Mercurial > ift6266

annotate deep/convolutional_dae/salah_exp/nist_csda.py @ 467:e0e57270b2af

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author Yoshua Bengio <bengioy@iro.umontreal.ca>
date Sat, 29 May 2010 16:50:03 -0400
parents c05680f8c92f
children
rev   line source
358
31641a84e0ae Initial commit for the experimental setup of the denoising convolutional network
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1 #!/usr/bin/python
31641a84e0ae Initial commit for the experimental setup of the denoising convolutional network
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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
31641a84e0ae Initial commit for the experimental setup of the denoising convolutional network
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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
31641a84e0ae Initial commit for the experimental setup of the denoising convolutional network
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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_new import CSdASgdOptimizer
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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 #REDUCE_TRAIN_TO = 40000
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50 if state.has_key('reduce_train_to'):
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51 rtt = state['reduce_train_to']
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52 elif REDUCE_TRAIN_TO:
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53 rtt = REDUCE_TRAIN_TO
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54
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55 if state.has_key('decrease_lr'):
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56 decrease_lr = state['decrease_lr']
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57 else :
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58 decrease_lr = 0
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59
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60 n_ins = 32*32
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61 n_outs = 62 # 10 digits, 26*2 (lower, capitals)
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62
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63 examples_per_epoch = 100000#NIST_ALL_TRAIN_SIZE
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64
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65 #To be sure variables will not be only in the if statement
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66 PATH = ''
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67 nom_reptrain = ''
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68 nom_serie = ""
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69 if state['pretrain_choice'] == 0:
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70 nom_serie="series_NIST.h5"
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71 elif state['pretrain_choice'] == 1:
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72 nom_serie="series_P07.h5"
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73
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74 series = create_series(state.num_hidden_layers,nom_serie)
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75
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76
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77 print "Creating optimizer with state, ", state
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78
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79 optimizer = CSdASgdOptimizer(dataset=datasets.nist_all(),
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80 hyperparameters=state, \
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81 n_ins=n_ins, n_outs=n_outs,\
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82 examples_per_epoch=examples_per_epoch, \
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83 series=series,
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84 max_minibatches=rtt)
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85
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86 parameters=[]
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87 #Number of files of P07 used for pretraining
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88 nb_file=0
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89 if state['pretrain_choice'] == 0:
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90 print('\n\tpretraining with NIST\n')
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91 optimizer.pretrain(datasets.nist_all())
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92 elif state['pretrain_choice'] == 1:
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93 #To know how many file will be used during pretraining
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94 nb_file = int(state['pretraining_epochs_per_layer'])
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95 state['pretraining_epochs_per_layer'] = 1 #Only 1 time over the dataset
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96 if nb_file >=100:
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97 sys.exit("The code does not support this much pretraining epoch (99 max with P07).\n"+
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98 "You have to correct the code (and be patient, P07 is huge !!)\n"+
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99 "or reduce the number of pretraining epoch to run the code (better idea).\n")
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100 print('\n\tpretraining with P07')
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101 optimizer.pretrain(datasets.nist_P07(min_file=0,max_file=nb_file))
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102 channel.save()
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103
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104 #Set some of the parameters used for the finetuning
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105 if state.has_key('finetune_set'):
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106 finetune_choice=state['finetune_set']
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107 else:
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108 finetune_choice=FINETUNE_SET
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109
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110 if state.has_key('max_finetuning_epochs'):
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111 max_finetune_epoch_NIST=state['max_finetuning_epochs']
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112 else:
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113 max_finetune_epoch_NIST=MAX_FINETUNING_EPOCHS
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114
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115 if state.has_key('max_finetuning_epochs_P07'):
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116 max_finetune_epoch_P07=state['max_finetuning_epochs_P07']
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117 else:
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118 max_finetune_epoch_P07=max_finetune_epoch_NIST
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119
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120 #Decide how the finetune is done
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121
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122 if finetune_choice == 0:
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123 print('\n\n\tfinetune with NIST\n\n')
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124 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1,decrease=decrease_lr)
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125 channel.save()
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126 if finetune_choice == 1:
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127 print('\n\n\tfinetune with P07\n\n')
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128 optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0,decrease=decrease_lr)
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129 channel.save()
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130 if finetune_choice == 2:
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131 print('\n\n\tfinetune with P07 followed by NIST\n\n')
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132 optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=20,decrease=decrease_lr)
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133 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=21,decrease=decrease_lr)
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134 channel.save()
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135 if finetune_choice == 3:
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136 print('\n\n\tfinetune with NIST only on the logistic regression on top (but validation on P07).\n\
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137 All hidden units output are input of the logistic regression\n\n')
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138 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1,special=1,decrease=decrease_lr)
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139
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140
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141 if finetune_choice==-1:
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142 print('\nSERIE OF 4 DIFFERENT FINETUNINGS')
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143 print('\n\n\tfinetune with NIST\n\n')
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144 sys.stdout.flush()
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145 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1,decrease=decrease_lr)
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146 channel.save()
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147 print('\n\n\tfinetune with P07\n\n')
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148 sys.stdout.flush()
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149 optimizer.reload_parameters('params_pretrain.txt')
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150 optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0,decrease=decrease_lr)
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151 channel.save()
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152 print('\n\n\tfinetune with P07 (done earlier) followed by NIST (written here)\n\n')
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153 sys.stdout.flush()
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154 optimizer.reload_parameters('params_finetune_P07.txt')
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155 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=21,decrease=decrease_lr)
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156 channel.save()
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157 print('\n\n\tfinetune with NIST only on the logistic regression on top.\n\
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158 All hidden units output are input of the logistic regression\n\n')
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159 sys.stdout.flush()
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160 optimizer.reload_parameters('params_pretrain.txt')
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161 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1,special=1,decrease=decrease_lr)
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162 channel.save()
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163
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164 channel.save()
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165
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166 return channel.COMPLETE
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167
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168 # These Series objects are used to save various statistics
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169 # during the training.
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170 def create_series(num_hidden_layers, nom_serie):
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171
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172 # Replace series we don't want to save with DummySeries, e.g.
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173 # series['training_error'] = DummySeries()
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174
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175 series = {}
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176
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177 basedir = os.getcwd()
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178
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179 h5f = tables.openFile(os.path.join(basedir, nom_serie), "w")
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180
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181 #REDUCE_EVERY=10
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182 # reconstruction
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183 reconstruction_base = \
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184 ErrorSeries(error_name="reconstruction_error",
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185 table_name="reconstruction_error",
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186 hdf5_file=h5f,
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187 index_names=('epoch','minibatch'),
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188 title="Reconstruction error (mean over "+str(REDUCE_EVERY)+" minibatches)")
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189 series['reconstruction_error'] = \
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190 AccumulatorSeriesWrapper(base_series=reconstruction_base,
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191 reduce_every=REDUCE_EVERY)
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192
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193 # train
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194 training_base = \
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195 ErrorSeries(error_name="training_error",
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196 table_name="training_error",
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197 hdf5_file=h5f,
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198 index_names=('epoch','minibatch'),
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199 title="Training error (mean over "+str(REDUCE_EVERY)+" minibatches)")
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200 series['training_error'] = \
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201 AccumulatorSeriesWrapper(base_series=training_base,
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202 reduce_every=REDUCE_EVERY)
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203
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204 # valid and test are not accumulated/mean, saved directly
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205 series['validation_error'] = \
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206 ErrorSeries(error_name="validation_error",
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207 table_name="validation_error",
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208 hdf5_file=h5f,
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209 index_names=('epoch','minibatch'))
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210
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211 series['test_error'] = \
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212 ErrorSeries(error_name="test_error",
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213 table_name="test_error",
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214 hdf5_file=h5f,
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215 index_names=('epoch','minibatch'))
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216
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217 param_names = []
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218 for i in range(num_hidden_layers):
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219 param_names += ['layer%d_W'%i, 'layer%d_b'%i, 'layer%d_bprime'%i]
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220 param_names += ['logreg_layer_W', 'logreg_layer_b']
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221
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222 # comment out series we don't want to save
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223 series['params'] = SharedParamsStatisticsWrapper(
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224 new_group_name="params",
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225 base_group="/",
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226 arrays_names=param_names,
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227 hdf5_file=h5f,
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228 index_names=('epoch',))
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229
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230 return series
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231
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232 # Perform insertion into the Postgre DB based on combination
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233 # of hyperparameter values above
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234 # (see comment for produit_cartesien_jobs() to know how it works)
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235 def jobman_insert_nist():
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236 jobs = produit_cartesien_jobs(JOB_VALS)
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237
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238 db = jobman.sql.db(JOBDB)
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239 for job in jobs:
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240 job.update({jobman.sql.EXPERIMENT: EXPERIMENT_PATH})
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241 jobman.sql.insert_dict(job, db)
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242
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243 print "inserted"
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244
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245 if __name__ == '__main__':
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246
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247 args = sys.argv[1:]
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248
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249 #if len(args) > 0 and args[0] == 'load_nist':
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250 # test_load_nist()
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251
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252 if len(args) > 0 and args[0] == 'jobman_insert':
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253 jobman_insert_nist()
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254
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255 elif len(args) > 0 and args[0] == 'test_jobman_entrypoint':
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256 chanmock = DD({'COMPLETE':0,'save':(lambda:None)})
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257 jobman_entrypoint(DD(DEFAULT_HP_NIST), chanmock)
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258
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259 else:
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260 print "Bad arguments"
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261