Can you please report this on the issue tracker? Thanks!
On 04/18/2016 09:28 AM, leg...@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" <afabi...@informatik.uni-bremen.de>
*An:* scikit-learn-general@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 leg...@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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