[scikit-learn] GridsearchCV

[Sebastian Raschka]

the “-1” means that it will run on all processors that are available

> On Mar 16, 2017, at 1:01 AM, Carlton Banks <noflaco at gmail.com> wrote:
> 
> Oh… totally forgot about that.. why -1?
>> Den 16. mar. 2017 kl. 05.58 skrev Joel Nothman <joel.nothman at 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 <noflaco at 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.raschka at 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 <noflaco at 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.
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