It wooeks!!!
thank you, thank you very much!!!
El miércoles, 3 de agosto de 2016, 3:38:36 (UTC+2), Jesse Livezey escribió:
>
> I just changed
>
> salidas_capa3[test_model(i)]
>
> to
>
> salidas_capa3(i)
>
>
> the function salidas_capa3 expects a batch index as an argument.
>
> On Sunday, July 31, 2016 at 3:16:45 PM UTC-4, Beatriz G. wrote:
>>
>> Is it not what I have given to salidas_capa3?
>>
>> I am really thankful for your help, really, really thankful.
>>
>>
>> El viernes, 29 de julio de 2016, 4:00:51 (UTC+2), Jesse Livezey escribió:
>>>
>>> I think you just want to do
>>>
>>> for i in range(n_test_batches):
>>> test_losses = [test_model(i)]
>>> y_pred_test = salidas_capa3(i)
>>> print y_pred_test
>>>
>>>
>>> The salidas_capa3 function expects a minibatch index as an argument.
>>>
>>> On Wednesday, July 27, 2016 at 11:27:08 PM UTC-7, Beatriz G. wrote:
>>>>
>>>> I am not able of extract the value of that function at that point, I
>>>> have debugged and I I have gotten the results of test_model in the
>>>> attached
>>>> pic.
>>>>
>>>> Thank you for your help.
>>>>
>>>>
>>>>
>>>> What is the value of test_model(i) at that point? I think it should be
>>>>> an array of indices.
>>>>>
>>>>> On Wednesday, July 27, 2016 at 1:52:27 AM UTC-7, Beatriz G. wrote:
>>>>>>
>>>>>> Hi Jesse, thank you for your reply.
>>>>>>
>>>>>> I have tried to use it when I test:
>>>>>>
>>>>>> #Aqui se tiene que cargar la red
>>>>>>
>>>>>> layer0.W.set_value(w0_test)
>>>>>> layer0.b.set_value(b0_test)
>>>>>>
>>>>>> layer1.W.set_value(w1_test)
>>>>>> layer1.b.set_value(b1_test)
>>>>>>
>>>>>> layer2.W.set_value(w2_test)
>>>>>> layer2.b.set_value(b2_test)
>>>>>>
>>>>>> # test it on the test set
>>>>>> for i in range(n_test_batches):
>>>>>> test_losses = [test_model(i)]
>>>>>> y_pred_test = salidas_capa3[test_model(i)]
>>>>>> print y_pred_test
>>>>>> test_score = numpy.mean(test_losses)
>>>>>>
>>>>>> print((' test error of best model %f %%') % (test_score * 100.))
>>>>>>
>>>>>>
>>>>>>
>>>>>> but I get the following error:
>>>>>>
>>>>>>
>>>>>> Traceback (most recent call last):
>>>>>> File "/home/beaa/Escritorio/Theano/Separando_Lenet.py", line 414, in
>>>>>> <module>
>>>>>> evaluate_lenet5()
>>>>>> File "/home/beaa/Escritorio/Theano/Separando_Lenet.py", line 390, in
>>>>>> evaluate_lenet5
>>>>>> y_pred_test = salidas_capa3[test_model(i)]
>>>>>> File
>>>>>> "/home/beaa/.local/lib/python2.7/site-packages/theano/compile/function_module.py",
>>>>>> line 545, in __getitem__
>>>>>> return self.value[item]
>>>>>> File
>>>>>> "/home/beaa/.local/lib/python2.7/site-packages/theano/compile/function_module.py",
>>>>>> line 480, in __getitem__
>>>>>> s = finder[item]
>>>>>> TypeError: unhashable type: 'numpy.ndarray'
>>>>>>
>>>>>>
>>>>>>
>>>>>> and I do not know what produces it.
>>>>>>
>>>>>>
>>>>>> Regards
>>>>>>
>>>>>>
>>>>>> El miércoles, 27 de julio de 2016, 2:29:24 (UTC+2), Jesse Livezey
>>>>>> escribió:
>>>>>>>
>>>>>>> You should be able to use this function to output y_pred
>>>>>>>
>>>>>>> salidas_capa3 = theano.function(
>>>>>>> [index],
>>>>>>> layer3.y_pred,
>>>>>>> givens={
>>>>>>> x: test_set_x[index * batch_size: (index + 1) * batch_size],
>>>>>>> }
>>>>>>> )
>>>>>>>
>>>>>>>
>>>>>>> On Monday, July 25, 2016 at 3:09:09 AM UTC-7, Beatriz G. wrote:
>>>>>>>>
>>>>>>>> Hi, anyone knows how to get the test labels that the classifier has
>>>>>>>> given to the data?
>>>>>>>>
>>>>>>>> I would like to extrat the data that has not been well classified.
>>>>>>>>
>>>>>>>> Regards.
>>>>>>>>
>>>>>>>
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