--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/deep/stacked_dae/v_youssouf/train_error.py	Sun Apr 25 12:31:22 2010 -0400
@@ -0,0 +1,118 @@
+#!/usr/bin/python
+# coding: utf-8
+
+import ift6266
+import pylearn
+
+import numpy 
+import theano
+import time
+
+import pylearn.version
+import theano.tensor as T
+from theano.tensor.shared_randomstreams import RandomStreams
+
+import copy
+import sys
+import os
+import os.path
+
+from jobman import DD
+import jobman, jobman.sql
+from pylearn.io import filetensor
+
+from utils import produit_cartesien_jobs
+from copy import copy
+
+from sgd_optimization import SdaSgdOptimizer
+
+#from ift6266.utils.scalar_series import *
+from ift6266.utils.seriestables import *
+import tables
+
+from ift6266 import datasets
+from config import *
+
+'''
+Function called by jobman upon launching each job
+Its path is the one given when inserting jobs: see EXPERIMENT_PATH
+'''
+def jobman_entrypoint(state, channel):
+    # record mercurial versions of each package
+    pylearn.version.record_versions(state,[theano,ift6266,pylearn])
+    # TODO: remove this, bad for number of simultaneous requests on DB
+    channel.save()
+
+    # For test runs, we don't want to use the whole dataset so
+    # reduce it to fewer elements if asked to.
+    rtt = None
+    if state.has_key('reduce_train_to'):
+        rtt = state['reduce_train_to']
+    elif REDUCE_TRAIN_TO:
+        rtt = REDUCE_TRAIN_TO
+ 
+    n_ins = 32*32
+    n_outs = 62 # 10 digits, 26*2 (lower, capitals)
+     
+    examples_per_epoch = NIST_ALL_TRAIN_SIZE
+
+    PATH = ''
+    maximum_exemples=int(500000) #Maximum number of exemples seen
+
+
+
+    print "Creating optimizer with state, ", state
+
+    optimizer = SdaSgdOptimizer(dataset=datasets.nist_all(), 
+                                    hyperparameters=state, \
+                                    n_ins=n_ins, n_outs=n_outs,\
+                                    examples_per_epoch=examples_per_epoch, \
+                                    max_minibatches=rtt)	
+
+
+    
+    
+
+    if os.path.exists(PATH+'params_finetune_NIST.txt'):
+        print ('\n finetune = NIST ')
+        optimizer.reload_parameters(PATH+'params_finetune_NIST.txt')
+        print "For" + str(maximum_exemples) + "over the NIST training set: "
+        optimizer.training_error(datasets.nist_all(maxsize=maximum_exemples))
+        
+    
+    if os.path.exists(PATH+'params_finetune_P07.txt'):
+        print ('\n finetune = P07 ')
+        optimizer.reload_parameters(PATH+'params_finetune_P07.txt')
+        print "For" + str(maximum_exemples) + "over the P07 training set: "
+        optimizer.training_error(datasets.nist_P07(maxsize=maximum_exemples))
+
+    
+    if os.path.exists(PATH+'params_finetune_NIST_then_P07.txt'):
+        print ('\n finetune = NIST then P07')
+        optimizer.reload_parameters(PATH+'params_finetune_NIST_then_P07.txt')
+        print "For" + str(maximum_exemples) + "over the NIST training set: "
+        optimizer.training_error(datasets.nist_all(maxsize=maximum_exemples))
+        print "For" + str(maximum_exemples) + "over the P07 training set: "
+        optimizer.training_error(datasets.nist_P07(maxsize=maximum_exemples))
+    
+    if os.path.exists(PATH+'params_finetune_P07_then_NIST.txt'):
+        print ('\n finetune = P07 then NIST')
+        optimizer.reload_parameters(PATH+'params_finetune_P07_then_NIST.txt')
+        print "For" + str(maximum_exemples) + "over the P07 training set: "
+        optimizer.training_error(datasets.nist_P07(maxsize=maximum_exemples))
+        print "For" + str(maximum_exemples) + "over the NIST training set: "
+        optimizer.training_error(datasets.nist_all(maxsize=maximum_exemples))
+    
+    channel.save()
+
+    return channel.COMPLETE
+
+
+
+if __name__ == '__main__':
+
+
+    chanmock = DD({'COMPLETE':0,'save':(lambda:None)})
+    jobman_entrypoint(DD(DEFAULT_HP_NIST), chanmock)
+
+