scikit-learn.org/stable/modules/classes.html On 1 May 2017 at 13:23, Carlton Banks <nofl...@gmail.com> wrote:
> BaseEstimator being? > > Den 1. maj 2017 kl. 05.22 skrev Joel Nothman <joel.noth...@gmail.com>: > > Sorry, I sent that incomplete (and this obviously remains untested): > > class ToMultiInput(TransformerMixin, BaseEstimator): > def fit(self, shapes): > self.shapes = shapes > def transform(self, X): > shape_sizes = [np.prod(shape) for shape in self.shapes] > offsets = np.cumsum([0] + shape_sizes) > return [X[start:stop].reshape(shape) > for start, stop, shape > in zip(offsets, offsets[1:], self.shapes)] > > tmi = ToMultiInput([single.shape for single in train_input]) > train_input = np.hstack([single.reshape(single.shape[0], -1) > for single in train_input]) > > GridSearchCV(make_pipeline(tmi, my_predictor), ...) > > > On 1 May 2017 at 13:19, Joel Nothman <joel.noth...@gmail.com> wrote: > >> Unless I'm mistaken about what we're looking at, you could use something >> like: >> >> class ToMultiInput(TransformerMixin, BaseEstimator): >> def fit(self, shapes): >> self.shapes = shapes >> def transform(self, X): >> return [X.] >> >> tmi = ToMultiInput([single.shape for single in train_input]) >> # this assumes that train_input is a sequence of ndarrays with the same >> first dimension: >> train_input = np.hstack([single.reshape(single.shape[0], -1) >> for single in train_input]) >> >> GridSearchCV(make_pipeline(tmi, my_predictor), ...) >> >> >> On 1 May 2017 at 11:45, Carlton Banks <nofl...@gmail.com> wrote: >> >>> How … batchsize could also be 1, I’ve just stored it like that. >>> >>> But how do reshape me data to be a matrix.. thats the big question.. is >>> possible? >>> >>> Den 1. maj 2017 kl. 02.21 skrev Joel Nothman <joel.noth...@gmail.com>: >>> >>> Do each of your 33 inputs have a batch of size 100? If you reshape your >>> data so that it all fits in one matrix, and then split it back out into its >>> 33 components as the first transformation in a Pipeline, there should be no >>> problem. >>> >>> On 1 May 2017 at 10:17, Joel Nothman <joel.noth...@gmail.com> wrote: >>> >>>> Sorry, I don't know enough about keras and its terminology. >>>> >>>> Scikit-learn usually limits itself to datasets where features and >>>> targets are a rectangular matrix. >>>> >>>> But grid search and other model selection tools should allow data of >>>> other shapes as long as they can be indexed on the first axis. You may be >>>> best off, however, getting support from the Keras folks. >>>> >>>> On 30 April 2017 at 23:23, Carlton Banks <nofl...@gmail.com> wrote: >>>> >>>>> It seems like scikit-learn is not able to handle network with multiple >>>>> inputs. >>>>> Keras documentation states: >>>>> >>>>> You can use Sequential Keras models (*single-input only*) as part of >>>>> your Scikit-Learn workflow via the wrappers found at >>>>> keras.wrappers.scikit_learn.py. >>>>> But besides what the wrapper can do.. can scikit-learn really not >>>>> handle multiple inputs?.. >>>>> >>>>> >>>>> Den 30. apr. 2017 kl. 14.18 skrev Carlton Banks <nofl...@gmail.com>: >>>>> >>>>> The shapes are >>>>> >>>>> print len(train_input)print train_input[0].shapeprint train_output.shape >>>>> 33(100, 8, 45, 3)(100, 1, 145) >>>>> >>>>> >>>>> 100 is the batch-size.. >>>>> >>>>> Den 30. apr. 2017 kl. 12.57 skrev Joel Nothman <joel.noth...@gmail.com >>>>> >: >>>>> >>>>> Scikit-learn should accept a list as X to grid search and index it >>>>> just fine. So I'm not sure that constraint applies to Grid Search >>>>> >>>>> On 30 April 2017 at 20:11, Julio Antonio Soto de Vicente < >>>>> ju...@esbet.es> wrote: >>>>> >>>>>> Tbh I've never tried, but I would say that te current sklearn API >>>>>> does not support multi-input data... >>>>>> >>>>>> El 30 abr 2017, a las 12:02, Joel Nothman <joel.noth...@gmail.com> >>>>>> escribió: >>>>>> >>>>>> What are the shapes of train_input and train_output? >>>>>> >>>>>> On 30 April 2017 at 12:59, Carlton Banks <nofl...@gmail.com> wrote: >>>>>> >>>>>>> I am currently trying to run some gridsearchCV on a keras model >>>>>>> which has multiple inputs. >>>>>>> The inputs is stored in a list in which each entry in the list is a >>>>>>> input for a specific channel. >>>>>>> >>>>>>> >>>>>>> Here is my model and how i use the gridsearch. >>>>>>> >>>>>>> https://pastebin.com/GMKH1L80 >>>>>>> >>>>>>> The error i am getting is: >>>>>>> >>>>>>> https://pastebin.com/A3cB0rMv >>>>>>> >>>>>>> Any idea how i can resolve this? >>>>>>> >>>>>>> >>>>>>> >>>>>>> _______________________________________________ >>>>>>> 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 > > > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > >
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