[scikit-learn] GridsearchCV

[Carlton Banks]

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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