diff deep/stacked_dae/v2/config.py.example @ 239:42005ec87747

Mergé (manuellement) les changements de Sylvain pour utiliser le code de dataset d'Arnaud, à cette différence près que je n'utilse pas les givens. J'ai probablement une approche différente pour limiter la taille du dataset dans mon débuggage, aussi.
author fsavard
date Mon, 15 Mar 2010 18:30:21 -0400
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/deep/stacked_dae/v2/config.py.example	Mon Mar 15 18:30:21 2010 -0400
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+'''
+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_sandbox_db/yourtablenamehere'
+EXPERIMENT_PATH = "ift6266.deep.stacked_dae.v2.nist_sda.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
+
+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': [0.1, 0.01],#, 0.001],#, 0.0001],
+        'pretraining_epochs_per_layer': [10,20],
+        'hidden_layers_sizes': [300,800],
+        'corruption_levels': [0.1,0.2,0.3],
+        'minibatch_size': [20],
+        'max_finetuning_epochs':[MAX_FINETUNING_EPOCHS],
+        'finetuning_lr':[0.1, 0.01], #0.001 was very bad, so we leave it out
+        'num_hidden_layers':[2,3]}
+
+# 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 = DD({'finetuning_lr':0.1,
+                       'pretraining_lr':0.1,
+                       'pretraining_epochs_per_layer':2,
+                       'max_finetuning_epochs':2,
+                       'hidden_layers_sizes':800,
+                       'corruption_levels':0.2,
+                       'minibatch_size':20,
+                       'reduce_train_to':10000,
+                       'num_hidden_layers':1})
+
+