--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/deep/stacked_dae/v2/sgd_optimization.py	Fri Mar 12 10:31:10 2010 -0500
@@ -0,0 +1,246 @@
+#!/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
+
+from jobman import DD
+import jobman, jobman.sql
+
+from stacked_dae import SdA
+
+from ift6266.utils.seriestables import *
+
+def shared_dataset(data_xy):
+    data_x, data_y = data_xy
+    if theano.config.device.startswith("gpu"):
+        print "TRANSFERING DATASETS (via shared()) TO GPU"
+        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')
+    else:
+        shared_x = theano.shared(data_x)
+        shared_y = theano.shared(data_y)
+    return shared_x, shared_y
+
+default_series = { \
+        'reconstruction_error' : DummySeries(),
+        'training_error' : DummySeries(),
+        'validation_error' : DummySeries(),
+        'test_error' : DummySeries(),
+        'params' : DummySeries()
+        }
+
+class SdaSgdOptimizer:
+    def __init__(self, dataset, hyperparameters, n_ins, n_outs, input_divider=1.0, series=default_series):
+        self.dataset = dataset
+        self.hp = hyperparameters
+        self.n_ins = n_ins
+        self.n_outs = n_outs
+        self.input_divider = input_divider
+   
+        self.series = series
+
+        self.rng = numpy.random.RandomState(1234)
+
+        self.init_datasets()
+        self.init_classifier()
+
+        sys.stdout.flush()
+     
+    def init_datasets(self):
+        print "init_datasets"
+        sys.stdout.flush()
+
+        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
+        # remove last batch in case it's incomplete
+        self.n_test_batches  = (self.test_set_x.value.shape[0]  / self.hp.minibatch_size) - 1
+
+    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( \
+                          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 = 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,\
+                          input_divider = self.input_divider )
+
+        #theano.printing.pydotprint(self.classifier.pretrain_functions[0], "function.graph")
+
+        sys.stdout.flush()
+
+    def train(self):
+        self.pretrain()
+        self.finetune()
+
+    def pretrain(self):
+        print "STARTING PRETRAINING, time = ", datetime.datetime.now()
+        sys.stdout.flush()
+
+        time_acc_func = 0.0
+        time_acc_total = 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):
+                    t1 = time.clock()
+                    c = self.classifier.pretrain_functions[i](batch_index)
+                    t2 = time.clock()
+
+                    time_acc_func += t2 - t1
+
+                    if batch_index % 500 == 0:
+                        print "acc / total", time_acc_func / (t2 - start_time), time_acc_func
+
+                    self.series["reconstruction_error"].append((epoch, batch_index), c)
+                        
+                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()
+
+    def finetune(self):
+        print "STARTING FINETUNING, time = ", datetime.datetime.now()
+
+        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
+        shared_divider = theano.shared(numpy.asarray(self.input_divider, dtype=theano.config.floatX))
+        test_model = theano.function([index], self.classifier.errors,
+                 givens = {
+                   self.classifier.x: self.test_set_x[index*minibatch_size:(index+1)*minibatch_size] / shared_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] / shared_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
+
+        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
+
+                self.series["training_error"].append((epoch, minibatch_index), cost_ij)
+
+                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)
+
+                    self.series["validation_error"].\
+                        append((epoch, minibatch_index), this_validation_loss*100.)
+
+                    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)
+
+                        self.series["test_error"].\
+                            append((epoch, minibatch_index), test_score*100.)
+
+                        print(('     epoch %i, minibatch %i/%i, test error of best '
+                              'model %f %%') % 
+                                     (epoch, minibatch_index+1, self.n_train_batches,
+                                      test_score*100.))
+
+                    sys.stdout.flush()
+
+            self.series['params'].append((epoch,), self.classifier.all_params)
+
+            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.))
+
+
+