First, I'd like to thank this library as it works like a charm on CUDA. Second I have stumbled upon an issue that I am not totally understanding regarding fixing a random_seed. The sklean version uses a parameter called random_state so I suppose this would be for the same purpose, however when setting like the following I receive a different result when executing the same code twice on the exactly same dataset.
When doing the exactly same with the sklearn.manifold.TSNE imported then I am getting the same result using a fixed randomness when executing twice. Also getting different results using sklearn vs tsne-cuda
Why is that, what am I missing?
Thanks,
adr
# Using t-SNE for dimensionality reduction
tsne = TSNE(n_components=2, random_seed=420, perplexity=30, n_iter=1000, verbose=1)
tsne_results = tsne.fit_transform(X)
Hey there! 👋🏻
First, I'd like to thank this library as it works like a charm on CUDA. Second I have stumbled upon an issue that I am not totally understanding regarding fixing a
random_seed
. Thesklean
version uses a parameter calledrandom_state
so I suppose this would be for the same purpose, however when setting like the following I receive a different result when executing the same code twice on the exactly same dataset.When doing the exactly same with the sklearn.manifold.TSNE imported then I am getting the same result using a fixed randomness when executing twice. Also getting different results using
sklearn
vstsne-cuda
Why is that, what am I missing?
Thanks, adr