Mercurial > ift6266
comparison deep/stacked_dae/v_sylvain/sgd_optimization.py @ 455:09e1c5872c2b
Ajout de trois lignes de code pour le calcul de l'erreur standart
| author | SylvainPL <sylvain.pannetier.lebeuf@umontreal.ca> |
|---|---|
| date | Wed, 26 May 2010 20:23:02 -0400 |
| parents | 5e11dda78995 |
| children | 78ed4628071d |
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| 454:df56627d5399 | 455:09e1c5872c2b |
|---|---|
| 393 self.classifier.params[idx].value=theano._asarray(copy(x),dtype=theano.config.floatX) | 393 self.classifier.params[idx].value=theano._asarray(copy(x),dtype=theano.config.floatX) |
| 394 else: | 394 else: |
| 395 self.classifier.params[idx].value=copy(x) | 395 self.classifier.params[idx].value=copy(x) |
| 396 | 396 |
| 397 def training_error(self,dataset,part=0): | 397 def training_error(self,dataset,part=0): |
| 398 import math | |
| 398 # create a function to compute the mistakes that are made by the model | 399 # create a function to compute the mistakes that are made by the model |
| 399 # on the validation set, or testing set | 400 # on the validation set, or testing set |
| 400 test_model = \ | 401 test_model = \ |
| 401 theano.function( | 402 theano.function( |
| 402 [self.classifier.x,self.classifier.y], self.classifier.errors) | 403 [self.classifier.x,self.classifier.y], self.classifier.errors) |
| 413 name = 'test' | 414 name = 'test' |
| 414 train_losses2 = [test_model(x,y) for x,y in iter2] | 415 train_losses2 = [test_model(x,y) for x,y in iter2] |
| 415 train_score2 = numpy.mean(train_losses2) | 416 train_score2 = numpy.mean(train_losses2) |
| 416 print 'On the ' + name + 'dataset' | 417 print 'On the ' + name + 'dataset' |
| 417 print(('\t the error is %f')%(train_score2*100.)) | 418 print(('\t the error is %f')%(train_score2*100.)) |
| 419 stderr = math.sqrt(train_score2-train_score2**2)/math.sqrt(len(train_losses2)*self.hp.minibatch_size) | |
| 420 print (('\t the stderr is %f')%(stderr*100.)) | |
| 418 | 421 |
| 419 #To see the prediction of the model, the real answer and the image to judge | 422 #To see the prediction of the model, the real answer and the image to judge |
| 420 def see_error(self, dataset): | 423 def see_error(self, dataset): |
| 421 import pylab | 424 import pylab |
| 422 #The function to know the prediction | 425 #The function to know the prediction |