3 ms·

Holy cow, I knew that MNIST was simple, but not that simple. Could you post a snippet of the code that you used to achieve this? It would be really, really nic

Holy cow, I knew that MNIST was simple, but not that simple. Could you post a snippet of the code that you used to achieve this? It would be really, really nice to have a baseline to work from I'm sure, and I feel like this could be really useful to a few other areas (my personal obsession is speed-training on CIFAR10 :')))) ) Holy cow, that's insane. :O

montebicyclelo 3y ago
I used the notebook linked in the original post [1]. It evaluates using 100 samples from the test set, (I'm guessing because the gzip method is slow - it would take ~7 hours on the full test set, on my machine). I plugged in the distance measures, for the `compute_ncd` function. (Jaccard/Dice have been negated and the -1 removed.) def euclidean(x1, x2): return np.sum(np.square(x1 - x2)) def jaccard(x1, x2): x1_binary = x1 > 0.5 x2_binary = x2 > 0.5 return np.logical_or(x1_binary, x2_binary).sum() / np.logical_and(x1_binary, x2_binary).sum() def dice(x1, x2): x1_binary = x1 > 0.5 x2_binary = x2 > 0.5 return 2 * np.logical_or(x1_binary, x2_binary).sum() / (x1_binary.sum() + x2_binary.sum()) [1] https://github.com/Jakob-98/mono/blob/73168bc0ea904e7586581573da5907020d88736f/python/gzip_mnist/mnist_gzip.ipynb https://github.com/Jakob-98/mono/blob/73168bc0ea904e75865815...
- tysam_and 3y ago
  Ohhh, you know what? I think this makes sense, since it's basically like making prototypes from the training set, only it's instead comparing against every training example, then averaging. Cool. Did not know that would be remotely close to 90%, but that makes sense to me. I wonder if something like a weighted max_mean would perform better or something L1. Or maybe L2 is ideal, it is at the center of a lot of things information theory, after all! ;PPPP
KateLawson 3y ago
MNIST is what the Post Office solved in the late 1990s. It's basically the "hello world" of visual recognition these days. https://www.govexec.com/federal-news/1999/02/postal-service-tests-handwriting-recognition-system/1746/ https://www.govexec.com/federal-news/1999/02/postal-service-...

montebicyclelo 3y ago

I used the notebook linked in the original post [1]. It evaluates using 100 samples from the test set, (I'm guessing because the gzip method is slow - it would take ~7 hours on the full test set, on my machine). I plugged in the distance measures, for the `compute_ncd` function. (Jaccard/Dice have been negated and the -1 removed.) def euclidean(x1, x2): return np.sum(np.square(x1 - x2)) def jaccard(x1, x2): x1_binary = x1 > 0.5 x2_binary = x2 > 0.5 return np.logical_or(x1_binary, x2_binary).sum() / np.logical_and(x1_binary, x2_binary).sum() def dice(x1, x2): x1_binary = x1 > 0.5 x2_binary = x2 > 0.5 return 2 * np.logical_or(x1_binary, x2_binary).sum() / (x1_binary.sum() + x2_binary.sum()) [1] https://github.com/Jakob-98/mono/blob/73168bc0ea904e7586581573da5907020d88736f/python/gzip_mnist/mnist_gzip.ipynb https://github.com/Jakob-98/mono/blob/73168bc0ea904e75865815...

tysam_and 3y ago
Ohhh, you know what? I think this makes sense, since it's basically like making prototypes from the training set, only it's instead comparing against every training example, then averaging. Cool. Did not know that would be remotely close to 90%, but that makes sense to me. I wonder if something like a weighted max_mean would perform better or something L1. Or maybe L2 is ideal, it is at the center of a lot of things information theory, after all! ;PPPP

KateLawson 3y ago

MNIST is what the Post Office solved in the late 1990s. It's basically the "hello world" of visual recognition these days. https://www.govexec.com/federal-news/1999/02/postal-service-tests-handwriting-recognition-system/1746/ https://www.govexec.com/federal-news/1999/02/postal-service-...