<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[FK103的专栏]]></title><description><![CDATA[爬虫]]></description><link>https://blog.youkuaiyun.com/FK103</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; FK103]]></copyright><item><title><![CDATA[linux处理文件]]></title><link>https://blog.youkuaiyun.com/FK103/article/details/135139878</link><guid>https://blog.youkuaiyun.com/FK103/article/details/135139878</guid><author>FK103</author><pubDate>Thu, 21 Dec 2023 20:38:39 +0800</pubDate><description><![CDATA[【代码】linux处理文件。]]></description><category></category></item><item><title><![CDATA[XenC数据选择工具的使用]]></title><link>https://blog.youkuaiyun.com/FK103/article/details/90664932</link><guid>https://blog.youkuaiyun.com/FK103/article/details/90664932</guid><author>FK103</author><pubDate>Wed, 29 May 2019 09:23:50 +0800</pubDate><description><![CDATA[XenC数据选择工具


系统要求：
gcc version 4.5.1 or higher
CMake 2.8.4 or higher
boost version 1.57.0 or higher
gzip


编译安装
解压XenC-master.zip文件
进入目录，执行cmake ./
执行make
成功后会在当前目录下生成XenC的可执行文件，可将其加到环境变量中


使用
需要文件：
...]]></description><category></category></item><item><title><![CDATA[WMT的英德翻译]]></title><link>https://blog.youkuaiyun.com/FK103/article/details/87078166</link><guid>https://blog.youkuaiyun.com/FK103/article/details/87078166</guid><author>FK103</author><pubDate>Tue, 12 Feb 2019 11:56:48 +0800</pubDate><description><![CDATA[The University of Cambridge’s Machine Translation Systems for WMT18
1. basic method
Combine the three most commonly used architectures: recurrent, convolutional, and self-attention-based models like t...]]></description><category></category></item><item><title><![CDATA[anaconda的一些操作]]></title><link>https://blog.youkuaiyun.com/FK103/article/details/86670320</link><guid>https://blog.youkuaiyun.com/FK103/article/details/86670320</guid><author>FK103</author><pubDate>Sun, 27 Jan 2019 22:04:56 +0800</pubDate><description><![CDATA[创建新环境

conda create --name xxx python=3.5


查看已创建的环境

conda list env


激活已安装的环境

source activate xxx


conda 换源

conda config --add channels 'https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/'...]]></description><category></category></item><item><title><![CDATA[tensorflow]]></title><link>https://blog.youkuaiyun.com/FK103/article/details/86569978</link><guid>https://blog.youkuaiyun.com/FK103/article/details/86569978</guid><author>FK103</author><pubDate>Mon, 21 Jan 2019 10:27:42 +0800</pubDate><description><![CDATA[tensorflow中的关键字global_step使用: https://blog.youkuaiyun.com/leviopku/article/details/78508951

]]></description><category></category></item><item><title><![CDATA[NLP训练所用到的一些方法]]></title><link>https://blog.youkuaiyun.com/FK103/article/details/86549329</link><guid>https://blog.youkuaiyun.com/FK103/article/details/86549329</guid><author>FK103</author><pubDate>Sat, 19 Jan 2019 10:13:52 +0800</pubDate><description><![CDATA[Xavier初始化方法： https://blog.youkuaiyun.com/shuzfan/article/details/51338178

]]></description><category></category></item><item><title><![CDATA[python学习]]></title><link>https://blog.youkuaiyun.com/FK103/article/details/86549086</link><guid>https://blog.youkuaiyun.com/FK103/article/details/86549086</guid><author>FK103</author><pubDate>Sat, 19 Jan 2019 09:55:32 +0800</pubDate><description><![CDATA[
Python—copy()、deepcopy()与赋值的区别: https://blog.youkuaiyun.com/u011630575/article/details/78604226


]]></description><category></category></item><item><title><![CDATA[× epoch、batch、training step（iteration）的区别]]></title><link>https://blog.youkuaiyun.com/FK103/article/details/86482992</link><guid>https://blog.youkuaiyun.com/FK103/article/details/86482992</guid><author>FK103</author><pubDate>Mon, 14 Jan 2019 19:56:54 +0800</pubDate><description><![CDATA[一次epoch=所有训练数据forward+backward后更新参数的过程。
一次iteration=[batch size]个训练数据forward+backward后更新参数过程。
另：一般是iteration译成“迭代”
（1）iteration：表示1次迭代（也叫training step），每次迭代更新1次网络结构的参数；
（2）batch-size：1次迭代所使用的样本量；
（3）e...]]></description><category></category></item><item><title><![CDATA[linux服务器安装anaconda，然后远程使用jupyter]]></title><link>https://blog.youkuaiyun.com/FK103/article/details/85057469</link><guid>https://blog.youkuaiyun.com/FK103/article/details/85057469</guid><author>FK103</author><pubDate>Mon, 17 Dec 2018 21:57:13 +0800</pubDate><description><![CDATA[
转载于 https://www.cnblogs.com/andylhc/p/9513504.html

1.linux服务器安装anaconda:
1.1 下载安装脚本：
wget https://repo.anaconda.com/archive/Anaconda3-5.2.0-Linux-x86_64.sh

1.2 运行安装向导：
bash Anaconda3-5.2.0-Linux-x8...]]></description><category></category></item><item><title><![CDATA[torch学习]]></title><link>https://blog.youkuaiyun.com/FK103/article/details/84950422</link><guid>https://blog.youkuaiyun.com/FK103/article/details/84950422</guid><author>FK103</author><pubDate>Tue, 11 Dec 2018 21:31:03 +0800</pubDate><description><![CDATA[torch.arange

torch.arange(start, end, step=1, out=None) → Tensor

返回一个1维张量，长度为 floor((end−start)/step)。包含从start到end，以step为步长的一组序列值(默认步长为1)。
参数:

start (float) – 序列的起始点
end (float) – 序列的终止点
step (floa...]]></description><category></category></item><item><title><![CDATA[Total Recorder去除噪音]]></title><link>https://blog.youkuaiyun.com/FK103/article/details/84647188</link><guid>https://blog.youkuaiyun.com/FK103/article/details/84647188</guid><author>FK103</author><pubDate>Fri, 30 Nov 2018 12:50:01 +0800</pubDate><description><![CDATA[
下载最新版本，v8.6 Build 6575
下载安装完毕之后用以下注册码激活：
用户： Mr N Goldsworthy
激活码：TRP6.KSAE.HFGM.NC5H.KAAE.EXXU.RWJR.5S7L.3FFP.NNBL.D3HA.P85H.7JED.VQ9M.65SA


...]]></description><category></category></item><item><title><![CDATA[tensorflow对tf.argmax()的理解]]></title><link>https://blog.youkuaiyun.com/FK103/article/details/84563199</link><guid>https://blog.youkuaiyun.com/FK103/article/details/84563199</guid><author>FK103</author><pubDate>Tue, 27 Nov 2018 11:36:38 +0800</pubDate><description><![CDATA[在tensorflow中，axis=1对列进行操作，axis=0对行进行操作

例如，argmax中axis=1就是对(每行中)不同列元素进行比较，得到最大值。

a = [[0, 0, 0, 1, 0, 0],
 [0, 0, 1, 0, 0, 0],
 [0, 1, 0, 0, 0, 0],
 [0, 0, 0, 0, 0, 1]]
 
 # axis=1, 每行的不同列进行比较，取对应最大值...]]></description><category></category></item><item><title><![CDATA[Tensorboard的简单使用]]></title><link>https://blog.youkuaiyun.com/FK103/article/details/84312629</link><guid>https://blog.youkuaiyun.com/FK103/article/details/84312629</guid><author>FK103</author><pubDate>Wed, 21 Nov 2018 01:09:05 +0800</pubDate><description><![CDATA[转自知乎灰灰
import tensorflow as tf

#定义一个计算图，实现两个向量的减法操作

#定义两个输入，a为常量，b为变量

a=tf.constant([10.0, 20.0, 40.0], name='a')

b=tf.Variable(tf.random_uniform([3]), name='b')

output=tf.add_n([a,b], name='add'...]]></description><category></category></item><item><title><![CDATA[deepin下解决Tensorflow 使用时cpu编译不支持警告]]></title><link>https://blog.youkuaiyun.com/FK103/article/details/84312571</link><guid>https://blog.youkuaiyun.com/FK103/article/details/84312571</guid><author>FK103</author><pubDate>Wed, 21 Nov 2018 00:46:40 +0800</pubDate><description><![CDATA[转自https://blog.youkuaiyun.com/Fourierrr_/article/details/79749899
deepin下使用TensorFlow模块时，弹出错误Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
原因是下载TensorFlow的版本不支...]]></description><category></category></item><item><title><![CDATA[NEURAL MACHINE TRANSLATION]]></title><link>https://blog.youkuaiyun.com/FK103/article/details/84254666</link><guid>https://blog.youkuaiyun.com/FK103/article/details/84254666</guid><author>FK103</author><pubDate>Mon, 19 Nov 2018 16:02:55 +0800</pubDate><description><![CDATA[NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATEABSTRACT1 INTRODUCTION
ABSTRACT
神经机器翻译是最近提出的机器翻译方法。与传统的统计机器翻译不同，神经机器翻译旨在构建单个神经网络，通过联合调整以最大化翻译性能。最近提出的用于神经机器翻译的模型通常属于一系列编码 - 解码器，并将源...]]></description><category></category></item><item><title><![CDATA[Sequence to Sequence Learning with Neural Networks]]></title><link>https://blog.youkuaiyun.com/FK103/article/details/83688235</link><guid>https://blog.youkuaiyun.com/FK103/article/details/83688235</guid><author>FK103</author><pubDate>Sat, 03 Nov 2018 16:25:19 +0800</pubDate><description><![CDATA[用神经网络进行序列到序列的学习摘要1.介绍
摘要
深度神经网络（DNN）是功能强大的模型，在复杂的学习任务中取得了优异的性能。虽然DNN在大型标记训练集可用时效果很好，但它们不能用于将序列映射到序列。在本文中，我们提出了一种通用的端到端序列学习方法，它对序列结构做出了最小的假设。我们的方法是使用多层长短期记忆（LSTM）将输入序列映射到固定维度的向量，然后使用另一个深度LSTM来解码来自向量的目标...]]></description><category></category></item><item><title><![CDATA[关于NLP]]></title><link>https://blog.youkuaiyun.com/FK103/article/details/83061479</link><guid>https://blog.youkuaiyun.com/FK103/article/details/83061479</guid><author>FK103</author><pubDate>Mon, 15 Oct 2018 18:39:35 +0800</pubDate><description><![CDATA[
转自博主 http://licstar.net/archives/328

1.词向量

One-hot Representation：每个词表示为一个很长的向量。这个向量的维度是词表大小，其中绝大多数元素为 0，只有一个维度的值为 1，这个维度就代表了当前的词。例如：
“话筒”表示为 [0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 …]
“麦克”表示为 [0 0 0 0 0 ...]]></description><category></category></item><item><title><![CDATA[神经概率语言模型]]></title><link>https://blog.youkuaiyun.com/FK103/article/details/83033086</link><guid>https://blog.youkuaiyun.com/FK103/article/details/83033086</guid><author>FK103</author><pubDate>Sat, 13 Oct 2018 13:09:00 +0800</pubDate><description><![CDATA[摘要

统计语言建模的目标是学习语言中单词序列的联合概率函数。由于 the curse of dimensionality，这本质上是困难的：测试模型的单词序列可能与训练期间看到的所有单词序列不同。一种基于n-gram的传统的但非常成功的方法是，通过连接训练集中看到的非常短的重叠序列来获得泛化。我们建议通过学习词的分布式表示来对抗 the curse of dimensionality，这允许每个...]]></description><category></category></item><item><title><![CDATA[markdown latex的快速入门]]></title><link>https://blog.youkuaiyun.com/FK103/article/details/82876895</link><guid>https://blog.youkuaiyun.com/FK103/article/details/82876895</guid><author>FK103</author><pubDate>Fri, 28 Sep 2018 09:59:20 +0800</pubDate><description><![CDATA[
转自博主https://blog.youkuaiyun.com/jmh1996/article/details/78289915

基本格式：

行间 公式与当前文字不在同一行，公式会换行。
$$公式$$
行内 公式与当前文字在同一行
$公式$
上标：$$a^{2}$$
a2a^{2}a2
下标：$$a_{2}$$
a2a_{2}a2​
上下标一起来：$$a^{i}_{j}$$
ajia^{i}_{j}aj...]]></description><category></category></item><item><title><![CDATA[自然语言处理神经网络模型入门]]></title><link>https://blog.youkuaiyun.com/FK103/article/details/82849402</link><guid>https://blog.youkuaiyun.com/FK103/article/details/82849402</guid><author>FK103</author><pubDate>Fri, 28 Sep 2018 00:21:21 +0800</pubDate><description><![CDATA[主要内容

input encoding for natural language tasks
feed-forward networks
convolutional networks,
recurrent networks
recursive networks
the computation graph abstraction for automatic gradient computation...]]></description><category></category></item></channel></rss>