blocks|key|2635189|text|好吧，你的代码有几个问题。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2635190|2635191|你正在创建一个摘要写入器(tf.summary.FileWriter)，但实际上你并没有用它写任何东西。如果你期望这会对tensorboard产生一些影响，那么print(sess.run(b))与tensorboard没有任何关系。它只是输出b|ordered-list-item|offset|length|style|CODE|2635192|You‘t+create和summary+object的值来连接一些值。|2635193|您可能为tensorboard.|2635194|输入了错误的文件夹|2635195|更具分析性的是：|2635196|2635197|你需要一个summary+object来写摘要。例如，将标量写入摘要的tf.summary.scalar。要将tensorboard的值写入summary.|2635198|Then，您实际上需要将汇总操作运行到一个会话中以使其正常工作，例如:使用您先前定义的编写器来执行b的值：writer.add_summary(summary).|2635199|Now在tensorboard中可以看到一些东西，在终端中使用tensorboard+--logdir=output+|2635200|一般使用时，您可能需要将tf.summary.merge_all()传递给run，以便将所有摘要汇总在一起。|2635201|2635202|希望这能有所帮助。|2635203|entityMap|0|LINK|mutability|MUTABLE|url|https://www.tensorflow.org/api_guides/python/summary^0|0|0|D|L|28|I|3E|1|0|D|7|0|0|0|0|0|Z|H|5|E|0|0|1D|1|1H|R|0|V|R|0|C|M|11|3|0|0|0^^$0|@$1|2|3|4|5|6|7|1B|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|1C|8|@]|9|@]|A|$]]|$1|C|3|D|5|E|7|1D|8|@$F|1E|G|1F|H|I]|$F|1G|G|1H|H|I]|$F|1I|G|1J|H|I]]|9|@]|A|$]]|$1|J|3|K|5|E|7|1K|8|@$F|1L|G|1M|H|I]]|9|@]|A|$]]|$1|L|3|M|5|E|7|1N|8|@]|9|@]|A|$]]|$1|N|3|O|5|6|7|1O|8|@]|9|@]|A|$]]|$1|P|3|Q|5|6|7|1P|8|@]|9|@]|A|$]]|$1|R|3|-4|5|6|7|1Q|8|@]|9|@]|A|$]]|$1|S|3|T|5|E|7|1R|8|@$F|1S|G|1T|H|I]]|9|@$F|1U|G|1V|1|1W]]|A|$]]|$1|U|3|V|5|E|7|1X|8|@$F|1Y|G|1Z|H|I]|$F|20|G|21|H|I]]|9|@]|A|$]]|$1|W|3|X|5|E|7|22|8|@$F|23|G|24|H|I]]|9|@]|A|$]]|$1|Y|3|Z|5|E|7|25|8|@$F|26|G|27|H|I]|$F|28|G|29|H|I]]|9|@]|A|$]]|$1|10|3|-4|5|6|7|2A|8|@]|9|@]|A|$]]|$1|11|3|12|5|6|7|2B|8|@]|9|@]|A|$]]|$1|13|3|-4|5|6|7|2C|8|@]|9|@]|A|$]]]|14|$15|$5|16|17|18|A|$19|1A]]]]

Well, you have several issues with your code.

<ol>
<li>You are creating a summary writer (<code>tf.summary.FileWriter</code>) but you don't actually write anything with it. <code>print(sess.run(b))</code> has nothing to do with tensorboard if you expected this to has some effect on it. It just prints the value of <code>b</code></li>
<li>You don't create and <code>summary</code> object to connect some value with.</li>
<li>You are probably entering wrong folder for tensorboard.</li>
</ol>

More analytically:

<ol>
<li>You need a <a href="https://www.tensorflow.org/api_guides/python/summary" rel="nofollow noreferrer">summary object</a> to write a summary. For example a <code>tf.summary.scalar</code> to write a scalar to a summary. Something like
<code>tf.summary.scalar("b_value", b)</code> to write the value of <code>b</code> to a summary.</li>
<li>Then you actually need to run your summary operation into a session to get it working, like: <code>summary = sess.run(summary_scalar)</code>.</li>
<li>Write the value with the writer you defined previously: <code>writer.add_summary(summary)</code>.</li>
<li>Now there is something to see in tensorboard, using
<code>tensorboard --logdir=output</code> in a terminal</li>
<li>In general use you will probably need <code>tf.summary.merge_all()</code> to pass to <code>run</code> in order to gather all your summaries together.</li>
</ol>

Hope this helps.

blocks|key|2846894|text|您的问题可能与您试图启动tensorboard的驱动器和logdir所在的驱动器有关。Tensorboard使用冒号分隔可选的运行名和logdir标志中的路径，因此您的路径被解释为\+path+\+to+\output\文件夹，名称为C。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|2846895|这可以通过从与日志目录相同的驱动器启动tensorboard或通过提供明确的运行名称(例如logdir=mylogs:C:\path\to\output\folder+)来解决|2846896|有关此问题的参考信息，请参阅here。|2846897|entityMap|0|LINK|mutability|MUTABLE|url|https://github.com/tensorflow/tensorflow/issues/7856^0|C|B|S|6|17|B|0|J|B|19|12|0|E|4|0|0^^$0|@$1|2|3|4|5|6|7|R|8|@$9|S|A|T|B|C]|$9|U|A|V|B|C]|$9|W|A|X|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|Y|8|@$9|Z|A|10|B|C]|$9|11|A|12|B|C]]|D|@]|E|$]]|$1|H|3|I|5|6|7|13|8|@]|D|@$9|14|A|15|1|16]]|E|$]]|$1|J|3|-4|5|6|7|17|8|@]|D|@]|E|$]]]|K|$L|$5|M|N|O|E|$P|Q]]]]

Your issue may be related to the drive you are attempting to start <code>tensorboard</code> from and the drive your <code>logdir</code> is on. <code>Tensorboard</code> uses a colon to separate the optional run name and the path in the logdir flag, so your path is being interpreted as \path\to\output\folder with name C. 

This can be worked around by either starting <code>tensorboard</code> from the same drive as your log directory or by providing an explicit run name, e.g. <code>logdir=mylogs:C:\path\to\output\folder</code>

See <a href="https://github.com/tensorflow/tensorflow/issues/7856" rel="noreferrer">here</a> for reference to the issue.

blocks|key|2846936|text|当我运行TensorFlow+(https://www.tensorflow.org/programmers_guide/tensorboard_histograms)教程时，我遇到了同样的问题。我继续尝试了上面hpabst引用的解决方案。它就像冠军一样工作。在终端中(我在CentOS中运行)-我运行：tensorboard+--log+=mydir:+'~/mlDemo/'|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|2846937|entityMap|0|LINK|mutability|MUTABLE|url|https://www.tensorflow.org/programmers_guide/tensorboard_histograms^0|48|11|G|1V|0|0^^$0|@$1|2|3|4|5|6|7|N|8|@$9|O|A|P|B|C]]|D|@$9|Q|A|R|1|S]]|E|$]]|$1|F|3|-4|5|6|7|T|8|@]|D|@]|E|$]]]|G|$H|$5|I|J|K|E|$L|M]]]]

When I ran the TensorFlow (<a href="https://www.tensorflow.org/programmers_guide/tensorboard_histograms" rel="nofollow noreferrer">https://www.tensorflow.org/programmers_guide/tensorboard_histograms</a>) tutorial, I ran across the same issue. I went ahead and tried the solution referenced by hpabst above. It worked like champ. In terminal (I am running in CentOS)- I ran: <code>tensorboard --log =mydir: '~/mlDemo/'</code>

blocks|key|99569|text|在tensorboard目录中找到main.py的路径，然后将其复制到。它应该是这样的：|type|unstyled|depth|inlineStyleRanges|offset|length|style|BOLD|CODE|entityRanges|data|99570|C:/Users/<Your+Username>/Anaconda3/envs/tensorflow/Lib/site-packages/tensorboard/main.py|code-block|syntax|javascript|99571|或|99572|C:/Users/<Your+Username>/anaconda/envs/tf/lib/python3.5/site-packages/tensorboard/main.py|99573|知道正确的路径后，在Anaconda提示符中使用tensorboard目录内的main.py路径运行此命令。这对我在Windows中是有效的。|99574|python+C:/Users/Username/Anaconda3/envs/tensorflow/Lib/site-packages/tensorboard/main.py+--logdir=foo:<path+to+your+log+directory>|99575|致词：KyungHoon+Kim|99576|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/a/46598482/6156635^0|1|B|H|7|0|0|0|0|O|B|1M|7|13|7|0|0|3|D|0|0^^$0|@$1|2|3|4|5|6|7|13|8|@$9|14|A|15|B|C]|$9|16|A|17|B|D]]|E|@]|F|$]]|$1|G|3|H|5|I|7|18|8|@]|E|@]|F|$J|K]]|$1|L|3|M|5|6|7|19|8|@]|E|@]|F|$]]|$1|N|3|O|5|I|7|1A|8|@]|E|@]|F|$J|K]]|$1|P|3|Q|5|6|7|1B|8|@$9|1C|A|1D|B|C]|$9|1E|A|1F|B|C]|$9|1G|A|1H|B|D]]|E|@]|F|$]]|$1|R|3|S|5|I|7|1I|8|@]|E|@]|F|$J|K]]|$1|T|3|U|5|6|7|1J|8|@]|E|@$9|1K|A|1L|1|1M]]|F|$]]|$1|V|3|-4|5|6|7|1N|8|@]|E|@]|F|$]]]|W|$X|$5|Y|Z|10|F|$11|12]]]]

Find the path to <code>main.py</code> inside tensorboard directory and copy it. It should be something like this:

<pre><code>C:/Users/&lt;Your Username&gt;/Anaconda3/envs/tensorflow/Lib/site-packages/tensorboard/main.py
</code></pre>

or

<pre><code>C:/Users/&lt;Your Username&gt;/anaconda/envs/tf/lib/python3.5/site-packages/tensorboard/main.py
</code></pre>

Once you know the correct path, Run this command in Anaconda Prompt using the path to <code>main.py</code> inside tensorboard directory. This worked for me in Windows.

<pre><code>python C:/Users/Username/Anaconda3/envs/tensorflow/Lib/site-packages/tensorboard/main.py --logdir=foo:&lt;path to your log directory&gt;
</code></pre>

Credits: <a href="https://stackoverflow.com/a/46598482/6156635">KyungHoon Kim</a>

blocks|key|102587|text|我也在使用windows10。我试着从相同的驱动器，不同的驱动器和本地路径运行tensorboard你的代码。在所有三种情况下，我都能看到图表。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|102588|一种解决方案是，也许你需要改变你的主机(我也不能用localhost:6006可视化)。尝试使用http://MYCOMP:6006查看是否发现了任何差异。|offset|length|102589|注意:我的tensorboard版本是1.8.0+(也许你可以更新一下tensorboard，看看有什么不同)|102590|entityMap|0|LINK|mutability|MUTABLE|url|http://mycomp:6006/^0|0|1C|I|0|0|0^^$0|@$1|2|3|4|5|6|7|P|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|Q|8|@]|9|@$D|R|E|S|1|T]]|A|$]]|$1|F|3|G|5|6|7|U|8|@]|9|@]|A|$]]|$1|H|3|-4|5|6|7|V|8|@]|9|@]|A|$]]]|I|$J|$5|K|L|M|A|$N|O]]]]

I am working with windows 10 as well. I tried your code with running tensorboard from same drive, different drive, and local path. In all three cases, I was able to see the graph.

One solution is, maybe you need to change your host (I cannot visualize with localhost:6006 as well). Try <a href="http://MYCOMP:6006" rel="nofollow noreferrer">http://MYCOMP:6006</a> to check if you see any difference.

Note: My tensorboard version is 1.8.0 (maybe you can update your tensorboard to see it gives any difference)

blocks|key|100212|text|对于Windows，我有一个变通方法。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|100213|日志cd+/path/to/+|100214|blockquote|100215|100216|tensorboard+--logdir=./|100217|100218|在这里，你可以像平常一样使用path。请记住，不要以logdir+=+./的形式给出空格。|100219|这给了我一个错误：|100220|100221|当前数据集没有处于活动状态的仪表板。可能的原因：-您尚未向事件文件中写入任何数据。-+TensorBoard找不到事件文件。|100222|100223|entityMap^0|0|0|0|0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|T|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|U|8|@]|9|@]|A|$]]|$1|D|3|-4|5|E|7|V|8|@]|9|@]|A|$]]|$1|F|3|-4|5|6|7|W|8|@]|9|@]|A|$]]|$1|G|3|H|5|6|7|X|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|Y|8|@]|9|@]|A|$]]|$1|J|3|K|5|6|7|Z|8|@]|9|@]|A|$]]|$1|L|3|M|5|6|7|10|8|@]|9|@]|A|$]]|$1|N|3|-4|5|6|7|11|8|@]|9|@]|A|$]]|$1|O|3|P|5|E|7|12|8|@]|9|@]|A|$]]|$1|Q|3|-4|5|6|7|13|8|@]|9|@]|A|$]]|$1|R|3|-4|5|6|7|14|8|@]|9|@]|A|$]]]|S|$]]

In case of Windows,I got a workaround.

<blockquote>
 cd /path/to/log
 
 tensorboard --logdir=./ 
</blockquote>

Here you can use path as normal.
Keep in mind not to give spaces with it as logdir = ./. 

This gave me an error:

<blockquote>
 No dashboards are active for the current data set. Probable causes: -
 You haven’t written any data to your event files. - TensorBoard can’t
 find your event files.
</blockquote>

blocks|key|2847072|text|试着这样做：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2847073|tensorboard+--logdir="C:\path\to\output\folder"|code-block|syntax|javascript|2847074|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

Try this instead:

<pre><code>tensorboard --logdir="C:\path\to\output\folder"
</code></pre>

blocks|key|102609|text|在Windows+10中，此命令有效|type|unstyled|depth|inlineStyleRanges|entityRanges|data|102610|tensorboard+--logdir=training/|code-block|syntax|javascript|102611|在这里，training是写入输出文件的目录。请注意，它没有任何引号，末尾有一个斜杠(/)。两者都很重要。|102612|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

In Windows 10, this command works

<pre><code>tensorboard --logdir=training/
</code></pre>

Here training is the directory where output files are written. Please note it does not have any quotes and has a slash (/) at the end. Both are important.

blocks|key|2847143|text|观察到，一旦tensorflow进入糟糕的状态，它每次之后都会抛出问题，因为在随后的运行中，|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2847144|在启动控制面板automatically|2847145|It+时，|unordered-list-item|2847146|不会终止以前使用的进程和以前的状态|ordered-list-item|2847147|2847148|2847149|缓解不良状态的步骤：|2847150|取消所有正在运行的tensorboard+processes.|2847151|Clear以前的tensorboard状态。|2847152|2847153|在jupyter+notebook中|2847154|!+powershell+"echo+'checking+for+existing+tensorboard+processes'"
!+powershell+"ps+%7C+Where-Object+{$_.ProcessName+-eq+'tensorboard'}"

!+powershell+"ps+%7C+Where-Object+{$_.ProcessName+-eq+'tensorboard'}%7C+%25{kill+$_}"

!+powershell+"echo+'cleaning+tensorboard+temp+dir'"
!+powershell+"rm+$env:TEMP\.tensorboard-info\*"

!+powershell+"ps+%7C+Where-Object+{$_.ProcessName+-eq+'tensorboard'}"


%25tensorboard+--logdir="logs\fit"+--host+localhost|code-block|syntax|javascript|2847155|如果在jupyter中超时，则转到浏览器中的http://localhost:6006/#scalars并检查|offset|length|2847156|entityMap|0|LINK|mutability|MUTABLE|url|http://localhost:6006/#scalars^0|0|0|0|0|0|0|0|0|0|0|0|0|M|U|0|0^^$0|@$1|2|3|4|5|6|7|1B|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|1C|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|1D|8|@]|9|@]|A|$]]|$1|G|3|H|5|I|7|1E|8|@]|9|@]|A|$]]|$1|J|3|-4|5|6|7|1F|8|@]|9|@]|A|$]]|$1|K|3|-4|5|6|7|1G|8|@]|9|@]|A|$]]|$1|L|3|M|5|6|7|1H|8|@]|9|@]|A|$]]|$1|N|3|O|5|6|7|1I|8|@]|9|@]|A|$]]|$1|P|3|Q|5|F|7|1J|8|@]|9|@]|A|$]]|$1|R|3|-4|5|6|7|1K|8|@]|9|@]|A|$]]|$1|S|3|T|5|6|7|1L|8|@]|9|@]|A|$]]|$1|U|3|V|5|W|7|1M|8|@]|9|@]|A|$X|Y]]|$1|Z|3|10|5|6|7|1N|8|@]|9|@$11|1O|12|1P|1|1Q]]|A|$]]|$1|13|3|-4|5|6|7|1R|8|@]|9|@]|A|$]]]|14|$15|$5|16|17|18|A|$19|1A]]]]

Observed that once tensorflow goes into a bad state, it throws problem everytime after because in subsequent runs,

<ol>
<li>It does not kill previous processes automatically</li>
<li>It uses previous states while starting the dashboard</li>
</ol>

Steps to mitigate the bad state:

<ol>
<li>Kill all running tensorboard processes.</li>
<li>Clear previous tensorboard state.</li>
</ol>

In jupyter notebook

<pre><code>! powershell "echo 'checking for existing tensorboard processes'"
! powershell "ps | Where-Object {$_.ProcessName -eq 'tensorboard'}"

! powershell "ps | Where-Object {$_.ProcessName -eq 'tensorboard'}| %{kill $_}"

! powershell "echo 'cleaning tensorboard temp dir'"
! powershell "rm $env:TEMP\.tensorboard-info\*"

! powershell "ps | Where-Object {$_.ProcessName -eq 'tensorboard'}"


%tensorboard --logdir="logs\fit" --host localhost
</code></pre>

If it times out in jupyter, then go to <a href="http://localhost:6006/#scalars" rel="nofollow noreferrer">http://localhost:6006/#scalars</a> in the browser and check

blocks|key|2635551|text|尝试将目录放在引号内。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2635552|示例：|2635553|tensorboard+--logdir="C:/Users/admin/Desktop/ML/log"|code-block|syntax|javascript|2635554|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|L|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|M|8|@]|9|@]|A|$G|H]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

Try placing the directory inside the quotes.
Example:
<pre><code>tensorboard --logdir=&quot;C:/Users/admin/Desktop/ML/log&quot;
</code></pre>

blocks|key|2847184|text|如果有人仍然遇到这个问题，我建议尝试不同的端口，如tensorboard+--logdir=logs+--port+5000。对我很管用。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|2847185|entityMap^0|P|11|0^^$0|@$1|2|3|4|5|6|7|H|8|@$9|I|A|J|B|C]]|D|@]|E|$]]|$1|F|3|-4|5|6|7|K|8|@]|D|@]|E|$]]]|G|$]]

In case anyone still meets this problem I suggest trying different port like <code>tensorboard --logdir=logs --port 5000</code>. Worked for me.

blocks|key|2635635|text|嗯，我尝试了几乎所有的解决方案，但都没有解决问题。几周后，我终于解决了这个问题。这就是我所做的。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2635636|2635637|终止tensorboard的现有进程。从我的本地目录-C：\+instances.|ordered-list-item|2635638|Deleted+\sethuri\AppData\+local+\Temp+|2635639|+-下载所有Jupiter笔记本电脑Tensorboard缓存文件|2635640|📷|atomic|offset|length|2635641|​|2635642|2635643|启动了Jupyter+notebook实例，并调用了tensorboard|2635644|I，但在重新加载页面后，它工作了|2635645|2635646|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/d93a97c598ab1a443cc7f83dd6147e94.png|imageAlt^0|0|0|0|0|0|0|1|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|15|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|16|8|@]|9|@]|A|$]]|$1|C|3|D|5|E|7|17|8|@]|9|@]|A|$]]|$1|F|3|G|5|E|7|18|8|@]|9|@]|A|$]]|$1|H|3|I|5|6|7|19|8|@]|9|@]|A|$]]|$1|J|3|K|5|L|7|1A|8|@]|9|@$M|1B|N|1C|1|1D]]|A|$]]|$1|O|3|P|5|6|7|1E|8|@]|9|@]|A|$]]|$1|Q|3|-4|5|6|7|1F|8|@]|9|@]|A|$]]|$1|R|3|S|5|E|7|1G|8|@]|9|@]|A|$]]|$1|T|3|U|5|E|7|1H|8|@]|9|@]|A|$]]|$1|V|3|-4|5|6|7|1I|8|@]|9|@]|A|$]]|$1|W|3|-4|5|6|7|1J|8|@]|9|@]|A|$]]]|X|$Y|$5|Z|10|11|A|$12|13|14|-4]]]]

Well, I have tried almost every solution here but nothing has worked out. Finally, after several weeks, I was able to fix this problem. Here is what I did.
<ol>
<li>Kill the existing processes of tensorboard. <a href="https://stackoverflow.com/a/60819054/9312772">Use this code</a></li>
<li>Shut down all Jupiter notebook instances.</li>
<li>Deleted Tensorboard cache files from my local - C:\Users\sethuri\AppData\Local\Temp
<a href="https://i.stack.imgur.com/NbY1v.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/NbY1v.png" alt="enter image description here" /></a></li>
<li>Launched Jupyter notebook instance and invoked the tensorboard</li>
<li>I didn't see the visual first but after reloading the page it worked</li>
</ol>

I am trying to use Tensorboard but every time I run any program with Tensorflow, I get an error when I go to localhost:6006 to view the Visualization

Here is my code

<pre><code>a = tf.add(1, 2,)
b = tf.multiply(a, 3)

with tf.Session() as sess:
 writer = tf.summary.FileWriter("output", sess.graph)
 print(sess.run(b))
 writer.close()
</code></pre>

When I go to the command prompt and enter

<pre><code>tensorboard --logdir=C:\path\to\output\folder
</code></pre>

It returns with 

<pre><code>TensorBoard 0.1.8 at http://MYCOMP:6006 (Press CTRL+C to quit)
</code></pre>

When I go to localhost:6006 it states

<blockquote>
 No dashboards are active for the current data set.
 Probable causes:
 - You haven’t written any data to your event files.
 - TensorBoard can’t find your event files.
</blockquote>

I have looked at this link (<a href="https://stackoverflow.com/questions/46108507/tensorboard-no-dashboards-are-active-for-the-current-data-set">Tensorboard: No dashboards are active for the current data set</a>) but it doesn't seem to fix this issue

And I am running this on Windows 10

What do I do to fix this issue? Am I giving the right path for Tensorboard in the command prompt? 

Thank you in advance

Tensorboard Error: No dashboards are active for current data set

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我正在尝试使用Tensorboard，但每次使用Tensorflow运行任何程序时，当我转到localhost:6006查看可视化时都会出现错误以下是我的代码a = tf.add(1, 2,)b = tf.multiply(a, 3)with tf.Session() as sess:    writer = tf.s...

问Tensorboard错误:当前数据集没有活动的仪表板
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