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()
class SdaSgdOptimizer:
def __init__(self, dataset, hyperparameters, n_ins, n_outs, input_divider=1.0, series_mux=None):
self.dataset = dataset
self.hp = hyperparameters
self.n_ins = n_ins
self.n_outs = n_outs
self.input_divider = input_divider
if not series_mux:
series_mux = DummyMux()
print "No series multiplexer set"
self.series_mux = series_mux
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_mux.append("reconstruction_error", c)
print 'Pre-training layer %i, epoch %d, cost '%(i,epoch),c
sys.stdout.flush()
self.series_mux.append("params", 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_mux.append("training_error", 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_mux.append("validation_error", this_validation_loss)
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_mux.append("test_error", test_score)
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_mux.append("params", 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.))
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)
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
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):
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) / 60.0))
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, dataset_test2=None):
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"
elif ind_test == 30:
nom_train = "PNIST07"
nom_test = "NIST"
nom_test2 = "P07"
elif ind_test == 31:
nom_train = "NIST"
nom_test = "PNIST07"
nom_test2 = "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 = 2.0 # 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 = 0.0
start_time = time.clock()
done_looping = False
epoch = 0
total_mb_index = 0
minibatch_index = 0
parameters_finetune = []
if ind_test == 21 | ind_test == 31:
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 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 | ind_test == 30:
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 * 100.0)
print (
"epoch %i, minibatch %i, validation error on NIST : %f %%"
% (epoch, minibatch_index + 1, this_validation_loss * 100.0)
)
# 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)
# test it on the third test set if there is one
iter3 = dataset_test2.test(minibatch_size, bufsize=buffersize)
if self.max_minibatches:
iter3 = itermax(iter3, self.max_minibatches)
test_losses3 = [test_model(x, y) for x, y in iter3]
test_score3 = numpy.mean(test_losses3)
self.series["test_error"].append((epoch, minibatch_index), test_score * 100.0)
print (
(
" epoch %i, minibatch %i, test error on dataset %s (train data) of best "
"model %f %%"
)
% (epoch, minibatch_index + 1, nom_train, test_score * 100.0)
)
print (
(" epoch %i, minibatch %i, test error on dataset %s of best " "model %f %%")
% (epoch, minibatch_index + 1, nom_test, test_score2 * 100.0)
)
print (
(" epoch %i, minibatch %i, test error on dataset %s of best " "model %f %%")
% (epoch, minibatch_index + 1, nom_test2, test_score3 * 100.0)
)
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
== 30 | (ind_test == 31 & 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 * 100.0, test_score * 100.0, nom_train)
)
print (("The test score on the %s dataset is %f") % (nom_test, test_score2 * 100.0))
print (("The test score on the %s dataset is %f") % (nom_test2, test_score3 * 100.0))
print ("The finetuning ran for %f minutes" % ((end_time - start_time) / 60.0))
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()
elif ind_test == 30:
f = open("params_finetune_PNIST07.txt", "w")
cPickle.dump(parameters_finetune, f, protocol=-1)
f.close()
elif ind_test == 31:
f = open("params_finetune_PNIST07_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, part=0):
import math
# 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)
# train
if part == 0:
iter2 = dataset.train(self.hp.minibatch_size, bufsize=buffersize)
name = "train"
# validation
if part == 1:
iter2 = dataset.valid(self.hp.minibatch_size, bufsize=buffersize)
name = "validation"
if part == 2:
iter2 = dataset.test(self.hp.minibatch_size, bufsize=buffersize)
name = "test"
train_losses2 = [test_model(x, y) for x, y in iter2]
train_score2 = numpy.mean(train_losses2)
print "On the " + name + "dataset"
print (("\t the error is %f") % (train_score2 * 100.0))
# print len(train_losses2)
stderr = math.sqrt(train_score2 - train_score2 ** 2) / math.sqrt(len(train_losses2) * self.hp.minibatch_size)
print (("\t the stderr is %f") % (stderr * 100.0))
# To see the prediction of the model, the real answer and the image to judge
def see_error(self, dataset):
import pylab
# The function to know the prediction
test_model = theano.function([self.classifier.x, self.classifier.y], self.classifier.logLayer.y_pred)
user = []
nb_total = 0 # total number of exemples seen
nb_error = 0 # total number of errors
for x, y in dataset.test(1):
nb_total += 1
pred = self.translate(test_model(x, y))
rep = self.translate(y)
error = pred != rep
print "prediction: " + str(pred) + "\t answer: " + str(rep) + "\t right: " + str(not (error))
pylab.imshow(x.reshape((32, 32)))
pylab.draw()
if error:
nb_error += 1
user.append(int(raw_input("1 = The error is normal, 0 = The error is not normal : ")))
print "\t\t character is hard to distinguish: " + str(user[-1])
else:
time.sleep(3)
print "\n Over the " + str(nb_total) + " exemples, there is " + str(
nb_error
) + " errors. \nThe percentage of errors is" + str(float(nb_error) / float(nb_total))
print "The percentage of errors done by the model that an human will also do: " + str(numpy.mean(user))
# To translate the numeric prediction in character if necessary
def translate(self, y):
if y <= 9:
return y[0]
elif y == 10:
return "A"
elif y == 11:
return "B"
elif y == 12:
return "C"
elif y == 13:
return "D"
elif y == 14:
return "E"
elif y == 15:
return "F"
elif y == 16:
return "G"
elif y == 17:
return "H"
elif y == 18:
return "I"
elif y == 19:
return "J"
elif y == 20:
return "K"
elif y == 21:
return "L"
elif y == 22:
return "M"
elif y == 23:
return "N"
elif y == 24:
return "O"
elif y == 25:
return "P"
elif y == 26:
return "Q"
elif y == 27:
return "R"
elif y == 28:
return "S"
elif y == 29:
return "T"
elif y == 30:
return "U"
elif y == 31:
return "V"
elif y == 32:
return "W"
elif y == 33:
return "X"
elif y == 34:
return "Y"
elif y == 35:
return "Z"
elif y == 36:
return "a"
elif y == 37:
return "b"
elif y == 38:
return "c"
elif y == 39:
return "d"
elif y == 40:
return "e"
elif y == 41:
return "f"
elif y == 42:
return "g"
elif y == 43:
return "h"
elif y == 44:
return "i"
elif y == 45:
return "j"
elif y == 46:
return "k"
elif y == 47:
return "l"
elif y == 48:
return "m"
elif y == 49:
return "n"
elif y == 50:
return "o"
elif y == 51:
return "p"
elif y == 52:
return "q"
elif y == 53:
return "r"
elif y == 54:
return "s"
elif y == 55:
return "t"
elif y == 56:
return "u"
elif y == 57:
return "v"
elif y == 58:
return "w"
elif y == 59:
return "x"
elif y == 60:
return "y"
elif y == 61:
return "z"
class SdaSgdOptimizer:
def __init__(self, dataset, hyperparameters, n_ins, n_outs,
examples_per_epoch, series=default_series,
save_params=False):
self.dataset = dataset
self.hp = hyperparameters
self.n_ins = n_ins
self.n_outs = n_outs
self.save_params = save_params
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):
print "STARTING PRETRAINING, time = ", datetime.datetime.now()
sys.stdout.flush()
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
for x,y in dataset.train(self.hp.minibatch_size):
c = self.classifier.pretrain_functions[i](x)
self.series["reconstruction_error"].append((epoch, batch_index), c)
batch_index+=1
#if batch_index % 100 == 0:
# print "100 batches"
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,dataset):
print "STARTING FINETUNING, time = ", datetime.datetime.now()
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(
[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
best_params = None
best_validation_loss = float('inf')
test_score = 0.
start_time = time.clock()
done_looping = False
epoch = 0
total_mb_index = 0
while (epoch < self.hp.max_finetuning_epochs) and (not done_looping):
epoch = epoch + 1
minibatch_index = -1
for x,y in dataset.train(minibatch_size):
minibatch_index += 1
cost_ij = self.classifier.finetune(x,y)
total_mb_index += 1
self.series["training_error"].append((epoch, minibatch_index), cost_ij)
if (total_mb_index+1) % validation_frequency == 0:
iter = dataset.valid(minibatch_size)
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/%i, validation error %f %%' % \
(epoch, minibatch_index+1, self.mb_per_epoch, \
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 and iteration number
best_validation_loss = this_validation_loss
best_iter = total_mb_index
# test it on the test set
iter = dataset.test(minibatch_size)
test_losses = [test_model(x,y) for x,y in iter]
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.mb_per_epoch,
test_score*100.))
sys.stdout.flush()
self.series['params'].append((epoch,), self.classifier.all_params)
if patience <= total_mb_index:
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})
if self.save_params:
save_params(self.classifier.all_params, "weights.dat")
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.))
