--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/deep/stacked_dae/sgd_optimization.py	Fri Feb 26 14:15:38 2010 -0500
@@ -0,0 +1,270 @@
+#!/usr/bin/python
+# coding: utf-8
+
+# Generic SdA optimization loop, adapted from the deeplearning.net tutorial
+
+import numpy 
+import theano
+import time
+import theano.tensor as T
+import copy
+import sys
+
+from jobman import DD
+import jobman, jobman.sql
+
+from stacked_dae import SdA
+
+def shared_dataset(data_xy):
+    data_x, data_y = data_xy
+    #shared_x = theano.shared(numpy.asarray(data_x, dtype=theano.config.floatX))
+    #shared_y = theano.shared(numpy.asarray(data_y, dtype=theano.config.floatX))
+    #shared_y = T.cast(shared_y, 'int32')
+    shared_x = theano.shared(data_x)
+    shared_y = theano.shared(data_y)
+    return shared_x, shared_y
+
+class SdaSgdOptimizer:
+    def __init__(self, dataset, hyperparameters, n_ins, n_outs, input_divider=1.0,\
+                job_tree=False, results_db=None,\
+                experiment="",\
+                num_hidden_layers_to_try=[1,2,3], \
+                finetuning_lr_to_try=[0.1, 0.01, 0.001, 0.0001, 0.00001]):
+
+        self.dataset = dataset
+        self.hp = copy.copy(hyperparameters)
+        self.n_ins = n_ins
+        self.n_outs = n_outs
+        self.input_divider = numpy.asarray(input_divider, dtype=theano.config.floatX)
+
+        self.job_tree = job_tree
+        self.results_db = results_db
+        self.experiment = experiment
+        if self.job_tree:
+            assert(not results_db is None)
+            # these hp should not be there, so we insert default values
+            # we use 3 hidden layers as we'll iterate through 1,2,3
+            self.hp.finetuning_lr = 0.1 # dummy value, will be replaced anyway
+            cl = self.hp.corruption_levels
+            nh = self.hp.hidden_layers_sizes
+            self.hp.corruption_levels = [cl,cl,cl]
+            self.hp.hidden_layers_sizes = [nh,nh,nh]
+            
+        self.num_hidden_layers_to_try = num_hidden_layers_to_try
+        self.finetuning_lr_to_try = finetuning_lr_to_try
+
+        self.printout_frequency = 1000
+
+        self.rng = numpy.random.RandomState(1234)
+
+        self.init_datasets()
+        self.init_classifier()
+     
+    def init_datasets(self):
+        print "init_datasets"
+        train_set, valid_set, test_set = self.dataset
+        self.test_set_x, self.test_set_y = shared_dataset(test_set)
+        self.valid_set_x, self.valid_set_y = shared_dataset(valid_set)
+        self.train_set_x, self.train_set_y = shared_dataset(train_set)
+
+        # compute number of minibatches for training, validation and testing
+        self.n_train_batches = self.train_set_x.value.shape[0] / self.hp.minibatch_size
+        self.n_valid_batches = self.valid_set_x.value.shape[0] / self.hp.minibatch_size
+        self.n_test_batches  = self.test_set_x.value.shape[0]  / self.hp.minibatch_size
+
+    def init_classifier(self):
+        print "Constructing classifier"
+        # construct the stacked denoising autoencoder class
+        self.classifier = SdA( \
+                          train_set_x= self.train_set_x, \
+                          train_set_y = self.train_set_y,\
+                          batch_size = self.hp.minibatch_size, \
+                          n_ins= self.n_ins, \
+                          hidden_layers_sizes = self.hp.hidden_layers_sizes, \
+                          n_outs = self.n_outs, \
+                          corruption_levels = self.hp.corruption_levels,\
+                          rng = self.rng,\
+                          pretrain_lr = self.hp.pretraining_lr, \
+                          finetune_lr = self.hp.finetuning_lr,\
+                          input_divider = self.input_divider )
+
+    def train(self):
+        self.pretrain()
+        if not self.job_tree:
+            # if job_tree is True, finetuning was already performed
+            self.finetune()
+
+    def pretrain(self):
+        print "STARTING PRETRAINING"
+
+        printout_acc = 0.0
+        last_error = 0.0
+
+        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
+                for batch_index in xrange(self.n_train_batches):
+                    c = self.classifier.pretrain_functions[i](batch_index)
+
+                    printout_acc += c / self.printout_frequency
+                    if (batch_index+1) % self.printout_frequency == 0:
+                        print batch_index, "reconstruction cost avg=", printout_acc
+                        last_error = printout_acc
+                        printout_acc = 0.0
+                        
+                print 'Pre-training layer %i, epoch %d, cost '%(i,epoch),c
+
+            self.job_splitter(i+1, time.clock()-start_time, last_error)
+     
+        end_time = time.clock()
+
+        print ('Pretraining took %f minutes' %((end_time-start_time)/60.))
+
+    # Save time by reusing intermediate results
+    def job_splitter(self, current_pretraining_layer, pretraining_time, last_error):
+
+        state_copy = None
+        original_classifier = None
+
+        if self.job_tree and current_pretraining_layer in self.num_hidden_layers_to_try:
+            for lr in self.finetuning_lr_to_try:
+                sys.stdout.flush()
+                sys.stderr.flush()
+
+                state_copy = copy.copy(self.hp)
+
+                self.hp.update({'num_hidden_layers':current_pretraining_layer, \
+                            'finetuning_lr':lr,\
+                            'pretraining_time':pretraining_time,\
+                            'last_reconstruction_error':last_error})
+
+                original_classifier = self.classifier
+                print "ORIGINAL CLASSIFIER MEANS",original_classifier.get_params_means()
+                self.classifier = SdA.copy_reusing_lower_layers(original_classifier, current_pretraining_layer, new_finetuning_lr=lr)
+                
+                self.finetune()
+            
+                self.insert_finished_job()
+
+                print "NEW CLASSIFIER MEANS AFTERWARDS",self.classifier.get_params_means()
+                print "ORIGINAL CLASSIFIER MEANS AFTERWARDS",original_classifier.get_params_means()
+                self.classifier = original_classifier
+                self.hp = state_copy
+
+    def insert_finished_job(self):
+        job = copy.copy(self.hp)
+        job[jobman.sql.STATUS] = jobman.sql.DONE
+        job[jobman.sql.EXPERIMENT] = self.experiment
+
+        # don,t try to store arrays in db
+        job['hidden_layers_sizes'] = job.hidden_layers_sizes[0]
+        job['corruption_levels'] = job.corruption_levels[0]
+
+        print "Will insert finished job", job
+        jobman.sql.insert_dict(jobman.flatten(job), self.results_db)
+
+    def finetune(self):
+        print "STARTING FINETUNING"
+
+        index   = T.lscalar()    # index to a [mini]batch 
+        minibatch_size = self.hp.minibatch_size
+
+        # create a function to compute the mistakes that are made by the model
+        # on the validation set, or testing set
+        test_model = theano.function([index], self.classifier.errors,
+                 givens = {
+                   self.classifier.x: self.test_set_x[index*minibatch_size:(index+1)*minibatch_size] / self.input_divider,
+                   self.classifier.y: self.test_set_y[index*minibatch_size:(index+1)*minibatch_size]})
+
+        validate_model = theano.function([index], self.classifier.errors,
+                givens = {
+                   self.classifier.x: self.valid_set_x[index*minibatch_size:(index+1)*minibatch_size] / self.input_divider,
+                   self.classifier.y: self.valid_set_y[index*minibatch_size:(index+1)*minibatch_size]})
+
+
+        # 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.n_train_batches, patience/2)
+                                      # go through this many 
+                                      # minibatche before checking the network 
+                                      # on the validation set; in this case we 
+                                      # check every epoch 
+
+        best_params          = None
+        best_validation_loss = float('inf')
+        test_score           = 0.
+        start_time = time.clock()
+
+        done_looping = False
+        epoch = 0
+
+        printout_acc = 0.0
+
+        if not self.hp.has_key('max_finetuning_epochs'):
+            self.hp.max_finetuning_epochs = 1000
+
+        while (epoch < self.hp.max_finetuning_epochs) and (not done_looping):
+            epoch = epoch + 1
+            for minibatch_index in xrange(self.n_train_batches):
+
+                cost_ij = self.classifier.finetune(minibatch_index)
+                iter    = epoch * self.n_train_batches + minibatch_index
+
+                printout_acc += cost_ij / float(self.printout_frequency * minibatch_size)
+                if (iter+1) % self.printout_frequency == 0:
+                    print iter, "cost avg=", printout_acc
+                    printout_acc = 0.0
+
+                if (iter+1) % validation_frequency == 0: 
+                    
+                    validation_losses = [validate_model(i) for i in xrange(self.n_valid_batches)]
+                    this_validation_loss = numpy.mean(validation_losses)
+                    print('epoch %i, minibatch %i/%i, validation error %f %%' % \
+                           (epoch, minibatch_index+1, self.n_train_batches, \
+                            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, iter * patience_increase)
+
+                        # save best validation score and iteration number
+                        best_validation_loss = this_validation_loss
+                        best_iter = iter
+
+                        # test it on the test set
+                        test_losses = [test_model(i) for i in xrange(self.n_test_batches)]
+                        test_score = numpy.mean(test_losses)
+                        print(('     epoch %i, minibatch %i/%i, test error of best '
+                              'model %f %%') % 
+                                     (epoch, minibatch_index+1, self.n_train_batches,
+                                      test_score*100.))
+
+
+            if patience <= iter :
+                done_looping = True
+                break
+
+        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(('Optimization complete with best validation score of %f %%,'
+               'with test performance %f %%') %  
+                     (best_validation_loss * 100., test_score*100.))
+        print ('The finetuning ran for %f minutes' % ((end_time-start_time)/60.))
+
+
+