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comparison deep/stacked_dae/v_sylvain/nist_sda_retrieve.py @ 328:c61b72d07676

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Rajout de la capacite de faire decroitre le taux d'apprentissage si demande
author SylvainPL <sylvain.pannetier.lebeuf@umontreal.ca>
date Sun, 11 Apr 2010 19:52:35 -0400
parents 71ffe2c9bfad
children 5c3935aa3f8a
comparison
equal deleted inserted replaced
327:4306796d60a8 328:c61b72d07676
48 rtt = None 48 rtt = None
49 if state.has_key('reduce_train_to'): 49 if state.has_key('reduce_train_to'):
50 rtt = state['reduce_train_to'] 50 rtt = state['reduce_train_to']
51 elif REDUCE_TRAIN_TO: 51 elif REDUCE_TRAIN_TO:
52 rtt = REDUCE_TRAIN_TO 52 rtt = REDUCE_TRAIN_TO
53
54 if state.has_key('decrease_lr'):
55 decrease_lr = state['decrease_lr']
56 else :
57 decrease_lr = 0
53 58
54 n_ins = 32*32 59 n_ins = 32*32
55 n_outs = 62 # 10 digits, 26*2 (lower, capitals) 60 n_outs = 62 # 10 digits, 26*2 (lower, capitals)
56 61
57 examples_per_epoch = NIST_ALL_TRAIN_SIZE 62 examples_per_epoch = NIST_ALL_TRAIN_SIZE
122 #Decide how the finetune is done 127 #Decide how the finetune is done
123 128
124 if finetune_choice == 0: 129 if finetune_choice == 0:
125 print('\n\n\tfinetune with NIST\n\n') 130 print('\n\n\tfinetune with NIST\n\n')
126 optimizer.reload_parameters(PATH+'params_pretrain.txt') 131 optimizer.reload_parameters(PATH+'params_pretrain.txt')
127 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1) 132 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1,decrease=decrease_lr)
128 channel.save() 133 channel.save()
129 if finetune_choice == 1: 134 if finetune_choice == 1:
130 print('\n\n\tfinetune with P07\n\n') 135 print('\n\n\tfinetune with P07\n\n')
131 optimizer.reload_parameters(PATH+'params_pretrain.txt') 136 optimizer.reload_parameters(PATH+'params_pretrain.txt')
132 optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0) 137 optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0,decrease=decrease_lr)
133 channel.save() 138 channel.save()
134 if finetune_choice == 2: 139 if finetune_choice == 2:
135 print('\n\n\tfinetune with P07 followed by NIST\n\n') 140 print('\n\n\tfinetune with P07 followed by NIST\n\n')
136 optimizer.reload_parameters(PATH+'params_pretrain.txt') 141 optimizer.reload_parameters(PATH+'params_pretrain.txt')
137 optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=20) 142 optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=20,decrease=decrease_lr)
138 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=21) 143 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=21,decrease=decrease_lr)
139 channel.save() 144 channel.save()
140 if finetune_choice == 3: 145 if finetune_choice == 3:
141 print('\n\n\tfinetune with NIST only on the logistic regression on top (but validation on P07).\n\ 146 print('\n\n\tfinetune with NIST only on the logistic regression on top (but validation on P07).\n\
142 All hidden units output are input of the logistic regression\n\n') 147 All hidden units output are input of the logistic regression\n\n')
143 optimizer.reload_parameters(PATH+'params_pretrain.txt') 148 optimizer.reload_parameters(PATH+'params_pretrain.txt')
144 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1,special=1) 149 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1,special=1,decrease=decrease_lr)
145 150
146 151
147 if finetune_choice==-1: 152 if finetune_choice==-1:
148 print('\nSERIE OF 3 DIFFERENT FINETUNINGS') 153 print('\nSERIE OF 4 DIFFERENT FINETUNINGS')
149 print('\n\n\tfinetune with NIST\n\n') 154 print('\n\n\tfinetune with NIST\n\n')
150 sys.stdout.flush() 155 sys.stdout.flush()
151 optimizer.reload_parameters(PATH+'params_pretrain.txt') 156 optimizer.reload_parameters(PATH+'params_pretrain.txt')
152 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1) 157 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1,decrease=decrease_lr)
153 channel.save() 158 channel.save()
154 print('\n\n\tfinetune with P07\n\n') 159 print('\n\n\tfinetune with P07\n\n')
155 sys.stdout.flush() 160 sys.stdout.flush()
156 optimizer.reload_parameters(PATH+'params_pretrain.txt') 161 optimizer.reload_parameters(PATH+'params_pretrain.txt')
157 optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0) 162 optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0,decrease=decrease_lr)
158 channel.save() 163 channel.save()
159 print('\n\n\tfinetune with P07 (done earlier) followed by NIST (written here)\n\n') 164 print('\n\n\tfinetune with P07 (done earlier) followed by NIST (written here)\n\n')
160 sys.stdout.flush() 165 sys.stdout.flush()
161 optimizer.reload_parameters('params_finetune_P07.txt') 166 optimizer.reload_parameters('params_finetune_P07.txt')
162 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=21) 167 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=21,decrease=decrease_lr)
163 channel.save() 168 channel.save()
164 print('\n\n\tfinetune with NIST only on the logistic regression on top.\n\ 169 print('\n\n\tfinetune with NIST only on the logistic regression on top.\n\
165 All hidden units output are input of the logistic regression\n\n') 170 All hidden units output are input of the logistic regression\n\n')
166 sys.stdout.flush() 171 sys.stdout.flush()
167 optimizer.reload_parameters(PATH+'params_pretrain.txt') 172 optimizer.reload_parameters(PATH+'params_pretrain.txt')
168 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1,special=1) 173 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1,special=1,decrease=decrease_lr)
169 channel.save() 174 channel.save()
170 175
171 channel.save() 176 channel.save()
172 177
173 return channel.COMPLETE 178 return channel.COMPLETE