Mercurial > ift6266
comparison deep/stacked_dae/v_sylvain/sgd_optimization.py @ 325:048898c1ee55
Ajout d'une fonction pour calculer l'erreur effectuee par le modele sur un ensemble pre-determine
| author | SylvainPL <sylvain.pannetier.lebeuf@umontreal.ca> |
|---|---|
| date | Fri, 09 Apr 2010 15:49:42 -0400 |
| parents | 403b9e6ecfaa |
| children | 18dc860a4ef4 |
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| 324:1763c64030d1 | 325:048898c1ee55 |
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| 339 for idx,x in enumerate(self.parameters_pre): | 339 for idx,x in enumerate(self.parameters_pre): |
| 340 if x.dtype=='float64': | 340 if x.dtype=='float64': |
| 341 self.classifier.params[idx].value=theano._asarray(copy(x),dtype=theano.config.floatX) | 341 self.classifier.params[idx].value=theano._asarray(copy(x),dtype=theano.config.floatX) |
| 342 else: | 342 else: |
| 343 self.classifier.params[idx].value=copy(x) | 343 self.classifier.params[idx].value=copy(x) |
| 344 | 344 |
| 345 | 345 #Calculate error over the training set (or a part of) |
| 346 | 346 def training_error(self,data): |
| 347 | 347 # create a function to compute the mistakes that are made by the model |
| 348 | 348 # on the validation set, or testing set |
| 349 test_model = \ | |
| 350 theano.function( | |
| 351 [self.classifier.x,self.classifier.y], self.classifier.errors) | |
| 352 | |
| 353 iter2 = data.train(self.hp.minibatch_size,bufsize=buffersize) | |
| 354 train_losses2 = [test_model(x,y) for x,y in iter2] | |
| 355 train_score2 = numpy.mean(train_losses2) | |
| 356 print "Training error is: " + str(train_score2) | |
| 357 | |
| 358 | |
| 359 | |
| 360 | |
| 361 |