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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_big/'
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23
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24 '''
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25 # change "sandbox" when you're ready
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26 JOBDB = 'postgres://ift6266h10@gershwin/ift6266h10_db/pannetis_SDA_retrieve'
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27 EXPERIMENT_PATH = "ift6266.deep.stacked_dae.v_sylvain.nist_sda_retrieve.jobman_entrypoint"
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28 '''
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29
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30 ##Pour lancer des travaux sur le cluster: (il faut etre ou se trouve les fichiers)
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31 ##python nist_sda_retrieve.py jobman_insert
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32 ##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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33
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34 ##Pour lancer sur GPU sur boltzmann (changer device=gpuX pour X le bon assigne)
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35 ##THEANO_FLAGS=floatX=float32,device=gpu2 python nist_sda_retrieve.py test_jobman_entrypoint
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36
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37
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38 # reduce training set to that many examples
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39 REDUCE_TRAIN_TO = None
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40 # that's a max, it usually doesn't get to that point
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41 MAX_FINETUNING_EPOCHS = 1000
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42 # number of minibatches before taking means for valid error etc.
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43 REDUCE_EVERY = 100
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44 #Set the finetune dataset
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45 FINETUNE_SET=0
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46 #Set the pretrain dataset used. 0: NIST, 1:P07
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47 PRETRAIN_CHOICE=0
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48
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49
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50 if TEST_CONFIG:
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51 REDUCE_TRAIN_TO = 1000
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52 MAX_FINETUNING_EPOCHS = 2
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53 REDUCE_EVERY = 10
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54
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55 # select detection or classification
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56 DETECTION_MODE = 0
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57 # consider maj and minuscule as the same
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58 REDUCE_LABEL = 1
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59
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60
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61 # This is to configure insertion of jobs on the cluster.
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62 # Possible values the hyperparameters can take. These are then
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63 # combined with produit_cartesien_jobs so we get a list of all
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64 # possible combinations, each one resulting in a job inserted
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65 # in the jobman DB.
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66 JOB_VALS = {'pretraining_lr': [0.1],#, 0.001],#, 0.0001],
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67 'pretraining_epochs_per_layer': [10],
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68 'hidden_layers_sizes': [800],
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69 'corruption_levels': [0.2],
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70 'minibatch_size': [100],
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71 'max_finetuning_epochs':[MAX_FINETUNING_EPOCHS],
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72 'max_finetuning_epochs_P07':[1],
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73 'finetuning_lr':[0.01], #0.001 was very bad, so we leave it out
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74 'num_hidden_layers':[4],
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75 'finetune_set':[-1],
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76 'pretrain_choice':[0,1]
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77 }
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78
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79 # Just useful for tests... minimal number of epochs
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80 # (This is used when running a single job, locally, when
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81 # calling ./nist_sda.py test_jobman_entrypoint
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82 DEFAULT_HP_NIST = {'finetuning_lr':0.05,
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83 'pretraining_lr':0.01,
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84 'pretraining_epochs_per_layer':15,
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85 'max_finetuning_epochs':MAX_FINETUNING_EPOCHS,
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86 #'max_finetuning_epochs':1,
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87 'max_finetuning_epochs_P07':7,
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88 'hidden_layers_sizes':1500,
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89 'corruption_levels':0.2,
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90 'minibatch_size':100,
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91 #'reduce_train_to':2000,
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92 'decrease_lr':1,
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93 'num_hidden_layers':4,
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94 'finetune_set':2,
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95 'pretrain_choice':1,
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96 'detection_mode':DETECTION_MODE,
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97 'reduce_label':REDUCE_LABEL}
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98
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