Mercurial > ift6266

comparison deep/stacked_dae/v_youssouf/config.py @ 371:8cf52a1c8055

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
initial commit of sda with 36 classes
author youssouf
date Sun, 25 Apr 2010 12:31:22 -0400
parents
children
comparison
equal deleted inserted replaced
336:a79db7cee035 371:8cf52a1c8055
1 '''
2 These are parameters used by nist_sda_retrieve.py. They'll end up as globals in there.
3
4 Rename this file to config.py and configure as needed.
5 DON'T add the renamed file to the repository, as others might use it
6 without realizing it, with dire consequences.
7 '''
8
9 # Set this to True when you want to run cluster tests, ie. you want
10 # to run on the cluster, many jobs, but want to reduce the training
11 # set size and the number of epochs, so you know everything runs
12 # fine on the cluster.
13 # Set this PRIOR to inserting your test jobs in the DB.
14 TEST_CONFIG = False
15
16 NIST_ALL_LOCATION = '/data/lisa/data/nist/by_class/all'
17 NIST_ALL_TRAIN_SIZE = 649081
18 # valid et test =82587 82587
19
20 #Path of two pre-train done earlier
21 PATH_NIST = '/u/pannetis/IFT6266/ift6266/deep/stacked_dae/v_sylvain/NIST_big'
22 PATH_P07 = '/u/pannetis/IFT6266/ift6266/deep/stacked_dae/v_sylvain/P07_big/'
23
24 '''
25 # change "sandbox" when you're ready
26 JOBDB = 'postgres://ift6266h10@gershwin/ift6266h10_db/pannetis_SDA_retrieve'
27 EXPERIMENT_PATH = "ift6266.deep.stacked_dae.v_sylvain.nist_sda_retrieve.jobman_entrypoint"
28 '''
29
30 ##Pour lancer des travaux sur le cluster: (il faut etre ou se trouve les fichiers)
31 ##python nist_sda_retrieve.py jobman_insert
32 ##dbidispatch --condor --repeat_jobs=2 jobman sql 'postgres://ift6266h10@gershwin/ift6266h10_db/pannetis_finetuningSDA0' . #C'est le path dans config.py
33
34 ##Pour lancer sur GPU sur boltzmann (changer device=gpuX pour X le bon assigne)
35 ##THEANO_FLAGS=floatX=float32,device=gpu2 python nist_sda_retrieve.py test_jobman_entrypoint
36
37
38 # reduce training set to that many examples
39 REDUCE_TRAIN_TO = None
40 # that's a max, it usually doesn't get to that point
41 MAX_FINETUNING_EPOCHS = 1000
42 # number of minibatches before taking means for valid error etc.
43 REDUCE_EVERY = 100
44 #Set the finetune dataset
45 FINETUNE_SET=0
46 #Set the pretrain dataset used. 0: NIST, 1:P07
47 PRETRAIN_CHOICE=0
48
49
50 if TEST_CONFIG:
51 REDUCE_TRAIN_TO = 1000
52 MAX_FINETUNING_EPOCHS = 2
53 REDUCE_EVERY = 10
54
55 # select detection or classification
56 DETECTION_MODE = 0
57 # consider maj and minuscule as the same
58 REDUCE_LABEL = 1
59
60
61 # This is to configure insertion of jobs on the cluster.
62 # Possible values the hyperparameters can take. These are then
63 # combined with produit_cartesien_jobs so we get a list of all
64 # possible combinations, each one resulting in a job inserted
65 # in the jobman DB.
66 JOB_VALS = {'pretraining_lr': [0.1],#, 0.001],#, 0.0001],
67 'pretraining_epochs_per_layer': [10],
68 'hidden_layers_sizes': [800],
69 'corruption_levels': [0.2],
70 'minibatch_size': [100],
71 'max_finetuning_epochs':[MAX_FINETUNING_EPOCHS],
72 'max_finetuning_epochs_P07':[1],
73 'finetuning_lr':[0.01], #0.001 was very bad, so we leave it out
74 'num_hidden_layers':[4],
75 'finetune_set':[-1],
76 'pretrain_choice':[0,1]
77 }
78
79 # Just useful for tests... minimal number of epochs
80 # (This is used when running a single job, locally, when
81 # calling ./nist_sda.py test_jobman_entrypoint
82 DEFAULT_HP_NIST = {'finetuning_lr':0.05,
83 'pretraining_lr':0.01,
84 'pretraining_epochs_per_layer':15,
85 'max_finetuning_epochs':MAX_FINETUNING_EPOCHS,
86 #'max_finetuning_epochs':1,
87 'max_finetuning_epochs_P07':7,
88 'hidden_layers_sizes':1500,
89 'corruption_levels':0.2,
90 'minibatch_size':100,
91 #'reduce_train_to':2000,
92 'decrease_lr':1,
93 'num_hidden_layers':4,
94 'finetune_set':2,
95 'pretrain_choice':1,
96 'detection_mode':DETECTION_MODE,
97 'reduce_label':REDUCE_LABEL}
98