Mercurial > ift6266

annotate deep/convolutional_dae/run_exp.py @ 331:c2331b8e4b89

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Ajout d'une ligne faisant rapetisser le finetune_lr pour NIST lorsqu'on a P07+NIST
author SylvainPL <sylvain.pannetier.lebeuf@umontreal.ca>
date Wed, 14 Apr 2010 10:17:33 -0400
parents 6eab220a7d70
children 69109e41983e
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 def save(self):
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5 pass
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6
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7 def go(state, channel):
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8 from ift6266 import datasets
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9 from ift6266.deep.convolutional_dae.sgd_opt import sgd_opt
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10 import pylearn, theano, ift6266
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11 import pylearn.version
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12 import sys
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13
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14 # params: bsize, pretrain_lr, train_lr, nfilts1, nfilts2, nftils3, nfilts4
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15 # pretrain_rounds, noise, mlp_sz
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16
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17 pylearn.version.record_versions(state, [theano, ift6266, pylearn])
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18 # TODO: maybe record pynnet version?
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19 channel.save()
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20
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21 dset = datasets.nist_all()
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22
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23 nfilts = []
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24 if state.nfilts1 != 0:
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25 nfilts.append(state.nfilts1)
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26 if state.nfilts2 != 0:
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27 nfilts.append(state.nfilts2)
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28 if state.nfilts3 != 0:
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29 nfilts.append(state.nfilts3)
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30 if state.nfilts4 != 0:
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31 nfilts.append(state.nfilts4)
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32
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33 fsizes = [(5,5)]*len(nfilts)
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34 subs = [(2,2)]*len(nfilts)
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35 noise = [state.noise]*len(nfilts)
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36
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37 pretrain_funcs, trainf, evalf, net = build_funcs(
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38 img_size=(32, 32),
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39 batch_size=state.bsize,
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40 filter_sizes=fsizes,
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41 num_filters=nfilts,
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42 subs=subs,
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43 noise=noise,
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44 mlp_sizes=[state.mlp_sz],
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45 out_size=62,
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46 dtype=numpy.float32,
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47 pretrain_lr=state.pretrain_lr,
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48 train_lr=state.train_lr)
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49
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50 t_it = repeat_itf(dset.train, state.bsize)
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51 pretrain_fs, train, valid, test = massage_funcs(
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52 t_it, t_it, dset, state.bsize,
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53 pretrain_funcs, trainf,evalf)
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54
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55 series = create_series()
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56
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57 print "pretraining ..."
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58 sys.stdout.flush()
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59 do_pretrain(pretrain_fs, state.pretrain_rounds, series['recons_error'])
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60
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61 print "training ..."
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62 sys.stdout.flush()
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63 best_valid, test_score = sgd_opt(train, valid, test,
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64 training_epochs=1000000, patience=2500,
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65 patience_increase=2.,
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66 improvement_threshold=0.995,
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67 validation_frequency=500,
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68 series=series, net=net)
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69 state.best_valid = best_valid
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70 state.test_score = test_score
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71 channel.save()
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72 return channel.COMPLETE
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73
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74 if __name__ == '__main__':
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75 st = dumb()
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76 st.bsize = 100
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77 st.pretrain_lr = 0.01
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78 st.train_lr = 0.1
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79 st.nfilts1 = 4
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80 st.nfilts2 = 4
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81 st.nfilts3 = 0
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82 st.pretrain_rounds = 500
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83 st.noise=0.2
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84 st.mlp_sz = 500
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85 go(st, dumb())