Mercurial > ift6266

annotate deep/stacked_dae/v_guillaume/config.py @ 638:677d1b1d8158

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author Yoshua Bengio <bengioy@iro.umontreal.ca>
date Sat, 19 Mar 2011 23:11:17 -0400
parents 0ca069550abd
children
rev   line source
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1 # -*- coding: utf-8 -*-
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2 '''
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3 These are parameters used by nist_sda_retrieve.py. They'll end up as globals in there.
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4
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5 Rename this file to config.py and configure as needed.
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6 DON'T add the renamed file to the repository, as others might use it
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7 without realizing it, with dire consequences.
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8 '''
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9
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10 # Set this to True when you want to run cluster tests, ie. you want
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11 # to run on the cluster, many jobs, but want to reduce the training
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12 # set size and the number of epochs, so you know everything runs
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13 # fine on the cluster.
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14 # Set this PRIOR to inserting your test jobs in the DB.
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15 TEST_CONFIG = False
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16
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17 NIST_ALL_LOCATION = '/data/lisa/data/nist/by_class/all'
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18 NIST_UPPER_LOCATION = '/data/lisa/data/nist/by_class/upper'
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19 NIST_LOWER_LOCATION = '/data/lisa/data/nist/by_class/lower'
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20 NIST_DIGITS_LOCATION = '/data/lisa/data/nist/by_class/digits'
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21
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22 NIST_ALL_TRAIN_SIZE = 649081
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23 # valid et test =82587 82587
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24 NIST_UPPER_TRAIN_SIZE = 196422
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25 NIST_LOWER_TRAIN_SIZE = 166998
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26 NIST_DIGITS_TRAIN_SIZE = 285661
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27
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28 SUBDATASET_NIST = 'all'
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29
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30 #Path of two pre-train done earlier
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31 PATH_NIST = '/u/pannetis/IFT6266/ift6266/deep/stacked_dae/v_sylvain/NIST_big'
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32 PATH_P07 = '/u/pannetis/IFT6266/ift6266/deep/stacked_dae/v_sylvain/P07_big/'
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33
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34 # change "sandbox" when you're ready
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35 JOBDB = 'postgres://ift6266h10@gershwin/ift6266h10_db/pannetis_SDA_retrieve'
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36 EXPERIMENT_PATH = "ift6266.deep.stacked_dae.v_sylvain.nist_sda_retrieve.jobman_entrypoint"
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37
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38 ##Pour lancer des travaux sur le cluster: (il faut etre ou se trouve les fichiers)
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39 ##python nist_sda_retrieve.py jobman_insert
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40 ##dbidispatch --condor --repeat_jobs=2 jobman sql 'postgres://ift6266h10@gershwin/ift6266h10_db/pannetis_finetuningSDA0' . #C'est le path dans config.py
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41
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42 ##Pour lancer sur GPU sur boltzmann (changer device=gpuX pour X le bon assigne)
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43 ##THEANO_FLAGS=floatX=float32,device=gpu2 python nist_sda_retrieve.py test_jobman_entrypoint
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44
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45
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46 # reduce training set to that many examples
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47 REDUCE_TRAIN_TO = None
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48 # that's a max, it usually doesn't get to that point
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49 MAX_FINETUNING_EPOCHS = 1000
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50 # number of minibatches before taking means for valid error etc.
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51 REDUCE_EVERY = 100
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52 #Set the finetune dataset
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53 FINETUNE_SET=0
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54 #Set the pretrain dataset used. 0: NIST, 1:P07
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55 PRETRAIN_CHOICE=0
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56
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57
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58 if TEST_CONFIG:
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59 REDUCE_TRAIN_TO = 1000
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60 MAX_FINETUNING_EPOCHS = 2
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61 REDUCE_EVERY = 10
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62
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63
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64 # This is to configure insertion of jobs on the cluster.
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65 # Possible values the hyperparameters can take. These are then
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66 # combined with produit_cartesien_jobs so we get a list of all
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67 # possible combinations, each one resulting in a job inserted
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68 # in the jobman DB.
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69 JOB_VALS = {'pretraining_lr': [0.1],#, 0.001],#, 0.0001],
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70 'pretraining_epochs_per_layer': [10],
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71 'hidden_layers_sizes': [800],
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72 'corruption_levels': [0.2],
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73 'minibatch_size': [100],
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74 'max_finetuning_epochs':[MAX_FINETUNING_EPOCHS],
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75 'max_finetuning_epochs_P07':[1],
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76 'finetuning_lr':[0.01], #0.001 was very bad, so we leave it out
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77 'num_hidden_layers':[4],
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78 'finetune_set':[-1],
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79 'pretrain_choice':[0,1]
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80 }
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81
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82 # Just useful for tests... minimal number of epochs
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83 # (This is used when running a single job, locally, when
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84 # calling ./nist_sda.py test_jobman_entrypoint
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85 DEFAULT_HP_NIST = {'finetuning_lr':0.1,
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86 'pretraining_lr':0.01,
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87 'pretraining_epochs_per_layer':15,
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88 'max_finetuning_epochs':MAX_FINETUNING_EPOCHS,
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89 #'max_finetuning_epochs':1,
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90 'max_finetuning_epochs_P07':7,
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91 'hidden_layers_sizes':1000,
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92 'corruption_levels':0.2,
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93 'minibatch_size':100,
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94 #'reduce_train_to':2000,
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95 'decrease_lr':1,
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96 'num_hidden_layers':3,
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97 'finetune_set':0,
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98 'pretrain_choice':0}
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99
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100