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
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