I am not sure what actually happens if you choose negative integers other than -1. Typically, you would choose either -1, 1 or a positive integer, sth like
-1: all available cpus 1: 1 process 2: 2 processes … 10: 10 process … > On Mar 16, 2017, at 1:08 AM, Carlton Banks <nofl...@gmail.com> wrote: > > 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 > > _______________________________________________ > 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
