Mercurial > ift6266

annotate deep/stacked_dae/v_guillaume/train_error.py @ 575:bff9ab360ef4

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nips_rebuttal_clean
author Yoshua Bengio <bengioy@iro.umontreal.ca>
date Sat, 07 Aug 2010 22:46:12 -0400
parents 0ca069550abd
children
rev   line source
436
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1 #!/usr/bin/python
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2 # coding: utf-8
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3
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4 import ift6266
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5 import pylearn
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6
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7 import numpy
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8 import theano
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9 import time
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10
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11 import pylearn.version
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12 import theano.tensor as T
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13 from theano.tensor.shared_randomstreams import RandomStreams
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14
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15 import copy
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16 import sys
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17 import os
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18 import os.path
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19
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20 from jobman import DD
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21 import jobman, jobman.sql
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22 from pylearn.io import filetensor
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23
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24 from utils import produit_cartesien_jobs
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25 from copy import copy
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26
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27 from sgd_optimization import SdaSgdOptimizer
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28
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29 #from ift6266.utils.scalar_series import *
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30 from ift6266.utils.seriestables import *
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31 import tables
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32
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33 from ift6266 import datasets
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34 from config import *
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35
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36 '''
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37 Function called by jobman upon launching each job
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38 Its path is the one given when inserting jobs: see EXPERIMENT_PATH
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39 '''
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40 def jobman_entrypoint(state, channel):
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41 # record mercurial versions of each package
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42 pylearn.version.record_versions(state,[theano,ift6266,pylearn])
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43 # TODO: remove this, bad for number of simultaneous requests on DB
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44 channel.save()
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45
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46 # For test runs, we don't want to use the whole dataset so
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47 # reduce it to fewer elements if asked to.
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48 rtt = None
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49 if state.has_key('reduce_train_to'):
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50 rtt = state['reduce_train_to']
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51 elif REDUCE_TRAIN_TO:
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52 rtt = REDUCE_TRAIN_TO
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53
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54 n_ins = 32*32
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55 n_outs = 62 # 10 digits, 26*2 (lower, capitals)
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56
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57 examples_per_epoch = NIST_ALL_TRAIN_SIZE
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58
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59 PATH = ''
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60 maximum_exemples=int(500000) #Maximum number of exemples seen
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61
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62
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63
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64 print "Creating optimizer with state, ", state
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65
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66 optimizer = SdaSgdOptimizer(dataset=datasets.nist_all(),
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67 hyperparameters=state, \
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68 n_ins=n_ins, n_outs=n_outs,\
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69 examples_per_epoch=examples_per_epoch, \
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70 max_minibatches=rtt)
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71
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72
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73
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74
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75
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76 if os.path.exists(PATH+'params_finetune_NIST.txt'):
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77 print ('\n finetune = NIST ')
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78 optimizer.reload_parameters(PATH+'params_finetune_NIST.txt')
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79 print "For" + str(maximum_exemples) + "over the NIST training set: "
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80 optimizer.training_error(datasets.nist_all(maxsize=maximum_exemples))
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81
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82
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83 if os.path.exists(PATH+'params_finetune_P07.txt'):
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84 print ('\n finetune = P07 ')
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85 optimizer.reload_parameters(PATH+'params_finetune_P07.txt')
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86 print "For" + str(maximum_exemples) + "over the P07 training set: "
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87 optimizer.training_error(datasets.nist_P07(maxsize=maximum_exemples))
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88
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89
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90 if os.path.exists(PATH+'params_finetune_NIST_then_P07.txt'):
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91 print ('\n finetune = NIST then P07')
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92 optimizer.reload_parameters(PATH+'params_finetune_NIST_then_P07.txt')
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93 print "For" + str(maximum_exemples) + "over the NIST training set: "
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94 optimizer.training_error(datasets.nist_all(maxsize=maximum_exemples))
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95 print "For" + str(maximum_exemples) + "over the P07 training set: "
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96 optimizer.training_error(datasets.nist_P07(maxsize=maximum_exemples))
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97
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98 if os.path.exists(PATH+'params_finetune_P07_then_NIST.txt'):
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99 print ('\n finetune = P07 then NIST')
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100 optimizer.reload_parameters(PATH+'params_finetune_P07_then_NIST.txt')
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101 print "For" + str(maximum_exemples) + "over the P07 training set: "
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102 optimizer.training_error(datasets.nist_P07(maxsize=maximum_exemples))
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103 print "For" + str(maximum_exemples) + "over the NIST training set: "
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104 optimizer.training_error(datasets.nist_all(maxsize=maximum_exemples))
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105
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106 channel.save()
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107
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108 return channel.COMPLETE
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109
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110
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111
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112 if __name__ == '__main__':
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113
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114
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115 chanmock = DD({'COMPLETE':0,'save':(lambda:None)})
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116 jobman_entrypoint(DD(DEFAULT_HP_NIST), chanmock)
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117
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118