--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/deep/convolutional_dae/salah_exp/sgd_optimization_new.py	Thu Apr 22 19:50:21 2010 -0400
@@ -0,0 +1,394 @@
+#!/usr/bin/python
+# coding: utf-8
+
+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_convolutional_dae_uit import CSdA
+
+from ift6266.utils.seriestables import *
+
+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
+def get_conv_shape(kernels,imgshp,batch_size,max_pool_layers):
+    # Returns the dimension at the output of the convoluational net
+    # and a list of Image and kernel shape for every
+    # Convolutional layer
+    conv_layers=[]
+    init_layer = [ [ kernels[0][0],1,kernels[0][1],kernels[0][2] ],\
+                   [ batch_size , 1, imgshp[0], imgshp[1] ],
+                    max_pool_layers[0] ]
+    conv_layers.append(init_layer)
+
+    conv_n_out = int((32-kernels[0][2]+1)/max_pool_layers[0][0])
+
+    for i in range(1,len(kernels)):
+        layer = [ [ kernels[i][0],kernels[i-1][0],kernels[i][1],kernels[i][2] ],\
+                  [ batch_size, kernels[i-1][0],conv_n_out,conv_n_out ],
+                   max_pool_layers[i] ]
+        conv_layers.append(layer)
+        conv_n_out = int( (conv_n_out - kernels[i][2]+1)/max_pool_layers[i][0])
+    conv_n_out=kernels[-1][0]*conv_n_out**2
+    return conv_n_out,conv_layers
+
+
+
+
+
+class CSdASgdOptimizer:
+    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 "CSdASgdOptimizer, 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"
+
+        n_ins,convlayers = get_conv_shape(self.hp.kernels,self.hp.imgshp,self.hp.minibatch_size,self.hp.max_pool_layers)
+
+        self.classifier = CSdA(n_ins_mlp = n_ins,
+                               batch_size = self.hp.minibatch_size,
+                               conv_hidden_layers_sizes = convlayers,
+                               mlp_hidden_layers_sizes = self.hp.mlp_size, 
+                               corruption_levels = self.hp.corruption_levels,
+                               rng = self.rng, 
+                               n_out = self.n_outs,
+                               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):
+        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):
+                    if x.shape[0] != self.hp.minibatch_size:
+                        continue
+                    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=[]
+        learning_rate = self.hp.finetuning_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 x.shape[0] != self.hp.minibatch_size:
+                    print 'bim'
+                    continue
+
+                cost_ij = self.classifier.finetune(x,y)#,learning_rate)
+                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)
+
+                    iter=dataset_test.valid(minibatch_size,bufsize=buffersize)
+ 
+
+                    if self.max_minibatches:
+                        iter = itermax(iter, self.max_minibatches)
+
+                    validation_losses = []
+
+                    for x,y in iter:
+                        if x.shape[0] != self.hp.minibatch_size:
+                            print 'bim'
+                            continue
+                        validation_losses.append(validate_model(x,y))
+
+                    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_losses2 = []
+                        for x,y in iter:
+                            if x.shape[0] != self.hp.minibatch_size:
+                                print 'bim'
+                                continue
+                            test_losses.append(test_model(x,y))
+
+                        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)
+                        for x,y in iter2:
+                            if x.shape[0] != self.hp.minibatch_size:
+                                continue
+                            test_losses2.append(test_model(x,y))
+
+                        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)