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
