科学校是什么类型的，研究所毕业设计打算做什么 From ac43c5bc7ba68fd4a42cedfce82e222c3f7f36da Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Tue, 17 Sep 2019 23:36:52 +0800 Subject: [PATCH 259/386] =?UTF-8?q?=E6=B7=BB=E5=8A=A0roi=20pooling?= =?UTF-8?q?=E5=92=8Croi=20align=E5=8E=9F=E7=90=86=EF=BC=8C=E8=BF=9B?= =?UTF-8?q?=E7=A8=8B=E9=80=9A=E4=BF=A1?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...4\271\240\351\235\242\350\257\225\351\242\230.md" | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index d8de9524..a9779bcc 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -487,6 +487,7 @@ faster rcnn将roi pooling替换为roi align效果有所提升 > [RoIPooling、RoIAlign笔记](https://www.cnblogs.com/wangyong/p/8523814.html) ROI Align操作过程如下图： + ![roi pooling](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/roi%20pooling.png) 1. `Conv layers` 使用的是 `VGG16`，`feat_stride=32`(即表示，经过网络层后图片缩小为原图的1/32),原图800*800,最后一层特征图feature map大小:25\*25 @@ -501,16 +502,17 @@ ROI Align操作过程如下图： > [RoIPooling、RoIAlign笔记](https://www.cnblogs.com/wangyong/p/8523814.html),这篇文章讲得很好。 ROI Align操作过程如下图： + ![roi align](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/roi%20align.png) -1. Conv layers使用的是VGG16，feat_stride=32(即表示，经过网络层后图片缩小为原图的1/32),原图800*800,最后一层特征图feature map大小:25*25 +1. `Conv layers` 使用的是VGG16，feat_stride=32(即表示，经过网络层后图片缩小为原图的1/32),原图800*800,最后一层特征图feature map大小:25*25 2. 假定原图中有一region proposal，大小为665*665，这样，映射到特征图中的大小：665/32=20.78,即20.78*20.78，此时，没有像RoiPooling那样就行取整操作，保留浮点数 -3. 假定pooled_w=7,pooled_h=7,即pooling后固定成7*7大小的特征图，所以，将在 feature map上映射的20.78*20.78的region proposal 划分成49个同等大小的小区域，每个小区域的大小20.78/7=2.97,即2.97*2.97 -4. 假定采样点数为4，即表示，对于每个2.97*2.97的小区域，平分四份，每一份取其中心点位置，而中心点位置的像素，采用双线性插值法进行计算，这样，就会得到四个点的像素值，如下图: +3. 假定pooled_w=7,pooled_h=7,即pooling后固定成 `7\*7` 大小的特征图，所以，将在 feature map上映射的 `20.78\*20.78` 的region proposal 划分成49个同等大小的小区域，每个小区域的大小20.78/7=2.97,即2.97*2.97 +4. 假定采样点数为`4`，即表示，对于每个 `2.97*2.97` 的小区域，平分四份，每一份取其中心点位置，而中心点位置的像素，采用双线性插值法进行计算，这样，就会得到四个点的像素值，如下图: + ![roi align双线性插值](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/roi%20align%E7%BA%BF%E6%80%A7%E6%8F%92%E5%80%BC.png) -上图中，四个红色叉叉`‘×’`的像素值是通过双线性插值算法计算得到的. -最后，取四个像素值中最大值作为这个小区域(即：2.97*2.97大小的区域)的像素值，如此类推，同样是49个小区域得到49个像素值，组成7*7大小的feature map +上图中，四个红色叉叉`‘×’`的像素值是通过双线性插值算法计算得到的.最后，取四个像素值中最大值作为这个小区域(即：2.97*2.97大小的区域)的像素值，如此类推，同样是49个小区域得到49个像素值，组成7*7大小的feature map 总结：知道了 `RoiPooling` 和 `RoiAlign` 实现原理，在以后的项目中可以根据实际情况进行方案的选择；对于检测图片中大目标物体时，两种方案的差别不大，而如果是图片中有较多小目标物体需要检测，则优先选择RoiAlign，更精准些。 ## Reference From 481ea5d9b463a3a353a286455acccd0efb14ddb7 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Tue, 17 Sep 2019 23:38:25 +0800 Subject: [PATCH 260/386] =?UTF-8?q?=E6=B7=BB=E5=8A=A0roi=20pooling?= =?UTF-8?q?=E5=92=8Croi=20align=E5=8E=9F=E7=90=86=EF=BC=8C=E8=BF=9B?= =?UTF-8?q?=E7=A8=8B=E9=80=9A=E4=BF=A1?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...46\344\271\240\351\235\242\350\257\225\351\242\230.md" | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index a9779bcc..9f8e4dcb 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -505,10 +505,10 @@ ROI Align操作过程如下图： ![roi align](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/roi%20align.png) -1. `Conv layers` 使用的是VGG16，feat_stride=32(即表示，经过网络层后图片缩小为原图的1/32),原图800*800,最后一层特征图feature map大小:25*25 -2. 假定原图中有一region proposal，大小为665*665，这样，映射到特征图中的大小：665/32=20.78,即20.78*20.78，此时，没有像RoiPooling那样就行取整操作，保留浮点数 -3. 假定pooled_w=7,pooled_h=7,即pooling后固定成 `7\*7` 大小的特征图，所以，将在 feature map上映射的 `20.78\*20.78` 的region proposal 划分成49个同等大小的小区域，每个小区域的大小20.78/7=2.97,即2.97*2.97 -4. 假定采样点数为`4`，即表示，对于每个 `2.97*2.97` 的小区域，平分四份，每一份取其中心点位置，而中心点位置的像素，采用双线性插值法进行计算，这样，就会得到四个点的像素值，如下图: +1. `Conv layers` 使用的是VGG16，feat_stride=32(即表示，经过网络层后图片缩小为原图的1/32),原图800\*800,最后一层特征图feature map大小:25\*25 +2. 假定原图中有一region proposal，大小为665\*665，这样，映射到特征图中的大小：665/32=20.78,即20.78\*20.78，此时，没有像RoiPooling那样就行取整操作，保留浮点数 +3. 假定pooled_w=7,pooled_h=7,即pooling后固定成 `7\*7` 大小的特征图，所以，将在 feature map上映射的 `20.78\*20.78` 的region proposal 划分成49个同等大小的小区域，每个小区域的大小20.78/7=2.97,即2.97\*2.97 +4. 假定采样点数为`4`，即表示，对于每个 `2.97\*2.97` 的小区域，平分四份，每一份取其中心点位置，而中心点位置的像素，采用双线性插值法进行计算，这样，就会得到四个点的像素值，如下图: ![roi align双线性插值](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/roi%20align%E7%BA%BF%E6%80%A7%E6%8F%92%E5%80%BC.png) From 2940dcf0497a9d53f13c275f7307ae1227dcb0c2 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Tue, 17 Sep 2019 23:39:49 +0800 Subject: [PATCH 261/386] =?UTF-8?q?=E6=B7=BB=E5=8A=A0roi=20pooling?= =?UTF-8?q?=E5=92=8Croi=20align=E5=8E=9F=E7=90=86=EF=BC=8C=E8=BF=9B?= =?UTF-8?q?=E7=A8=8B=E9=80=9A=E4=BF=A1?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 9f8e4dcb..3d431a2b 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -505,10 +505,10 @@ ROI Align操作过程如下图： ![roi align](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/roi%20align.png) -1. `Conv layers` 使用的是VGG16，feat_stride=32(即表示，经过网络层后图片缩小为原图的1/32),原图800\*800,最后一层特征图feature map大小:25\*25 +1. `Conv layers` 使用的是VGG16，feat_stride=32(即表示，经过网络层后图片缩小为原图的1/32),原图800\*800,最后一层特征图feature map大小: 25\*25 2. 假定原图中有一region proposal，大小为665\*665，这样，映射到特征图中的大小：665/32=20.78,即20.78\*20.78，此时，没有像RoiPooling那样就行取整操作，保留浮点数 -3. 假定pooled_w=7,pooled_h=7,即pooling后固定成 `7\*7` 大小的特征图，所以，将在 feature map上映射的 `20.78\*20.78` 的region proposal 划分成49个同等大小的小区域，每个小区域的大小20.78/7=2.97,即2.97\*2.97 -4. 假定采样点数为`4`，即表示，对于每个 `2.97\*2.97` 的小区域，平分四份，每一份取其中心点位置，而中心点位置的像素，采用双线性插值法进行计算，这样，就会得到四个点的像素值，如下图: +3. 假定pooled_w=7,pooled_h=7,即pooling后固定成 `7*7` 大小的特征图，所以，将在 feature map上映射的 `20.78\*20.78` 的region proposal 划分成49个同等大小的小区域，每个小区域的大小 20.78/7=2.97, 即 2.97\*2.97 +4. 假定采样点数为`4`，即表示，对于每个 `2.97*2.97` 的小区域，平分四份，每一份取其中心点位置，而中心点位置的像素，采用双线性插值法进行计算，这样，就会得到四个点的像素值，如下图: ![roi align双线性插值](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/roi%20align%E7%BA%BF%E6%80%A7%E6%8F%92%E5%80%BC.png) From fadaa44b8544de32143da57f9ef54546c2d93e31 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Tue, 17 Sep 2019 23:43:38 +0800 Subject: [PATCH 262/386] =?UTF-8?q?=E6=B7=BB=E5=8A=A0roi=20pooling?= =?UTF-8?q?=E5=92=8Croi=20align=E5=8F=8D=E5=90=91=E4=BC=A0=E6=92=AD?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...5\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 3d431a2b..3e412b96 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -483,7 +483,7 @@ def calculateIoU(candidateBound, groundTruthBound): ## ROI Pooling替换为ROI Align，及各自原理 faster rcnn将roi pooling替换为roi align效果有所提升 #### ROI Pooling原理 -`RPN`　生成的 `ROI` 区域大小是对应与输入图像大小（每个roi区域大小各不相同），为了能够共享权重，所以需要将这些 `ROI`　映射回特征图上，并固定大小。`ROI Pooling` 具体操作如下： +`RPN` 生成的 `ROI` 区域大小是对应与输入图像大小（每个roi区域大小各不相同），为了能够共享权重，所以需要将这些 `ROI` 映射回特征图上，并固定大小。`ROI Pooling` 具体操作如下： > [RoIPooling、RoIAlign笔记](https://www.cnblogs.com/wangyong/p/8523814.html) ROI Align操作过程如下图： @@ -515,6 +515,9 @@ ROI Align操作过程如下图： 上图中，四个红色叉叉`‘×’`的像素值是通过双线性插值算法计算得到的.最后，取四个像素值中最大值作为这个小区域(即：2.97*2.97大小的区域)的像素值，如此类推，同样是49个小区域得到49个像素值，组成7*7大小的feature map 总结：知道了 `RoiPooling` 和 `RoiAlign` 实现原理，在以后的项目中可以根据实际情况进行方案的选择；对于检测图片中大目标物体时，两种方案的差别不大，而如果是图片中有较多小目标物体需要检测，则优先选择RoiAlign，更精准些。 +## ROI Pooling/ROI Align 反向传播 +> 参考[你真的学会RoI Pooling了吗?](https://zhuanlan.zhihu.com/p/59692298)，自己的理解稍后补充。 + ## Reference 1.[深度学习中的数据增强](https://blog.csdn.net/zhelong3205/article/details/81810743 ) ## Reference From 51170f58a587bd81c6e7b213ad4f367604290503 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Wed, 18 Sep 2019 13:19:10 +0800 Subject: [PATCH 263/386] =?UTF-8?q?C++=E9=9D=A2=E8=AF=95=E9=A2=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...n-shell\351\235\242\350\257\225\351\242\230.md" | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git "a/\347\274\226\347\250\213\350\257\255\350\250\200/cpp-python-shell\351\235\242\350\257\225\351\242\230.md" "b/\347\274\226\347\250\213\350\257\255\350\250\200/cpp-python-shell\351\235\242\350\257\225\351\242\230.md" index 86a9eb1e..a59ecbf7 100644 --- "a/\347\274\226\347\250\213\350\257\255\350\250\200/cpp-python-shell\351\235\242\350\257\225\351\242\230.md" +++ "b/\347\274\226\347\250\213\350\257\255\350\250\200/cpp-python-shell\351\235\242\350\257\225\351\242\230.md" @@ -1,5 +1,13 @@ # Python/C/C++/计算机基础/图像处理基础 # C/C++ 面试题 +## C++中的new delete和C语言中的malloc free有什么区别 +虽然这两者都分别是完成分配内存和释放内存的功能，但是 `C++` 用 `new` 分配内存时会调用构造函数，用 `delete` 释放内存时会调用析构函数。 +## static关键字作用 ++ **声明全局静态变量**：在全局变量前加上关键字 `static`，全局变量就定义成一个全局静态变量，作用域在声明它的文件之外是不可见的，即**从定义之处开始到文件结尾**。 ++ **局部静态变量**：在局部变量前加上关键字 `static`，作用域仍然为局部作用域，即当定义它的函数或者语句块结束的时候，作用域结束。 ++ **静态函数**： 在函数返回类型前加 `static`，静态函数只在声明他的文件中可见，不能被其他文件使用。 ++ **类的静态成员**：在类中，静态成员可以实现多个对象之间的数据共享，即静态成员是类的所有对象中共享的成员，而不是某个对象成员。并且使用静态数据成员不会破坏隐藏的原则，保证了数据的安全性。 ++ **类的静态函数**：把函数成员声明为静态的，就可以把函数与类的任何特定对象独立开来。`静态成员函数即使在类对象不存在的情况下也能被调用`，静态函数只要使用类名加范围解析运算符 `::` 就可以访问(`<类名>::<静态成员函数名>(<参数表>)`) ## C++ 变量作用域 作用域即是程序的一个区域，在程序中变量的作用域一般有三个地方： + 在函数或者一个代码块内部声明的变量，称为局部变量； @@ -234,7 +242,13 @@ b = [1, 2, 3, ['a', 'b', 'c', 'd'], 4] c = [1, 2, 3, ['a', 'b', 'c', 'd']] d = [1, 2, 3, ['a', 'b', 'c']] +## Python 继承多态理解 +多态是指对不同类型的变量进行相同的操作，它会根据对象（或类）类型的不同而表现出不同的行为。 +### 总结 ++ 继承可以拿到父类的所有数据和方法，子类可以重写父类的方法，也可以新增自己特有的方法。 ++ 有了继承，才有了多态，不同类的对象对同一消息会作出不同的相应。 ## 其他面试题 1. 参考[这里](https://zhuanlan.zhihu.com/p/23526961) 2. [110道Python面试题（真题）](https://zhuanlan.zhihu.com/p/54430650) 3. [关于Python的面试题](https://github.com/taizilongxu/interview_Python#15-__new__%E5%92%8C__init__%E7%9A%84%E5%8C%BA%E5%88%AB) +4. [继承和多态](http://funhacks.net/explore-python/Class/inheritance_and_polymorphism.html) From dde512f36e02b0141e556bc54c2184264e10b1aa Mon Sep 17 00:00:00 2001 From: Zhang Date: Sat, 21 Sep 2019 11:15:39 +0800 Subject: [PATCH 264/386] =?UTF-8?q?Update=20=E6=95=B0=E5=AD=97=E5=9B=BE?= =?UTF-8?q?=E5=83=8F=E5=A4=84=E7=90=86=E5=9F=BA=E7=A1=80.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 增加图像平滑和高斯模糊的解释 --- ...03\217\345\244\204\347\220\206\345\237\272\347\241\200.md" | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git "a/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206\345\237\272\347\241\200.md" "b/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206\345\237\272\347\241\200.md" index 4a360c7c..3470a272 100644 --- "a/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206\345\237\272\347\241\200.md" +++ "b/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206\345\237\272\347\241\200.md" @@ -1,5 +1,7 @@ # 数字图像处理 ## 图像处理中平滑和锐化操作是什么？ +> 所谓"模糊"，可以理解成每一个像素都取周边像素的平均值，在图形上，就相当于产生"模糊"效果，但是在数值上，这是一种"平滑化"，"中间点"会失去细节。 + **平滑处理（smoothing）也称模糊处理（bluring）**，主要用于**消除图像中的噪声部分**，平滑处理常用的用途是用来减少图像上的噪点或失真，平滑主要使用图像滤波。在这里，我个人认为可以把图像平滑和图像滤波联系起来，因为图像平滑常用的方法就是图像滤波器。在OpenCV3中常用的图像滤波器有以下几种： + 方框滤波——BoxBlur函数 @@ -22,4 +24,4 @@ ### Reference [图像增强－图像锐化](https://www.cnblogs.com/BYTEMAN/archive/2012/07/21/2603021.html) ## numpy手撕代码 -人脸识别的场景下，输入105*12的feature map，用这个在1000512的特征库当中用欧氏距离去匹配105*12的feature map，用这个在1000*512的特征库当中用欧氏距离去匹配10*1000的特征，得到这个output。 \ No newline at end of file +人脸识别的场景下，输入105*12的feature map，用这个在1000512的特征库当中用欧氏距离去匹配105*12的feature map，用这个在1000*512的特征库当中用欧氏距离去匹配10*1000的特征，得到这个output。 From c4b2fc0807cb5e015d69718a263f6e7fe012684e Mon Sep 17 00:00:00 2001 From: Zhang Date: Sat, 21 Sep 2019 11:16:01 +0800 Subject: [PATCH 265/386] Add files via upload --- ...230\346\226\257\346\250\241\347\263\212.jpg" | Bin 0 -> 18564 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 "images/\351\253\230\346\226\257\346\250\241\347\263\212.jpg" diff --git "a/images/\351\253\230\346\226\257\346\250\241\347\263\212.jpg" "b/images/\351\253\230\346\226\257\346\250\241\347\263\212.jpg" new file mode 100644 index 0000000000000000000000000000000000000000..e31a02f9b6ab89f409b84e87980efbad6313b3d5 GIT binary patch literal 18564 zcmaI71z1~6&^8>Tl(s-|iaQi{*J4Eypg^DmD^7smu5}6&cYjKQ1}T;x1=^y40!4xa zDee?^`O@cm?Z2+?{WrI4Aj=$^lM@jY7ZDd1!nF|c4fgP}4HEM3<^0bcG#q{HeV|@`P)`rG zzdhR8dHVY)ap11>|K0-3>%Y4Fe?F`K(GUds|8EL|{e1xccI)eB 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zcYll>yRu~AD&t#l)^&d>Z}0<;Hb;9mZ9EZ_>J2pYDyp+Kw0S{LN=Bjgra+AQ4ai03ZL^ DlNsmZ literal 0 HcmV?d00001 From 593a51b1489ad05527a1d637c9c4200a14a2572b Mon Sep 17 00:00:00 2001 From: Zhang Date: Sat, 21 Sep 2019 11:17:42 +0800 Subject: [PATCH 266/386] =?UTF-8?q?Update=20=E6=95=B0=E5=AD=97=E5=9B=BE?= =?UTF-8?q?=E5=83=8F=E5=A4=84=E7=90=86=E5=9F=BA=E7=A1=80.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...03\217\345\244\204\347\220\206\345\237\272\347\241\200.md" | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git "a/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206\345\237\272\347\241\200.md" "b/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206\345\237\272\347\241\200.md" index 3470a272..6f57ec30 100644 --- "a/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206\345\237\272\347\241\200.md" +++ "b/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206\345\237\272\347\241\200.md" @@ -1,6 +1,8 @@ # 数字图像处理 ## 图像处理中平滑和锐化操作是什么？ -> 所谓"模糊"，可以理解成每一个像素都取周边像素的平均值，在图形上，就相当于产生"模糊"效果，但是在数值上，这是一种"平滑化"，"中间点"会失去细节。 +> 所谓"模糊"，可以理解成每一个像素都取周边像素的平均值，在图形上，就相当于产生"模糊"效果，但是在数值上，这是一种"平滑化"，"中间点"会失去细节。（高斯模糊效果如下图所示：） + +![高斯模糊效果图](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E9%AB%98%E6%96%AF%E6%A8%A1%E7%B3%8A.jpg) **平滑处理（smoothing）也称模糊处理（bluring）**，主要用于**消除图像中的噪声部分**，平滑处理常用的用途是用来减少图像上的噪点或失真，平滑主要使用图像滤波。在这里，我个人认为可以把图像平滑和图像滤波联系起来，因为图像平滑常用的方法就是图像滤波器。在OpenCV3中常用的图像滤波器有以下几种： From 4692b043a2e2a30bb1cbceaf79acadc3996645da Mon Sep 17 00:00:00 2001 From: Zhang Date: Sat, 21 Sep 2019 11:22:17 +0800 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z3VW`M81|5^7vVZ|woBXN^)Pq4?cY$`w(EIt-Msi}GynmTs*C^?;r7Yc@3);k{{Yl% zuCBwOm;O4j-Mg}uYB^5sL`^y*+jf(R?8 zAUH(XYNQU?)2C#W@%kR0uKw%YTLGE>~t9Nr_cKfF{_P~n|=^6tHHkDNih|dnm z7JAQB+wSh~tZrW3=KVd_8ffHXSv2TAQ>RYJKk=2j)%u<7?Y;fo%f9Z{`d3XVm=Q%< zX#*T^?~7l%b8&X%`^;YL)-7?(D#f!VfDeS}(sEPT=wEfc-R~C4ZmyfFx3(==4L}tD z0)t2&#~A7T&DYA>Pp`TKnrNX=O$9w|rmy~Qwsh&)BiILP)_ShbaeH;O-94+@l3M!! zq+DW2)W}y&YI#Y!*1EOtYu?@5bGNu}+ALI+xJ`8fr^>z)r%uV@3JJ=k4=l#OF zf2JGzzStT;qU)w#>S@>U@tI&@P7|k2XI4E4omtbQ5_IX)q@99fPMsuzYdW*1PB`k$ btm)H^I(27GoN*PMS<|N+b!SeUID`M$bThgj literal 0 HcmV?d00001 From c9e7dc635e68664bad61a9bd2edb1c828daa745b Mon Sep 17 00:00:00 2001 From: Zhang Date: Sat, 21 Sep 2019 11:30:08 +0800 Subject: [PATCH 268/386] =?UTF-8?q?Update=20=E6=95=B0=E5=AD=97=E5=9B=BE?= =?UTF-8?q?=E5=83=8F=E5=A4=84=E7=90=86=E5=9F=BA=E7=A1=80.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...7\345\244\204\347\220\206\345\237\272\347\241\200.md" | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git "a/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206\345\237\272\347\241\200.md" "b/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206\345\237\272\347\241\200.md" index 6f57ec30..ffc3f56a 100644 --- "a/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206\345\237\272\347\241\200.md" +++ "b/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206\345\237\272\347\241\200.md" @@ -1,9 +1,11 @@ # 数字图像处理 ## 图像处理中平滑和锐化操作是什么？ -> 所谓"模糊"，可以理解成每一个像素都取周边像素的平均值，在图形上，就相当于产生"模糊"效果，但是在数值上，这是一种"平滑化"，"中间点"会失去细节。（高斯模糊效果如下图所示：） - +所谓"模糊"，可以理解成每一个像素都取周边像素的平均值(**这种叫均值模糊**)，在图形上，就相当于产生"模糊"效果，但是在数值上，这是一种"平滑化"，"中间点"会失去细节。（高斯模糊效果如下图所示：） ![高斯模糊效果图](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E9%AB%98%E6%96%AF%E6%A8%A1%E7%B3%8A.jpg) +注意这里，计算平均值时，取值范围越大，"模糊效果"越强烈，也就是模糊半径越大，图像就越模糊。从数值角度看，就是数值越平滑。下图是分别是原图、模糊半径3像素、模糊半径10像素的效果： +[不同模糊半径效果图](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E4%B8%8D%E5%90%8C%E6%A8%A1%E7%B3%8A%E5%8D%8A%E5%BE%84%E6%95%88%E6%9E%9C%E5%9B%BE.jpg) + **平滑处理（smoothing）也称模糊处理（bluring）**，主要用于**消除图像中的噪声部分**，平滑处理常用的用途是用来减少图像上的噪点或失真，平滑主要使用图像滤波。在这里，我个人认为可以把图像平滑和图像滤波联系起来，因为图像平滑常用的方法就是图像滤波器。在OpenCV3中常用的图像滤波器有以下几种： + 方框滤波——BoxBlur函数 @@ -24,6 +26,7 @@ 一般来说，图像的能量主要集中在其低频部分，噪声所在的频段主要在高频段，同时图像边缘信息也主要集中在其高频部分。这将导致原始图像在平滑处理之后，图像边缘和图像轮廓模糊的情况出现。为了减少这类不利效果的影响，就需要利用图像锐化技术，使图像的边缘变得清晰。图像锐化处理的目的是为了使图像的边缘、轮廓线以及图像的细节变得清晰，经过平滑的图像变得模糊的根本原因是因为图像受到了平均或积分运算，因此可以对其进行逆运算(如微分运算)就可以使图像变得清晰。微分运算是求信号的变化率，由傅立叶变换的微分性质可知，微分运算具有较强高频分量作用。从频率域来考虑，图像模糊的实质是因为其高频分量被衰减，因此可以用高通滤波器来使图像清晰。但要注意能够进行锐化处理的图像必须有较高的性噪比，否则锐化后图像性噪比反而更低，从而使得噪声增加的比信号还要多，因此一般是先去除或减轻噪声后再进行锐化处理。 ### Reference -[图像增强－图像锐化](https://www.cnblogs.com/BYTEMAN/archive/2012/07/21/2603021.html) ++ [图像增强－图像锐化](https://www.cnblogs.com/BYTEMAN/archive/2012/07/21/2603021.html) ++ [高斯模糊算法](http://www.ruanyifeng.com/blog/2012/11/gaussian_blur.html) ## numpy手撕代码 人脸识别的场景下，输入105*12的feature map，用这个在1000512的特征库当中用欧氏距离去匹配105*12的feature map，用这个在1000*512的特征库当中用欧氏距离去匹配10*1000的特征，得到这个output。 From c0481093d9b3f5eba6abada9b3917be807369856 Mon Sep 17 00:00:00 2001 From: Zhang Date: Sat, 21 Sep 2019 11:43:10 +0800 Subject: [PATCH 269/386] Add files via upload --- ...6\345\270\203\346\226\271\347\250\213.png" | 63 +++++++++++++++++ ...6\345\270\203\346\226\271\347\250\213.png" | 68 +++++++++++++++++++ 2 files changed, 131 insertions(+) create mode 100644 "images/N\347\273\264\347\251\272\351\227\264\346\255\243\345\244\252\345\210\206\345\270\203\346\226\271\347\250\213.png" create mode 100644 "images/\344\272\214\347\273\264\347\251\272\351\227\264\347\232\204\346\255\243\345\244\252\345\210\206\345\270\203\346\226\271\347\250\213.png" diff --git "a/images/N\347\273\264\347\251\272\351\227\264\346\255\243\345\244\252\345\210\206\345\270\203\346\226\271\347\250\213.png" "b/images/N\347\273\264\347\251\272\351\227\264\346\255\243\345\244\252\345\210\206\345\270\203\346\226\271\347\250\213.png" new file mode 100644 index 00000000..e7444ce5 --- /dev/null +++ "b/images/N\347\273\264\347\251\272\351\227\264\346\255\243\345\244\252\345\210\206\345\270\203\346\226\271\347\250\213.png" @@ -0,0 +1,63 @@ + +{\displaystyle G(r)={\frac {1}{{\sqrt {2\pi \sigma ^{2}}}^{N}}}e^{-r^{2}/(2\sigma ^{2})}} + + + \ No newline at end of file diff --git "a/images/\344\272\214\347\273\264\347\251\272\351\227\264\347\232\204\346\255\243\345\244\252\345\210\206\345\270\203\346\226\271\347\250\213.png" "b/images/\344\272\214\347\273\264\347\251\272\351\227\264\347\232\204\346\255\243\345\244\252\345\210\206\345\270\203\346\226\271\347\250\213.png" new file mode 100644 index 00000000..a345a8e1 --- /dev/null +++ "b/images/\344\272\214\347\273\264\347\251\272\351\227\264\347\232\204\346\255\243\345\244\252\345\210\206\345\270\203\346\226\271\347\250\213.png" @@ -0,0 +1,68 @@ + +{\displaystyle G(u,v)={\frac {1}{2\pi \sigma ^{2}}}e^{-(u^{2}+v^{2})/(2\sigma ^{2})}} + + + \ No newline at end of file From 59383d2b74bb1feebd49b170769edbba32a6bcc7 Mon Sep 17 00:00:00 2001 From: Zhang Date: Sat, 21 Sep 2019 11:45:17 +0800 Subject: [PATCH 270/386] =?UTF-8?q?Update=20=E6=95=B0=E5=AD=97=E5=9B=BE?= =?UTF-8?q?=E5=83=8F=E5=A4=84=E7=90=86=E5=9F=BA=E7=A1=80.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...04\347\220\206\345\237\272\347\241\200.md" | 19 +++++++++++++++++-- 1 file changed, 17 insertions(+), 2 deletions(-) diff --git "a/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206\345\237\272\347\241\200.md" "b/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206\345\237\272\347\241\200.md" index ffc3f56a..a25aac34 100644 --- "a/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206\345\237\272\347\241\200.md" +++ "b/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206\345\237\272\347\241\200.md" @@ -1,10 +1,24 @@ # 数字图像处理 -## 图像处理中平滑和锐化操作是什么？ +## 均值模糊与高斯模糊 +### 均值模糊（不常用） 所谓"模糊"，可以理解成每一个像素都取周边像素的平均值(**这种叫均值模糊**)，在图形上，就相当于产生"模糊"效果，但是在数值上，这是一种"平滑化"，"中间点"会失去细节。（高斯模糊效果如下图所示：） ![高斯模糊效果图](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E9%AB%98%E6%96%AF%E6%A8%A1%E7%B3%8A.jpg) 注意这里，计算平均值时，取值范围越大，"模糊效果"越强烈，也就是模糊半径越大，图像就越模糊。从数值角度看，就是数值越平滑。下图是分别是原图、模糊半径3像素、模糊半径10像素的效果： -[不同模糊半径效果图](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E4%B8%8D%E5%90%8C%E6%A8%A1%E7%B3%8A%E5%8D%8A%E5%BE%84%E6%95%88%E6%9E%9C%E5%9B%BE.jpg) + +### 高斯模糊 +从数学的角度来看，图像的高斯模糊过程就是图像像素与像素的正态分布做卷积, 同时高斯模糊对图像来说就是一个低通滤波器。简单来说高斯模糊就是一种图像模糊滤波器，它用正态分布计算图像中每个像素的变换。其中N维空间正态分布方程为： + +![N维空间的正太分布方程](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/N%E7%BB%B4%E7%A9%BA%E9%97%B4%E6%AD%A3%E5%A4%AA%E5%88%86%E5%B8%83%E6%96%B9%E7%A8%8B.png) + +在二维空间定义为： +![二维空间的正太分布方程](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E4%BA%8C%E7%BB%B4%E7%A9%BA%E9%97%B4%E7%9A%84%E6%AD%A3%E5%A4%AA%E5%88%86%E5%B8%83%E6%96%B9%E7%A8%8B.png) + +其中r是模糊半径 ($r^2 = u^2 + v^2$），`σ` 是正态分布的标准偏差。在二维空间中，这个公式生成的曲面的等高线是从中心开始呈正态分布的同心圆。分布不为零的像素组成的卷积矩阵与原始图像做变换。每个像素的值都是周围相邻像素值的加权平均。原始像素的值有最大的高斯分布值，所以有最大的权重，相邻像素随着距离原始像素越来越远，其权重也越来越小。这样进行模糊处理比其它的均衡模糊滤波器更高地保留了边缘效果，效果如下图所示： + +![不同标准偏差大小(σ)效果图](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E4%B8%8D%E5%90%8C%E6%A8%A1%E7%B3%8A%E5%8D%8A%E5%BE%84%E6%95%88%E6%9E%9C%E5%9B%BE.jpg) + +## 图像处理中平滑和锐化操作是什么？ **平滑处理（smoothing）也称模糊处理（bluring）**，主要用于**消除图像中的噪声部分**，平滑处理常用的用途是用来减少图像上的噪点或失真，平滑主要使用图像滤波。在这里，我个人认为可以把图像平滑和图像滤波联系起来，因为图像平滑常用的方法就是图像滤波器。在OpenCV3中常用的图像滤波器有以下几种： @@ -28,5 +42,6 @@ ### Reference + [图像增强－图像锐化](https://www.cnblogs.com/BYTEMAN/archive/2012/07/21/2603021.html) + [高斯模糊算法](http://www.ruanyifeng.com/blog/2012/11/gaussian_blur.html) ++ 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080abf562e7279d73c66fcca7c528fa6b082dfc5 Mon Sep 17 00:00:00 2001 From: Zhang Date: Sat, 21 Sep 2019 11:48:59 +0800 Subject: [PATCH 274/386] Add files via upload --- ...\206\345\270\203\346\226\271\347\250\213.png" | Bin 0 -> 3356 bytes ...\206\345\270\203\346\226\271\347\250\213.png" | Bin 0 -> 3405 bytes 2 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 "images/N\347\273\264\347\251\272\351\227\264\346\255\243\345\244\252\345\210\206\345\270\203\346\226\271\347\250\213.png" create mode 100644 "images/\344\272\214\347\273\264\347\251\272\351\227\264\347\232\204\346\255\243\345\244\252\345\210\206\345\270\203\346\226\271\347\250\213.png" diff --git "a/images/N\347\273\264\347\251\272\351\227\264\346\255\243\345\244\252\345\210\206\345\270\203\346\226\271\347\250\213.png" "b/images/N\347\273\264\347\251\272\351\227\264\346\255\243\345\244\252\345\210\206\345\270\203\346\226\271\347\250\213.png" new file mode 100644 index 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zPf9aorG&+P8^!XfnSB7s%LzI>Sg$VF5JQSsPWE+oD?&>U%Rp-ph?Xos=J6cydWk z4#l(A4VTTvE>th5UmRx@a6Y5qfCd97jjc# zD`}3a@`}QUa|8aK_Vt?bK+jiN&qoe(cfRcL7FvqBAv#3g?SCmXh)EVfKuG=UxW-uh zH@UwgID-F2eSj&gy}be;Aoj;dK|rV>Bz-F}peqgT$}TDrEv?VU$~raeN7M}I&u%#9 z%BmJNTx#dPpJpYNs?++VFX7Imp7?zlkCt?fB8jJ1eSCbJ@vgSEHsk1KjoROXw;|cj z1UYA_rK;A2^@Y52hJkjdtG8FVb|O8Ny%^ETAx(vxAf0vpja;xAN*A%d@_U!;e9Q&+ zl0Zjn$5q<~$mG4gmTPzqJpk0))|S65fuW8$zk3O1eWAwjkEvgw09%=9rk*;LfAmmF zK;182SL+b#*GZ`htC!jo2zh#46i9ra9oy`lPL_Ew$<0-)nS_0mm^9yE!D^xJP?Ax1 z^E4y|BNdd;hnI%qM8Ngda(qXRp0%}II*w5pO>w?05E2_}e&jStqbUd*Gwqe;VDG>g zy`TH{<;YV1c1OxvJY;wX>D_u`!Xf3jFpbN}D@;_&2TuEe~C T1)~y}hd@YUOQTl?E=m6ZVg97C literal 0 HcmV?d00001 From b4e25685f5d5cc3dbf3e364d42eb30d98a118d77 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Fri, 27 Sep 2019 00:09:09 +0800 Subject: [PATCH 275/386] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E6=89=8B=E6=92=95?= =?UTF-8?q?=E4=BB=A3=E7=A0=81=E9=A2=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...6\214\207offer\351\242\230\350\247\243.md" | 23 ++++++++++++++++++- ...40\351\235\242\350\257\225\351\242\230.md" | 4 ++++ ...40\351\235\242\350\257\225\351\242\230.md" | 6 +++++ ...ll\351\235\242\350\257\225\351\242\230.md" | 3 ++- ...72\347\241\200\347\237\245\350\257\206.md" | 16 +++++++++++-- 5 files changed, 48 insertions(+), 4 deletions(-) diff --git "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\345\211\221\346\214\207offer\351\242\230\350\247\243.md" "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\345\211\221\346\214\207offer\351\242\230\350\247\243.md" index dd9064ae..70e318d4 100644 --- "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\345\211\221\346\214\207offer\351\242\230\350\247\243.md" +++ "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\345\211\221\346\214\207offer\351\242\230\350\247\243.md" @@ -10,4 +10,25 @@ + 遍历 `k-n` 个数，得到元素　`X`，如果　`X`　比堆顶元素 `Y` 大，则不改变原来的堆，因为这个元素比大根堆中所有元素都大； + 如果　`X`　比堆顶元素 `Y` 小，那么用 `X` 替换堆顶的元素 `Y`。在 X 替换堆顶元素 Y 之后，大根堆的结构可能被破坏，需要进行向下调整(重新排序)。调整过程的时间复杂度是 $O(log_2k)$ 。 -## 最长公共子序列问题 \ No newline at end of file +## 最长公共子序列问题 + +# 手撕代码题 +## 瓜子二手车 +1. 给出二叉树中的两个结点，找出它们的最低公共父节点. +2. [牛顿迭代求sqrt](https://leetcode.com/problems/sqrtx/). ✓ +3. 通过均匀硬币生成1到N随机数 +4. Logistic回归如何防止过拟合. ✓ +5. 打印二叉树的某一行. +6. [维护一个数据流的滚动方差](https://leetcode.com/problems/moving-average-from-data-stream/description/) +7. 如何判断两个链表是否相交并找出交点. +8. 两个栈实现队列, 判断链表是否有环. ✓ +9. 数组中出现次数超过一半的数字. ✓ +10. 杨辉三角给定行和列输出这个数. ✓ +11. 连续数组最小子序和=target,求这个子序的长度 +10. 概率题 ✓ + + 一米长的绳子，任意分成三份，能构成三角形的概率 + + 两个人约定在七点到八点之间见面，先到的人等20分钟就可以走，问两人见面的概率 + +## 58同城 +1. 手撕硬nms ✓ +2. 判断链表是否对称 ✓ \ No newline at end of file diff --git "a/\346\234\272\345\231\250\345\255\246\344\271\240/\346\234\272\345\231\250\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\234\272\345\231\250\345\255\246\344\271\240/\346\234\272\345\231\250\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index a8e8ce8f..89d352ae 100644 --- "a/\346\234\272\345\231\250\345\255\246\344\271\240/\346\234\272\345\231\250\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\234\272\345\231\250\345\255\246\344\271\240/\346\234\272\345\231\250\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -8,6 +8,10 @@ + 随机森林 + 聚类与降维 +## 逻辑回归如何防止过拟合 +1. 优化模型，引入正则化(数据稀疏使用`L1`正则化)，在优化目标中加入正则项，通过惩罚过大的参数来防止过拟合. +2. 增加样本数据(这个方法适合所有的模型). +3. 通过特征选择，剔除一些不重要的特征，从而降低模型复杂度. ## 数据不平衡怎么办? ## AUC的理解 diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 3e412b96..e1dbf426 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -518,6 +518,12 @@ ROI Align操作过程如下图： ## ROI Pooling/ROI Align 反向传播 > 参考[你真的学会RoI Pooling了吗?](https://zhuanlan.zhihu.com/p/59692298)，自己的理解稍后补充。 +## DNN的反向传播算法推导 +> 参考[机器学习必刷题-手撕推导篇(2)：BP算法推导](https://www.jianshu.com/p/d52626b39382) + +## CNN的反向传播算法推导 +> 参考[卷积神经网络(CNN)反向传播算法](https://www.cnblogs.com/pinard/p/6494810.html) + ## Reference 1.[深度学习中的数据增强](https://blog.csdn.net/zhelong3205/article/details/81810743 ) ## Reference diff --git "a/\347\274\226\347\250\213\350\257\255\350\250\200/cpp-python-shell\351\235\242\350\257\225\351\242\230.md" "b/\347\274\226\347\250\213\350\257\255\350\250\200/cpp-python-shell\351\235\242\350\257\225\351\242\230.md" index a59ecbf7..16c635a5 100644 --- "a/\347\274\226\347\250\213\350\257\255\350\250\200/cpp-python-shell\351\235\242\350\257\225\351\242\230.md" +++ "b/\347\274\226\347\250\213\350\257\255\350\250\200/cpp-python-shell\351\235\242\350\257\225\351\242\230.md" @@ -201,7 +201,7 @@ print(str2int('12300')) print(str2int('0012345')) # 0012345 ``` ## Python深拷贝、浅拷贝区别 -> Python中的大多数对象，比如列表、字典、NumPy数组，和用户定义的类型（类），都是可变的。意味着这些对象或包含的值可以被修改。但也有些对象是不可变的，例如数值型、字符串型string和元组tuple。 +> Python中的大多数对象，比如列表list、字典dict、集合set、NumPy数组，和用户定义的类型（类），都是可变的。意味着这些对象或包含的值可以被修改。但也有些对象是不可变的，例如数值型int、字符串型str和元组tuple。 **1、复制不可变数据类型：** @@ -252,3 +252,4 @@ d = [1, 2, 3, ['a', 'b', 'c']] 2. [110道Python面试题（真题）](https://zhuanlan.zhihu.com/p/54430650) 3. [关于Python的面试题](https://github.com/taizilongxu/interview_Python#15-__new__%E5%92%8C__init__%E7%9A%84%E5%8C%BA%E5%88%AB) 4. [继承和多态](http://funhacks.net/explore-python/Class/inheritance_and_polymorphism.html) + diff --git "a/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200\347\237\245\350\257\206.md" "b/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200\347\237\245\350\257\206.md" index 7e0ffb52..b8a4408a 100644 --- "a/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200\347\237\245\350\257\206.md" +++ "b/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200\347\237\245\350\257\206.md" @@ -21,6 +21,7 @@ + 读脏数据 + 不可重复读 + 脏影读 + ## 多进程与多线程区别 + **进程是资源分配的最小单位，线程是CPU调度（独立调度）的最小单位**。 + 多个进程之间相互独立，一个任务就是一个进程，进程内的“子任务”称为线程；线程是进程的一部分，一个进程至少有一个线程。 @@ -33,7 +34,10 @@ 进程间通信（IPC, InterProcess Communication）：是指在不同进程之间传播或交换信息。`IPC` 的方式有：管道、消息队列、信号量、共享存储、Socket等。`Socket`　支持不同主机的两个进程的　`IPC`。 > python Process类参数：`target`表示调用的对象，就是子进程要执行的任务 -`Python` 多线程通信可以通过导入`Process`（创建进程）　和`Queue`(创建队列)：｀from multiprocessing import Process, Queue｀ +`Python` 多线程通信可以通过导入`Process`（创建进程）　和`Queue`(创建队列)：`from multiprocessing import Process, Queue`，具体通信过程如下： +1. 父进程创建 `Queue`,并传递给各个子进程：`q = Queue()` `pw = Process(target=write, args=(q,))` +2. 分别启动写入数据和读数据子进程：`pw.start()` `pr.start()` +3. 等待进程技术：`pw.join()` ### 线程通信 一个进程所含的不同线程是共享内存的，所以线程之间共享数据有一个问题是多个线程共享一个变量，导致内容改乱了。解决办法是，如果不同线程间有共享的变量，其中一个方法就是在修改前给其上一把锁`lock`，确保一次只有一个线程能修改。`Python` 创建线程方式如下： ```python @@ -61,4 +65,12 @@ def run_thread(n): 要让单核　`CPU` 运行多任务，就得用到**并发技术**，实现并发技术相当复杂，最容易理解的是“时间片轮转进程调度算法”，它的思想简单介绍如下：在操作系统的管理下，所有正在运行的进程轮流使用CPU，每个进程允许占用CPU的时间非常短(比如10毫秒)，这样用户根本感觉不出来CPU是在轮流为多个进程服务，就好象所有的进程都在不间断地运行一样。但实际上在任何一个时间内有且仅有一个进程占有CPU。如果一台计算机有多个CPU，情况就不同了，如果进程数小于CPU数，则不同的进程可以分配给不同的CPU来运行，这样，多个进程就是真正同时运行的，这便是并行。但如果进程数大于CPU数，则仍然需要使用并发技术。因此，我们可以得出以下结论： + 总线程数<= CPU数量：并行运行 -+ 总线程数 > CPU数量：并发运行 \ No newline at end of file ++ 总线程数 > CPU数量：并发运行 + +# 手撕代码题 +1. 十个数平分两组，差最小. +2. 给出五个会议起始和终止的时间，固定时间内最多安排几场会议. +3. 合并k个链表. +4. 01矩阵中最大的1矩阵面积. +5. 海洋陆地的动态规划题. + From 56e22d4691a2e2e2336513b65b6e0fa3b3beb83f Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Sun, 29 Sep 2019 12:43:28 +0800 Subject: [PATCH 276/386] =?UTF-8?q?=E7=A7=8B=E6=8B=9B=E7=AE=97=E6=B3=95?= =?UTF-8?q?=E5=B2=97=E9=9D=A2=E7=BB=8F=E6=9B=B4=E6=96=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...40\351\235\242\350\257\225\351\242\230.md" | 4 +- ...13\346\213\233\351\235\242\347\273\217.md" | 108 +++++++++++++++++- 2 files changed, 109 insertions(+), 3 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index e1dbf426..1ee75dc9 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -70,10 +70,10 @@ $$L = -\sum_{c=1}^{M}y_{c}log(p_{c})或者H(p,q)=-\sum p(x)logq(x)$$（p代表 + 深层卷积层的感受野大小和它之前所有层的滤波器大小和步长有关系 + 计算感受野大小时，忽略了图像边缘的影响 -感受野大小的计算采用 `top to down` 的方式， 即先计算最深层在前一层上的感受野，然后逐渐传递到第一层，使用的公式可以表示如下: $$RF = ((RF-)*stride + fsize +感受野大小的计算采用 `top to down` 的方式， 即先计算最深层在前一层上的感受野，然后逐渐传递到第一层，使用的公式可以表示如下: $$RF = ((RF-1)*stride + fsize ```　 for layer in （top layer To down layer）: 　　　　 - RF = ((RF -1)* stride) + fsize + RF = ((RF - 1)* stride) + fsize ``` + `stride`: 表示卷积的步长； + `fsize`: 表示卷积层滤波器的大小 diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" index d1b9fa6e..4ab104c9 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" @@ -28,6 +28,7 @@ + 进程之间的通信方式 8. 一道编程题 +## 涂鸦移动 ### 涂鸦移动一面(45分钟) 1. 实习项目介绍 2. Python多进程介绍下 @@ -36,7 +37,102 @@ 5. 两道编程题 + 二维数组矩阵 + 求top k数 +### 涂鸦移动二面(40分钟) +1. 多进程与多线程区别 +2. 两数之和 `leetcode2` +3. 三数之和 `leetcode15` +## 平安智慧城-图像识别算法工程师（15分钟）-玄学挂, 问题都答上来，反手就是挂，感觉看学校 +1. `Faster RCNN` 结构画下，讲下过程 +2. `VGG` 和 `ResNet` 结构画下 +3. `ResNet` 结构解决了什么问题，怎么解决的 +4. `pooling` 层作用 +5. `Inception` 结构画下 + +## 华为海思-人工智能算法工程师（一面体验不好，二面编程题自己状态出问题了，导致用错了方法（时间复杂度O(1)就能解决，我没想出来）） +### 华为海思一面 +1. `Faster RCNN` 为什么能精准定位到检测框的位置 +2. 知识蒸馏方法的一个问题（这个问题问的太抽象了，我始终没有搞懂面试官想要问我什么） +3. 你有没有对现有网络做过改进，怎么改进的（说了对压缩模型的一些设计，但是面试官不满意） +4. 手撕代码：指定位置反转链表， 并把代码每一行解释清楚 +5. 建议我转通用软件开发，说我不适合做算法，我不同意，导致后面二面心情有点糟糕，思维不在线 +### 华为海思二面 +1. `Python` 的一些基础知识 +2. `Inception` 画下，以其中一个模块为例，从头到尾解释下包括： + + 卷积核参数的选择 + + 为什么 `Inception` 能为了增加网络对尺度的适应性 + + 感受野是什么 +3. 聊天， 给我挑道简单的代码题 +4. 手撕代码，代码题不难，面试官人也很好，提醒了有更少的时间复杂度，但是自己状态不好，没想到时间复杂度可以为 `O(1)` +### 总结 +华为今年感觉不关心你的项目了， 很看中手撕代码，只要代码那关没过，就算你基础知识原理和项目做的再出色，估计也过不了， 相对，只要手撕代码过了，前面就算答得很烂，也还是能面试通过。 +## 奥比中光-算法优化 +### 笔试 +1. 选择题：笔试都是考一些算法优化的问题，没有专门了解过的话，真的很多人估计都不会做。 +2. 编程题：代码优化 +### 一面 +1. 实习项目介绍： + + 解决了什么问题 + + 怎么解决的 + + `inception` 结构原理描述 + +2. 博客写了什么内容 +3. 传统图像处理方法说下 +4. 深度学习（神经网络）的一些基本结构说下 +### HR面 +1. 自我介绍 +2. 实习项目介绍： + + 用了什么方法 + + 为什么用`inception`结构 + +3. 目标检测了解哪些 +4. 平时喜欢看什么书 +5. 大学期间有参加什么活动吗 +6. 手里有其他`offer`没 +### 总结 +面试官和`hr`人都很友好，会耐心的听你把话说完，面试体验很舒服。 +## 58同城-机器学习算法工程师（问的很细致，公式的每一个参数都要解释清楚） +### 58同城一面 +1. `KNN` 原理讲下，以一个实际问题为例，讲下 `KNN` 怎么做的 +2. `retinanet` 网络介绍下 + + `FPN` 网络画下，讲下原理 + + `Focal loss` 讲下，写下公式，讲下$\alpha_{t}$ 和 $\gamma$ 两个参数作用，为什么 $\alpha_{t}$ 可以解决正负样本的不平衡， $\gamma$ 可以解决解决难易样本的不平衡问题。 + +3. `soft-nms` 和硬 `nms` 原理和过程各自介绍下，为什么`soft-nms` 能解决漏检问题（我没讲清楚） +4. `dropout` 和 `bn` 在训练阶段和预测阶段有什么不同，具体原理和过程说下 +5. 手撕 `nms` 代码，并讲清楚过程(10分钟) +### 58同城二面 +1. 逻辑回归怎么解决过拟合问题 +2. `dropou` 原理，训练和测试阶段有什么不同 +3. `tensorflow` 一些框架的问题，有用 `tensorflow` 写过模型和项目吗（有） +4. 实习项目介绍， `inception`结构和原理介绍下 +5. 模型压缩都有哪些方法，说下知识蒸馏怎么做的 +### 58同城hr面 +1. 介绍自己 +2. 奖学金拿过吗 +3. 在大学期间的一些活动 +4. 实习项目介绍下，解决了什么问题，还有什么问题没解决，打算怎么解决 +5. 手里有其他 `offer` 没和秋招情况 + +## 瓜子二手车-机器学习算法工程师 +### 一面（30分钟） +1. 概率题，并写出相关代码 +2. 实习项目介绍， `inception` 网络原理 +3. `faster rcnn` 网络讲下 +### 二面（70分钟）-中途接了个电话 +1. 排好序的有重复数字的数组，从中找一个指定元素，并返回最小的那个索引值（同步IDE, 要求AC并且尽可能最优解） +2. 回文整数（不能使用`str`） +3. 传统图像处理方法 + + `opencv` 图像处理基本方法 + + 边缘检测算子有哪些，说下`canny` 算子具体怎么做的 + + `HOG` 特征算法过程说下 + +4. `pooling` 层作用 +5. 实习项目介绍，`arm` 端模型如何部署的 + +### 总结 +瓜子二手车也没怎么问项目，就是手撕代码和问一些基础知识，秋招好几家公司都是这样了，不是很关心项目，比较看中手撕代码。 +## 卡斯柯信号 ## 卡斯柯信号一面(35分钟)(人工智能->C++开发) 1. 项目介绍 2. 你是如何学习机器学习、深度学习的 @@ -44,10 +140,20 @@ 4. `c++` 面向对象的三个特性：继承、多态、接口 5. 基类和派生类析构函数有什么区别 6. 讲下继承 +## 卡斯柯信号二面(50分钟)(人工智能->C++开发) +1. 做4道编程题（35分钟） +2. 讲笔试题 +3. 聊家常（为什么想来上海，有女朋友吗） ## 工商银行(15分钟)（软件开发中心） 1. 核心项目介绍(围绕项目提了一些问题) 2. 你的本科和研究所成绩，为什么投成都岗 3. 你的本科学校是什么类型的，研究所毕业设计打算做什么 4. 你的博客主要写了什么内容 5. 你的比赛经历介绍下 -6. 你有什么想问的 \ No newline at end of file +6. 你有什么想问的 +## 经纬恒润 +1. 自我介绍 +2. 实习项目介绍 +3. 期望薪资和工作地点 +### 面试总结 +感觉凉了，虽然问题都答上来了，但是薪资那里经纬恒润工资好像很低。 \ No newline at end of file From dd9bb6cb5dd71000de26389cdff065d0089e72ed Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Sun, 29 Sep 2019 12:50:49 +0800 Subject: [PATCH 277/386] =?UTF-8?q?=E7=A7=8B=E6=8B=9B=E7=AE=97=E6=B3=95?= =?UTF-8?q?=E5=B2=97=E9=9D=A2=E7=BB=8F=E6=9B=B4=E6=96=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...13\346\213\233\351\235\242\347\273\217.md" | 37 ++++++++++++------- 1 file changed, 24 insertions(+), 13 deletions(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" index 4ab104c9..62a6a240 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" @@ -1,10 +1,12 @@ -### 字节跳动AI Lab一面（挂） +## 字节跳动AI Lab-计算机视觉算法工程师 +### 一面（挂） 1. 钢筋检测项目深挖 2. `Roi Pooling` 和 `Roi Algin`区别 3. `F1 Score` 如何计算 4. `Siamese` 网络原理，`loss` 计算方式 5. 算法题（忘了） -### 地平线提前批一面（挂）(1个小时) +## 地平线机器人-计算机时间算法工程师 +### 提前批一面（挂）(1个小时) 1. 两道算法题： + 求二叉树的右视图 + 输入一个数，找出含 7 的数字的数的最大个数 @@ -12,7 +14,9 @@ 3. `Roi pooling` 和 `Roi Align` 怎么做的，`Roi Align`的反向传播公式写下 4. 几种优化器说下，说下区别及优缺点 5. `SGD` 公式和带动量`SGD`公式写下 -### 元戎启行一面（50分钟）(挂) + +## 元戎启行-计算机视觉算法工程师 +### 一面（50分钟）(挂) 1. 自我介绍 2. 项目细节，深挖项目（图像增强怎么做的，过采样具体怎么实现，模型融合怎么融合等） 3. 实习项目，具体怎么解决了问题 @@ -28,8 +32,8 @@ + 进程之间的通信方式 8. 一道编程题 -## 涂鸦移动 -### 涂鸦移动一面(45分钟) +## 涂鸦移动-软件开发工程师 +### 一面(45分钟) 1. 实习项目介绍 2. Python多进程介绍下 3. 面向对象讲解下 @@ -37,12 +41,17 @@ 5. 两道编程题 + 二维数组矩阵 + 求top k数 -### 涂鸦移动二面(40分钟) +### 二面(40分钟) 1. 多进程与多线程区别 2. 两数之和 `leetcode2` 3. 三数之和 `leetcode15` +4. 排序算法 + + 讲下常用的排序算法，及各自时间复杂度 + + 快排原理过程说下，什么情况下时间复杂度最高 -## 平安智慧城-图像识别算法工程师（15分钟）-玄学挂, 问题都答上来，反手就是挂，感觉看学校 +4. 其他问题 +## 平安智慧城-图像识别算法工程师-玄学挂, 问题都答上来，反手就是挂，感觉看学校 +### 一面（15分钟） 1. `Faster RCNN` 结构画下，讲下过程 2. `VGG` 和 `ResNet` 结构画下 3. `ResNet` 结构解决了什么问题，怎么解决的 @@ -132,26 +141,28 @@ ### 总结 瓜子二手车也没怎么问项目，就是手撕代码和问一些基础知识，秋招好几家公司都是这样了，不是很关心项目，比较看中手撕代码。 -## 卡斯柯信号 -## 卡斯柯信号一面(35分钟)(人工智能->C++开发) +## 卡斯柯信号-C++软件开发工程师 +### 一面(35分钟) 1. 项目介绍 2. 你是如何学习机器学习、深度学习的 3. 指针和引用的区别 4. `c++` 面向对象的三个特性：继承、多态、接口 5. 基类和派生类析构函数有什么区别 6. 讲下继承 -## 卡斯柯信号二面(50分钟)(人工智能->C++开发) -1. 做4道编程题（35分钟） +### 二面(50分钟) +1. 做4道编程题（38分钟） 2. 讲笔试题 3. 聊家常（为什么想来上海，有女朋友吗） -## 工商银行(15分钟)（软件开发中心） +## 工商银行-软件开发中心: 大数据及机器学习算法工程师 +### 一面(15分钟) 1. 核心项目介绍(围绕项目提了一些问题) 2. 你的本科和研究所成绩，为什么投成都岗 3. 你的本科学校是什么类型的，研究所毕业设计打算做什么 4. 你的博客主要写了什么内容 5. 你的比赛经历介绍下 6. 你有什么想问的 -## 经纬恒润 +## 经纬恒润-无人驾驶算法工程师 +### 一面（20分钟） 1. 自我介绍 2. 实习项目介绍 3. 期望薪资和工作地点 From 846d987552394a556be00c6c9a3c1ec083414783 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Sun, 29 Sep 2019 12:56:57 +0800 Subject: [PATCH 278/386] =?UTF-8?q?=E7=A7=8B=E6=8B=9B=E7=AE=97=E6=B3=95?= =?UTF-8?q?=E5=B2=97=E9=9D=A2=E7=BB=8F=E6=9B=B4=E6=96=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...13\346\213\233\351\235\242\347\273\217.md" | 20 ++++++++++--------- 1 file changed, 11 insertions(+), 9 deletions(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" index 62a6a240..ae4b6950 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" @@ -45,11 +45,11 @@ 1. 多进程与多线程区别 2. 两数之和 `leetcode2` 3. 三数之和 `leetcode15` -4. 排序算法 +4. 排序算法: + 讲下常用的排序算法，及各自时间复杂度 - + 快排原理过程说下，什么情况下时间复杂度最高 - -4. 其他问题 + + 快排原理过程说下，什么情况下时间复杂度最高 + +5. 其他问题 ## 平安智慧城-图像识别算法工程师-玄学挂, 问题都答上来，反手就是挂，感觉看学校 ### 一面（15分钟） 1. `Faster RCNN` 结构画下，讲下过程 @@ -84,7 +84,7 @@ + 解决了什么问题 + 怎么解决的 + `inception` 结构原理描述 - + 2. 博客写了什么内容 3. 传统图像处理方法说下 4. 深度学习（神经网络）的一些基本结构说下 @@ -94,7 +94,7 @@ + 用了什么方法 + 为什么用`inception`结构 -3. 目标检测了解哪些 +3. 目标检测了解哪些方法 4. 平时喜欢看什么书 5. 大学期间有参加什么活动吗 6. 手里有其他`offer`没 @@ -103,10 +103,11 @@ ## 58同城-机器学习算法工程师（问的很细致，公式的每一个参数都要解释清楚） ### 58同城一面 1. `KNN` 原理讲下，以一个实际问题为例，讲下 `KNN` 怎么做的 -2. `retinanet` 网络介绍下 +2. `retinanet` 网络相关： + + `retinanet` 结构讲下 + `FPN` 网络画下，讲下原理 + `Focal loss` 讲下，写下公式，讲下$\alpha_{t}$ 和 $\gamma$ 两个参数作用，为什么 $\alpha_{t}$ 可以解决正负样本的不平衡， $\gamma$ 可以解决解决难易样本的不平衡问题。 - + 3. `soft-nms` 和硬 `nms` 原理和过程各自介绍下，为什么`soft-nms` 能解决漏检问题（我没讲清楚） 4. `dropout` 和 `bn` 在训练阶段和预测阶段有什么不同，具体原理和过程说下 5. 手撕 `nms` 代码，并讲清楚过程(10分钟) @@ -116,6 +117,7 @@ 3. `tensorflow` 一些框架的问题，有用 `tensorflow` 写过模型和项目吗（有） 4. 实习项目介绍， `inception`结构和原理介绍下 5. 模型压缩都有哪些方法，说下知识蒸馏怎么做的 +6. 防止过拟合的方法有哪些，这些方法都是怎么做的 ### 58同城hr面 1. 介绍自己 2. 奖学金拿过吗 @@ -135,7 +137,7 @@ + `opencv` 图像处理基本方法 + 边缘检测算子有哪些，说下`canny` 算子具体怎么做的 + `HOG` 特征算法过程说下 - + 4. `pooling` 层作用 5. 实习项目介绍，`arm` 端模型如何部署的 From adcd32aa52dd9b5a6fe8aed952ae8e13872517f2 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Sun, 29 Sep 2019 12:59:16 +0800 Subject: [PATCH 279/386] =?UTF-8?q?=E7=A7=8B=E6=8B=9B=E7=AE=97=E6=B3=95?= =?UTF-8?q?=E5=B2=97=E9=9D=A2=E7=BB=8F=E6=9B=B4=E6=96=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...47\247\213\346\213\233\351\235\242\347\273\217.md" | 11 +++-------- 1 file changed, 3 insertions(+), 8 deletions(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" index ae4b6950..d15ca7cb 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" @@ -47,8 +47,7 @@ 3. 三数之和 `leetcode15` 4. 排序算法: + 讲下常用的排序算法，及各自时间复杂度 - + 快排原理过程说下，什么情况下时间复杂度最高 - + + 快排原理过程说下，什么情况下时间复杂度最高 5. 其他问题 ## 平安智慧城-图像识别算法工程师-玄学挂, 问题都答上来，反手就是挂，感觉看学校 ### 一面（15分钟） @@ -83,8 +82,7 @@ 1. 实习项目介绍： + 解决了什么问题 + 怎么解决的 - + `inception` 结构原理描述 - + + `inception` 结构原理描述 2. 博客写了什么内容 3. 传统图像处理方法说下 4. 深度学习（神经网络）的一些基本结构说下 @@ -93,7 +91,6 @@ 2. 实习项目介绍： + 用了什么方法 + 为什么用`inception`结构 - 3. 目标检测了解哪些方法 4. 平时喜欢看什么书 5. 大学期间有参加什么活动吗 @@ -107,7 +104,6 @@ + `retinanet` 结构讲下 + `FPN` 网络画下，讲下原理 + `Focal loss` 讲下，写下公式，讲下$\alpha_{t}$ 和 $\gamma$ 两个参数作用，为什么 $\alpha_{t}$ 可以解决正负样本的不平衡， $\gamma$ 可以解决解决难易样本的不平衡问题。 - 3. `soft-nms` 和硬 `nms` 原理和过程各自介绍下，为什么`soft-nms` 能解决漏检问题（我没讲清楚） 4. `dropout` 和 `bn` 在训练阶段和预测阶段有什么不同，具体原理和过程说下 5. 手撕 `nms` 代码，并讲清楚过程(10分钟) @@ -136,8 +132,7 @@ 3. 传统图像处理方法 + `opencv` 图像处理基本方法 + 边缘检测算子有哪些，说下`canny` 算子具体怎么做的 - + `HOG` 特征算法过程说下 - + + `HOG` 特征算法过程说下 4. `pooling` 层作用 5. 实习项目介绍，`arm` 端模型如何部署的 From da5701d7ba0cb2691dca00f70bc2cca3e20ce33f Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Sun, 29 Sep 2019 13:02:41 +0800 Subject: [PATCH 280/386] =?UTF-8?q?=E7=A7=8B=E6=8B=9B=E7=AE=97=E6=B3=95?= =?UTF-8?q?=E5=B2=97=E9=9D=A2=E7=BB=8F=E6=9B=B4=E6=96=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...62\227\347\247\213\346\213\233\351\235\242\347\273\217.md" | 4 ++++ 1 file changed, 4 insertions(+) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" index d15ca7cb..9147b131 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" @@ -162,6 +162,10 @@ ### 一面（20分钟） 1. 自我介绍 2. 实习项目介绍 + + 解决了什么问题，怎么解决的 + + 类别过采样方法原理 + + 解决数据不平衡问题有哪些方法 3. 期望薪资和工作地点 +4. 手里有哪些offer ### 面试总结 感觉凉了，虽然问题都答上来了，但是薪资那里经纬恒润工资好像很低。 \ No newline at end of file From 2802d966caa98e5fe542aea084d08fe5e8af8932 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Sun, 29 Sep 2019 21:19:46 +0800 Subject: [PATCH 281/386] =?UTF-8?q?=E7=A7=8B=E6=8B=9B=E7=AE=97=E6=B3=95?= =?UTF-8?q?=E5=B2=97=E9=9D=A2=E7=BB=8F=E6=9B=B4=E6=96=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" index 9147b131..13fe4d13 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" @@ -168,4 +168,4 @@ 3. 期望薪资和工作地点 4. 手里有哪些offer ### 面试总结 -感觉凉了，虽然问题都答上来了，但是薪资那里经纬恒润工资好像很低。 \ No newline at end of file +感觉凉了，虽然问题都答上来了，但是薪资那里经纬恒润工资好像不高。 \ No newline at end of file From 38eff99e0da60d8a91e26230982a1491f6ce8e2e Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Sun, 6 Oct 2019 19:57:31 +0800 Subject: [PATCH 282/386] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E4=BA=8C=E5=88=86?= =?UTF-8?q?=E6=9F=A5=E6=89=BE=E7=9A=84python=E4=BB=A3=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...46\344\271\240\347\254\224\350\256\260.md" | 20 ++++++++++++++++++- 1 file changed, 19 insertions(+), 1 deletion(-) diff --git "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" index bacc9ca0..689d822c 100644 --- "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" +++ "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" @@ -6,7 +6,25 @@ # 第一章，算法简介 ## 1.2，二分法查找元素 -一般而言，对于包含n个元素的列表查找某个元素，使用二分法最多需要$log_{2}n$步(**时间复杂度为$log_{2}n$**)，简单查找最多需要n步。大O表示法指出了算法最糟糕情况下的运行时间 +一般而言，对于包含n个元素的列表查找某个元素，使用二分法最多需要$log_{2}n$步(**时间复杂度为$log_{2}n$**)，简单查找最多需要n步。大O表示法指出了算法最糟糕情况下的运行时间。二分法实例代码如下： +```python +def binary_search(list, item): + low = 0 + high = len(list)-1 + while low <= high: + mid = (low + high) // 2 + if list[mid] == item: + return mid + elif list[mid] > item: + high = mid - 1 + else: + low = mid + 1 + return None + + +if __name__ == "__main__": + print(binary_search([1,2,3,4,6,7], 3)) # 输出 2 +``` # 第二章，选择排序 ## 2.1，内存工作原理 在计算机中，存储多项数据时，有两种基本方式－数组和链表。但它们并非适用于所有情形。 From b302045924fba61cf9ce5c98960a81da2b8dd268 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Sun, 6 Oct 2019 20:24:18 +0800 Subject: [PATCH 283/386] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E6=9C=89=E5=BA=8F?= =?UTF-8?q?=E6=95=B0=E7=BB=84=E6=9C=89=E9=87=8D=E5=A4=8D=E5=85=83=E7=B4=A0?= =?UTF-8?q?=E7=9A=84=E4=BA=8C=E5=88=86=E6=9F=A5=E6=89=BE=E7=9A=84python?= =?UTF-8?q?=E4=BB=A3=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...46\344\271\240\347\254\224\350\256\260.md" | 23 +++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" index 689d822c..667fec26 100644 --- "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" +++ "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" @@ -25,6 +25,29 @@ def binary_search(list, item): if __name__ == "__main__": print(binary_search([1,2,3,4,6,7], 3)) # 输出 2 ``` +### 1.2.1，特殊的二分查找 +有序数组中的**目标出现多次**，利用二分查找返回在最左边出现的目标值或者是最右边出现的目标值，查找最左边出现的目标值实例代码如下： +```python +def binary_search2(arr, target): + if not arr: + return None + low = 0 + high = len(arr)-1 + while low < high: + mid = (low + high) // 2 + + if arr[mid] < target: + low = mid + 1 + else: + high = mid + if arr[high] == target: + return high + return None + + +if __name__ == "__main__": + print(binary_search2([1,1,1,3,3,3,4], 3)) # 输出3 +``` # 第二章，选择排序 ## 2.1，内存工作原理 在计算机中，存储多项数据时，有两种基本方式－数组和链表。但它们并非适用于所有情形。 From e49ee542f8b302ace9d8b6cb67750f2466ccbd89 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Sun, 6 Oct 2019 21:23:19 +0800 Subject: [PATCH 284/386] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E6=9C=89=E5=BA=8F?= =?UTF-8?q?=E6=95=B0=E7=BB=84=E6=9C=89=E9=87=8D=E5=A4=8D=E5=85=83=E7=B4=A0?= =?UTF-8?q?=E7=9A=84=E4=BA=8C=E5=88=86=E6=9F=A5=E6=89=BE=E7=9A=84python?= =?UTF-8?q?=E4=BB=A3=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...46\344\271\240\347\254\224\350\256\260.md" | 33 ++++++++++++------- 1 file changed, 21 insertions(+), 12 deletions(-) diff --git "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" index 667fec26..5684e590 100644 --- "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" +++ "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" @@ -26,27 +26,36 @@ if __name__ == "__main__": print(binary_search([1,2,3,4,6,7], 3)) # 输出 2 ``` ### 1.2.1，特殊的二分查找 -有序数组中的**目标出现多次**，利用二分查找返回在最左边出现的目标值或者是最右边出现的目标值，查找最左边出现的目标值实例代码如下： +有序数组中的**目标出现多次**，利用二分查找返回在最左边出现的目标值或者是最右边出现的目标值，实例代码如下： ```python -def binary_search2(arr, target): +def binary_search2(arr, target, flag="left"): if not arr: return None - low = 0 - high = len(arr)-1 - while low < high: - mid = (low + high) // 2 - + left = 0 + right = len(arr) - 1 + while left <= right: + mid = left + (right - left) // 2 if arr[mid] < target: - low = mid + 1 + left = mid + 1 + elif arr[mid] > target: + right = mid -1 else: - high = mid - if arr[high] == target: - return high + if flag == "left": + if mid > 0 and arr[mid-1] == target: + right = mid -1 # 不断向最左边逼近 + else: + return mid + elif flag == "right": + if mid + 1 < len(arr) and arr[mid + 1] == target: + left = mid + 1 # 不断向最右边逼近 + else: + return mid return None if __name__ == "__main__": - print(binary_search2([1,1,1,3,3,3,4], 3)) # 输出3 + print(binary_search2([1,1,1,3,3,3,4], 3, "left")) # 查找最左边出现的目标值, 输出3 + print(binary_search2([1,1,1,3,3,3,4,4,4], 3, "right")) # 查找最右边出现的目标值, 输出5 ``` # 第二章，选择排序 ## 2.1，内存工作原理 From 0dc4991ef59f7ee1e05b85e1f5d97596fe4e227e Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Sun, 6 Oct 2019 21:35:21 +0800 Subject: [PATCH 285/386] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E6=9C=89=E5=BA=8F?= =?UTF-8?q?=E6=95=B0=E7=BB=84=E6=9C=89=E9=87=8D=E5=A4=8D=E5=85=83=E7=B4=A0?= =?UTF-8?q?=E7=9A=84=E4=BA=8C=E5=88=86=E6=9F=A5=E6=89=BE=E7=9A=84python?= =?UTF-8?q?=E4=BB=A3=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" index 5684e590..76193c98 100644 --- "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" +++ "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" @@ -34,7 +34,7 @@ def binary_search2(arr, target, flag="left"): left = 0 right = len(arr) - 1 while left <= right: - mid = left + (right - left) // 2 + mid = left + (right - left) // 2 # 防止数据过大溢出? if arr[mid] < target: left = mid + 1 elif arr[mid] > target: From 7f300c9bcbb10af00827a539a243f0cf7ca7ec9f Mon Sep 17 00:00:00 2001 From: Zhang Date: Sat, 19 Oct 2019 17:37:10 +0800 Subject: [PATCH 286/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 1ee75dc9..c924ecc2 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -391,7 +391,7 @@ HOG特征提取方法就是将一个image： 1. 卷积层有参数，池化层没有参数 2. **经过卷积层节点矩阵深度会改变**，池化层不会改变节点矩阵的深度，但是它可以缩小节点矩阵的大小 ## 卷积层参数数量计算方法 -假设输入层矩阵维度是96\*96\*3，第一层卷积层使用尺寸为5\*5、深度为16的过滤器（卷积核尺寸为5\*5、卷积核数量为16），那么这层卷积层的参数个数为５\*5\*3\*16+16=1216个 +假设输入层矩阵维度是96\*96\*3，第一层卷积层使用尺寸为5\*5、深度为16的过滤器（卷积核尺寸为5\*5、卷积核数量为16），那么这层卷积层的参数个数为５\*5\*3\*16+16=1216个，公式总结如下：**卷积层参数量=（filter size * 前一层特征图的通道数 ）* 当前层filter数量 + 当前层filter数量** ## 卷积层输出大小计算 卷积中的特征图大小计算方式有两种，分别是‘VALID’和‘SAME’，卷积和池化都适用，除不尽的结果都向下取整。公式：`O = (W-F+2P)/S+1`，输入图片（Input）大小为I=W*W，卷积核（Filter）大小为F*F，步长（stride）为S，填充（Padding）的像素数为P。 From 9ddfdc4863a83646a95f05d987432c3498dcbf40 Mon Sep 17 00:00:00 2001 From: Zhang Date: Sat, 19 Oct 2019 17:38:19 +0800 Subject: [PATCH 287/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index c924ecc2..6f8fe85e 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -391,7 +391,7 @@ HOG特征提取方法就是将一个image： 1. 卷积层有参数，池化层没有参数 2. **经过卷积层节点矩阵深度会改变**，池化层不会改变节点矩阵的深度，但是它可以缩小节点矩阵的大小 ## 卷积层参数数量计算方法 -假设输入层矩阵维度是96\*96\*3，第一层卷积层使用尺寸为5\*5、深度为16的过滤器（卷积核尺寸为5\*5、卷积核数量为16），那么这层卷积层的参数个数为５\*5\*3\*16+16=1216个，公式总结如下：**卷积层参数量=（filter size * 前一层特征图的通道数 ）* 当前层filter数量 + 当前层filter数量** +假设输入层矩阵维度是96\*96\*3，第一层卷积层使用尺寸为5\*5、深度为16的过滤器（卷积核尺寸为5\*5、卷积核数量为16），那么这层卷积层的参数个数为５\*5\*3\*16+16=1216个，公式总结如下：**卷积层参数量=（filter size \* 前一层特征图的通道数 ）\* 当前层filter数量 + 当前层filter数量** ## 卷积层输出大小计算 卷积中的特征图大小计算方式有两种，分别是‘VALID’和‘SAME’，卷积和池化都适用，除不尽的结果都向下取整。公式：`O = (W-F+2P)/S+1`，输入图片（Input）大小为I=W*W，卷积核（Filter）大小为F*F，步长（stride）为S，填充（Padding）的像素数为P。 From 3063eccf69240afc290981db465a0f55c87f89bf Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Sat, 7 Dec 2019 21:08:15 +0800 Subject: [PATCH 288/386] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E5=A4=A7=E8=AF=9D?= =?UTF-8?q?=E6=95=B0=E6=8D=AE=E7=BB=93=E6=9E=84=E4=B9=A6=E7=9A=84=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E7=AC=94=E8=AE=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...63\225\345\255\246\344\271\240\347\254\224\350\256\260.md" | 4 ++++ ...55\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 3 ++- 2 files changed, 6 insertions(+), 1 deletion(-) diff --git "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" index bacc9ca0..d64c8abc 100644 --- "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" +++ "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" @@ -4,6 +4,10 @@ 10大排序算法时间复杂度及空间复杂度如下图： ![10大排序算法时间复杂度及空间复杂度](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E6%8E%92%E5%BA%8F%E7%AE%97%E6%B3%95%E6%97%B6%E9%97%B4%E5%A4%8D%E6%9D%82%E5%BA%A6.jpg) +*** +# 大话数据结构学习笔记 +## 第三章 线性表 +*** # 第一章，算法简介 ## 1.2，二分法查找元素 一般而言，对于包含n个元素的列表查找某个元素，使用二分法最多需要$log_{2}n$步(**时间复杂度为$log_{2}n$**)，简单查找最多需要n步。大O表示法指出了算法最糟糕情况下的运行时间 diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 1ee75dc9..eb366eca 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -523,7 +523,8 @@ ROI Align操作过程如下图： ## CNN的反向传播算法推导 > 参考[卷积神经网络(CNN)反向传播算法](https://www.cnblogs.com/pinard/p/6494810.html) - +[四张图彻底搞懂CNN反向传播算法（通俗易懂）](https://zhuanlan.zhihu.com/p/81675803) +[反向传播算法推导过程（非常详细）](https://zhuanlan.zhihu.com/p/79657669) ## Reference 1.[深度学习中的数据增强](https://blog.csdn.net/zhelong3205/article/details/81810743 ) ## Reference From e919ae2f8071cb53bc0cadded46ba13192e7b55b Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Sat, 7 Dec 2019 21:14:01 +0800 Subject: [PATCH 289/386] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E5=A4=A7=E8=AF=9D?= =?UTF-8?q?=E6=95=B0=E6=8D=AE=E7=BB=93=E6=9E=84=E4=B9=A6=E7=9A=84=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E7=AC=94=E8=AE=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...36\204\345\255\246\344\271\240\347\254\224\350\256\260.md" | 1 + ...63\225\345\255\246\344\271\240\347\254\224\350\256\260.md" | 4 ---- 2 files changed, 1 insertion(+), 4 deletions(-) create mode 100644 "\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\345\244\247\350\257\235\346\225\260\346\215\256\347\273\223\346\236\204\345\255\246\344\271\240\347\254\224\350\256\260.md" diff --git "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\345\244\247\350\257\235\346\225\260\346\215\256\347\273\223\346\236\204\345\255\246\344\271\240\347\254\224\350\256\260.md" "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\345\244\247\350\257\235\346\225\260\346\215\256\347\273\223\346\236\204\345\255\246\344\271\240\347\254\224\350\256\260.md" new file mode 100644 index 00000000..74b10b63 --- /dev/null +++ "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\345\244\247\350\257\235\346\225\260\346\215\256\347\273\223\346\236\204\345\255\246\344\271\240\347\254\224\350\256\260.md" @@ -0,0 +1 @@ +# 大话数据结构学习笔记 \ No newline at end of file diff --git "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" index 56331619..76193c98 100644 --- "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" +++ "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225\345\255\246\344\271\240\347\254\224\350\256\260.md" @@ -4,10 +4,6 @@ 10大排序算法时间复杂度及空间复杂度如下图： ![10大排序算法时间复杂度及空间复杂度](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E6%8E%92%E5%BA%8F%E7%AE%97%E6%B3%95%E6%97%B6%E9%97%B4%E5%A4%8D%E6%9D%82%E5%BA%A6.jpg) -*** -# 大话数据结构学习笔记 -## 第三章 线性表 -*** # 第一章，算法简介 ## 1.2，二分法查找元素 一般而言，对于包含n个元素的列表查找某个元素，使用二分法最多需要$log_{2}n$步(**时间复杂度为$log_{2}n$**)，简单查找最多需要n步。大O表示法指出了算法最糟糕情况下的运行时间。二分法实例代码如下： From b454cdb57d303ed63266be2c3735b8acef1ab9ef Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Mon, 16 Dec 2019 22:45:31 +0800 Subject: [PATCH 290/386] add some notes about 3d detection --- ...04\347\220\206\345\237\272\347\241\200.md" | 17 +++ ...6\214\207offer\351\242\230\350\247\243.md" | 2 +- ...46\344\271\240\347\254\224\350\256\260.md" | 6 +- ...26\347\250\213\345\237\272\347\241\200.md" | 129 ++++++++++++++++++ 4 files changed, 152 insertions(+), 2 deletions(-) create mode 100644 "\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200/Linux\347\275\221\347\273\234\347\274\226\347\250\213\345\237\272\347\241\200.md" diff --git "a/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206\345\237\272\347\241\200.md" "b/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206\345\237\272\347\241\200.md" index a25aac34..11730f1a 100644 --- "a/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206\345\237\272\347\241\200.md" +++ "b/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206/\346\225\260\345\255\227\345\233\276\345\203\217\345\244\204\347\220\206\345\237\272\347\241\200.md" @@ -1,4 +1,21 @@ +## 3D目标检测笔记 +1. EfficientNet is a backbone. In the original paper, they used Hourglass backbone, which has a structure similar to UNet. +CenterNet is only a type of decoder, not the whole network. +2. 8 outputs, not exactly classes. +1 for binary mask +7 for regression variables x, y, z, yaw, pitch_sin, pitch_cos, roll +3. Regression predicts raw 3d coordinates of a car x, y, z. And binary mask predicts the 2d position on the image row, col. 2d map is much more precise but contains incomplete information. Function optimize_xy is used to shift x, y (on the road) so that they correspond to row, col (in image) +4. CenterNet模型：作者在构建模型时将目标作为一个点——即目标BBox的中心点。检测器采用关键点估计来找到中心点，并回归到其他目标属性，例如尺寸，3D位置，方向，甚至姿态。 +5. I use this idea to predict x, y, z coordinates of the vehicle and also yaw, pitch_cos, pitch_sin, roll angles. +For pitch I predict sin and cos, because, as we will see, this angle can be both near 0 and near 3.14. +These 7 parameters are my regression target variables instead of shift_x, shift_y, size_x, size_y +6. In this competition the world coordinate (Xw, Yw, Zw) is same with camera coordinate (Xc, Yc, Zc), the camera is origin. + # 数字图像处理 +## 相机成像的原理 +相机成像的原理：针孔相机(Pinhole Camera)通过投影变换，可以将三维Camera坐标转换为二维的图像坐标。这个变换矩阵解释相机的内在属性，称为`相机内参（Camera Intrinsic） K`。 +## yaw航向角，pitch俯仰角，roll翻滾角 + ## 均值模糊与高斯模糊 ### 均值模糊（不常用） 所谓"模糊"，可以理解成每一个像素都取周边像素的平均值(**这种叫均值模糊**)，在图形上，就相当于产生"模糊"效果，但是在数值上，这是一种"平滑化"，"中间点"会失去细节。（高斯模糊效果如下图所示：） diff --git "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\345\211\221\346\214\207offer\351\242\230\350\247\243.md" "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\345\211\221\346\214\207offer\351\242\230\350\247\243.md" index 70e318d4..c6fc4cbf 100644 --- "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\345\211\221\346\214\207offer\351\242\230\350\247\243.md" +++ "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\345\211\221\346\214\207offer\351\242\230\350\247\243.md" @@ -18,7 +18,7 @@ 2. [牛顿迭代求sqrt](https://leetcode.com/problems/sqrtx/). ✓ 3. 通过均匀硬币生成1到N随机数 4. Logistic回归如何防止过拟合. ✓ -5. 打印二叉树的某一行. +5. 打印二叉树的某一行(层序遍历). 6. [维护一个数据流的滚动方差](https://leetcode.com/problems/moving-average-from-data-stream/description/) 7. 如何判断两个链表是否相交并找出交点. 8. 两个栈实现队列, 判断链表是否有环. ✓ diff --git "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\345\244\247\350\257\235\346\225\260\346\215\256\347\273\223\346\236\204\345\255\246\344\271\240\347\254\224\350\256\260.md" "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\345\244\247\350\257\235\346\225\260\346\215\256\347\273\223\346\236\204\345\255\246\344\271\240\347\254\224\350\256\260.md" index 74b10b63..8e5dc6e1 100644 --- "a/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\345\244\247\350\257\235\346\225\260\346\215\256\347\273\223\346\236\204\345\255\246\344\271\240\347\254\224\350\256\260.md" +++ "b/\346\225\260\346\215\256\347\273\223\346\236\204\344\270\216\347\256\227\346\263\225/\345\244\247\350\257\235\346\225\260\346\215\256\347\273\223\346\236\204\345\255\246\344\271\240\347\254\224\350\256\260.md" @@ -1 +1,5 @@ -# 大话数据结构学习笔记 \ No newline at end of file +# 大话数据结构学习笔记 +## 第三章 线性表 ++ 线性表（List）：零个或多个数据元素的有限序列。 + + diff --git "a/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200/Linux\347\275\221\347\273\234\347\274\226\347\250\213\345\237\272\347\241\200.md" "b/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200/Linux\347\275\221\347\273\234\347\274\226\347\250\213\345\237\272\347\241\200.md" new file mode 100644 index 00000000..504365c3 --- /dev/null +++ "b/\350\256\241\347\256\227\346\234\272\345\237\272\347\241\200/Linux\347\275\221\347\273\234\347\274\226\347\250\213\345\237\272\347\241\200.md" @@ -0,0 +1,129 @@ +# Libevent C++高并发网络编程 +## 3.1 课程内容概述 +1. Libevent源码的跨平台编译和测试 +2. Libevent原理和网络模型设置 +3. event事件处理原理和实战 +4. bufferevent缓冲IO +5. bufferevent、zlib实现过滤器中压缩和解压缩 +6. libevent的http接口实现服务端和客户端 +7. 搭建基于libevent的C++跨平台线程池 +8. 基于libevent和线程池完成FTP服务器开发 + +# 大并发服务器架构学习 +## 第一章 高性能的服务器架构 ++ 网络I/O + 服务器高性能编程技术 + 数据库 ++ 超出数据库连接数 ++ 超出时限（队列+连接池，缓冲更新，缓冲换页，数据库读写分离进行负载均衡-replication机制） + +服务器性能四大杀手： ++ 数据拷贝-缓存技术解决 ++ 环境切换-有理性创建线程，单线程还是多线程好，单核服务器（采用状态机编程，效率最佳，减少线程间的切换开销），多线程能够充分发挥多核服务器的性能。 ++ 内存分配-内存池 ++ 锁竞争 + +## 第二章 大型网站架构演变过程 +1. web动静资源分离 +2. 缓存处理 + + 减少对网站的访问-客户端（浏览器）缓存 + + 减少对Web应用服务器的请求-前端页面缓存（squid） + + 减少对数据库的查询-页面片段缓存ESI（Edge Side Includes） + + 减少对文件系统I/O操作-本地数据缓存 + +3. Web server集群+读写分离（负载均衡） + + 前端负载均衡：DNS负载均衡、反向代理、基于NAT的负载均衡技术、LVS、F5硬件负载均衡 + + 应用服务器负载均衡 + + 数据库负载均衡 +4. CDN、分布式缓存、分库分表 +5. 多数据中心+分布式存储与计算（技术点DFS分布式文件系统、Key-Value DB、Map/Reduce算法） + +## 第三章 poll +Linux下有三种I/O复用模型：select、poll、epoll。 + +# Linux网络编程基础 +## 网络基础-综述 +### 1. **协议的概念** +TCP协议注重数据的传输，HTTP协议注重数据的解释。 +### 2. b/s（浏览器/服务端模式） c/s（客户端/服务端模式） ++ `C/S模式优点`：协议选用灵活，提前对数据进行缓存 ++ C/S模式缺点：对用户安全构成威胁，开发任务工作量大 ++ `B/S模式优点`：安全性高，跨平台 ++ B/S模式缺点：协议选用不灵活，数据加载不缓存 ++ 两者使用场景不同。 +### 3. **分层模型：OSI七层模型-TCP/IP四层模型** ++ `OSI七层模型`: 物理层->数据链路层->网络层（IP协议）->传输层（TCP/UDP协议）->会话层->表示层->应用层 ++ `TCP/IP四层模型`：网络接口层------>网络层（IP协议）->传输层（TCP/UDP协议）--------------->应用层（FTP协议） +### 4. **协议格式** ++ `数据包基本格式`：操作系统封装数据和解析数据包；路由器寻路（寻找下一路由节点）的一般思想。 ++ `以太网帧格式`：|目的地址(6字节)|源地址6字节|类型2字节|数据|CRC校应2字节| ++ `arp数据包格式`:|目的地址(6字节)|源地址6字节|帧类型0806|硬件类型-协议类型-硬件地址长度-协议地址长度-op-发送端以太网地址-发送端IP地址目的以太网地址-目的IP地址（28字节ARP请求/应答）|PAD填充18字节|。`arp数据报的目的：获取下一跳mac的地址`。 ++ `IP段格式`：在网络层。|4位版本号|4位首部长度|8位服务类型（TOS）|16位总长度（字节数）| ++ `TCP/UDP数据报格式` +### 5. **NAT映射 打洞机制** ++ `NAT（Network Address Translation，网络地址转换）`，也叫做网络掩蔽或者IP掩蔽。NAT是一种网络地址翻译技术，主要是将内部的私有IP地址（private IP）转换成可以在公网使用的公网IP（public IP）。 ++ NAT可以同时让多个计算机同时联网，并隐藏其内网IP，因此也增加了内网的网络安全性；此外，NAT对来自外部的数据查看其NAT映射记录，对没有相应记录的数据包进行拒绝，提高了网络安全性。 ++ 打洞机制需要借助公网 `IP` 实现； ++ 公-公通信（局域网内IP通信）：直接访问；公-私：NAT映射；私-公：NAT映射；私-私：NAT映射、打洞机制。 +### 6. **套接字socket通信原理概念** ++ IP地址：在网络环境中唯一标识一台主机 ++ 端口号：在主机中唯一标识一个进程 ++ `IP地址 + 端口`：唯一标识网络通讯中的一个进程，对应一个socket。 ++ `socket` 成对出现、必须绑定IP+端口、一个文件描述符指向两个缓冲区（一个读一个写）。 + +#### 6.1 网络字节序列化 ++ 大端存储：低地址--高位(高地址存低位) ++ 小端存储：高地址--低位 + +TCP/IP协议规定，`网络数据流应采用大端字节序`。为了使网络程序具有可移植性，使同样的 c 代码在大端和小端计算机上编译后都能正常运行，可以调用以下库函数做**网络字节序和主机字节序的转换**。 +```C++ +#include + +uint32_t htonl(uint32_t hostlong); +uint16_t htons(uint16_t hostshort); +uint32_t ntohl(uint32_t netlong); +uint16_t ntohs(uint16_t netshort); +``` +`h` 标识`host`， `n` 表示 `network`， `I` 表示 32 位长整数， `s` 表示 `16`位短整数。 +#### 6.2 ip地址转换函数 +``` C++ +#include +int inet_pton(int af, const char *src, void *dst) //字符串ip转换网络字节序; +const char *inet_ntop(int af,const void *src,char *dst, socklen_t size) //网络字节序转换字符串ip; +``` +#### 6.3 socketaddr数据结构 +```C++ +// sockaddr_in 结构体定义 +struct sockaddr_in { + sa_family_t sin_family; /* address family: AF_INET */ + in_port_t sin_port; /* port in network byte order */ + struct in_addr sin_addr; /* internet address */ +}; + +/* Internet address. */ +struct in_addr { + uint32_t s_addr; /* address in network byte order */ +}; +``` +#### 6.4 网络套接字函数 +1. 创建套接字`socket`函数 +```cpp +int socket(int domain, int type, int protocol); +返回值： + 成功：返回指向创建的socket的文件描述符，失败：返回-1，设置errno。 +``` +2. 绑定ip和端口号函数 bind +```cpp +#include +#include +int bind(int cockfd, const struct sockaddr *addr, socklen_t addrlen); +sockfd: + socket 文件描述符 +addr: + 构造出IP地址+端口号 +addrlen： + sizeof(addr)长度 +返回值： + 成功返回0，失败返回-1，设置errno +``` +### 7. **TCP C/S模型**：server.c client.c + + From 90cd9e543479a3eca6c859d3c600da3a4e6981cc Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Tue, 17 Dec 2019 00:05:00 +0800 Subject: [PATCH 291/386] add some notes about 3d detection --- README.md | 50 +++++++++++++++++++++++++------------------------- 1 file changed, 25 insertions(+), 25 deletions(-) diff --git a/README.md b/README.md index 5ec217cc..e34c3e25 100644 --- a/README.md +++ b/README.md @@ -64,7 +64,7 @@ |------------|---------------|---------------|-------------|----------------| |百度|美团|地平线机器人|顺丰科技|否| |阿里巴巴|滴滴出行|图森未来|招银网络科技|否| -|腾讯|拼多多|momenta|平安科技|，是，| +|腾讯|菜鸟网络|momenta|平安科技|，是，| |字节跳动|京东|小马智行|cvte|是，，| |微软|网易|蔚来汽车|海康威视|，是，| |谷歌|快手|小鹏汽车|虹软科技|否| @@ -73,28 +73,28 @@ |依图|陌陌|格灵深瞳|中兴|否| |大疆无人机|美图MTlab|文行知远|联想|否| |蚂蚁金服|360安全|云天励飞|汇顶科技|否| -|云从科技|搜狗|松鼠AI|美的中央研究院|，是，| -|英伟达|新浪|思必驰|锐明技术|否| -|Intel|搜狐/金山|码隆科技|联发科|否| -|亚马逊|小红书/蘑菇街|优必选|奥比中光科技|否| -|博士中国|作业帮/猿辅导/好未来/跟谁学|度小满金融|戴尔|否| -|无|英语流利说|深睿医疗|TP-LINK|否| -|无|oppo/vivo/一加|京东数科|ZOOM|否| -|无|欢聚时代/虎牙/斗鱼直播/BIGO|猎豹移动|广联达|否| -|无|keep/shopee|京东数科|深信服|否| -|无|汽车之家/瓜子二手车|追一科技|中国电信云计算|否| -|无|巨人网络/盛大游戏/|深兰科技|三星电子研究所|否| -|无|搜狐畅游/tap2fun|用友科技|苏宁|否| -|无|阅文集团/58集团|数美科技|微众银行|是，否| -|无|B站/酷狗音乐/当当网|驭势科技|中国移动成研院|否| -|无|最右/快看漫画/猫眼娱乐|随手科技|远景智能|否| -|无|乐信/有赞|智加科技|联影医疗|否| -|无|去哪儿/携程/途家|星环科技|中科创达|否| -|无|同花顺/老虎证券|趋势科技|恒润科技|否| -|无|转转/珍爱网/知乎|暗物智能DMAI|航天二院706所|否| -|无|金蝶软件(中国)|第四范式(有毁约风险)|吉利汽车|否| -|无|酷狗音乐/同花顺||碧桂园机器人|否| -|无|趣头条/一点资讯|||无| -|无|多牛传媒|||无| -|无|华米/绿米|||无| +|拼多多|搜狗|松鼠AI|美的中央研究院|，是，| +|英伟达|猿辅导|思必驰|锐明技术|否| +|Intel|新浪/搜狐/金山|奥比中光|联发科|否| +|亚马逊|YY/虎牙/BIGO/斗鱼|优必选||否| +|博世中国|oppo/vivo/一加|度小满金融|戴尔|否| +|无|贝壳找房|深睿医疗|TP-LINK|否| +|无|携程/去哪儿/途家|京东数科|ZOOM|否| +|无|瓜子二手车|猎豹移动|广联达|否| +|无|作业帮/VIPKID/好未来|京东数科|深信服|否| +|无|阅文集团/58集团|追一科技|中国电信云计算|否| +|无|B站|深兰科技|三星电子研究所|否| +|无|小红书/英语流利说|明略科技|苏宁|否| +|无|趣头条/一点资讯|数美科技|微众银行|是，否| +|无|知乎|驭势科技|中国移动成研院|否| +|无|蘑菇街|随手科技|远景智能|否| +|无|转转|智加科技|联影医疗|否| +|无|同花顺/老虎证券|星环科技|中科创达|否| +|无|乐信/有赞|趋势科技|恒润科技|否| +|无|金蝶软件(中国)|暗物智能DMAI|航天二院706所|否| +|无|汽车之家|第四范式(有毁约风险)|吉利汽车|否| +|无|珍爱网/酷狗音乐|云从科技|碧桂园机器人|否| +|无|巨人网络/盛大游戏|酷家乐|华米/绿米|无| +|无|最右/快看漫画|码隆科技||无| +|无|猫眼娱乐/多牛传媒|||无| From bf3ac484aae0132fef2ab6466ba6e9898b44441b Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Sun, 23 Feb 2020 16:54:07 +0800 Subject: [PATCH 292/386] add a intern summary article --- ...36\344\271\240\346\200\273\347\273\223.md" | 25 +++++++++++++++++++ 1 file changed, 25 insertions(+) create mode 100644 "\345\234\260\345\271\263\347\272\277\346\234\272\345\231\250\344\272\272\345\256\236\344\271\240\346\200\273\347\273\223.md" diff --git "a/\345\234\260\345\271\263\347\272\277\346\234\272\345\231\250\344\272\272\345\256\236\344\271\240\346\200\273\347\273\223.md" "b/\345\234\260\345\271\263\347\272\277\346\234\272\345\231\250\344\272\272\345\256\236\344\271\240\346\200\273\347\273\223.md" new file mode 100644 index 00000000..cabcc74e --- /dev/null +++ "b/\345\234\260\345\271\263\347\272\277\346\234\272\345\231\250\344\272\272\345\256\236\344\271\240\346\200\273\347\273\223.md" @@ -0,0 +1,25 @@ +## 关于工作内容 +来地平线实习差不多3个月了，在这边完成的工作内容，主要有以下几个方面： ++ 抽烟检测模型的输出，包括`arm`、`gpu`、`j2定点化`、`mimic`模型的输出及相关训练集及模型测试分析报告的撰写； ++ `ARM` 端抽烟检测工程代码的优化，主要是针对数据预处理、数据可视化、模型训练、模型评测、fp/fn定性分析等代码； ++ 针对不同平台的模型难以统一评测的问题，编写了统一的评测程序，配置相关 `yaml` 文件即可完成不同平台模型的评测并输出 `pr`曲线； ++ 通过对数据过采样的方式解决了 `阴阳脸` 误报严重的问题； ++ 还有一些其他工作：比如数据送标、训练集/测试集制作、日常标注答疑等。 + +关于工作内容上，自己做的抽烟检测问题，是属于多分类问题，在学术界多分类问题不是什么大问题，早已经有可行的解决方案了，但是在实际工业中发现，即使是三分类的这样简单的问题，在实际项目中也会碰到各种各样的问题，这让我认识到 AI 要想真正落地，是真的需要一批真心喜欢技术，又踏实的人去把 AI 技术落地，这中间也许会碰到一些 `dirty work` 吧，对于实习生的成长也可能是不利的，但是对于业务项目来说，这些工作可能又是必须做的。 +## 关于工作环境 +地平线是 `to B` 型公司，因此底层技术和业务项目显得尤为重要，需要每一个地平线研发人员扎实做底层技术和踏实做好业务产品。在这边整体工作环境还不错，虽然会加班，但是公司为每个员工配备了升降桌和电竞椅，以考虑员工的身体健康，每天也有零食来补充能量。身边的同事很友好，让我`感动`的在于有些同事虽然平时很忙，但是有问题找他的时候，他也会很耐心的帮你解决疑惑。最后，感谢周围每一个帮助过我的同事：轩爷、丽娜、方神、根利、辉哥、弈霖、翔哥、华哥等，还有一起实习的小伙伴：圣琦、家康、成武。 +## 关于个人成长 +有些遗憾，在这边实习个人成长方面没有达到自己的要求，虽然刚来第一个月自己成长很快，但是后期成长有限，`自己的成长跟付出不成正比吧`。这一点就不细说了，希望地平线以后能完善对于实习生的培养规范，不能只是一味的要求干活。值得让我注意的是，我发现光干活真的并不一定能让你得到很多成长，但是学习别人的优秀工程代码和学习新知识，然后再应用到实际项目中，那样不仅自己能得到成长，工作效率也能大大提升。自己后期就是稍微看了一个代码优秀的系统性的项目代码，短时间内就让自己收获很多，可惜后期时间有限，留给自己学习的时间不多，否则自己的成长能更多些。 +## 关于技术收获 +虽然在公司很多事，但是自己回家后偶尔也会看会书，在公式有时间也会看些基础知识和新知识，总的来说技术收获还是有些： ++ 开发工具熟悉/了解：`git/shell/tmux/cmake/hadoop命令` ++ 抽烟工程代码优化及数据处理程序编写，熟练掌握 `Python` ++ 了解`模型压缩知识`（浅层网络设计/知识蒸馏等）及熟悉模型评测指标代码编写及分析 ++ 熟悉 `mxnet` 框架 +## 展望 +实习近 3 个月的时间匆匆而过，这段时间虽然过得很累，但是实习是研究生必须经历的一个阶段，不管是找工作还是对于自己以后的人生职业规划都是有用的，而且**越早实习越好**！！！在研三接下来的一年时间里： ++ 我会先刷题，复习`机器学习/深度学习/图像处理/C++/Python/`的基础知识和项目细节，为找工作做准备，这几个月要为找工作而努力 ++ 之后，做一些之前没做的事：复现论文，`faster rcnn/mask rcnn/unet/yolo/ssd` 等论文，一个就可以，尽力发一个还不错的论文 ++ 掌握 `C++`，完整的写一个 C++ 项目 ++ 继续学习目标检测/语义分割的知识，参加 `Kaggle`比赛，尽力拿一个金牌吧（很难，但是当作目标） \ No newline at end of file From 60d333197ed5fd2a8f2cb0e1c1ea9b89dc903a00 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Sun, 23 Feb 2020 17:01:19 +0800 Subject: [PATCH 293/386] Add something to the interview experience --- ...62\227\347\247\213\346\213\233\351\235\242\347\273\217.md" | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" index 13fe4d13..afae1342 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" @@ -151,13 +151,15 @@ 2. 讲笔试题 3. 聊家常（为什么想来上海，有女朋友吗） ## 工商银行-软件开发中心: 大数据及机器学习算法工程师 -### 一面(15分钟) +### 一面(15分钟)(过) 1. 核心项目介绍(围绕项目提了一些问题) 2. 你的本科和研究所成绩，为什么投成都岗 3. 你的本科学校是什么类型的，研究所毕业设计打算做什么 4. 你的博客主要写了什么内容 5. 你的比赛经历介绍下 6. 你有什么想问的 +### 面试总结 +银行感觉比较看奖学金(成绩)和比赛,技术问题问的很少,很玄学的就通过面试了,后期有事回家就没去最后的笔试(提前批先面试再笔试)。 ## 经纬恒润-无人驾驶算法工程师 ### 一面（20分钟） 1. 自我介绍 From dcfbede03e6810a04252ef55f89e5ad461a40852 Mon Sep 17 00:00:00 2001 From: HarleysZhang Date: Sun, 23 Feb 2020 17:02:49 +0800 Subject: [PATCH 294/386] Add something to the interview experience --- ...\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" index afae1342..b1ac37e0 100644 --- "a/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" +++ "b/\350\256\241\347\256\227\346\234\272\350\247\206\350\247\211\345\262\227\347\247\213\346\213\233\351\235\242\347\273\217.md" @@ -159,7 +159,7 @@ 5. 你的比赛经历介绍下 6. 你有什么想问的 ### 面试总结 -银行感觉比较看奖学金(成绩)和比赛,技术问题问的很少,很玄学的就通过面试了,后期有事回家就没去最后的笔试(提前批先面试再笔试)。 +银行感觉比较看`奖学金`(成绩)和比赛，技术问题问的很少，很玄学的就通过面试了，后期有事回家就没去最后的笔试(提前批先面试再笔试)。 ## 经纬恒润-无人驾驶算法工程师 ### 一面（20分钟） 1. 自我介绍 From 57ec9119bfed74737a6f60ae8159ee4223b5c5b4 Mon Sep 17 00:00:00 2001 From: Zhang Date: Fri, 10 Apr 2020 16:32:01 +0800 Subject: [PATCH 295/386] Add files via upload --- ...234\347\232\204\346\257\224\350\276\203.jpg" | Bin 0 -> 25333 bytes ...out\347\232\204\346\223\215\344\275\234.jpg" | Bin 0 -> 17874 bytes 2 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 "images/\346\240\207\345\207\206\347\275\221\347\273\234\345\222\214\345\270\246\346\234\211Dropout\347\275\221\347\273\234\347\232\204\346\257\224\350\276\203.jpg" create mode 100644 "images/\351\242\204\346\265\213\346\250\241\345\236\213\346\227\266Dropout\347\232\204\346\223\215\344\275\234.jpg" diff --git "a/images/\346\240\207\345\207\206\347\275\221\347\273\234\345\222\214\345\270\246\346\234\211Dropout\347\275\221\347\273\234\347\232\204\346\257\224\350\276\203.jpg" "b/images/\346\240\207\345\207\206\347\275\221\347\273\234\345\222\214\345\270\246\346\234\211Dropout\347\275\221\347\273\234\347\232\204\346\257\224\350\276\203.jpg" new file mode 100644 index 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Mon Sep 17 00:00:00 2001 From: Zhang Date: Fri, 10 Apr 2020 16:36:44 +0800 Subject: [PATCH 297/386] =?UTF-8?q?Update=20=E6=B7=B1=E5=BA=A6=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E9=9D=A2=E8=AF=95=E9=A2=98.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 更新图片链接失效问题 --- ...\246\344\271\240\351\235\242\350\257\225\351\242\230.md" | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index 735fc6af..f42dc89b 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -326,7 +326,7 @@ Python深度学习p127. + `dropout`效果跟`bagging`效果类似（bagging是减少方差variance，而boosting是减少偏差bias） + 加入dropout会使神经网络训练时间边长，模型预测时不需要dropout，记得关掉。 -![dropou直观展示](https://www.armcvai.com/wp-content/uploads/2019/03/dropout.png) +![dropou直观展示](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E6%A0%87%E5%87%86%E7%BD%91%E7%BB%9C%E5%92%8C%E5%B8%A6%E6%9C%89Dropout%E7%BD%91%E7%BB%9C%E7%9A%84%E6%AF%94%E8%BE%83.jpg) #### dropout具体工作流程 以 标准神经网络为例，正常的流程是：我们首先把输入数据x通过网络前向传播，然后把误差反向传播一决定如何更新参数让网络进行学习。使用dropout之后，过程变成如下： @@ -342,13 +342,13 @@ Python深度学习p127. 不可避免的，在训练网络中的每个单元都要添加一道概率流程，标准网络和带有dropout网络的比较图如下所示： -![dropout在训练阶段](https://www.armcvai.com/wp-content/uploads/2019/03/添加了dropou的神经网络图.png) +![dropout在训练阶段](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E6%B7%BB%E5%8A%A0%E4%BA%86dropou%E7%9A%84%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E5%9B%BE.png) (2). 在测试模型阶段 预测模型的时候，输入是当前输入，每个神经单元的权重参数要乘以概率p。 -![dropout在测试模型时](https://www.armcvai.com/wp-content/uploads/2019/03/dropout_test.png) +![dropout在测试模型时](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E9%A2%84%E6%B5%8B%E6%A8%A1%E5%9E%8B%E6%97%B6Dropout%E7%9A%84%E6%93%8D%E4%BD%9C.jpg) #### 如何选择dropout 的概率 input 的dropout概率推荐是0.8， hidden layer 推荐是0.5， 但是也可以在一定的区间上取值。（All dropout nets use p = 0.5 for hidden units and p = 0.8 for input units.） From f9a62ed7b57edafd8526e2bf3f29738e21874c97 Mon Sep 17 00:00:00 2001 From: Zhang Date: Sat, 2 May 2020 12:31:01 +0800 Subject: [PATCH 298/386] Add files via upload --- ...200\346\265\213\346\255\245\351\252\244.jpg" | Bin 0 -> 22638 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 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00:00:00 2001 From: HarleysZhang Date: Sat, 2 May 2020 12:39:09 +0800 Subject: [PATCH 299/386] =?UTF-8?q?=E6=9B=B4=E6=96=B0HOG=E7=89=B9=E5=BE=81?= =?UTF-8?q?=E6=A3=80=E6=B5=8B=E6=B5=81=E7=A8=8B=E5=9B=BE=E5=9B=BE=E7=89=87?= =?UTF-8?q?=E9=93=BE=E6=8E=A5=E5=A4=B1=E6=95=88=E9=97=AE=E9=A2=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...40\351\235\242\350\257\225\351\242\230.md" | 65 ++++--------------- 1 file changed, 13 insertions(+), 52 deletions(-) diff --git "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" index f42dc89b..3e323df2 100644 --- "a/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" +++ "b/\346\267\261\345\272\246\345\255\246\344\271\240/\346\267\261\345\272\246\345\255\246\344\271\240\351\235\242\350\257\225\351\242\230.md" @@ -70,10 +70,10 @@ $$L = -\sum_{c=1}^{M}y_{c}log(p_{c})或者H(p,q)=-\sum p(x)logq(x)$$（p代表 + 深层卷积层的感受野大小和它之前所有层的滤波器大小和步长有关系 + 计算感受野大小时，忽略了图像边缘的影响 -感受野大小的计算采用 `top to down` 的方式， 即先计算最深层在前一层上的感受野，然后逐渐传递到第一层，使用的公式可以表示如下: $$RF = ((RF-1)*stride + fsize +感受野大小的计算采用 `top to down` 的方式， 即先计算最深层在前一层上的感受野，然后逐渐传递到第一层，使用的公式可以表示如下: $$RF = ((RF-)*stride + fsize ```　 for layer in （top layer To down layer）: 　　　　 - RF = ((RF - 1)* stride) + fsize + RF = ((RF -1)* stride) + fsize ``` + `stride`: 表示卷积的步长； + `fsize`: 表示卷积层滤波器的大小 @@ -96,6 +96,9 @@ net_struct = {'alexnet': {'net':[[11,4,0],[3,2,0],[5,1,2],[3,2,0],[3,1,1],[3,1,1 def outFromIn(isz, net, layernum): + """ + 计算feature map大小 + """ totstride = 1 insize = isz for layer in range(layernum): @@ -106,6 +109,9 @@ def outFromIn(isz, net, layernum): return outsize, totstride def inFromOut(net, layernum): + """ + 计算感受野receptive file大小 + """ RF = 1 for layer in reversed(range(layernum)): fsize, stride, pad = net[layer] @@ -326,7 +332,7 @@ Python深度学习p127. + `dropout`效果跟`bagging`效果类似（bagging是减少方差variance，而boosting是减少偏差bias） + 加入dropout会使神经网络训练时间边长，模型预测时不需要dropout，记得关掉。 -![dropou直观展示](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E6%A0%87%E5%87%86%E7%BD%91%E7%BB%9C%E5%92%8C%E5%B8%A6%E6%9C%89Dropout%E7%BD%91%E7%BB%9C%E7%9A%84%E6%AF%94%E8%BE%83.jpg) +![dropou直观展示](https://www.armcvai.com/wp-content/uploads/2019/03/dropout.png) #### dropout具体工作流程 以 标准神经网络为例，正常的流程是：我们首先把输入数据x通过网络前向传播，然后把误差反向传播一决定如何更新参数让网络进行学习。使用dropout之后，过程变成如下： @@ -342,13 +348,13 @@ Python深度学习p127. 不可避免的，在训练网络中的每个单元都要添加一道概率流程，标准网络和带有dropout网络的比较图如下所示： -![dropout在训练阶段](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E6%B7%BB%E5%8A%A0%E4%BA%86dropou%E7%9A%84%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E5%9B%BE.png) +![dropout在训练阶段](https://www.armcvai.com/wp-content/uploads/2019/03/添加了dropou的神经网络图.png) (2). 在测试模型阶段 预测模型的时候，输入是当前输入，每个神经单元的权重参数要乘以概率p。 -![dropout在测试模型时](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/%E9%A2%84%E6%B5%8B%E6%A8%A1%E5%9E%8B%E6%97%B6Dropout%E7%9A%84%E6%93%8D%E4%BD%9C.jpg) +![dropout在测试模型时](https://www.armcvai.com/wp-content/uploads/2019/03/dropout_test.png) #### 如何选择dropout 的概率 input 的dropout概率推荐是0.8， hidden layer 推荐是0.5， 但是也可以在一定的区间上取值。（All dropout nets use p = 0.5 for hidden units and p = 0.8 for input units.） @@ -370,7 +376,7 @@ HOG特征提取方法就是将一个image： 3. 计算每个cell中每个pixel的gradient（即orientation） 4. 统计每个cell的梯度直方图（不同梯度的个数），即可形成每个cell的descriptor。 #### HOG特征检测步骤 -![HOG特征检测步骤](https://www.armcvai.com/wp-content/uploads/2019/03/image.png) +![HOG特征检测步骤](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/HOG%E7%89%B9%E5%BE%81%E6%A3%80%E6%B5%8B%E6%AD%A5%E9%AA%A4.jpg) 颜色空间归一化——–>梯度计算————->梯度方向直方图———->重叠块直方图归一化———–>HOG特征 #### 参考资料 @@ -391,7 +397,7 @@ HOG特征提取方法就是将一个image： 1. 卷积层有参数，池化层没有参数 2. **经过卷积层节点矩阵深度会改变**，池化层不会改变节点矩阵的深度，但是它可以缩小节点矩阵的大小 ## 卷积层参数数量计算方法 -假设输入层矩阵维度是96\*96\*3，第一层卷积层使用尺寸为5\*5、深度为16的过滤器（卷积核尺寸为5\*5、卷积核数量为16），那么这层卷积层的参数个数为５\*5\*3\*16+16=1216个，公式总结如下：**卷积层参数量=（filter size \* 前一层特征图的通道数 ）\* 当前层filter数量 + 当前层filter数量** +假设输入层矩阵维度是96\*96\*3，第一层卷积层使用尺寸为5\*5、深度为16的过滤器（卷积核尺寸为5\*5、卷积核数量为16），那么这层卷积层的参数个数为５\*5\*3\*16+16=1216个 ## 卷积层输出大小计算 卷积中的特征图大小计算方式有两种，分别是‘VALID’和‘SAME’，卷积和池化都适用，除不尽的结果都向下取整。公式：`O = (W-F+2P)/S+1`，输入图片（Input）大小为I=W*W，卷积核（Filter）大小为F*F，步长（stride）为S，填充（Padding）的像素数为P。 @@ -478,53 +484,10 @@ def calculateIoU(candidateBound, groundTruthBound): 数据增强分两类，一类是离线增强，一类是在线增强： 1. 离线增强 ： 直接对数据集进行处理，数据的数目会变成增强因子 x 原数据集的数目 ，这种方法常常用于数据集很小的时候 2. 在线增强 ： 这种增强的方法用于，获得 batch 数据之后，然后对这个batch的数据进行增强，如旋转、平移、翻折等相应的变化，由于有些数据集不能接受线性级别的增长，这种方法长用于大的数据集，很多机器学习框架已经支持了这种数据增强方式，并且可以使用GPU优化计算。 -## 为什么需要使用ROI Pooling/ROI Align? -为了使得最后面的两个全连接层能够共享 `conv layers`(VGG) 权重。在所有的 `RoIs` 都被 `pooling` 成（512×7×7）的`feature map`后，将它 `reshape` 成一个一维的向量，就可以利用 `VGG16` 的预训练的权重来初始化前两层全连接。 ## ROI Pooling替换为ROI Align，及各自原理 faster rcnn将roi pooling替换为roi align效果有所提升 #### ROI Pooling原理 -`RPN` 生成的 `ROI` 区域大小是对应与输入图像大小（每个roi区域大小各不相同），为了能够共享权重，所以需要将这些 `ROI` 映射回特征图上，并固定大小。`ROI Pooling` 具体操作如下： -> [RoIPooling、RoIAlign笔记](https://www.cnblogs.com/wangyong/p/8523814.html) - -ROI Align操作过程如下图： - -![roi pooling](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/roi%20pooling.png) - -1. `Conv layers` 使用的是 `VGG16`，`feat_stride=32`(即表示，经过网络层后图片缩小为原图的1/32),原图800*800,最后一层特征图feature map大小:25\*25 -2. 假定原图中有一 `region proposal`，大小为665\*665，这样，映射到特征图中的大小：665/32=20.78,即20.78\*20.78，源码中，在计算的时候会进行取整操作，于是，进行所谓的第一次量化，即映射的特征图大小为20*20 -3. 假定pooled_w=7,pooled_h=7,即 `pooling` 后固定成7\*7大小的特征图，所以，将上面在 `feature map`上映射的20\*20的 region proposal划分成 `49`个同等大小的小区域，每个小区域的大小20/7=2.86,即2.86\*2.86，此时，进行第二次量化，故小区域大小变成2\*2 -4. 每个2\*2的小区域里，取出其中最大的像素值，作为这一个区域的‘代表’，这样，49个小区域就输出49个像素值，组成7\*7大小的feature map - -所以，通过上面可以看出，经过**两次量化，即将浮点数取整**，原本在特征图上映射的20\*20大小的region proposal，偏差成大小为14\*14的，这样的像素偏差势必会对后层的回归定位产生影响 - -所以，产生了替代方案: `RoiAlign`。 #### ROI Align原理 -> [RoIPooling、RoIAlign笔记](https://www.cnblogs.com/wangyong/p/8523814.html),这篇文章讲得很好。 - -ROI Align操作过程如下图： - -![roi align](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/roi%20align.png) - -1. `Conv layers` 使用的是VGG16，feat_stride=32(即表示，经过网络层后图片缩小为原图的1/32),原图800\*800,最后一层特征图feature map大小: 25\*25 -2. 假定原图中有一region proposal，大小为665\*665，这样，映射到特征图中的大小：665/32=20.78,即20.78\*20.78，此时，没有像RoiPooling那样就行取整操作，保留浮点数 -3. 假定pooled_w=7,pooled_h=7,即pooling后固定成 `7*7` 大小的特征图，所以，将在 feature map上映射的 `20.78\*20.78` 的region proposal 划分成49个同等大小的小区域，每个小区域的大小 20.78/7=2.97, 即 2.97\*2.97 -4. 假定采样点数为`4`，即表示，对于每个 `2.97*2.97` 的小区域，平分四份，每一份取其中心点位置，而中心点位置的像素，采用双线性插值法进行计算，这样，就会得到四个点的像素值，如下图: - -![roi align双线性插值](https://github.com/HarleysZhang/2019_algorithm_intern_information/blob/master/images/roi%20align%E7%BA%BF%E6%80%A7%E6%8F%92%E5%80%BC.png) - -上图中，四个红色叉叉`‘×’`的像素值是通过双线性插值算法计算得到的.最后，取四个像素值中最大值作为这个小区域(即：2.97*2.97大小的区域)的像素值，如此类推，同样是49个小区域得到49个像素值，组成7*7大小的feature map - -总结：知道了 `RoiPooling` 和 `RoiAlign` 实现原理，在以后的项目中可以根据实际情况进行方案的选择；对于检测图片中大目标物体时，两种方案的差别不大，而如果是图片中有较多小目标物体需要检测，则优先选择RoiAlign，更精准些。 -## ROI Pooling/ROI Align 反向传播 -> 参考[你真的学会RoI Pooling了吗?](https://zhuanlan.zhihu.com/p/59692298)，自己的理解稍后补充。 - -## DNN的反向传播算法推导 -> 参考[机器学习必刷题-手撕推导篇(2)：BP算法推导](https://www.jianshu.com/p/d52626b39382) - -## CNN的反向传播算法推导 -> 参考[卷积神经网络(CNN)反向传播算法](https://www.cnblogs.com/pinard/p/6494810.html) -[四张图彻底搞懂CNN反向传播算法（通俗易懂）](https://zhuanlan.zhihu.com/p/81675803) -[反向传播算法推导过程（非常详细）](https://zhuanlan.zhihu.com/p/79657669) ## Reference 1.[深度学习中的数据增强](https://blog.csdn.net/zhelong3205/article/details/81810743 ) ## Reference @@ -536,5 +499,3 @@ ROI Align操作过程如下图： - - From 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