Mercurial > ift6266

annotate deep/stacked_dae/aistats_review/m_mlp_ift.py @ 647:47af8a002530 tip

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changed Theano to ift6266 and remove numpy as we do not use code from numpy in this repository
author Razvan Pascanu <r.pascanu@gmail.com>
date Wed, 17 Oct 2012 09:26:14 -0400
parents 820764689d2f
children
rev   line source
617
820764689d2f Experiment to test the performance of shallower networks.
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1 import pdb,bricks.costs,datetime,os,theano,sys
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2 from bricks.experiments import *
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3 from bricks.networks import *
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4 from bricks import *
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5 from datasets import *
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6 from bricks.optimizer import *
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7 from monitor.exp_monitoring import *
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8
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9 #from monitor.series import *
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10 import numpy
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11 #import jobman,jobman.sql,pylearn.version
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12 #from jobman import DD
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13 #from utils.JobmanHandling import *
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14
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15 class MnistTSdaeExperiment(ExperimentObject):
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16 # Todo : Write down the interface
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17
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18 def _init_dataset(self):
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19 self.dataset_list = [ PNIST07(), nist_all() ]
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20 self.dataset = self.dataset_list[0]
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21
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22
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23 def _init_outputs(self):
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24 self.ds_output = { 'Pnist_Train' : self.dataset_list[0].train,
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25 'Pnist_Valid' : self.dataset_list[0].valid,
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26 'Pnist_Test' : self.dataset_list[0].test,
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27 'nist_Train' : self.dataset_list[1].train,
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28 'nist_Valid' : self.dataset_list[1].valid,
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29 'nist_Test' : self.dataset_list[1].test}
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30
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31 self.outputs = { 'CC' : costs.classification_error(self.network.layers[-1][0].out_dict['argmax_softmax_output'],self.network.in_dict['pred']) }
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32 #'L1' : costs.L1(self.network.layers[0][0].out_dict['sigmoid_output']) }
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33 #'LL' : costs.negative_ll(self.network.layers[-1][0].out_dict['softmax_output'],self.network.in_dict['pred']) }
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34
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35
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36
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37 def _init_network(self):
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38 """ Choose wich network to initialize """
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39 #x,y = self.dataset.train(1).next()
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40 n_i = 1024
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41 n_o = 62
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42 numpy.random.seed(self.hp['seed'])
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43 self.network = MLPNetwork(n_i,n_o,size=self.hp['size'])
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44 default.load_pickled_network(self.network,'best_params/1/')
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45
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46 def _init_costs_params(self):
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47 #finetuning
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48 self.costs = [ [costs.negative_ll(self.network.layers[-1][0].out_dict['softmax_output'],self.network.in_dict['pred'])] ]
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49 self.params = [ [self.network.get_all_params(),self.network.get_all_params()] ]
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50
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51
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52 def _init_monitor(self):
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53 self.monitor = monitor(self.outputs,self.ds_output,self.network,self.sub_paths,save_criterion='Pnist_Valid')
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54
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55 def startexp(self):
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56 print self.info()
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57 for j,optimizer in enumerate(self.optimizers):
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58 print 'Optim', '#'+str(j+1)
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59 sys.stdout.flush()
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60 for i in range(self.hp['ft_ep']):
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61 optimizer.tune(self.dataset.train,self.hp['bs'])
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62 print repr(i).rjust(3),self.monitor.get_str_output()
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63 sys.stdout.flush()
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64
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65
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66 def run(self):
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67 self.startexp()
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68 self.monitor.dump()
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69 return True
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70
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71 def jobman_entrypoint(state, channel):
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72 import jobman,jobman.sql,pylearn.version
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73 from jobman import DD
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74 from utils.JobmanHandling import JobHandling,jobman_insert,cartesian_product_jobs
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75 exp = MnistTSdaeExperiment(state,channel)
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76 return exp.jobhandler.start(state,channel)
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77
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78 def standalone(state):
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79 exp = MnistTSdaeExperiment(state)
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80 exp.run()
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81
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82
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83 if __name__ == '__main__':
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84 HP = { 'lr':[ [ .1] ],
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85 'ft_ep':[100],
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86 'bs':[100],
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87 'size':[ [300],[4000],[5000],[6000],[7000] ],
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88 'seed':[0]}
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89
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90 job_db_path = 'postgres://mullerx:b9f6ed1ee4@gershwin/mullerx_db/m_mlp_ift'
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91 exp_path = "m_mlp_ift.jobman_entrypoint"
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92
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93 args = sys.argv[1:]
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94
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95 if len(args) > 0 and args[0] == 'jobman_insert':
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96 jobman_insert(HP,job_db_path,exp_path)
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97
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98 elif len(args) > 0 and args[0] == 'jobman_test':
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99 chanmock = DD({'COMPLETE':0,'save':(lambda:None)})
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100 dd_hp = cartesian_product_jobs(HP)
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101 print dd_hp[0]
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102 jobman_entrypoint(dd_hp[0], chanmock)
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103
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104 elif len(args) > 0 and args[0] == 'standalone':
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105 hp = { 'lr':[ .1],
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106 'ft_ep':100,
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107 'bs':100,
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108 'size':[ 3000 ],
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109 'seed':0}
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110 standalone(hp)
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111
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112
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113 else:
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114 print "Bad arguments"
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115
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116
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117 #jobman sqlview postgres://mullerx:b9f6ed1ee4@gershwin/mullerx_db/m_mlp_ift m_mlp_ift_view
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118 #psql -h gershwin -U mullerx -d mullerx_db
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119 #b9f6ed1ee4
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120
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121 #jobdispatch --condor --env=THEANO_FLAGS=floatX=float32 --repeat_jobs=5 jobman sql -n0 'postgres://mullerx:b9f6ed1ee4@gershwin/mullerx_db/m_mlp_ift' .