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comparison deep/stacked_dae/v_guillaume/config.py @ 436:0ca069550abd

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Added : single class version of SDA
author Guillaume Sicard <guitch21@gmail.com>
date Mon, 03 May 2010 06:14:05 -0400
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435:d8129a09ffb1 436:0ca069550abd
1 # -*- coding: utf-8 -*-
2 '''
3 These are parameters used by nist_sda_retrieve.py. They'll end up as globals in there.
4
5 Rename this file to config.py and configure as needed.
6 DON'T add the renamed file to the repository, as others might use it
7 without realizing it, with dire consequences.
8 '''
9
10 # Set this to True when you want to run cluster tests, ie. you want
11 # to run on the cluster, many jobs, but want to reduce the training
12 # set size and the number of epochs, so you know everything runs
13 # fine on the cluster.
14 # Set this PRIOR to inserting your test jobs in the DB.
15 TEST_CONFIG = False
16
17 NIST_ALL_LOCATION = '/data/lisa/data/nist/by_class/all'
18 NIST_UPPER_LOCATION = '/data/lisa/data/nist/by_class/upper'
19 NIST_LOWER_LOCATION = '/data/lisa/data/nist/by_class/lower'
20 NIST_DIGITS_LOCATION = '/data/lisa/data/nist/by_class/digits'
21
22 NIST_ALL_TRAIN_SIZE = 649081
23 # valid et test =82587 82587
24 NIST_UPPER_TRAIN_SIZE = 196422
25 NIST_LOWER_TRAIN_SIZE = 166998
26 NIST_DIGITS_TRAIN_SIZE = 285661
27
28 SUBDATASET_NIST = 'all'
29
30 #Path of two pre-train done earlier
31 PATH_NIST = '/u/pannetis/IFT6266/ift6266/deep/stacked_dae/v_sylvain/NIST_big'
32 PATH_P07 = '/u/pannetis/IFT6266/ift6266/deep/stacked_dae/v_sylvain/P07_big/'
33
34 # change "sandbox" when you're ready
35 JOBDB = 'postgres://ift6266h10@gershwin/ift6266h10_db/pannetis_SDA_retrieve'
36 EXPERIMENT_PATH = "ift6266.deep.stacked_dae.v_sylvain.nist_sda_retrieve.jobman_entrypoint"
37
38 ##Pour lancer des travaux sur le cluster: (il faut etre ou se trouve les fichiers)
39 ##python nist_sda_retrieve.py jobman_insert
40 ##dbidispatch --condor --repeat_jobs=2 jobman sql 'postgres://ift6266h10@gershwin/ift6266h10_db/pannetis_finetuningSDA0' . #C'est le path dans config.py
41
42 ##Pour lancer sur GPU sur boltzmann (changer device=gpuX pour X le bon assigne)
43 ##THEANO_FLAGS=floatX=float32,device=gpu2 python nist_sda_retrieve.py test_jobman_entrypoint
44
45
46 # reduce training set to that many examples
47 REDUCE_TRAIN_TO = None
48 # that's a max, it usually doesn't get to that point
49 MAX_FINETUNING_EPOCHS = 1000
50 # number of minibatches before taking means for valid error etc.
51 REDUCE_EVERY = 100
52 #Set the finetune dataset
53 FINETUNE_SET=0
54 #Set the pretrain dataset used. 0: NIST, 1:P07
55 PRETRAIN_CHOICE=0
56
57
58 if TEST_CONFIG:
59 REDUCE_TRAIN_TO = 1000
60 MAX_FINETUNING_EPOCHS = 2
61 REDUCE_EVERY = 10
62
63
64 # This is to configure insertion of jobs on the cluster.
65 # Possible values the hyperparameters can take. These are then
66 # combined with produit_cartesien_jobs so we get a list of all
67 # possible combinations, each one resulting in a job inserted
68 # in the jobman DB.
69 JOB_VALS = {'pretraining_lr': [0.1],#, 0.001],#, 0.0001],
70 'pretraining_epochs_per_layer': [10],
71 'hidden_layers_sizes': [800],
72 'corruption_levels': [0.2],
73 'minibatch_size': [100],
74 'max_finetuning_epochs':[MAX_FINETUNING_EPOCHS],
75 'max_finetuning_epochs_P07':[1],
76 'finetuning_lr':[0.01], #0.001 was very bad, so we leave it out
77 'num_hidden_layers':[4],
78 'finetune_set':[-1],
79 'pretrain_choice':[0,1]
80 }
81
82 # Just useful for tests... minimal number of epochs
83 # (This is used when running a single job, locally, when
84 # calling ./nist_sda.py test_jobman_entrypoint
85 DEFAULT_HP_NIST = {'finetuning_lr':0.1,
86 'pretraining_lr':0.01,
87 'pretraining_epochs_per_layer':15,
88 'max_finetuning_epochs':MAX_FINETUNING_EPOCHS,
89 #'max_finetuning_epochs':1,
90 'max_finetuning_epochs_P07':7,
91 'hidden_layers_sizes':1000,
92 'corruption_levels':0.2,
93 'minibatch_size':100,
94 #'reduce_train_to':2000,
95 'decrease_lr':1,
96 'num_hidden_layers':3,
97 'finetune_set':0,
98 'pretrain_choice':0}
99
100