ift6266: deep/stacked_dae/v_youssouf/sgd_optimization.py comparison

comparison deep/stacked_dae/v_youssouf/sgd_optimization.py @ 371:8cf52a1c8055

initial commit of sda with 36 classes

author	youssouf
date	Sun, 25 Apr 2010 12:31:22 -0400
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children

comparison

equal deleted inserted replaced

-:a79db7cee035
+:8cf52a1c8055
+#!/usr/bin/python
+# coding: utf-8
+# Generic SdA optimization loop, adapted from the deeplearning.net tutorial
+import numpy
+import theano
+import time
+import datetime
+import theano.tensor as T
+import sys
+import pickle
+from jobman import DD
+import jobman, jobman.sql
+from copy import copy
+from stacked_dae import SdA
+from ift6266.utils.seriestables import *
+#For test purpose only
+buffersize=1000
+default_series = { \
+'reconstruction_error' : DummySeries(),
+'training_error' : DummySeries(),
+'validation_error' : DummySeries(),
+'test_error' : DummySeries(),
+'params' : DummySeries()
+}
+def itermax(iter, max):
+for i,it in enumerate(iter):
+if i >= max:
+break
+yield it
+class SdaSgdOptimizer:
+def __init__(self, dataset, hyperparameters, n_ins, n_outs,
+examples_per_epoch, series=default_series, max_minibatches=None):
+self.dataset = dataset
+self.hp = hyperparameters
+self.n_ins = n_ins
+self.n_outs = n_outs
+self.parameters_pre=[]
+self.max_minibatches = max_minibatches
+print "SdaSgdOptimizer, max_minibatches =", max_minibatches
+print "Reduce Label: ", self.hp.reduce_label
+self.ex_per_epoch = examples_per_epoch
+self.mb_per_epoch = examples_per_epoch / self.hp.minibatch_size
+self.series = series
+self.rng = numpy.random.RandomState(1234)
+self.init_classifier()
+sys.stdout.flush()
+def init_classifier(self):
+print "Constructing classifier"
+# we don't want to save arrays in DD objects, so
+# we recreate those arrays here
+nhl = self.hp.num_hidden_layers
+layers_sizes = [self.hp.hidden_layers_sizes] * nhl
+corruption_levels = [self.hp.corruption_levels] * nhl
+# construct the stacked denoising autoencoder class
+self.classifier = SdA( \
+batch_size = self.hp.minibatch_size, \
+n_ins= self.n_ins, \
+hidden_layers_sizes = layers_sizes, \
+n_outs = self.n_outs, \
+corruption_levels = corruption_levels,\
+rng = self.rng,\
+pretrain_lr = self.hp.pretraining_lr, \
+finetune_lr = self.hp.finetuning_lr, \
+detection_mode = self.hp.detection_mode, \
+)
+#theano.printing.pydotprint(self.classifier.pretrain_functions[0], "function.graph")
+sys.stdout.flush()
+def train(self):
+self.pretrain(self.dataset)
+self.finetune(self.dataset)
+def pretrain(self,dataset):
+print "STARTING PRETRAINING, time = ", datetime.datetime.now()
+sys.stdout.flush()
+un_fichier=int(819200.0/self.hp.minibatch_size) #Number of batches in a P07 file
+start_time = time.clock()
+## Pre-train layer-wise
+for i in xrange(self.classifier.n_layers):
+# go through pretraining epochs
+for epoch in xrange(self.hp.pretraining_epochs_per_layer):
+# go through the training set
+batch_index=0
+count=0
+num_files=0
+for x,y in dataset.train(self.hp.minibatch_size):
+c = self.classifier.pretrain_functions[i](x)
+count +=1
+self.series["reconstruction_error"].append((epoch, batch_index), c)
+batch_index+=1
+#if batch_index % 100 == 0:
+#    print "100 batches"
+# useful when doing tests
+if self.max_minibatches and batch_index >= self.max_minibatches:
+break
+#When we pass through the data only once (the case with P07)
+#There is approximately 800*1024=819200 examples per file (1k per example and files are 800M)
+if self.hp.pretraining_epochs_per_layer == 1 and count%un_fichier == 0:
+print 'Pre-training layer %i, epoch %d, cost '%(i,num_files),c
+num_files+=1
+sys.stdout.flush()
+self.series['params'].append((num_files,), self.classifier.all_params)
+#When NIST is used
+if self.hp.pretraining_epochs_per_layer > 1:
+print 'Pre-training layer %i, epoch %d, cost '%(i,epoch),c
+sys.stdout.flush()
+self.series['params'].append((epoch,), self.classifier.all_params)
+end_time = time.clock()
+print ('Pretraining took %f minutes' %((end_time-start_time)/60.))
+self.hp.update({'pretraining_time': end_time-start_time})
+sys.stdout.flush()
+#To be able to load them later for tests on finetune
+self.parameters_pre=[copy(x.value) for x in self.classifier.params]
+f = open('params_pretrain.txt', 'w')
+pickle.dump(self.parameters_pre,f)
+f.close()
+def finetune(self,dataset,dataset_test,num_finetune,ind_test,special=0,decrease=0):
+if special != 0 and special != 1:
+sys.exit('Bad value for variable special. Must be in {0,1}')
+print "STARTING FINETUNING, time = ", datetime.datetime.now()
+minibatch_size = self.hp.minibatch_size
+if ind_test == 0 or ind_test == 20:
+nom_test = "NIST"
+nom_train="P07"
+else:
+nom_test = "P07"
+nom_train = "NIST"
+# create a function to compute the mistakes that are made by the model
+# on the validation set, or testing set
+test_model = \
+theano.function(
+[self.classifier.x,self.classifier.y], self.classifier.errors)
+#         givens = {
+#           self.classifier.x: ensemble_x,
+#           self.classifier.y: ensemble_y]})
+validate_model = \
+theano.function(
+[self.classifier.x,self.classifier.y], self.classifier.errors)
+#        givens = {
+#           self.classifier.x: ,
+#           self.classifier.y: ]})
+# early-stopping parameters
+patience              = 10000 # look as this many examples regardless
+patience_increase     = 2.    # wait this much longer when a new best is
+# found
+improvement_threshold = 0.995 # a relative improvement of this much is
+# considered significant
+validation_frequency  = min(self.mb_per_epoch, patience/2)
+# go through this many
+# minibatche before checking the network
+# on the validation set; in this case we
+# check every epoch
+if self.max_minibatches and validation_frequency > self.max_minibatches:
+validation_frequency = self.max_minibatches / 2
+best_params          = None
+best_validation_loss = float('inf')
+test_score           = 0.
+start_time = time.clock()
+done_looping = False
+epoch = 0
+total_mb_index = 0
+minibatch_index = 0
+parameters_finetune=[]
+if ind_test == 21:
+learning_rate = self.hp.finetuning_lr / 10.0
+else:
+learning_rate = self.hp.finetuning_lr  #The initial finetune lr
+while (epoch < num_finetune) and (not done_looping):
+epoch = epoch + 1
+for x,y in dataset.train(minibatch_size,bufsize=buffersize):
+minibatch_index += 1
+if self.hp.reduce_label:
+y[y > 35] = y[y > 35]-26
+if special == 0:
+cost_ij = self.classifier.finetune(x,y,learning_rate)
+elif special == 1:
+cost_ij = self.classifier.finetune2(x,y)
+total_mb_index += 1
+self.series["training_error"].append((epoch, minibatch_index), cost_ij)
+if (total_mb_index+1) % validation_frequency == 0:
+#minibatch_index += 1
+#The validation set is always NIST (we want the model to be good on NIST)
+if ind_test == 0 | ind_test == 20:
+iter=dataset_test.valid(minibatch_size,bufsize=buffersize)
+else:
+iter = dataset.valid(minibatch_size,bufsize=buffersize)
+if self.max_minibatches:
+iter = itermax(iter, self.max_minibatches)
+validation_losses = [validate_model(x,y) for x,y in iter]
+this_validation_loss = numpy.mean(validation_losses)
+self.series["validation_error"].\
+append((epoch, minibatch_index), this_validation_loss*100.)
+print('epoch %i, minibatch %i, validation error on NIST : %f %%' % \
+(epoch, minibatch_index+1, \
+this_validation_loss*100.))
+# if we got the best validation score until now
+if this_validation_loss < best_validation_loss:
+#improve patience if loss improvement is good enough
+if this_validation_loss < best_validation_loss *  \
+improvement_threshold :
+patience = max(patience, total_mb_index * patience_increase)
+# save best validation score, iteration number and parameters
+best_validation_loss = this_validation_loss
+best_iter = total_mb_index
+parameters_finetune=[copy(x.value) for x in self.classifier.params]
+# test it on the test set
+iter = dataset.test(minibatch_size,bufsize=buffersize)
+if self.max_minibatches:
+iter = itermax(iter, self.max_minibatches)
+test_losses = [test_model(x,y) for x,y in iter]
+test_score = numpy.mean(test_losses)
+#test it on the second test set
+iter2 = dataset_test.test(minibatch_size,bufsize=buffersize)
+if self.max_minibatches:
+iter2 = itermax(iter2, self.max_minibatches)
+test_losses2 = [test_model(x,y) for x,y in iter2]
+test_score2 = numpy.mean(test_losses2)
+self.series["test_error"].\
+append((epoch, minibatch_index), test_score*100.)
+print(('     epoch %i, minibatch %i, test error on dataset %s  (train data) of best '
+'model %f %%') %
+(epoch, minibatch_index+1,nom_train,
+test_score*100.))
+print(('     epoch %i, minibatch %i, test error on dataset %s of best '
+'model %f %%') %
+(epoch, minibatch_index+1,nom_test,
+test_score2*100.))
+if patience <= total_mb_index:
+done_looping = True
+break   #to exit the FOR loop
+sys.stdout.flush()
+# useful when doing tests
+if self.max_minibatches and minibatch_index >= self.max_minibatches:
+break
+if decrease == 1:
+learning_rate /= 2 #divide the learning rate by 2 for each new epoch
+self.series['params'].append((epoch,), self.classifier.all_params)
+if done_looping == True:    #To exit completly the fine-tuning
+break   #to exit the WHILE loop
+end_time = time.clock()
+self.hp.update({'finetuning_time':end_time-start_time,\
+'best_validation_error':best_validation_loss,\
+'test_score':test_score,
+'num_finetuning_epochs':epoch})
+print(('\nOptimization complete with best validation score of %f %%,'
+'with test performance %f %% on dataset %s ') %
+(best_validation_loss * 100., test_score*100.,nom_train))
+print(('The test score on the %s dataset is %f')%(nom_test,test_score2*100.))
+print ('The finetuning ran for %f minutes' % ((end_time-start_time)/60.))
+sys.stdout.flush()
+#Save a copy of the parameters in a file to be able to get them in the future
+if special == 1:    #To keep a track of the value of the parameters
+f = open('params_finetune_stanford.txt', 'w')
+pickle.dump(parameters_finetune,f)
+f.close()
+elif ind_test == 0 | ind_test == 20:    #To keep a track of the value of the parameters
+f = open('params_finetune_P07.txt', 'w')
+pickle.dump(parameters_finetune,f)
+f.close()
+elif ind_test== 1:    #For the run with 2 finetunes. It will be faster.
+f = open('params_finetune_NIST.txt', 'w')
+pickle.dump(parameters_finetune,f)
+f.close()
+elif ind_test== 21:    #To keep a track of the value of the parameters
+f = open('params_finetune_P07_then_NIST.txt', 'w')
+pickle.dump(parameters_finetune,f)
+f.close()
+#Set parameters like they where right after pre-train or finetune
+def reload_parameters(self,which):
+#self.parameters_pre=pickle.load('params_pretrain.txt')
+f = open(which)
+self.parameters_pre=pickle.load(f)
+f.close()
+for idx,x in enumerate(self.parameters_pre):
+if x.dtype=='float64':
+self.classifier.params[idx].value=theano._asarray(copy(x),dtype=theano.config.floatX)
+else:
+self.classifier.params[idx].value=copy(x)
+def training_error(self,dataset):
+# create a function to compute the mistakes that are made by the model
+# on the validation set, or testing set
+test_model = \
+theano.function(
+[self.classifier.x,self.classifier.y], self.classifier.errors)
+iter2 = dataset.train(self.hp.minibatch_size,bufsize=buffersize)
+train_losses2 = [test_model(x,y) for x,y in iter2]
+train_score2 = numpy.mean(train_losses2)
+print "Training error is: " + str(train_score2)

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comparison deep/stacked_dae/v_youssouf/sgd_optimization.py @ 371:8cf52a1c8055