--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/deep/stacked_dae/v_youssouf/nist_sda.py	Sun Apr 25 12:31:22 2010 -0400
@@ -0,0 +1,266 @@
+#!/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
+        
+    if state.has_key('decrease_lr'):
+        decrease_lr = state['decrease_lr']
+    else :
+        decrease_lr = 0
+ 
+    n_ins = 32*32
+    n_outs = 36 # 10 digits, 26 characters (merged lower and capitals)
+     
+    examples_per_epoch = NIST_ALL_TRAIN_SIZE
+    
+    #To be sure variables will not be only in the if statement
+    PATH = ''
+    nom_reptrain = ''
+    nom_serie = ""
+    if state['pretrain_choice'] == 0:
+        nom_serie="series_NIST.h5"
+    elif state['pretrain_choice'] == 1:
+        nom_serie="series_P07.h5"
+
+    series = create_series(state.num_hidden_layers,nom_serie)
+
+
+    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, \
+                                    series=series,
+                                    max_minibatches=rtt)
+
+    parameters=[]
+    #Number of files of P07 used for pretraining
+    nb_file=0
+    if state['pretrain_choice'] == 0:
+        print('\n\tpretraining with NIST\n')
+        optimizer.pretrain(datasets.nist_all()) 
+    elif state['pretrain_choice'] == 1:
+        #To know how many file will be used during pretraining
+        nb_file = int(state['pretraining_epochs_per_layer']) 
+        state['pretraining_epochs_per_layer'] = 1 #Only 1 time over the dataset
+        if nb_file >=100:
+            sys.exit("The code does not support this much pretraining epoch (99 max with P07).\n"+
+            "You have to correct the code (and be patient, P07 is huge !!)\n"+
+             "or reduce the number of pretraining epoch to run the code (better idea).\n")
+        print('\n\tpretraining with P07')
+        optimizer.pretrain(datasets.nist_P07(min_file=0,max_file=nb_file)) 
+    channel.save()
+    
+    #Set some of the parameters used for the finetuning
+    if state.has_key('finetune_set'):
+        finetune_choice=state['finetune_set']
+    else:
+        finetune_choice=FINETUNE_SET
+    
+    if state.has_key('max_finetuning_epochs'):
+        max_finetune_epoch_NIST=state['max_finetuning_epochs']
+    else:
+        max_finetune_epoch_NIST=MAX_FINETUNING_EPOCHS
+    
+    if state.has_key('max_finetuning_epochs_P07'):
+        max_finetune_epoch_P07=state['max_finetuning_epochs_P07']
+    else:
+        max_finetune_epoch_P07=max_finetune_epoch_NIST
+    
+    #Decide how the finetune is done
+    
+    if finetune_choice == 0:
+        print('\n\n\tfinetune with NIST\n\n')
+        optimizer.reload_parameters('params_pretrain.txt')
+        optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1,decrease=decrease_lr)
+        channel.save()
+    if finetune_choice == 1:
+        print('\n\n\tfinetune with P07\n\n')
+        optimizer.reload_parameters('params_pretrain.txt')
+        optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0,decrease=decrease_lr)
+        channel.save()
+    if finetune_choice == 2:
+        print('\n\n\tfinetune with P07\n\n')
+        optimizer.reload_parameters('params_pretrain.txt')
+        optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=20,decrease=decrease_lr)
+        #optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=21,decrease=decrease_lr)
+        channel.save()
+    if finetune_choice == 3:
+        print('\n\n\tfinetune with NIST only on the logistic regression on top (but validation on P07).\n\
+        All hidden units output are input of the logistic regression\n\n')
+        optimizer.reload_parameters('params_pretrain.txt')
+        optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1,special=1,decrease=decrease_lr)
+        
+        
+    if finetune_choice==-1:
+        print('\nSERIE OF 4 DIFFERENT FINETUNINGS')
+        print('\n\n\tfinetune with NIST\n\n')
+        sys.stdout.flush()
+        optimizer.reload_parameters('params_pretrain.txt')
+        optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1,decrease=decrease_lr)
+        channel.save()
+        print('\n\n\tfinetune with P07\n\n')
+        sys.stdout.flush()
+        optimizer.reload_parameters('params_pretrain.txt')
+        optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0,decrease=decrease_lr)
+        channel.save()
+        print('\n\n\tfinetune with P07 (done earlier) followed by NIST (written here)\n\n')
+        sys.stdout.flush()
+        optimizer.reload_parameters('params_finetune_P07.txt')
+        optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=21,decrease=decrease_lr)
+        channel.save()
+        print('\n\n\tfinetune with NIST only on the logistic regression on top.\n\
+        All hidden units output are input of the logistic regression\n\n')
+        sys.stdout.flush()
+        optimizer.reload_parameters('params_pretrain.txt')
+        optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1,special=1,decrease=decrease_lr)
+        channel.save()
+    
+    channel.save()
+
+    return channel.COMPLETE
+
+# These Series objects are used to save various statistics
+# during the training.
+def create_series(num_hidden_layers, nom_serie):
+
+    # Replace series we don't want to save with DummySeries, e.g.
+    # series['training_error'] = DummySeries()
+
+    series = {}
+
+    basedir = os.getcwd()
+
+    h5f = tables.openFile(os.path.join(basedir, nom_serie), "w")
+
+    # reconstruction
+    reconstruction_base = \
+                ErrorSeries(error_name="reconstruction_error",
+                    table_name="reconstruction_error",
+                    hdf5_file=h5f,
+                    index_names=('epoch','minibatch'),
+                    title="Reconstruction error (mean over "+str(REDUCE_EVERY)+" minibatches)")
+    series['reconstruction_error'] = \
+                AccumulatorSeriesWrapper(base_series=reconstruction_base,
+                    reduce_every=REDUCE_EVERY)
+
+    # train
+    training_base = \
+                ErrorSeries(error_name="training_error",
+                    table_name="training_error",
+                    hdf5_file=h5f,
+                    index_names=('epoch','minibatch'),
+                    title="Training error (mean over "+str(REDUCE_EVERY)+" minibatches)")
+    series['training_error'] = \
+                AccumulatorSeriesWrapper(base_series=training_base,
+                    reduce_every=REDUCE_EVERY)
+
+    # valid and test are not accumulated/mean, saved directly
+    series['validation_error'] = \
+                ErrorSeries(error_name="validation_error",
+                    table_name="validation_error",
+                    hdf5_file=h5f,
+                    index_names=('epoch','minibatch'))
+
+    series['test_error'] = \
+                ErrorSeries(error_name="test_error",
+                    table_name="test_error",
+                    hdf5_file=h5f,
+                    index_names=('epoch','minibatch'))
+
+    param_names = []
+    for i in range(num_hidden_layers):
+        param_names += ['layer%d_W'%i, 'layer%d_b'%i, 'layer%d_bprime'%i]
+    param_names += ['logreg_layer_W', 'logreg_layer_b']
+
+    # comment out series we don't want to save
+    series['params'] = SharedParamsStatisticsWrapper(
+                        new_group_name="params",
+                        base_group="/",
+                        arrays_names=param_names,
+                        hdf5_file=h5f,
+                        index_names=('epoch',))
+
+    return series
+
+# 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_nist():
+    jobs = produit_cartesien_jobs(JOB_VALS)
+
+    db = jobman.sql.db(JOBDB)
+    for job in jobs:
+        job.update({jobman.sql.EXPERIMENT: EXPERIMENT_PATH})
+        jobman.sql.insert_dict(job, db)
+
+    print "inserted"
+
+if __name__ == '__main__':
+
+    args = sys.argv[1:]
+
+    #if len(args) > 0 and args[0] == 'load_nist':
+    #    test_load_nist()
+
+    if len(args) > 0 and args[0] == 'jobman_insert':
+        jobman_insert_nist()
+
+    elif len(args) > 0 and args[0] == 'test_jobman_entrypoint':
+        chanmock = DD({'COMPLETE':0,'save':(lambda:None)})
+        jobman_entrypoint(DD(DEFAULT_HP_NIST), chanmock)
+
+    else:
+        print "Bad arguments"
+