[scikit-learn] GridsearchCV

[Carlton Banks]

I changed it to -48?.. and it seem to be running.. 
> Den 16. mar. 2017 kl. 06.06 skrev Sebastian Raschka <se.raschka at 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 <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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