Mercurial > ift6266
comparison deep/stacked_dae/v_sylvain/nist_sda.py @ 312:bd6085d77706
Avoir exactement le meme jeu de donnees pour pre-train et finetune
author | SylvainPL <sylvain.pannetier.lebeuf@umontreal.ca> |
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date | Thu, 01 Apr 2010 14:25:40 -0400 |
parents | ed0443f7aad4 |
children | 8de3bef71458 |
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311:8b31280129a9 | 312:bd6085d77706 |
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104 #Decide how the finetune is done | 104 #Decide how the finetune is done |
105 | 105 |
106 if finetune_choice == 0: | 106 if finetune_choice == 0: |
107 print('\n\n\tfinetune with NIST\n\n') | 107 print('\n\n\tfinetune with NIST\n\n') |
108 optimizer.reload_parameters('params_pretrain.txt') | 108 optimizer.reload_parameters('params_pretrain.txt') |
109 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(min_file=nb_file),max_finetune_epoch_NIST,ind_test=1) | 109 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1) |
110 channel.save() | 110 channel.save() |
111 if finetune_choice == 1: | 111 if finetune_choice == 1: |
112 print('\n\n\tfinetune with P07\n\n') | 112 print('\n\n\tfinetune with P07\n\n') |
113 optimizer.reload_parameters('params_pretrain.txt') | 113 optimizer.reload_parameters('params_pretrain.txt') |
114 optimizer.finetune(datasets.nist_P07(min_file=nb_file),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0) | 114 optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0) |
115 channel.save() | 115 channel.save() |
116 if finetune_choice == 2: | 116 if finetune_choice == 2: |
117 print('\n\n\tfinetune with NIST followed by P07\n\n') | 117 print('\n\n\tfinetune with NIST followed by P07\n\n') |
118 optimizer.reload_parameters('params_pretrain.txt') | 118 optimizer.reload_parameters('params_pretrain.txt') |
119 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(min_file=nb_file),max_finetune_epoch_NIST,ind_test=21) | 119 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=21) |
120 optimizer.finetune(datasets.nist_P07(min_file=nb_file),datasets.nist_all(),max_finetune_epoch_P07,ind_test=20) | 120 optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=20) |
121 channel.save() | 121 channel.save() |
122 if finetune_choice == 3: | 122 if finetune_choice == 3: |
123 print('\n\n\tfinetune with NIST only on the logistic regression on top (but validation on P07).\n\ | 123 print('\n\n\tfinetune with NIST only on the logistic regression on top (but validation on P07).\n\ |
124 All hidden units output are input of the logistic regression\n\n') | 124 All hidden units output are input of the logistic regression\n\n') |
125 optimizer.reload_parameters('params_pretrain.txt') | 125 optimizer.reload_parameters('params_pretrain.txt') |
126 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(min_file=nb_file),max_finetune_epoch_NIST,ind_test=1,special=1) | 126 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1,special=1) |
127 | 127 |
128 | 128 |
129 if finetune_choice==-1: | 129 if finetune_choice==-1: |
130 print('\nSERIE OF 3 DIFFERENT FINETUNINGS') | 130 print('\nSERIE OF 3 DIFFERENT FINETUNINGS') |
131 print('\n\n\tfinetune with NIST\n\n') | 131 print('\n\n\tfinetune with NIST\n\n') |
132 optimizer.reload_parameters('params_pretrain.txt') | 132 optimizer.reload_parameters('params_pretrain.txt') |
133 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(min_file=nb_file),max_finetune_epoch_NIST,ind_test=1) | 133 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1) |
134 channel.save() | 134 channel.save() |
135 print('\n\n\tfinetune with P07\n\n') | 135 print('\n\n\tfinetune with P07\n\n') |
136 optimizer.reload_parameters('params_pretrain.txt') | 136 optimizer.reload_parameters('params_pretrain.txt') |
137 optimizer.finetune(datasets.nist_P07(min_file=nb_file),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) |
138 channel.save() | 138 channel.save() |
139 print('\n\n\tfinetune with NIST (done earlier) followed by P07 (written here)\n\n') | 139 print('\n\n\tfinetune with NIST (done earlier) followed by P07 (written here)\n\n') |
140 optimizer.reload_parameters('params_finetune_NIST.txt') | 140 optimizer.reload_parameters('params_finetune_NIST.txt') |
141 optimizer.finetune(datasets.nist_P07(min_file=nb_file),datasets.nist_all(),max_finetune_epoch_P07,ind_test=20) | 141 optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=20) |
142 channel.save() | 142 channel.save() |
143 print('\n\n\tfinetune with NIST only on the logistic regression on top.\n\ | 143 print('\n\n\tfinetune with NIST only on the logistic regression on top.\n\ |
144 All hidden units output are input of the logistic regression\n\n') | 144 All hidden units output are input of the logistic regression\n\n') |
145 optimizer.reload_parameters('params_pretrain.txt') | 145 optimizer.reload_parameters('params_pretrain.txt') |
146 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(min_file=nb_file),max_finetune_epoch_NIST,ind_test=1,special=1) | 146 optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1,special=1) |
147 channel.save() | 147 channel.save() |
148 | 148 |
149 channel.save() | 149 channel.save() |
150 | 150 |
151 return channel.COMPLETE | 151 return channel.COMPLETE |