Mercurial > ift6266
comparison deep/stacked_dae/old/mnist_sda.py @ 265:c8fe09a65039
Déplacer le nouveau code de stacked_dae de v2 vers le répertoire de base 'stacked_dae', et bougé le vieux code vers le répertoire 'old'
| author | fsavard |
|---|---|
| date | Fri, 19 Mar 2010 10:54:39 -0400 |
| parents | deep/stacked_dae/mnist_sda.py@3632e6258642 |
| children |
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| 243:3c54cb3713ef | 265:c8fe09a65039 |
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| 1 #!/usr/bin/python | |
| 2 # coding: utf-8 | |
| 3 | |
| 4 # TODO: This probably doesn't work anymore, adapt to new code in sgd_opt | |
| 5 # Parameterize call to sgd_optimization for MNIST | |
| 6 | |
| 7 import numpy | |
| 8 import theano | |
| 9 import time | |
| 10 import theano.tensor as T | |
| 11 from theano.tensor.shared_randomstreams import RandomStreams | |
| 12 | |
| 13 from sgd_optimization import SdaSgdOptimizer | |
| 14 import cPickle, gzip | |
| 15 from jobman import DD | |
| 16 | |
| 17 MNIST_LOCATION = '/u/savardf/datasets/mnist.pkl.gz' | |
| 18 | |
| 19 def sgd_optimization_mnist(learning_rate=0.1, pretraining_epochs = 2, \ | |
| 20 pretrain_lr = 0.1, training_epochs = 5, \ | |
| 21 dataset='mnist.pkl.gz'): | |
| 22 # Load the dataset | |
| 23 f = gzip.open(dataset,'rb') | |
| 24 # this gives us train, valid, test (each with .x, .y) | |
| 25 dataset = cPickle.load(f) | |
| 26 f.close() | |
| 27 | |
| 28 n_ins = 28*28 | |
| 29 n_outs = 10 | |
| 30 | |
| 31 hyperparameters = DD({'finetuning_lr':learning_rate, | |
| 32 'pretraining_lr':pretrain_lr, | |
| 33 'pretraining_epochs_per_layer':pretraining_epochs, | |
| 34 'max_finetuning_epochs':training_epochs, | |
| 35 'hidden_layers_sizes':[100], | |
| 36 'corruption_levels':[0.2], | |
| 37 'minibatch_size':20}) | |
| 38 | |
| 39 optimizer = SdaSgdOptimizer(dataset, hyperparameters, n_ins, n_outs) | |
| 40 optimizer.pretrain() | |
| 41 optimizer.finetune() | |
| 42 | |
| 43 if __name__ == '__main__': | |
| 44 sgd_optimization_mnist(dataset=MNIST_LOCATION) | |
| 45 |