I changed it to -48?.. and it seem to be running.. > Den 16. mar. 2017 kl. 06.06 skrev Sebastian Raschka <se.rasc...@gmail.com>: > > the “-1” means that it will run on all processors that are available > >> On Mar 16, 2017, at 1:01 AM, Carlton Banks <nofl...@gmail.com> wrote: >> >> Oh… totally forgot about that.. why -1? >>> Den 16. mar. 2017 kl. 05.58 skrev Joel Nothman <joel.noth...@gmail.com>: >>> >>> If you're using something like n_jobs=-1, that will explode memory usage in >>> proportion to the number of cores, and particularly so if you're passing >>> the data as a list rather than array and hence can't take advantage of >>> memmapped data parallelism. >>> >>> On 16 March 2017 at 15:46, Carlton Banks <nofl...@gmail.com> wrote: >>> The ndarray (6,3,3) => (row, col,color channels) >>> >>> I tried fixing it converting the list of numpy.ndarray to >>> numpy.asarray(list) >>> >>> but this causes a different problem: >>> >>> is grid use a lot a memory.. I am running on a super computer, and seem to >>> have problems with memory.. already used 62 gb ram.. >>> >>>> Den 16. mar. 2017 kl. 05.30 skrev Sebastian Raschka <se.rasc...@gmail.com>: >>>> >>>> Sklearn estimators typically assume 2d inputs (as numpy arrays) with >>>> shape=[n_samples, n_features]. >>>> >>>>> list of Np.ndarrays of shape (6,3,3) >>>> >>>> I assume you mean a 3D tensor (3D numpy array) with shape=[n_samples, >>>> n_pixels, n_pixels]? What you could do is to reshape it before you put it >>>> in, i.e., >>>> >>>> data_ary = your_ary.reshape(n_samples, -1).shape >>>> >>>> then, you need to add a line at the beginning your CNN class that does the >>>> reverse, i.e., data_ary.reshape(6, n_pixels, n_pixels).shape. Numpy’s >>>> reshape typically returns view objects, so that these additional steps >>>> shouldn’t be “too” expensive. >>>> >>>> Best, >>>> Sebastian >>>> >>>> >>>> >>>>> On Mar 16, 2017, at 12:00 AM, Carlton Banks <nofl...@gmail.com> wrote: >>>>> >>>>> Hi… >>>>> >>>>> I currently trying to optimize my CNN model using gridsearchCV, but seem >>>>> to have some problems feading my input data.. >>>>> >>>>> My training data is stored as a list of Np.ndarrays of shape (6,3,3) and >>>>> my output is stored as a list of np.array with one entry. >>>>> >>>>> Why am I having problems parsing my data to it? >>>>> >>>>> best regards >>>>> Carl B. >>>>> _______________________________________________ >>>>> scikit-learn mailing list >>>>> scikit-learn@python.org >>>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>> >>>> _______________________________________________ >>>> scikit-learn mailing list >>>> scikit-learn@python.org >>>> https://mail.python.org/mailman/listinfo/scikit-learn >>> >>> _______________________________________________ >>> scikit-learn mailing list >>> scikit-learn@python.org >>> https://mail.python.org/mailman/listinfo/scikit-learn >>> >>> _______________________________________________ >>> scikit-learn mailing list >>> scikit-learn@python.org >>> https://mail.python.org/mailman/listinfo/scikit-learn >> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn
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