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?
>>>>>
>>>>>
>>>>>
>>>>> _______________________________________________
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>>>>>
>>>>>
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