<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[小颜颜6035的博客]]></title><description><![CDATA[共同进步，共同学习。]]></description><link>https://blog.youkuaiyun.com/yinjun3215</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; yinjun3215]]></copyright><item><title><![CDATA[jupyter notebook如何使用anaconda的虚拟环境]]></title><link>https://blog.youkuaiyun.com/yinjun3215/article/details/130751996</link><guid>https://blog.youkuaiyun.com/yinjun3215/article/details/130751996</guid><author>yinjun3215</author><pubDate>Thu, 18 May 2023 17:43:59 +0800</pubDate><description><![CDATA[刷新一下jupyter notebook的页面，发现刚才的虚拟环境被添加进来了。1.打开anaconda prompt。切换到要使用的虚拟环境。]]></description><category></category></item><item><title><![CDATA[windows安装tensorflow cpu详细教程]]></title><link>https://blog.youkuaiyun.com/yinjun3215/article/details/130742742</link><guid>https://blog.youkuaiyun.com/yinjun3215/article/details/130742742</guid><author>yinjun3215</author><pubDate>Thu, 18 May 2023 11:44:38 +0800</pubDate><description><![CDATA[3.接下来创建虚拟环境，输入conda create -n 虚拟环境名称 python=python版本，示例：conda create -n tf_cpu python=3.7。安装后进行验证，进入python解释器，输入import tensorflow as tf。首先要知道自己用tensorflow搭建的神经网络是用cpu训练还是gpu训练。至此windows安装tensorflow cpu完毕！]]></description><category></category></item><item><title><![CDATA[windows搭建pyspark环境详细教程]]></title><link>https://blog.youkuaiyun.com/yinjun3215/article/details/130732856</link><guid>https://blog.youkuaiyun.com/yinjun3215/article/details/130732856</guid><author>yinjun3215</author><pubDate>Wed, 17 May 2023 19:53:42 +0800</pubDate><description><![CDATA[将hadoop.dll和winutils.exe均拷贝到D:\hadoop-2.7.1\bin下和C:\Windows\System32下（两个文件各拷贝一份）此时bin目录（D:\hadoop-2.7.1\bin）下可能没有hadoop.dll及winutils.exe文件，接下来安装py4j,在cmd中输入pip install py4j。变量值：D:\spark-3.0.0-bin-hadoop2.7。变量值：D:\jdk      也就是jdk的安装路径。变量值：D:\hadoop-2.7.1。]]></description><category></category></item><item><title><![CDATA[win10如何快速打开高级系统设置]]></title><link>https://blog.youkuaiyun.com/yinjun3215/article/details/123707021</link><guid>https://blog.youkuaiyun.com/yinjun3215/article/details/123707021</guid><author>yinjun3215</author><pubDate>Thu, 24 Mar 2022 11:40:56 +0800</pubDate><description><![CDATA[找到电脑屏幕左下角的windows图标，右键鼠标点击运行，输入sysdm.cpl,确定，

点高级即可


]]></description><category></category></item><item><title><![CDATA[anaconda如何配置环境变量]]></title><link>https://blog.youkuaiyun.com/yinjun3215/article/details/123705879</link><guid>https://blog.youkuaiyun.com/yinjun3215/article/details/123705879</guid><author>yinjun3215</author><pubDate>Thu, 24 Mar 2022 11:34:04 +0800</pubDate><description><![CDATA[anaconda安装好后，在cmd输入conda，显示：
‘conda’ 不是内部或外部命令，也不是可运行的程序
或批处理文件。
原因是：
anaconda没有配置环境变量
那接下来我就教大家如何配置环境变量，网上有些文章的步骤是错误的
本地开发时，我们最好保持pycharm所用的python解释器是anaconda的python,这样在以后的开发过程当中，pycharm和jupyter notebook的环境保持一致，便于管理。
那么如何配置anaconda的环境变量呢？
1.找到anaconda的安装路]]></description><category></category></item><item><title><![CDATA[UnicodeEncodeError: ‘gbk‘ codec can‘t encode character ‘\xa0‘ in position 2: illegal multibyte seque]]></title><link>https://blog.youkuaiyun.com/yinjun3215/article/details/122244273</link><guid>https://blog.youkuaiyun.com/yinjun3215/article/details/122244273</guid><author>yinjun3215</author><pubDate>Thu, 30 Dec 2021 19:58:01 +0800</pubDate><description><![CDATA[python将列表各元素写入txt文件，一个元素占一行：
# df3是一个dataframe
file_handle = open(r"E:\work_notebook\区域+人群-商品类目相关性分析\df3_cols.txt",mode='w')
for col in df3.columns:
    col1 = col + ' ' + 'float,'
    file_handle.write(col1 + '\n')
file_handle.close()   # 必须要加close,不然数据无法]]></description><category></category></item><item><title><![CDATA[pandas修改dataframe的索引未成功]]></title><link>https://blog.youkuaiyun.com/yinjun3215/article/details/122238361</link><guid>https://blog.youkuaiyun.com/yinjun3215/article/details/122238361</guid><author>yinjun3215</author><pubDate>Thu, 30 Dec 2021 15:34:42 +0800</pubDate><description><![CDATA[刚开始使用df.reindex(new_index),发现索引并没有修改成功，查看答案，发现网上说的方法是指定axis参数，即：
df.reindex(new_index, axis='rows')

但是依然没有解决：
故使用粗暴方法，解决了：
df.index = new_index


...]]></description><category></category></item><item><title><![CDATA[sns.heatmap 画图报错TypeError: ufunc ‘isnan‘ not supported for the input types, and the inputs could not]]></title><link>https://blog.youkuaiyun.com/yinjun3215/article/details/122132956</link><guid>https://blog.youkuaiyun.com/yinjun3215/article/details/122132956</guid><author>yinjun3215</author><pubDate>Fri, 24 Dec 2021 17:45:57 +0800</pubDate><description><![CDATA[报错原因：
我画的是pandas  的DataFrame 的类型。然后问题就在于我的数据是float的，但是我的特征是object于是就引发了上述的错误：

于是就修改特征数据类型：
df3[list(df3.columns)]=df3[list(df3.columns)].astype(float)

再运行如下代码，问题解决：
sns.set(font='SimHei',font_scale=1.0)  # 解决Seaborn中文显示问题并调整字体大小
fig, ax = plt.subplots(fi]]></description><category></category></item><item><title><![CDATA[如何通过命令行从hbase导出数据到本地]]></title><link>https://blog.youkuaiyun.com/yinjun3215/article/details/121358091</link><guid>https://blog.youkuaiyun.com/yinjun3215/article/details/121358091</guid><author>yinjun3215</author><pubDate>Tue, 16 Nov 2021 16:11:36 +0800</pubDate><description><![CDATA[1.首先要切换到hbase目录下：

如果不知道在哪里，可以通过find / -name  hbase-daemon.sh查找路径
我的是在/opt/cloudera/parcels/CDH-5.12.2-1.cdh5.12.2.p0.4/lib/hbase/bin/目录下
2.执行hbase shell
3.输入list查看hbase有多少表

4.退出hbase shell命令行 输入echo “scan ‘recs_user_label’”|hbase shell &gt;test1.csv
进行导出]]></description><category></category></item><item><title><![CDATA[如何分享conda环境]]></title><link>https://blog.youkuaiyun.com/yinjun3215/article/details/121357307</link><guid>https://blog.youkuaiyun.com/yinjun3215/article/details/121357307</guid><author>yinjun3215</author><pubDate>Tue, 16 Nov 2021 15:40:39 +0800</pubDate><description><![CDATA[如何想保持自己的python环境及各种第三方库的版本和开发同事保持一致，可以将同事的conda环境存为.yml文件，然后执行命令就可以了。流程如下：
1.打开Anaconda Prompt
2.执行：conda env export --name root &gt;environment.yml
3.在本地C:\Users\admin路径下会生成.yml文件

4.拿到environment.yml文件后，将该文件放在工作目录下，可以通过以下命令从该文件创建环境
conda env create -f en]]></description><category></category></item><item><title><![CDATA[模型训练报错InvalidArgumentError: indices[12,0] = 4 is not in [0, 4) [[node model_1/emb_lookup_user_sex]]></title><link>https://blog.youkuaiyun.com/yinjun3215/article/details/121235922</link><guid>https://blog.youkuaiyun.com/yinjun3215/article/details/121235922</guid><author>yinjun3215</author><pubDate>Tue, 09 Nov 2021 20:20:04 +0800</pubDate><description><![CDATA[运行代码：
train_df = pd.read_csv('E:/work_notebook/09.06_个性化推荐点击率预估/user_item_act_train_df.csv')
val_df = pd.read_csv('E:/work_notebook/09.06_个性化推荐点击率预估/user_item_act_val_df.csv')
x_train = train_df.iloc[:, :-1].values
y_train = train_df['label'].values
x_val ]]></description><category></category></item><item><title><![CDATA[Error response from daemon: You cannot remove a running container 96683146ee5bcc37ef65707f559ffcd37a]]></title><link>https://blog.youkuaiyun.com/yinjun3215/article/details/121143041</link><guid>https://blog.youkuaiyun.com/yinjun3215/article/details/121143041</guid><author>yinjun3215</author><pubDate>Thu, 04 Nov 2021 15:08:24 +0800</pubDate><description><![CDATA[解决方法：
sudo docker ps -a


根据ID进行删除：
sudo docker rm -f 96683146ee5b

至此问题已解决

]]></description><category></category></item><item><title><![CDATA[linux卸载docker旧版本，安装最新稳定版本，并使用tensoflow serving部署深度学习模型]]></title><link>https://blog.youkuaiyun.com/yinjun3215/article/details/121138051</link><guid>https://blog.youkuaiyun.com/yinjun3215/article/details/121138051</guid><author>yinjun3215</author><pubDate>Thu, 04 Nov 2021 11:41:42 +0800</pubDate><description><![CDATA[一：如果在linux第一次使用docker部署深度学习模型，则应先查看以下docker版本：
停止docker服务：
systemctl stop docker
查看当前版本：
rpm -qa | grep docker
如果不是最新版本，则卸载当前版本：
卸载软件包：
yum erase docker \
                  docker-client \
                  docker-client-latest \
                  docker-c]]></description><category></category></item><item><title><![CDATA[如何让jupyter notebook使用anaconda 的base(虚拟)环境]]></title><link>https://blog.youkuaiyun.com/yinjun3215/article/details/120772126</link><guid>https://blog.youkuaiyun.com/yinjun3215/article/details/120772126</guid><author>yinjun3215</author><pubDate>Thu, 14 Oct 2021 20:32:11 +0800</pubDate><description><![CDATA[打开anaconda prompt,默认进入base环境
使用命令jupyter notebook打开即可：


]]></description><category></category></item><item><title><![CDATA[linux安装python faiss模块]]></title><link>https://blog.youkuaiyun.com/yinjun3215/article/details/120765434</link><guid>https://blog.youkuaiyun.com/yinjun3215/article/details/120765434</guid><author>yinjun3215</author><pubDate>Thu, 14 Oct 2021 15:35:32 +0800</pubDate><description><![CDATA[我们安装cpu版本，因此执行：
conda install faiss-cpu -c pytorch

中途输入y

安装成功：


]]></description><category></category></item><item><title><![CDATA[inverse_transform()的用法]]></title><link>https://blog.youkuaiyun.com/yinjun3215/article/details/120749643</link><guid>https://blog.youkuaiyun.com/yinjun3215/article/details/120749643</guid><author>yinjun3215</author><pubDate>Wed, 13 Oct 2021 18:21:36 +0800</pubDate><description><![CDATA[将编码之后的数据还原为原始数据

]]></description><category></category></item><item><title><![CDATA[np.random.permutation()解析]]></title><link>https://blog.youkuaiyun.com/yinjun3215/article/details/120744699</link><guid>https://blog.youkuaiyun.com/yinjun3215/article/details/120744699</guid><author>yinjun3215</author><pubDate>Wed, 13 Oct 2021 15:23:19 +0800</pubDate><description><![CDATA[作用：对列表或数组进行随机打乱，类似于sklearn.until.shuffle
a = np.array([1, 2, 3, 4])
b = np.random.permutation(a)
b   # array([4, 2, 3, 1])


]]></description><category></category></item><item><title><![CDATA[raise RuntimeError(“tf.placeholder() is not compatible with “ RuntimeError: tf.placeholder() is not]]></title><link>https://blog.youkuaiyun.com/yinjun3215/article/details/120730776</link><guid>https://blog.youkuaiyun.com/yinjun3215/article/details/120730776</guid><author>yinjun3215</author><pubDate>Tue, 12 Oct 2021 19:53:54 +0800</pubDate><description><![CDATA[解决方法：
代码开头加：
tf.compat.v1.disable_eager_execution()


]]></description><category></category></item><item><title><![CDATA[如何删除pandas处理数据中产生的Unnamed:0列]]></title><link>https://blog.youkuaiyun.com/yinjun3215/article/details/120722639</link><guid>https://blog.youkuaiyun.com/yinjun3215/article/details/120722639</guid><author>yinjun3215</author><pubDate>Tue, 12 Oct 2021 14:20:21 +0800</pubDate><description><![CDATA[解决方案：
pd.read_csv(path, index_col=0)
或：
pd.to_csv(path, index=False)


]]></description><category></category></item><item><title><![CDATA[如何解压上传到jupyter的zip文件]]></title><link>https://blog.youkuaiyun.com/yinjun3215/article/details/120720376</link><guid>https://blog.youkuaiyun.com/yinjun3215/article/details/120720376</guid><author>yinjun3215</author><pubDate>Tue, 12 Oct 2021 11:32:34 +0800</pubDate><description><![CDATA[import zipfile

f = zipfile.ZipFile("./EGES_embedding_rec.zip",'r') # 原压缩文件在服务器的位置
for file in f.namelist():
    f.extract(file,"./")               # 解压到的位置
f.close()


]]></description><category></category></item></channel></rss>