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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