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TSNE sets kl_divergence wrong #6507

ssaeger opened this Issue Mar 8, 2016 · 2 comments


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ssaeger commented Mar 8, 2016

In commit 6bf63f6 error was replaced by kl_divergence, but line 850 was missed so that the final kl_divergence value is actually an old value that misses the final optimization step. This is especially a problem since people can access the kl_divergence in the current version as described in #6477.

Even in the verbose output one can see that the error/kl_divergence value from iteration 100 is kept as the final value at iteration 200.

[t-SNE] Iteration 25: error = 8.0242269, gradient norm = 0.0014062
[t-SNE] Iteration 50: error = 7.9942951, gradient norm = 0.0014150
[t-SNE] Iteration 75: error = 7.9053034, gradient norm = 0.0014402
[t-SNE] Iteration 100: error = 7.8706691, gradient norm = 0.0014515
[t-SNE] KL divergence after 100 iterations with early exaggeration: 7.870669
[t-SNE] Iteration 125: error = 0.5025748, gradient norm = 0.0009721
[t-SNE] Iteration 150: error = 0.4773589, gradient norm = 0.0009048
[t-SNE] Iteration 175: error = 0.4710594, gradient norm = 0.0008880
[t-SNE] Iteration 200: error = 0.4693623, gradient norm = 0.0008834
[t-SNE] Error after 200 iterations: 7.870669

If this confirms to be a bug, I will create a PR for it.


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AlexanderFabisch commented Mar 21, 2016

Yes, that seems to be an error. It has been introduced during the merge of the master and Barnes-Hut SNE.


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ssaeger commented Mar 21, 2016

Ok, then I will create a PR to fix it. :)

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