https://github.com/kjw0612/awesome-rnn Faster Recurrent Neural Network Language Modeling Toolkit with Noise Contrastive Estimation and Hierarchical Softmax…

一.线程的基本概念…

-----------------Migration Toolkit-----------------Migration Toolkit is a command line utility that imports data or schema objectdefinitions immediately, or generates scripts that can be used at a later timeto duplicate data and database objects. Mig…

(* * Super Object Toolkit * * Usage allowed under the restrictions of the Lesser GNU General Public License * or alternatively the restrictions of the Mozilla Public License 1.1 * * Software distributed under the License is distributed on an "AS IS&q…

The GUI Toolkit, Framework Page User interfaces occupy an important part of software development. This page provides a comprehensive reference on toolkits for building graphical user interfaces (GUIs), with emphasis on resources for Free Software (Op…

转载请注明出处: https://www.cnblogs.com/darkknightzh/p/10043864.html 参考网址: 论文:https://arxiv.org/abs/1506.01497 tf的第三方faster rcnn:https://github.com/endernewton/tf-faster-rcnn IOU:https://www.cnblogs.com/darkknightzh/p/9043395.html faster rcnn主要包括两部分:rpn网络和r…

Recurrent Neural Network Language Modeling Toolkit  工具使用点击打开链接 本博客地址:http://blog.csdn.net/wangxinginnlp/article/details/38385471 依照训练的进度学习代码: RNN训练过程(摘自Mikolov的博士论文): 1. Set time counter t = 0, initialize state of the neurons in the hidden layer s(t)…

Faster R-CNN教程 最后更新日期:2016年4月29日 本教程主要基于python版本的faster R-CNN,因为python layer的使用,这个版本会比matlab的版本速度慢10%,但是准确率应该是差不多的. 目前已经实现的有两种方式: Alternative training Approximate joint training 推荐使用第二种,因为第二种使用的显存更小,而且训练会更快,同时准确率差不多甚至略高一点. Contents 配置环境 安装步骤 Demo 建立自…

系列前言 參考文献: RNNLM - Recurrent Neural Network  Language Modeling Toolkit(点此阅读) Recurrent neural network based language model(点此阅读) EXTENSIONS OF RECURRENT NEURAL NETWORK LANGUAGE MODEL(点此阅读) Strategies for Training Large Scale Neural Network  Language…

步骤:1.build_head()函数: 构建CNN基层网络图像被缩放16倍2.build_rpn()函数: 在feature map上生成box的坐标和判断是否有物体 generate_anchors:生成9个坐标框(思想:先定义一个基准框(0,0,16,16),因为经过多层卷积池化之后,feature map上一点的感受野对应到原始图像就会是一个区域,这里设置的是16,也就是feature map上一点对应到原图的大小为16x16的区域根据基准框生成一个中心坐标长宽为( (16,16,7.5…