I checked on 0.20.1 using scikit-learn shipped by Anaconda and both seem to have the same default.
On Mon, 17 Dec 2018 at 16:01, Guillaume Lemaître <g.lemaitr...@gmail.com> wrote: > could you provide the scikit-learn version in both case? > > Sent from my phone - sorry to be brief and potential misspell. > *From:* kouichi.mats...@gmail.com > *Sent:* 17 December 2018 15:56 > *To:* scikit-learn@python.org > *Reply to:* scikit-learn@python.org > *Subject:* Re: [scikit-learn] MLPClassifier on WIndows 10 is 4 times > slower than that on macOS? > > Thank you for your quick reply. It's very helpful. > It's because of Anaconda: Its python stops the iteration soon as follows > (w/ verbose=True). > I am not sure why 'n_iter_no_change=10' is changed in Anaconda. > Anaconda might modify the MLPClassifier implementation. > > > python learn.py (in pure Python+Scikit-Learn) > ... > > Iteration 125, loss = 0.26152263 > > Iteration 126, loss = 0.25705940 > > Iteration 127, loss = 0.25957841 > > Training loss did not improve more than tol=0.000100 for 10 consecutive > epochs. Stopping. > 0.8496 > > > python learn.py (in Anaconda) > ... > Iteration 23, loss = 0.34410594 > Iteration 24, loss = 0.34663903 > Iteration 25, loss = 0.34376815 > Training loss did not improve more than tol=0.000100 for two consecutive > epochs. Stopping. > 0.852 > > Thanks, > > > --- > 松田晃一 MATSUDA, Kouichi, Ph.D. > > > 2018年12月16日(日) 0:50 Gael Varoquaux <gael.varoqu...@normalesup.org>: > >> I suspect that it is probably due to the linear-algebra libraries: your >> scientific Python install on macOS is probably using optimized >> linear-algebra (ie optimized numpy and scipy), but not your install on >> Windows. >> >> I would recommend you to look at how you installed you Python >> distribution on macOS and on Windows, as you likely have installed an >> optimized one on one of the platforms and not on the other. >> >> Cheers, >> >> Gaël >> >> On Sat, Dec 15, 2018 at 09:02:06AM -0500, Kouichi Matsuda wrote: >> > Hi Hi everyone, >> >> > I am writing a scikit-learn program to use MLPClassifier to learn >> > Fashion-MNIST. >> > The following is the program. It's very simple. >> > When I ran it on Windows 10 (Core-i7-8565U, 1.8GHz, 16GB) note book, it >> took >> > about 4 minutes. >> > However, when I ran it on MacBook(macOS), it took about 1 minutes. >> > Does anyone help me to understand the reason why Windows 10 is so slow? >> > Am I missing something? >> >> > Thanks, >> >> > import os import gzip import numpy as np #from https://github.com/ >> > zalandoresearch/fashion-mnist/blob/master/utils/mnist_reader.py def >> load_mnist >> > (path, kind='train'): labels_path = os.path.join(path,'% >> s-labels-idx1-ubyte.gz' >> > % kind) images_path = os.path.join(path,'%s-images-idx3-ubyte.gz' % >> kind) with >> > gzip.open(labels_path, 'rb') as lbpath: labels = np.frombuffer( >> lbpath.read(), >> > dtype=np.uint8, offset=8) with gzip.open(images_path, 'rb') as >> imgpath: images >> > = np.frombuffer(imgpath.read(), dtype=np.uint8, offset=16) images = >> > images.reshape(len(labels), 784) return images, labels x_train, >> y_train = >> > load_mnist('data', kind='train') x_test, y_test = load_mnist('data', >> kind= >> > 't10k') from sklearn.neural_network import MLPClassifier import time >> import >> > datetime print(datetime.datetime.today()) start = time.time() mlp = >> > MLPClassifier() mlp.fit(x_train, y_train) print((time.time() - start)/ >> 60) >> >> >> > --- >> > MATSUDA, Kouichi, Ph.D. >> >> > _______________________________________________ >> > scikit-learn mailing list >> > scikit-learn@python.org >> > https://mail.python.org/mailman/listinfo/scikit-learn >> >> >> -- >> Gael Varoquaux >> Senior Researcher, INRIA Parietal >> NeuroSpin/CEA Saclay , Bat 145, 91191 Gif-sur-Yvette France >> Phone: ++ 33-1-69-08-79-68 <+33169087968> >> http://gael-varoquaux.info >> http://twitter.com/GaelVaroquaux >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> > -- Guillaume Lemaitre INRIA Saclay - Parietal team Center for Data Science Paris-Saclay https://glemaitre.github.io/
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