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Tensorboard projector will compute PCA endlessly


I have just over 100k word embeddings which I created using gensim, originally each containing 200 dimensions. I've been trying to visualize them within tensorboard's projector but I have only failed so far. My problem is that tensorboard seems to freeze while computing PCA. At first, I left the page open for 16 hours, imagining that it was just too much to be calculated, but nothing happened. At this point, I started to try and test different scenarios just in case all I needed was more time and I was trying to rush things. The following is a list of my testing so far, all of which failed at the same spot, computing PCA:

  • I plotted only 10 points of 200 dimensions;
  • I retrained my gensim model so that I could reduce its dimensionality to 100;
  • Then I reduced it to 10;
  • Then to 2;
  • Then I tried plotting only 2 points, i.e. 2 two dimensional points;

I am using Tensorflow 1.11; You can find my last saved tensor flow session here, would you mind trying it out?

I am still a beginner, therefore I used a couple tutorial to get me started; I used Sud Harsan work so far.

Any help is much appreciated. Thanks.


Updates:

A) I've found someone else dealing with the same problem; I tried the solution provided, but it didn't change anything.

B) I thought it could have something to do with my installation, therefore I tried uninstalling tensorflow and installing it back; no luck. I then proceeded to create a new environment dedicated to tensorflow and that also didn't work.

C) Assuming there was something wrong with my code, I ran tensorflow's basic embedding tutorial to check if I could open its projector's results. And guess what?! I still can't go past "Calculating PCA"

Now, I did visit the online projector example and that loads perfectly.

Again, Any help would be more than appreciated. Thanks!


Solution

  • As mentioned by Bluedrops, updating tensorboard and tensorflow seems to fix the problem.

    I created a new environment with conda and installed the newest versions of Tensorflow, Tensorboard and their dependencies and that seems to fix the issue.