Mercurial > ift6266

annotate deep/convolutional_dae/run_exp.py @ 352:cfb79f9fd1a4

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Ajout d'une fonctionnalite pour pouvoir avoir un taux d'apprentissage decroissant dans le pretrain
author SylvainPL <sylvain.pannetier.lebeuf@umontreal.ca>
date Wed, 21 Apr 2010 14:50:59 -0400
parents 69109e41983e
children 01445a75c702
rev   line source
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1 from ift6266.deep.convolutional_dae.scdae import *
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2
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3 class dumb(object):
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4 COMPLETE = None
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5 def save(self):
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6 pass
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7
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8 def go(state, channel):
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9 from ift6266 import datasets
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10 from ift6266.deep.convolutional_dae.sgd_opt import sgd_opt
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11 import pylearn, theano, ift6266
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12 import pylearn.version
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13 import sys
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15 # params: bsize, pretrain_lr, train_lr, nfilts1, nfilts2, nftils3, nfilts4
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16 # pretrain_rounds, noise, mlp_sz
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17
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18 pylearn.version.record_versions(state, [theano, ift6266, pylearn])
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19 # TODO: maybe record pynnet version?
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20 channel.save()
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21
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22 dset = datasets.nist_P07()
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23
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24 nfilts = []
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25 if state.nfilts1 != 0:
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26 nfilts.append(state.nfilts1)
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27 if state.nfilts2 != 0:
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28 nfilts.append(state.nfilts2)
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29 if state.nfilts3 != 0:
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30 nfilts.append(state.nfilts3)
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31 if state.nfilts4 != 0:
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32 nfilts.append(state.nfilts4)
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33
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34 fsizes = [(5,5)]*len(nfilts)
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35 subs = [(2,2)]*len(nfilts)
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36 noise = [state.noise]*len(nfilts)
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37
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38 pretrain_funcs, trainf, evalf, net = build_funcs(
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39 img_size=(32, 32),
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40 batch_size=state.bsize,
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41 filter_sizes=fsizes,
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42 num_filters=nfilts,
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43 subs=subs,
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44 noise=noise,
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45 mlp_sizes=[state.mlp_sz],
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46 out_size=62,
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47 dtype=numpy.float32,
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48 pretrain_lr=state.pretrain_lr,
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49 train_lr=state.train_lr)
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50
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51 t_it = repeat_itf(dset.train, state.bsize)
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52 pretrain_fs, train, valid, test = massage_funcs(
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53 t_it, t_it, dset, state.bsize,
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54 pretrain_funcs, trainf,evalf)
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55
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56 series = create_series()
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57
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58 print "pretraining ..."
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59 sys.stdout.flush()
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60 do_pretrain(pretrain_fs, state.pretrain_rounds, series['recons_error'])
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61
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62 print "training ..."
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63 sys.stdout.flush()
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64 best_valid, test_score = sgd_opt(train, valid, test,
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65 training_epochs=800000, patience=2000,
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66 patience_increase=2.,
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67 improvement_threshold=0.995,
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68 validation_frequency=500,
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69 series=series, net=net)
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70 state.best_valid = best_valid
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71 state.test_score = test_score
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72 channel.save()
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73 return channel.COMPLETE
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74
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75 if __name__ == '__main__':
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76 st = dumb()
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77 st.bsize = 100
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78 st.pretrain_lr = 0.01
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79 st.train_lr = 0.1
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80 st.nfilts1 = 4
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81 st.nfilts2 = 4
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82 st.nfilts3 = 0
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83 st.pretrain_rounds = 500
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84 st.noise=0.2
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85 st.mlp_sz = 500
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86 go(st, dumb())