def test_GRBM_DBN(finetune_lr=1, pretraining_epochs=100,
pretrain_lr=0.01, k=1, training_epochs=500,
batch_size=200, annealing_learning_rate=0.99999):
"""
Demonstrates how to train and test a Deep Belief Network.
This is demonstrated on MNIST.
:type learning_rate: float
:param learning_rate: learning rate used in the finetune stage
:type pretraining_epochs: int
:param pretraining_epochs: number of epoch to do pretraining
:type pretrain_lr: float
:param pretrain_lr: learning rate to be used during pre-training
:type k: int
:param k: number of Gibbs steps in CD/PCD
:type training_epochs: int
:param training_epochs: maximal number of iterations ot run the optimizer
:type dataset: string
:param dataset: path the the pickled dataset
:type batch_size: int
:param batch_size: the size of a minibatch
"""
datasets = load_CodaLab_skel(ratio_train=0.9, ration_valid=0.08)
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size
# numpy random generator
numpy_rng = numpy.random.RandomState(123)
print '... building the model'
# construct the Deep Belief Network
dbn = GRBM_DBN(numpy_rng=numpy_rng, n_ins=528,
hidden_layers_sizes=[2000, 2000, 1000],
n_outs=201, finetune_lr=finetune_lr)
#########################
# PRETRAINING THE MODEL #
#########################
print '... getting the pretraining functions'
pretraining_fns = dbn.pretraining_functions(train_set_x=train_set_x,
batch_size=batch_size,
k=k)
print '... pre-training the model'
start_time = time.clock()
## Pre-train layer-wise
for i in xrange(dbn.n_layers):
if i==0:
# for GRBM, the The learning rate needs to be about one or
#two orders of magnitude smaller than when using
#binary visible units and some of the failures reported in the
# literature are probably due to using a
pretrain_lr_new = pretrain_lr*0.1
else:
pretrain_lr_new = pretrain_lr
# go through pretraining epochs
for epoch in xrange(pretraining_epochs):
start_time_temp = time.clock()
# go through the training set
c = []
for batch_index in xrange(n_train_batches):
c.append(pretraining_fns[i](index=batch_index,
lr=pretrain_lr_new))
end_time_temp = time.clock()
print 'Pre-training layer %i, epoch %d, cost %f ' % (i, epoch, numpy.mean(c)) + ' ran for %d sec' % ((end_time_temp - start_time_temp) )
end_time = time.clock()
print >> sys.stderr, ('The pretraining code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
########################
# FINETUNING THE MODEL #
########################
# get the training, validation and testing function for the model
print '... getting the finetuning functions'
train_fn, validate_model, test_model = dbn.build_finetune_functions(
datasets=datasets, batch_size=batch_size,
annealing_learning_rate=annealing_learning_rate)
print '... finetunning the model'
# early-stopping parameters
patience = 4 * n_train_batches # look as this many examples regardless
patience_increase = 2. # wait this much longer when a new best is
# found
improvement_threshold = 0.999 # a relative improvement of this much is
# considered significant
validation_frequency = min(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 = numpy.inf
test_score = 0.
start_time = time.clock()
done_looping = False
epoch = 0
while (epoch < training_epochs) and (not done_looping):
start_time_temp = time.clock()
epoch = epoch + 1
for minibatch_index in xrange(n_train_batches):
minibatch_avg_cost = train_fn(minibatch_index)
iter = (epoch - 1) * n_train_batches + minibatch_index
if (iter + 1) % validation_frequency == 0:
import warnings
warnings.filterwarnings("ignore")
validation_losses = validate_model()
this_validation_loss = numpy.mean(validation_losses)
# 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()
test_score = numpy.mean(test_losses)
end_time_temp = time.clock()
print(('epoch %i, minibatch %i/%i, validation error %f %%' \
'test error of best model %f %%, used time %d sec') %
(epoch, minibatch_index + 1, n_train_batches,this_validation_loss * 100.,
test_score * 100., (end_time_temp - start_time_temp)))
if patience <= iter:
done_looping = True
break
end_time = time.clock()
print(('Optimization complete with best validation score of %f %%,'
'with test performance %f %%') %
(best_validation_loss * 100., test_score * 100.))
print >> sys.stderr, ('The fine tuning code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time)
/ 60.))
from time import gmtime, strftime
filename = 'dbn_'+strftime("%Y-%m-%d-%H-%M-%S", gmtime())
dbn.save(filename)
if 0: # here for testing, where we never used
## Now for testing
dbn = GRBM_DBN(numpy_rng=numpy_rng, n_ins=528,
hidden_layers_sizes=[1000, 1000, 500],
n_outs=201)
dbn.load('dbn_2014-05-22-18-39-37.npy')
# compiling a Theano function that computes the mistakes that are made by
# the model on a minibatch
index = T.lscalar('index')
validate_model = theano.function(inputs=[index],
outputs=dbn.logLayer.p_y_given_x,
givens={
dbn.x: valid_set_x[index * batch_size:(index + 1) * batch_size]})
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]
n_valid_batches /= batch_size
temp = [validate_model(i)
for i in xrange(n_valid_batches)]
