Can you please report this on the issue tracker? Thanks!
On 04/18/2016 09:28 AM, ***@web.de wrote:
> Thanks for your response Alexander! Here is a simplified version of my
> script applied to the MNIST data set. It wasn't clear from my first
> mail but I don't want to train it incrementally but instead apply tsne
> to each batch within the data set (for several epochs). This works for
> an unspecified number of epochs/batches until the program crashes.
> for epoch in range(20)
> for batch in batch_iterator(mnist_data):
> # reshape from (500, 1, 28, 28) to (500, 784)
> data = batch.reshape(batch.shape[0], -1)
> tsne = TSNE(n_components=2, random_state=0, init='pca',
> verbose=0).fit_transform(data)
> # continue with scatter plot visualization...
> plt.scatter(tsne[:,0], tsne[:,1], c=labels)
> plt.show()
> *Gesendet:* Montag, 18. April 2016 um 14:44 Uhr
> *Von:* "Alexander Fabisch" <***@informatik.uni-bremen.de>
> *An:* scikit-learn-***@lists.sourceforge.net
> *Betreff:* Re: [Scikit-learn-general] tSNE assertion errors
> Hi Sarah,
>
> t-SNE does not support incremental training. Your model will be
> retrained every time you fit a new batch of data (see
> https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/manifold/t_sne.py#L664).
> That means you might have found a dataset that reveals an error in
> implementation. Could you provide a small script that reproduces the
> error?
>
> Best regards,
>
> Alexander
> Am 18.04.2016 um 14:28 schrieb ***@web.de:
>
> Hey everyone!
> I am new to Python and the scikit learn package so I hope someone
> can help me with the two issues I encountered during use of the
> sklearn.manifold implementation of the t-SNE algorithm. First a
> little bit of context: I am repeatedly feeding batches of
> dimensionality 500x784 to the algorithm for visualization.
> However, before my script finishes, one of the two following error
> messages occurs:
> AssertionError:[t-SNE]Insertionfailed
> or
> AssertionError:Treeconsistency failed:unexpected number of
> points=499at root node=500
> Furthermore, these messages do not occur at a fixed time but their
> behaviour seems rather non-deterministic. Hopefully someone came
> across this problem before and can help me to fix it.
> Best,
> Sarah
>
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