--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/deep/convolutional_dae/salah_exp/config.py	Thu Apr 22 19:50:21 2010 -0400
@@ -0,0 +1,177 @@
+'''
+These are parameters used by nist_sda.py. They'll end up as globals in there.
+
+Rename this file to config.py and configure as needed.
+DON'T add the renamed file to the repository, as others might use it
+without realizing it, with dire consequences.
+'''
+
+# Set this to True when you want to run cluster tests, ie. you want
+# to run on the cluster, many jobs, but want to reduce the training
+# set size and the number of epochs, so you know everything runs
+# fine on the cluster.
+# Set this PRIOR to inserting your test jobs in the DB.
+TEST_CONFIG = False
+
+NIST_ALL_LOCATION = '/data/lisa/data/nist/by_class/all'
+NIST_ALL_TRAIN_SIZE = 649081
+# valid et test =82587 82587 
+
+# change "sandbox" when you're ready
+JOBDB = 'postgres://ift6266h10@gershwin/ift6266h10_db/rifaisal_csda'
+EXPERIMENT_PATH = "ift6266.deep.convolutional_dae.salah_exp.nist_csda.jobman_entrypoint"
+
+##Pour lancer des travaux sur le cluster: (il faut etre ou se trouve les fichiers)
+##python nist_sda.py jobman_insert
+##dbidispatch --condor --repeat_jobs=2 jobman sql 'postgres://ift6266h10@gershwin/ift6266h10_db/pannetis_finetuningSDA0' .  #C'est le path dans config.py
+
+# reduce training set to that many examples
+REDUCE_TRAIN_TO = None
+# that's a max, it usually doesn't get to that point
+MAX_FINETUNING_EPOCHS = 1000
+# number of minibatches before taking means for valid error etc.
+REDUCE_EVERY = 100
+#Set the finetune dataset
+FINETUNE_SET=1
+#Set the pretrain dataset used. 0: NIST, 1:P07
+PRETRAIN_CHOICE=1
+
+
+if TEST_CONFIG:
+    REDUCE_TRAIN_TO = 1000
+    MAX_FINETUNING_EPOCHS = 2
+    REDUCE_EVERY = 10
+
+
+# This is to configure insertion of jobs on the cluster.
+# Possible values the hyperparameters can take. These are then
+# combined with produit_cartesien_jobs so we get a list of all
+# possible combinations, each one resulting in a job inserted
+# in the jobman DB.
+
+
+JOB_VALS = {'pretraining_lr': [0.01],#, 0.001],#, 0.0001],
+        'pretraining_epochs_per_layer': [10],
+        'kernels' : [[[52,5,5], [32,3,3]], [[52,7,7], [52,3,3]]],
+        'mlp_size' : [[1000],[500]],
+        'imgshp' : [[32,32]],
+        'max_pool_layers' : [[[2,2],[2,2]]],
+        'corruption_levels': [[0.2,0.1]],
+        'minibatch_size': [100],
+        'max_finetuning_epochs':[MAX_FINETUNING_EPOCHS],
+        'max_finetuning_epochs_P07':[1000],
+        'finetuning_lr':[0.1,0.01], #0.001 was very bad, so we leave it out
+        'num_hidden_layers':[2],
+        'finetune_set':[1],
+        'pretrain_choice':[1]
+        }
+
+DEFAULT_HP_NIST = {'pretraining_lr': 0.01,
+        'pretraining_epochs_per_layer': 1,
+        'kernels' : [[4,5,5], [2,3,3]],
+        'mlp_size' : [10],
+        'imgshp' : [32,32],
+        'max_pool_layers' : [[2,2],[2,2]],
+        'corruption_levels': [0.1,0.2],
+        'minibatch_size': 20,
+        'max_finetuning_epochs':MAX_FINETUNING_EPOCHS,
+        'max_finetuning_epochs_P07':1000,
+        'finetuning_lr':0.1, #0.001 was very bad, so we leave it out
+        'num_hidden_layers':2,
+        'finetune_set':1,
+        'pretrain_choice':1,
+        #'reduce_train_to':1000,
+        }
+
+                    
+                    
+##[pannetis@ceylon test]$ python nist_sda.py test_jobman_entrypoint
+##WARNING: untracked file /u/pannetis/IFT6266/ift6266/deep/stacked_dae/v_sylvain/TMP_DBI/configobj.py
+##WARNING: untracked file /u/pannetis/IFT6266/ift6266/deep/stacked_dae/v_sylvain/TMP_DBI/utils.py
+##WARNING: untracked file /u/pannetis/IFT6266/ift6266/deep/stacked_dae/v_sylvain/config.py
+##WARNING: untracked file /u/pannetis/IFT6266/ift6266/deep/stacked_dae/v_sylvain/config2.py
+##Creating optimizer with state,  DD{'reduce_train_to': 11000, 'pretraining_epochs_per_layer': 2, 'hidden_layers_sizes': 300, 'num_hidden_layers': 2, 'corruption_levels': 0.20000000000000001, 'finetuning_lr': 0.10000000000000001, 'pretrain_choice': 0, 'max_finetuning_epochs': 2, 'version_pylearn': '08b37147dec1', 'finetune_set': -1, 'pretraining_lr': 0.10000000000000001, 'version_ift6266': 'a6b6b1140de9', 'version_theano': 'fb6c3a06cb65', 'minibatch_size': 20}
+##SdaSgdOptimizer, max_minibatches = 11000
+##C##n_outs 62
+##pretrain_lr 0.1
+##finetune_lr 0.1
+##----
+##
+##pretraining with NIST
+##
+##STARTING PRETRAINING, time =  2010-03-29 15:07:43.945981
+##Pre-training layer 0, epoch 0, cost  113.562562494
+##Pre-training layer 0, epoch 1, cost  113.410032944
+##Pre-training layer 1, epoch 0, cost  98.4539954687
+##Pre-training layer 1, epoch 1, cost  97.8658966686
+##Pretraining took 9.011333 minutes
+##
+##SERIE OF 3 DIFFERENT FINETUNINGS
+##
+##
+##finetune with NIST
+##
+##
+##STARTING FINETUNING, time =  2010-03-29 15:16:46.512235
+##epoch 1, minibatch 4999, validation error on P07 : 29.511250 %
+##     epoch 1, minibatch 4999, test error on dataset NIST  (train data) of best model 40.408509 %
+##     epoch 1, minibatch 4999, test error on dataset P07 of best model 96.700000 %
+##epoch 1, minibatch 9999, validation error on P07 : 25.560000 %
+##     epoch 1, minibatch 9999, test error on dataset NIST  (train data) of best model 34.778969 %
+##     epoch 1, minibatch 9999, test error on dataset P07 of best model 97.037500 %
+##
+##Optimization complete with best validation score of 25.560000 %,with test performance 34.778969 % on dataset NIST 
+##The test score on the P07 dataset is 97.037500
+##The finetuning ran for 3.281833 minutes
+##
+##
+##finetune with P07
+##
+##
+##STARTING FINETUNING, time =  2010-03-29 15:20:06.346009
+##epoch 1, minibatch 4999, validation error on NIST : 65.226250 %
+##     epoch 1, minibatch 4999, test error on dataset P07  (train data) of best model 84.465000 %
+##     epoch 1, minibatch 4999, test error on dataset NIST of best model 65.965237 %
+##epoch 1, minibatch 9999, validation error on NIST : 58.745000 %
+##     epoch 1, minibatch 9999, test error on dataset P07  (train data) of best model 80.405000 %
+##     epoch 1, minibatch 9999, test error on dataset NIST of best model 61.341923 %
+##
+##Optimization complete with best validation score of 58.745000 %,with test performance 80.405000 % on dataset P07 
+##The test score on the NIST dataset is 61.341923
+##The finetuning ran for 3.299500 minutes
+##
+##
+##finetune with NIST (done earlier) followed by P07 (written here)
+##
+##
+##STARTING FINETUNING, time =  2010-03-29 15:23:27.947374
+##epoch 1, minibatch 4999, validation error on NIST : 83.975000 %
+##     epoch 1, minibatch 4999, test error on dataset P07  (train data) of best model 83.872500 %
+##     epoch 1, minibatch 4999, test error on dataset NIST of best model 43.170010 %
+##epoch 1, minibatch 9999, validation error on NIST : 79.775000 %
+##     epoch 1, minibatch 9999, test error on dataset P07  (train data) of best model 80.971250 %
+##     epoch 1, minibatch 9999, test error on dataset NIST of best model 49.017468 %
+##
+##Optimization complete with best validation score of 79.775000 %,with test performance 80.971250 % on dataset P07 
+##The test score on the NIST dataset is 49.017468
+##The finetuning ran for 2.851500 minutes
+##
+##
+##finetune with NIST only on the logistic regression on top.
+##        All hidden units output are input of the logistic regression
+##
+##
+##STARTING FINETUNING, time =  2010-03-29 15:26:21.430557
+##epoch 1, minibatch 4999, validation error on P07 : 95.223750 %
+##     epoch 1, minibatch 4999, test error on dataset NIST  (train data) of best model 93.268765 %
+##     epoch 1, minibatch 4999, test error on dataset P07 of best model 96.535000 %
+##epoch 1, minibatch 9999, validation error on P07 : 95.223750 %
+##
+##Optimization complete with best validation score of 95.223750 %,with test performance 93.268765 % on dataset NIST 
+##The test score on the P07 dataset is 96.535000
+##The finetuning ran for 2.013167 minutes
+##Closing remaining open files: /u/pannetis/IFT6266/test/series.h5... done
+##[pannetis@ceylon test]$ 
+
+
+