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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