--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/deep/stacked_dae/v_guillaume/sgd_optimization.py	Mon May 03 06:14:05 2010 -0400
@@ -0,0 +1,395 @@
+#!/usr/bin/python
+# -*- coding: utf-8 -*-
+# 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
+import cPickle
+
+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_name, dataset, hyperparameters, n_ins, n_outs,
+                    examples_per_epoch, series=default_series, max_minibatches=None):
+	self.dataset_name = dataset_name
+        self.dataset = dataset
+        self.hp = hyperparameters
+        self.n_ins = n_ins
+        self.n_outs = n_outs
+        self.parameters_pre=[]
+   
+	if (self.dataset_name == "upper"):
+	  self.class_offset = 10
+	elif (self.dataset_name == "lower"):
+	  self.class_offset = 36
+	else:
+	  self.class_offset = 0
+
+
+        self.max_minibatches = max_minibatches
+        print "SdaSgdOptimizer, max_minibatches =", max_minibatches
+
+        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)
+
+        #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,decrease=0):
+        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()  
+        
+        ########  This is hardcoaded. THe 0.95 parameter is hardcoaded and can be changed at will  ###
+        #Set the decreasing rate of the learning rate. We want the final learning rate to
+        #be 5% of the original learning rate. The decreasing factor is linear
+        decreasing = (decrease*self.hp.pretraining_lr)/float(self.hp.pretraining_epochs_per_layer*800000/self.hp.minibatch_size)
+        
+        ## Pre-train layer-wise 
+        for i in xrange(self.classifier.n_layers):
+            # go through pretraining epochs 
+            
+            #To reset the learning rate to his original value
+            learning_rate=self.hp.pretraining_lr
+            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):
+		    y = y - self.class_offset
+                    c = self.classifier.pretrain_functions[i](x,learning_rate)
+                    count +=1
+
+                    self.series["reconstruction_error"].append((epoch, batch_index), c)
+                    batch_index+=1
+
+                    #If we need to decrease the learning rate for the pretrain
+                    if decrease != 0:
+                        learning_rate -= decreasing
+
+                    # 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')
+        cPickle.dump(self.parameters_pre,f,protocol=-1)
+        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
+                
+                y = y - self.class_offset
+
+                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 - self.class_offset) 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 - self.class_offset) 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 - self.class_offset) 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:
+                if (ind_test == 21 & epoch % 100 == 0) | ind_test == 20 | (ind_test == 1 & epoch % 100 == 0) :
+                    learning_rate /= 2 #divide the learning rate by 2 for each new epoch of P07 (or 100 of NIST)
+            
+            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')
+            cPickle.dump(parameters_finetune,f,protocol=-1)
+            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')
+            cPickle.dump(parameters_finetune,f,protocol=-1)
+            f.close()
+               
+
+        elif ind_test== 1:    #For the run with 2 finetunes. It will be faster.
+            f = open('params_finetune_NIST.txt', 'w')
+            cPickle.dump(parameters_finetune,f,protocol=-1)
+            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')
+            cPickle.dump(parameters_finetune,f,protocol=-1)
+            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=cPickle.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 - self.class_offset) for x,y in iter2]
+        train_score2 = numpy.mean(train_losses2)
+        print "Training error is: " + str(train_score2)
+
+
+
+