# HG changeset patch # User SylvainPL # Date 1269634676 14400 # Node ID f9b93ae45723deef4a9c7cfb3f04d9d667893bee # Parent 1cc535f3e254b76ae2a7324df28c0118dae797d0 Programme pour reprendre une partie des experiences seulement. Utile seulement pour un usage tres specifique diff -r 1cc535f3e254 -r f9b93ae45723 deep/stacked_dae/v_sylvain/nist_sda_retrieve.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/deep/stacked_dae/v_sylvain/nist_sda_retrieve.py Fri Mar 26 16:17:56 2010 -0400 @@ -0,0 +1,251 @@ +#!/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 config2 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 + + series = create_series(state.num_hidden_layers) + + 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 = 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)) + print ('Retrieve pre-train done earlier') + + sys.stdout.flush() + + #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('/u/pannetis/IFT6266/ift6266/deep/stacked_dae/v_sylvain/ift6266h10_db/pannetis_finetuningSDA/1/params_pretrain.txt') + optimizer.finetune(datasets.nist_all(),datasets.nist_P07(min_file=nb_file),max_finetune_epoch_NIST,ind_test=1) + channel.save() + if finetune_choice == 1: + print('\n\n\tfinetune with P07\n\n') + optimizer.reload_parameters('/u/pannetis/IFT6266/ift6266/deep/stacked_dae/v_sylvain/ift6266h10_db/pannetis_finetuningSDA/1/params_pretrain.txt') + optimizer.finetune(datasets.nist_P07(min_file=nb_file),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0) + channel.save() + if finetune_choice == 2: + print('\n\n\tfinetune with NIST followed by P07\n\n') + optimizer.reload_parameters('/u/pannetis/IFT6266/ift6266/deep/stacked_dae/v_sylvain/ift6266h10_db/pannetis_finetuningSDA/1/params_pretrain.txt') + optimizer.finetune(datasets.nist_all(),datasets.nist_P07(min_file=nb_file),max_finetune_epoch_NIST,ind_test=21) + optimizer.finetune(datasets.nist_P07(min_file=nb_file),datasets.nist_all(),max_finetune_epoch_P07,ind_test=20) + 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('/u/pannetis/IFT6266/ift6266/deep/stacked_dae/v_sylvain/ift6266h10_db/pannetis_finetuningSDA/1/params_pretrain.txt') + optimizer.finetune(datasets.nist_all(),datasets.nist_P07(min_file=nb_file),max_finetune_epoch_NIST,ind_test=1,special=1) + + + if finetune_choice==-1: + print('\nSERIE OF 3 DIFFERENT FINETUNINGS') + print('\n\n\tfinetune with NIST\n\n') + sys.stdout.flush() + optimizer.reload_parameters('/u/pannetis/IFT6266/ift6266/deep/stacked_dae/v_sylvain/ift6266h10_db/pannetis_finetuningSDA/1/params_pretrain.txt') + optimizer.finetune(datasets.nist_all(),datasets.nist_P07(min_file=nb_file),max_finetune_epoch_NIST,ind_test=1) + channel.save() + print('\n\n\tfinetune with P07\n\n') + sys.stdout.flush() + optimizer.reload_parameters('/u/pannetis/IFT6266/ift6266/deep/stacked_dae/v_sylvain/ift6266h10_db/pannetis_finetuningSDA/1/params_pretrain.txt') + optimizer.finetune(datasets.nist_P07(min_file=nb_file),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0) + channel.save() + print('\n\n\tfinetune with NIST (done earlier) followed by P07 (written here)\n\n') + sys.stdout.flush() + optimizer.reload_parameters('/u/pannetis/IFT6266/ift6266/deep/stacked_dae/v_sylvain/ift6266h10_db/pannetis_finetuningSDA/1/params_finetune_NIST.txt') + optimizer.finetune(datasets.nist_P07(min_file=nb_file),datasets.nist_all(),max_finetune_epoch_P07,ind_test=20) + 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('/u/pannetis/IFT6266/ift6266/deep/stacked_dae/v_sylvain/ift6266h10_db/pannetis_finetuningSDA/1/params_pretrain.txt') + optimizer.finetune(datasets.nist_all(),datasets.nist_P07(min_file=nb_file),max_finetune_epoch_NIST,ind_test=1,special=1) + 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): + + # 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, "series.h5"), "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" +