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comparison deep/stacked_dae/v_sylvain/nist_sda.py @ 354:ffc06af1c543

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Ajout d'une fonctionnalite pour pouvoir avoir un taux d'apprentissage decroissant dans le pretrain
author SylvainPL <sylvain.pannetier.lebeuf@umontreal.ca>
date Wed, 21 Apr 2010 14:54:54 -0400
parents 4306796d60a8
children 87e684bfe538
comparison
equal deleted inserted replaced
353:bc4464c0894c 354:ffc06af1c543
53 53
54 if state.has_key('decrease_lr'): 54 if state.has_key('decrease_lr'):
55 decrease_lr = state['decrease_lr'] 55 decrease_lr = state['decrease_lr']
56 else : 56 else :
57 decrease_lr = 0 57 decrease_lr = 0
58
59 if state.has_key('decrease_lr_pretrain'):
60 dec=state['decrease_lr_pretrain']
61 else :
62 dec=0
58 63
59 n_ins = 32*32 64 n_ins = 32*32
60 n_outs = 62 # 10 digits, 26*2 (lower, capitals) 65 n_outs = 62 # 10 digits, 26*2 (lower, capitals)
61 66
62 examples_per_epoch = NIST_ALL_TRAIN_SIZE 67 examples_per_epoch = NIST_ALL_TRAIN_SIZE
85 parameters=[] 90 parameters=[]
86 #Number of files of P07 used for pretraining 91 #Number of files of P07 used for pretraining
87 nb_file=0 92 nb_file=0
88 if state['pretrain_choice'] == 0: 93 if state['pretrain_choice'] == 0:
89 print('\n\tpretraining with NIST\n') 94 print('\n\tpretraining with NIST\n')
90 optimizer.pretrain(datasets.nist_all()) 95 optimizer.pretrain(datasets.nist_all(), decrease = dec)
91 elif state['pretrain_choice'] == 1: 96 elif state['pretrain_choice'] == 1:
92 #To know how many file will be used during pretraining 97 #To know how many file will be used during pretraining
93 nb_file = int(state['pretraining_epochs_per_layer']) 98 nb_file = int(state['pretraining_epochs_per_layer'])
94 state['pretraining_epochs_per_layer'] = 1 #Only 1 time over the dataset 99 state['pretraining_epochs_per_layer'] = 1 #Only 1 time over the dataset
95 if nb_file >=100: 100 if nb_file >=100:
96 sys.exit("The code does not support this much pretraining epoch (99 max with P07).\n"+ 101 sys.exit("The code does not support this much pretraining epoch (99 max with P07).\n"+
97 "You have to correct the code (and be patient, P07 is huge !!)\n"+ 102 "You have to correct the code (and be patient, P07 is huge !!)\n"+
98 "or reduce the number of pretraining epoch to run the code (better idea).\n") 103 "or reduce the number of pretraining epoch to run the code (better idea).\n")
99 print('\n\tpretraining with P07') 104 print('\n\tpretraining with P07')
100 optimizer.pretrain(datasets.nist_P07(min_file=0,max_file=nb_file)) 105 optimizer.pretrain(datasets.nist_P07(min_file=0,max_file=nb_file),decrease = dec)
101 channel.save() 106 channel.save()
102 107
103 #Set some of the parameters used for the finetuning 108 #Set some of the parameters used for the finetuning
104 if state.has_key('finetune_set'): 109 if state.has_key('finetune_set'):
105 finetune_choice=state['finetune_set'] 110 finetune_choice=state['finetune_set']