Mercurial > ift6266

annotate deep/stacked_dae/v_guillaume/nist_sda.py @ 548:34cb28249de0

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author Yoshua Bengio <bengioy@iro.umontreal.ca>
date Wed, 02 Jun 2010 13:30:35 -0400
parents 0ca069550abd
children
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436
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1 #!/usr/bin/python
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2 # -*- coding: utf-8 -*-
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3 # coding: utf-8
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4
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5 import ift6266
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6 import pylearn
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7
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8 import numpy
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9 import theano
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10 import time
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11
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12 import pylearn.version
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13 import theano.tensor as T
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14 from theano.tensor.shared_randomstreams import RandomStreams
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15
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16 import copy
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17 import sys
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18 import os
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19 import os.path
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20
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21 from jobman import DD
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22 import jobman, jobman.sql
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23 from pylearn.io import filetensor
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24
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25 from utils import produit_cartesien_jobs
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26 from copy import copy
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27
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28 from sgd_optimization import SdaSgdOptimizer
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29
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30 #from ift6266.utils.scalar_series import *
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31 from ift6266.utils.seriestables import *
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32 import tables
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33
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34 from ift6266 import datasets
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35 from config import *
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36
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37 '''
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38 Function called by jobman upon launching each job
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39 Its path is the one given when inserting jobs: see EXPERIMENT_PATH
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40 '''
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41 def jobman_entrypoint(state, channel):
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42 # record mercurial versions of each package
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43 pylearn.version.record_versions(state,[theano,ift6266,pylearn])
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44 # TODO: remove this, bad for number of simultaneous requests on DB
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45 channel.save()
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46
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47 # For test runs, we don't want to use the whole dataset so
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48 # reduce it to fewer elements if asked to.
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49 rtt = None
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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 if state.has_key('decrease_lr_pretrain'):
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61 dec=state['decrease_lr_pretrain']
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62 else :
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63 dec=0
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64
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65 n_ins = 32*32
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66
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67 if state.has_key('subdataset'):
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68 subdataset_name=state['subdataset']
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69 else:
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70 subdataset_name=SUBDATASET_NIST
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71
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72 #n_outs = 62 # 10 digits, 26*2 (lower, capitals)
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73 if subdataset_name == "upper":
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74 n_outs = 26
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75 subdataset = datasets.nist_upper()
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76 examples_per_epoch = NIST_UPPER_TRAIN_SIZE
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77 elif subdataset_name == "lower":
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78 n_outs = 26
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79 subdataset = datasets.nist_lower()
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80 examples_per_epoch = NIST_LOWER_TRAIN_SIZE
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81 elif subdataset_name == "digits":
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82 n_outs = 10
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83 subdataset = datasets.nist_digits()
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84 examples_per_epoch = NIST_DIGITS_TRAIN_SIZE
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85 else:
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86 n_outs = 62
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87 subdataset = datasets.nist_all()
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88 examples_per_epoch = NIST_ALL_TRAIN_SIZE
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89
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90 print 'Using subdataset ', subdataset_name
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91
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92 #To be sure variables will not be only in the if statement
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93 PATH = ''
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94 nom_reptrain = ''
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95 nom_serie = ""
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96 if state['pretrain_choice'] == 0:
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97 nom_serie="series_NIST.h5"
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98 elif state['pretrain_choice'] == 1:
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99 nom_serie="series_P07.h5"
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100
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101 series = create_series(state.num_hidden_layers,nom_serie)
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102
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103
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104 print "Creating optimizer with state, ", state
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105
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106 optimizer = SdaSgdOptimizer(dataset_name=subdataset_name,\
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107 dataset=subdataset,\
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108 hyperparameters=state, \
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109 n_ins=n_ins, n_outs=n_outs,\
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110 examples_per_epoch=examples_per_epoch, \
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111 series=series,
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112 max_minibatches=rtt)
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113
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114 parameters=[]
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115 #Number of files of P07 used for pretraining
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116 nb_file=0
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117
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118 print('\n\tpretraining with NIST\n')
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119
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120 optimizer.pretrain(subdataset, decrease = dec)
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121
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122 channel.save()
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123
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124 #Set some of the parameters used for the finetuning
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125 if state.has_key('finetune_set'):
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126 finetune_choice=state['finetune_set']
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127 else:
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128 finetune_choice=FINETUNE_SET
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129
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130 if state.has_key('max_finetuning_epochs'):
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131 max_finetune_epoch_NIST=state['max_finetuning_epochs']
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132 else:
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133 max_finetune_epoch_NIST=MAX_FINETUNING_EPOCHS
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134
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135 if state.has_key('max_finetuning_epochs_P07'):
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136 max_finetune_epoch_P07=state['max_finetuning_epochs_P07']
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137 else:
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138 max_finetune_epoch_P07=max_finetune_epoch_NIST
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139
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140 #Decide how the finetune is done
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141
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142 if finetune_choice == 0:
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143 print('\n\n\tfinetune with NIST\n\n')
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144 optimizer.reload_parameters('params_pretrain.txt')
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145 optimizer.finetune(subdataset,subdataset,max_finetune_epoch_NIST,ind_test=1,decrease=decrease_lr)
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146 channel.save()
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147 if finetune_choice == 1:
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148 print('\n\n\tfinetune with P07\n\n')
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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 if finetune_choice == 2:
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153 print('\n\n\tfinetune with P07 followed by NIST\n\n')
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154 optimizer.reload_parameters('params_pretrain.txt')
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diff changeset
155 optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=20,decrease=decrease_lr)
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156 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=21,decrease=decrease_lr)
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157 channel.save()
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158 if finetune_choice == 3:
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159 print('\n\n\tfinetune with NIST only on the logistic regression on top (but validation on P07).\n\
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160 All hidden units output are input of the logistic regression\n\n')
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parents:
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161 optimizer.reload_parameters('params_pretrain.txt')
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parents:
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162 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1,special=1,decrease=decrease_lr)
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163
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164
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165 if finetune_choice==-1:
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166 print('\nSERIE OF 4 DIFFERENT FINETUNINGS')
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167 print('\n\n\tfinetune with NIST\n\n')
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168 sys.stdout.flush()
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169 optimizer.reload_parameters('params_pretrain.txt')
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170 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1,decrease=decrease_lr)
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171 channel.save()
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172 print('\n\n\tfinetune with P07\n\n')
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173 sys.stdout.flush()
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174 optimizer.reload_parameters('params_pretrain.txt')
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175 optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0,decrease=decrease_lr)
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176 channel.save()
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177 print('\n\n\tfinetune with P07 (done earlier) followed by NIST (written here)\n\n')
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178 sys.stdout.flush()
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179 optimizer.reload_parameters('params_finetune_P07.txt')
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180 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=21,decrease=decrease_lr)
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181 channel.save()
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182 print('\n\n\tfinetune with NIST only on the logistic regression on top.\n\
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183 All hidden units output are input of the logistic regression\n\n')
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184 sys.stdout.flush()
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185 optimizer.reload_parameters('params_pretrain.txt')
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186 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1,special=1,decrease=decrease_lr)
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187 channel.save()
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188
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189 channel.save()
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190
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191 return channel.COMPLETE
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192
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193 # These Series objects are used to save various statistics
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194 # during the training.
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195 def create_series(num_hidden_layers, nom_serie):
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196
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197 # Replace series we don't want to save with DummySeries, e.g.
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198 # series['training_error'] = DummySeries()
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199
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200 series = {}
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201
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202 basedir = os.getcwd()
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203
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204 h5f = tables.openFile(os.path.join(basedir, nom_serie), "w")
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205
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206 # reconstruction
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207 reconstruction_base = \
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208 ErrorSeries(error_name="reconstruction_error",
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209 table_name="reconstruction_error",
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210 hdf5_file=h5f,
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211 index_names=('epoch','minibatch'),
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212 title="Reconstruction error (mean over "+str(REDUCE_EVERY)+" minibatches)")
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213 series['reconstruction_error'] = \
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214 AccumulatorSeriesWrapper(base_series=reconstruction_base,
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215 reduce_every=REDUCE_EVERY)
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216
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217 # train
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218 training_base = \
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219 ErrorSeries(error_name="training_error",
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220 table_name="training_error",
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221 hdf5_file=h5f,
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222 index_names=('epoch','minibatch'),
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diff changeset
223 title="Training error (mean over "+str(REDUCE_EVERY)+" minibatches)")
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diff changeset
224 series['training_error'] = \
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225 AccumulatorSeriesWrapper(base_series=training_base,
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diff changeset
226 reduce_every=REDUCE_EVERY)
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227
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228 # valid and test are not accumulated/mean, saved directly
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229 series['validation_error'] = \
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parents:
diff changeset
230 ErrorSeries(error_name="validation_error",
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parents:
diff changeset
231 table_name="validation_error",
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diff changeset
232 hdf5_file=h5f,
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parents:
diff changeset
233 index_names=('epoch','minibatch'))
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parents:
diff changeset
234
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235 series['test_error'] = \
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diff changeset
236 ErrorSeries(error_name="test_error",
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parents:
diff changeset
237 table_name="test_error",
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diff changeset
238 hdf5_file=h5f,
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parents:
diff changeset
239 index_names=('epoch','minibatch'))
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diff changeset
240
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241 param_names = []
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diff changeset
242 for i in range(num_hidden_layers):
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parents:
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243 param_names += ['layer%d_W'%i, 'layer%d_b'%i, 'layer%d_bprime'%i]
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parents:
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244 param_names += ['logreg_layer_W', 'logreg_layer_b']
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245
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246 # comment out series we don't want to save
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parents:
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247 series['params'] = SharedParamsStatisticsWrapper(
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248 new_group_name="params",
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parents:
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249 base_group="/",
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parents:
diff changeset
250 arrays_names=param_names,
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parents:
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251 hdf5_file=h5f,
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parents:
diff changeset
252 index_names=('epoch',))
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253
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254 return series
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255
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parents:
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256 # Perform insertion into the Postgre DB based on combination
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parents:
diff changeset
257 # of hyperparameter values above
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parents:
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258 # (see comment for produit_cartesien_jobs() to know how it works)
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parents:
diff changeset
259 def jobman_insert_nist():
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parents:
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260 jobs = produit_cartesien_jobs(JOB_VALS)
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261
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parents:
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262 db = jobman.sql.db(JOBDB)
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parents:
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263 for job in jobs:
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parents:
diff changeset
264 job.update({jobman.sql.EXPERIMENT: EXPERIMENT_PATH})
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parents:
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265 jobman.sql.insert_dict(job, db)
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parents:
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266
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parents:
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267 print "inserted"
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268
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parents:
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269 if __name__ == '__main__':
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270
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271 args = sys.argv[1:]
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272
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273 #if len(args) > 0 and args[0] == 'load_nist':
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274 # test_load_nist()
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275
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276 if len(args) > 0 and args[0] == 'jobman_insert':
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277 jobman_insert_nist()
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278
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279 elif len(args) > 0 and args[0] == 'test_jobman_entrypoint':
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parents:
diff changeset
280 chanmock = DD({'COMPLETE':0,'save':(lambda:None)})
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parents:
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281 jobman_entrypoint(DD(DEFAULT_HP_NIST), chanmock)
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282
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283 else:
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parents:
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284 print "Bad arguments"
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parents:
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285