--- a/deep/crbm/mnist_config.py.example	Thu Apr 22 10:19:07 2010 -0400
+++ b/deep/crbm/mnist_config.py.example	Thu Apr 22 10:34:26 2010 -0400
@@ -54,7 +54,7 @@
 '''
 
 # change "sandbox" when you're ready
-JOBDB = 'postgres://ift6266h10@gershwin/ift6266h10_sandbox_db/fsavard_mnistcrbm_exp1'
+JOBDB = 'postgres://ift6266h10@gershwin/ift6266h10_sandbox_db/yourtablenamehere'
 
 # 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
@@ -74,8 +74,7 @@
 # print series to stdout too (otherwise just produce the HDF5 file)
 SERIES_STDOUT_TOO = False
 
-# every X minibatches, not every X examples
-VISUALIZE_EVERY = 1000
+VISUALIZE_EVERY = 20000
 GIBBS_STEPS_IN_VIZ_CHAIN = 1000
 
 if TEST_CONFIG:

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/deep/crbm/utils.py	Thu Apr 22 10:34:26 2010 -0400
@@ -0,0 +1,118 @@
+#!/usr/bin/python
+# coding: utf-8
+
+from __future__ import with_statement
+
+import jobman
+from jobman import DD
+
+from pylearn.io.seriestables import *
+import tables
+
+
+
+# from pylearn codebase
+# useful in __init__(param1, param2, etc.) to save
+# values in self.param1, self.param2... just call
+# update_locals(self, locals())
+def update_locals(obj, dct):
+    if 'self' in dct:
+        del dct['self']
+    obj.__dict__.update(dct)
+
+# from a dictionary of possible values for hyperparameters, e.g.
+# hp_values = {'learning_rate':[0.1, 0.01], 'num_layers': [1,2]}
+# create a list of other dictionaries representing all the possible
+# combinations, thus in this example creating:
+# [{'learning_rate': 0.1, 'num_layers': 1}, ...]
+# (similarly for combinations (0.1, 2), (0.01, 1), (0.01, 2))
+def produit_cartesien_jobs(val_dict):
+    job_list = [DD()]
+    all_keys = val_dict.keys()
+
+    for key in all_keys:
+        possible_values = val_dict[key]
+        new_job_list = []
+        for val in possible_values:
+            for job in job_list:
+                to_insert = job.copy()
+                to_insert.update({key: val})
+                new_job_list.append(to_insert)
+        job_list = new_job_list
+
+    return job_list
+
+def jobs_from_reinsert_list(cols, job_vals):
+    job_list = []
+    for vals in job_vals:
+        job = DD()
+        for i, col in enumerate(cols):
+            job[col] = vals[i]
+        job_list.append(job)
+
+    return job_list
+
+def save_params(all_params, filename):
+    import pickle
+    with open(filename, 'wb') as f:
+        values = [p.value for p in all_params]
+
+        # -1 for HIGHEST_PROTOCOL
+        pickle.dump(values, f, -1)
+
+# Perform insertion into the Postgre DB based on combination
+# of hyperparameter values above
+# (see comment for produit_cartesien_jobs() to know how it works)
+def jobman_insert_job_vals(job_db, experiment_path, job_vals):
+    jobs = produit_cartesien_jobs(job_vals)
+
+    db = jobman.sql.db(job_db)
+    for job in jobs:
+        job.update({jobman.sql.EXPERIMENT: experiment_path})
+        jobman.sql.insert_dict(job, db)
+
+def jobman_insert_specific_jobs(job_db, experiment_path,
+                        insert_cols, insert_vals):
+    jobs = jobs_from_reinsert_list(insert_cols, insert_vals)
+
+    db = jobman.sql.db(job_db)
+    for job in jobs:
+        job.update({jobman.sql.EXPERIMENT: experiment_path})
+        jobman.sql.insert_dict(job, db)
+
+# Just a shortcut for a common case where we need a few
+# related Error (float) series
+def get_accumulator_series_array( \
+                hdf5_file, group_name, series_names, 
+                reduce_every,
+                index_names=('epoch','minibatch'),
+                stdout_too=True,
+                skip_hdf5_append=False):
+    all_series = []
+
+    new_group = hdf5_file.createGroup('/', group_name)
+
+    other_targets = []
+    if stdout_too:
+        other_targets = [StdoutAppendTarget()]
+
+    for sn in series_names:
+        series_base = \
+            ErrorSeries(error_name=sn,
+                table_name=sn,
+                hdf5_file=hdf5_file,
+                hdf5_group=new_group._v_pathname,
+                index_names=index_names,
+                other_targets=other_targets,
+                skip_hdf5_append=skip_hdf5_append)
+
+        all_series.append( \
+            AccumulatorSeriesWrapper( \
+                    base_series=series_base,
+                    reduce_every=reduce_every))
+
+    ret_wrapper = SeriesArrayWrapper(all_series)
+
+    return ret_wrapper
+
+