i was wondering about the minus in front? > Den 16. mar. 2017 kl. 06.00 skrev Sebastian Raschka <[email protected]>: > > Hm, if you set n_jobs>1, then I think it’s using multiprocessing, which will > pass a copy of the input data to each process. That could be one reason for > the relatively large memory consumption. > >> On Mar 16, 2017, at 12:46 AM, Carlton Banks <[email protected]> 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 <[email protected]>: >>> >>> 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 <[email protected]> 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 >>>> [email protected] >>>> https://mail.python.org/mailman/listinfo/scikit-learn >>> >>> _______________________________________________ >>> scikit-learn mailing list >>> [email protected] >>> https://mail.python.org/mailman/listinfo/scikit-learn >> >> _______________________________________________ >> scikit-learn mailing list >> [email protected] >> https://mail.python.org/mailman/listinfo/scikit-learn > > _______________________________________________ > scikit-learn mailing list > [email protected] > https://mail.python.org/mailman/listinfo/scikit-learn
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