Mercurial > ift6266

annotate deep/convolutional_dae/run_exp.py @ 292:8108d271c30c

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Fix stuff (imports, ...) so that it can run under jobman properly.
author Arnaud Bergeron <abergeron@gmail.com>
date Fri, 26 Mar 2010 18:49:27 -0400
parents 7d1fa2d7721c
children d89820070ea0
rev   line source
291
7d1fa2d7721c Split out the run_exp method.
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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
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13 # params: bsize, pretrain_lr, train_lr, nfilts1, nfilts2, nftils3, nfilts4
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14 # pretrain_rounds, noise, mlp_sz
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15
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16 pylearn.version.record_versions(state, [theano, ift6266, pylearn])
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17 # TODO: maybe record pynnet version?
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18 channel.save()
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19
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20 dset = datasets.nist_all(1000)
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21
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22 nfilts = []
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23 if state.nfilts1 != 0:
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24 nfilts.append(state.nfilts1)
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25 if state.nfilts2 != 0:
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26 nfilts.append(state.nfilts2)
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27 if state.nfilts3 != 0:
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28 nfilts.append(state.nfilts3)
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29 if state.nfilts4 != 0:
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30 nfilts.append(state.nfilts4)
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31
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32 fsizes = [(5,5)]*len(nfilts)
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33 subs = [(2,2)]*len(nfilts)
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34 noise = [state.noise]*len(nfilts)
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35
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36 pretrain_funcs, trainf, evalf, net = build_funcs(
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37 img_size=(32, 32),
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38 batch_size=state.bsize,
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39 filter_sizes=fsizes,
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40 num_filters=nfilts,
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41 subs=subs,
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42 noise=noise,
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43 mlp_sizes=[state.mlp_sz],
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44 out_size=62,
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45 dtype=numpy.float32,
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46 pretrain_lr=state.pretrain_lr,
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47 train_lr=state.train_lr)
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48
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49 pretrain_fs, train, valid, test = massage_funcs(
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50 repeat_itf(dset.train, state.bsize),
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51 dset, state.bsize,
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52 pretrain_funcs, trainf,evalf)
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53
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54 series = create_series()
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55
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56 do_pretrain(pretrain_fs, state.pretrain_rounds, series['recons_error'])
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57
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58 sgd_opt(train, valid, test, training_epochs=100000, patience=10000,
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59 patience_increase=2., improvement_threshold=0.995,
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60 validation_frequency=2500, series=series, net=net)
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61
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62 if __name__ == '__main__':
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63 st = dumb()
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64 st.bsize = 100
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65 st.pretrain_lr = 0.01
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66 st.train_lr = 0.1
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67 st.nfilts1 = 4
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68 st.nfilts2 = 4
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69 st.nfilts3 = 0
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70 st.pretrain_rounds = 500
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71 st.noise=0.2
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72 st.mlp_sz = 500
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73 go(st, dumb())