[scikit-learn] Is something wrong with this gridsearchCV?

[Julio Antonio Soto de Vicente]

You totally can. In fact, there's a tradeoff between how many cpu cores each network uses and the number of parallel networks you can train.

By default, any Keras network will use "as many cores it makes sense for the depth of your network and the amount of data you have" (both over Theano and Tensorflow). Given you are using an convnet, chances are that each network will decide to use, probably, 5 or more. Unless, the n_jobs in your GridSearchCV is "quite high" (high depends on the number of cpu cores on your machine). If your machine has 10 cores and n_jobs=10, each Keras network will use 1 core. If n_jobs=2, each network will use 5 cores, and so on.

--
Julio

> El 16 mar 2017, a las 18:08, Carlton Banks <noflaco at gmail.com> escribió:
> 
> ahh.. makes sense.. but would have hoped i could parelize it as i have so many cores to run on.. 
>> Den 16. mar. 2017 kl. 18.05 skrev Julio Antonio Soto de Vicente <julio at esbet.es>:
>> 
>> Your code is perfectly fine.
>> 
>> You are training 10 networks in parallel (since you have n_jobs=10), so each network started training in its own, and outputing its progress independently.
>> 
>> Given enough amount of time, you will see that all 10 networks will eventually get to epoch number 2, and 10 messages of epoch #2 will be printed out.
>> 
>> --
>> Julio
>> 
>>> El 16 mar 2017, a las 17:59, Carlton Banks <noflaco at gmail.com> escribió:
>>> 
>>> I haven’t a verbosity level in the code?… but set it to 3 as suggested by Julio… It did not seem to work.. 
>>> 
>>> https://www.dropbox.com/s/nr5rattzts0wuvd/Screenshot%20from%202017-03-16%2017%3A56%3A26.png?dl=0
>>> 
>>>> Den 16. mar. 2017 kl. 17.51 skrev Carlton Banks <noflaco at gmail.com>:
>>>> 
>>>> Ohh.. actually the data size cannot be wrong.. 
>>>> input_train and output_train are both lists… which i then only take a part of … and then make then to a np.array…
>>>> 
>>>> So that should not be incorrect. 
>>>> 
>>>>> Den 16. mar. 2017 kl. 17.33 skrev Carlton Banks <noflaco at gmail.com>:
>>>>> 
>>>>> I am running this on a super computer, so yes  I am running a few training sessions. 
>>>>> I guess i will look at the verbose, and the adjust the training data size. 
>>>>> 
>>>>>> Den 16. mar. 2017 kl. 17.30 skrev Julio Antonio Soto de Vicente <julio at esbet.es>:
>>>>>> 
>>>>>> IMO this has nothing to do with GridSearchCV itself...
>>>>>> 
>>>>>> It rather looks like different (verbose) keras models are being trained simultaneously, and therefore "collapsing" your stdout.
>>>>>> 
>>>>>> I recommend setting Keras verbosity level to 3, in order to avoid printing the progress bars during GridSearchCV (which can be misleading).
>>>>>> 
>>>>>> --
>>>>>> Julio
>>>>>> 
>>>>>>> El 16 mar 2017, a las 16:50, Carlton Banks <noflaco at gmail.com> escribió:
>>>>>>> 
>>>>>>> I am currently using grid search to optimize my keras model… 
>>>>>>> 
>>>>>>> Something seemed  a bit off during the training?
>>>>>>> 
>>>>>>> https://www.dropbox.com/s/da0ztv2kqtkrfpu/Screenshot%20from%202017-03-16%2016%3A43%3A42.png?dl=0
>>>>>>> 
>>>>>>> For some reason is the training for each epoch not done for all datapoints?… 
>>>>>>> 
>>>>>>> What could be wrong?
>>>>>>> 
>>>>>>> Here is the code:
>>>>>>> 
>>>>>>> http://pastebin.com/raw/itJFm5a6
>>>>>>> 
>>>>>>> Anything that seems off?
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