Mercurial > ift6266

comparison deep/stacked_dae/v_sylvain/nist_sda.py @ 275:7b4507295eba

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merge
author Xavier Glorot <glorotxa@iro.umontreal.ca>
date Mon, 22 Mar 2010 10:20:10 -0400
parents f14fb56b3f8d
children 698313f8f6e6
comparison
equal deleted inserted replaced
274:44409b6652aa 275:7b4507295eba
58 58
59 series = create_series(state.num_hidden_layers) 59 series = create_series(state.num_hidden_layers)
60 60
61 print "Creating optimizer with state, ", state 61 print "Creating optimizer with state, ", state
62 62
63 optimizer = SdaSgdOptimizer(dataset=datasets.nist_all, 63 optimizer = SdaSgdOptimizer(dataset=datasets.nist_all(),
64 hyperparameters=state, \ 64 hyperparameters=state, \
65 n_ins=n_ins, n_outs=n_outs,\ 65 n_ins=n_ins, n_outs=n_outs,\
66 examples_per_epoch=examples_per_epoch, \ 66 examples_per_epoch=examples_per_epoch, \
67 series=series, 67 series=series,
68 max_minibatches=rtt) 68 max_minibatches=rtt)
69 69
70 parameters=[] 70 parameters=[]
71 optimizer.pretrain(datasets.nist_all) 71 optimizer.pretrain(datasets.nist_P07())
72 channel.save() 72 channel.save()
73 73
74 #Set some of the parameters used for the finetuning 74 #Set some of the parameters used for the finetuning
75 if state.has_key('finetune_set'): 75 if state.has_key('finetune_set'):
76 finetune_choice=state['finetune_set'] 76 finetune_choice=state['finetune_set']
90 #Decide how the finetune is done 90 #Decide how the finetune is done
91 91
92 if finetune_choice==0: 92 if finetune_choice==0:
93 print('\n\n\tfinetune avec nist\n\n') 93 print('\n\n\tfinetune avec nist\n\n')
94 optimizer.reload_parameters() 94 optimizer.reload_parameters()
95 optimizer.finetune(datasets.nist_all,max_finetune_epoch_NIST) 95 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1)
96 if finetune_choice==1: 96 if finetune_choice==1:
97 print('\n\n\tfinetune avec P07\n\n') 97 print('\n\n\tfinetune avec P07\n\n')
98 optimizer.reload_parameters() 98 optimizer.reload_parameters()
99 optimizer.finetune(datasets.nist_P07,max_finetune_epoch_P07) 99 optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0)
100 if finetune_choice==2: 100 if finetune_choice==2:
101 print('\n\n\tfinetune avec nist suivi de P07\n\n') 101 print('\n\n\tfinetune avec nist suivi de P07\n\n')
102 optimizer.reload_parameters() 102 optimizer.reload_parameters()
103 optimizer.finetune(datasets.nist_all,max_finetune_epoch_NIST) 103 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1)
104 optimizer.finetune(datasets.nist_P07,max_finetune_epoch_P07) 104 optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0)
105 105
106 if finetune_choice==-1: 106 if finetune_choice==-1:
107 print('\nSerie de 3 essais de fine-tuning') 107 print('\nSerie de 3 essais de fine-tuning')
108 print('\n\n\tfinetune avec nist\n\n') 108 print('\n\n\tfinetune avec nist\n\n')
109 optimizer.reload_parameters() 109 optimizer.reload_parameters()
110 optimizer.finetune(datasets.nist_all,max_finetune_epoch_NIST) 110 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1)
111 channel.save() 111 channel.save()
112 print('\n\n\tfinetune avec P07\n\n') 112 print('\n\n\tfinetune avec P07\n\n')
113 optimizer.reload_parameters() 113 optimizer.reload_parameters()
114 optimizer.finetune(datasets.nist_P07,max_finetune_epoch_P07) 114 optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0)
115 channel.save() 115 channel.save()
116 print('\n\n\tfinetune avec nist suivi de P07\n\n') 116 print('\n\n\tfinetune avec nist suivi de P07\n\n')
117 optimizer.reload_parameters() 117 optimizer.reload_parameters()
118 optimizer.finetune(datasets.nist_all,max_finetune_epoch_NIST) 118 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1)
119 optimizer.finetune(datasets.nist_P07,max_finetune_epoch_P07) 119 optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0)
120 channel.save() 120 channel.save()
121 121
122 channel.save() 122 channel.save()
123 123
124 return channel.COMPLETE 124 return channel.COMPLETE