--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/deep/stacked_dae/v_youssouf/config.py	Sun Apr 25 12:31:22 2010 -0400
@@ -0,0 +1,98 @@
+'''
+These are parameters used by nist_sda_retrieve.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 
+
+#Path of two pre-train done earlier
+PATH_NIST = '/u/pannetis/IFT6266/ift6266/deep/stacked_dae/v_sylvain/NIST_big'
+PATH_P07 = '/u/pannetis/IFT6266/ift6266/deep/stacked_dae/v_sylvain/P07_big/'
+
+'''
+# change "sandbox" when you're ready
+JOBDB = 'postgres://ift6266h10@gershwin/ift6266h10_db/pannetis_SDA_retrieve'
+EXPERIMENT_PATH = "ift6266.deep.stacked_dae.v_sylvain.nist_sda_retrieve.jobman_entrypoint"
+'''
+
+##Pour lancer des travaux sur le cluster: (il faut etre ou se trouve les fichiers)
+##python nist_sda_retrieve.py jobman_insert
+##dbidispatch --condor --repeat_jobs=2 jobman sql 'postgres://ift6266h10@gershwin/ift6266h10_db/pannetis_finetuningSDA0' .  #C'est le path dans config.py
+
+##Pour lancer sur GPU sur boltzmann (changer device=gpuX pour X le bon assigne)
+##THEANO_FLAGS=floatX=float32,device=gpu2 python nist_sda_retrieve.py test_jobman_entrypoint
+
+
+# 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=0
+#Set the pretrain dataset used. 0: NIST, 1:P07
+PRETRAIN_CHOICE=0
+
+
+if TEST_CONFIG:
+    REDUCE_TRAIN_TO = 1000
+    MAX_FINETUNING_EPOCHS = 2
+    REDUCE_EVERY = 10
+	
+# select detection or classification
+DETECTION_MODE = 0
+# consider maj and minuscule as the same
+REDUCE_LABEL = 1
+
+
+# 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': [0.1],#, 0.001],#, 0.0001],
+        'pretraining_epochs_per_layer': [10],
+        'hidden_layers_sizes': [800],
+        'corruption_levels': [0.2],
+        'minibatch_size': [100],
+        'max_finetuning_epochs':[MAX_FINETUNING_EPOCHS],
+        'max_finetuning_epochs_P07':[1],
+        'finetuning_lr':[0.01], #0.001 was very bad, so we leave it out
+        'num_hidden_layers':[4],
+        'finetune_set':[-1],
+        'pretrain_choice':[0,1]
+        }
+
+# Just useful for tests... minimal number of epochs
+# (This is used when running a single job, locally, when
+# calling ./nist_sda.py test_jobman_entrypoint
+DEFAULT_HP_NIST = {'finetuning_lr':0.05,
+                       'pretraining_lr':0.01,
+                       'pretraining_epochs_per_layer':15,
+                       'max_finetuning_epochs':MAX_FINETUNING_EPOCHS,
+                       #'max_finetuning_epochs':1,
+                       'max_finetuning_epochs_P07':7,
+                       'hidden_layers_sizes':1500,
+                       'corruption_levels':0.2,
+                       'minibatch_size':100,
+                       #'reduce_train_to':2000,
+		       'decrease_lr':1,
+                       'num_hidden_layers':4,
+                       'finetune_set':2,
+                       'pretrain_choice':1,
+					   'detection_mode':DETECTION_MODE,
+					   'reduce_label':REDUCE_LABEL}
+