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 > http://gael-varoquaux.info http://twitter.com/GaelVaroquaux > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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