Great! Thanks Woops, the latest Anaconda does not support the latest scikit-learn...
>>> print(sklearn.__version__) 0.19.2 I should have checked the change log ... orz >> n_iter_no_change parameter now at 10 from previously hardcoded 2. #9456 by Nicholas Nadeau. It might be confusing to change it to be severer. Thanks and sorry for bothering you. --- 松田晃一 MATSUDA, Kouichi, Ph.D. 2018年12月18日(火) 0:17 Guillaume Lemaître <g.lemaitr...@gmail.com>: > 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/ > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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