Mercurial > ift6266
annotate deep/stacked_dae/v_guillaume/config2.py @ 618:14ba0120baff
review response changes
author | Yoshua Bengio <bengioy@iro.umontreal.ca> |
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date | Sun, 09 Jan 2011 14:13:23 -0500 |
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1 ''' |
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2 These are parameters used by nist_sda_retrieve.py. They'll end up as globals in there. |
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3 |
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4 Rename this file to config.py and configure as needed. |
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5 DON'T add the renamed file to the repository, as others might use it |
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6 without realizing it, with dire consequences. |
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7 ''' |
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8 |
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9 # Set this to True when you want to run cluster tests, ie. you want |
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10 # to run on the cluster, many jobs, but want to reduce the training |
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11 # set size and the number of epochs, so you know everything runs |
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12 # fine on the cluster. |
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13 # Set this PRIOR to inserting your test jobs in the DB. |
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14 TEST_CONFIG = False |
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15 |
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16 NIST_ALL_LOCATION = '/data/lisa/data/nist/by_class/all' |
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17 NIST_ALL_TRAIN_SIZE = 649081 |
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18 # valid et test =82587 82587 |
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19 |
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20 #Path of two pre-train done earlier |
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21 PATH_NIST = '/u/pannetis/IFT6266/ift6266/deep/stacked_dae/v_sylvain/NIST_big' |
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22 PATH_P07 = '/u/pannetis/IFT6266/ift6266/deep/stacked_dae/v_sylvain/P07_demo/' |
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23 |
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24 # change "sandbox" when you're ready |
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25 JOBDB = 'postgres://ift6266h10@gershwin/ift6266h10_db/pannetis_SDA_retrieve' |
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26 EXPERIMENT_PATH = "ift6266.deep.stacked_dae.v_sylvain.nist_sda_retrieve.jobman_entrypoint" |
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27 |
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28 ##Pour lancer des travaux sur le cluster: (il faut etre ou se trouve les fichiers) |
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29 ##python nist_sda_retrieve.py jobman_insert |
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30 ##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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31 |
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32 ##Pour lancer sur GPU sur boltzmann (changer device=gpuX pour X le bon assigne) |
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33 ##THEANO_FLAGS=floatX=float32,device=gpu2 python nist_sda_retrieve.py test_jobman_entrypoint |
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34 |
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35 |
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36 # reduce training set to that many examples |
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37 REDUCE_TRAIN_TO = None |
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38 # that's a max, it usually doesn't get to that point |
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39 MAX_FINETUNING_EPOCHS = 1000 |
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40 # number of minibatches before taking means for valid error etc. |
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41 REDUCE_EVERY = 100 |
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42 #Set the finetune dataset |
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43 FINETUNE_SET=0 |
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44 #Set the pretrain dataset used. 0: NIST, 1:P07 |
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45 PRETRAIN_CHOICE=0 |
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46 |
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47 |
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48 if TEST_CONFIG: |
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49 REDUCE_TRAIN_TO = 1000 |
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50 MAX_FINETUNING_EPOCHS = 2 |
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51 REDUCE_EVERY = 10 |
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52 |
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53 |
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54 # This is to configure insertion of jobs on the cluster. |
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55 # Possible values the hyperparameters can take. These are then |
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56 # combined with produit_cartesien_jobs so we get a list of all |
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57 # possible combinations, each one resulting in a job inserted |
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58 # in the jobman DB. |
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59 JOB_VALS = {'pretraining_lr': [0.1],#, 0.001],#, 0.0001], |
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60 'pretraining_epochs_per_layer': [10], |
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61 'hidden_layers_sizes': [800], |
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62 'corruption_levels': [0.2], |
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63 'minibatch_size': [100], |
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64 'max_finetuning_epochs':[MAX_FINETUNING_EPOCHS], |
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65 'max_finetuning_epochs_P07':[1], |
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66 'finetuning_lr':[0.01], #0.001 was very bad, so we leave it out |
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67 'num_hidden_layers':[4], |
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68 'finetune_set':[-1], |
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69 'pretrain_choice':[0,1] |
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70 } |
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71 |
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72 # Just useful for tests... minimal number of epochs |
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73 # (This is used when running a single job, locally, when |
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74 # calling ./nist_sda.py test_jobman_entrypoint |
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75 DEFAULT_HP_NIST = {'finetuning_lr':0.1, |
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76 'pretraining_lr':0.01, |
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77 'pretraining_epochs_per_layer':15, |
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78 'max_finetuning_epochs':MAX_FINETUNING_EPOCHS, |
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79 #'max_finetuning_epochs':1, |
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80 'max_finetuning_epochs_P07':7, |
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81 'hidden_layers_sizes':1000, |
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82 'corruption_levels':0.2, |
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83 'minibatch_size':100, |
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84 #'reduce_train_to':2000, |
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85 'decrease_lr':1, |
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86 'num_hidden_layers':3, |
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87 'finetune_set':2, |
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88 'pretrain_choice':1} |
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90 |