Tensorboard Error: No Dashboards Are Active For Current Data Set

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 a = tf.add(1,

Solution 1:

Your issue may be related to the drive you are attempting to start tensorboard from and the drive your logdir is on. Tensorboard 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 tensorboard from the same drive as your log directory or by providing an explicit run name, e.g. logdir=mylogs:C:\path\to\output\folder

See here for reference to the issue.

Solution 2:

In case of Windows,I got a workaround.

cd /path/to/log

tensorboard --logdir=./

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

This gave me an error:

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.

Solution 3:

In Windows 10, this command works

tensorboard --logdir=training/

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.

Solution 4:

Try this instead:

tensorboard --logdir="C:\path\to\output\folder"

Solution 5:

Well, you have several issues with your code.

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

More analytically:

1. You need a summary object to write a summary. For example a tf.summary.scalar to write a scalar to a summary. Something like tf.summary.scalar("b_value", b) to write the value of b to a summary.
2. Then you actually need to run your summary operation into a session to get it working, like: summary = sess.run(summary_scalar).
3. Write the value with the writer you defined previously: writer.add_summary(summary).
4. Now there is something to see in tensorboard, using tensorboard --logdir=output in a terminal
5. In general use you will probably need tf.summary.merge_all() to pass to run in order to gather all your summaries together.

Hope this helps.