def test_dA(learning_rate=0.1, training_epochs=15,
dataset='mnist.pkl.gz',
batch_size=20, output_folder='dA_plots'):
datasets = load_data(dataset)
train_set_x, train_set_y = datasets[0]
# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size
# start-snippet-2
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
x = T.matrix('x') # the data is presented as rasterized images
# end-snippet-2
if not os.path.isdir(output_folder):
os.makedirs(output_folder)
os.chdir(output_folder)
####################################
# BUILDING THE MODEL NO CORRUPTION #
####################################
rng = numpy.random.RandomState(123)
theano_rng = RandomStreams(rng.randint(2 ** 30))
da = dA(
numpy_rng=rng,
theano_rng=theano_rng,
input=x,
n_visible=28 * 28,
n_hidden=500
)
cost, updates = da.get_cost_updates(
corruption_level=0.,
learning_rate=learning_rate
)
train_da = theano.function(
[index],
cost,
updates=updates,
givens={
x: train_set_x[index * batch_size: (index + 1) * batch_size]
}
)
start_time = timeit.default_timer()
############
# TRAINING #
############
# go through training epochs
for epoch in xrange(training_epochs):
# go through trainng set
c = []
for batch_index in xrange(n_train_batches):
c.append(train_da(batch_index))
print 'Training epoch %d, cost ' % epoch, numpy.mean(c)
end_time = timeit.default_timer()
training_time = (end_time - start_time)
print >> sys.stderr, ('The no corruption code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((training_time) / 60.))
image = Image.fromarray(
tile_raster_images(X=da.W.get_value(borrow=True).T,
img_shape=(28, 28), tile_shape=(10, 10),
tile_spacing=(1, 1)))
image.save('filters_corruption_0.png')
# start-snippet-3
#####################################
# BUILDING THE MODEL CORRUPTION 30% #
#####################################
rng = numpy.random.RandomState(123)
theano_rng = RandomStreams(rng.randint(2 ** 30))
da = dA(
numpy_rng=rng,
theano_rng=theano_rng,
input=x,
n_visible=28 * 28,
n_hidden=500
)
cost, updates = da.get_cost_updates(
corruption_level=0.3,
learning_rate=learning_rate
)
train_da = theano.function(
[index],
cost,
updates=updates,
givens={
x: train_set_x[index * batch_size: (index + 1) * batch_size]
}
)
start_time = timeit.default_timer()
############
# TRAINING #
############
# go through training epochs
for epoch in xrange(training_epochs):
# go through trainng set
c = []
for batch_index in xrange(n_train_batches):
c.append(train_da(batch_index))
print 'Training epoch %d, cost ' % epoch, numpy.mean(c)
end_time = timeit.default_timer()
training_time = (end_time - start_time)
print >> sys.stderr, ('The 30% corruption code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % (training_time / 60.))
# end-snippet-3
# start-snippet-4
image = Image.fromarray(tile_raster_images(
X=da.W.get_value(borrow=True).T,
img_shape=(28, 28), tile_shape=(10, 10),
tile_spacing=(1, 1)))
image.save('filters_corruption_30.png')
# end-snippet-4
os.chdir('../')
def test_dA(learning_rate=0.1, training_epochs=15,output_folder='dA_plots'):
size = [160, 120] #[width,height]
batch_size=20
dataset="/home/coskun/PycharmProjects/data/rgbd_dataset_freiburg3_large_cabinet/"
X_Pairs=dataset_loader.load_pairs(dataset,step_size=[])
n_train_batches = len(X_Pairs)
n_train_batches /= batch_size
Fx = T.matrix(name='Fx_input') # the data is presented as rasterized images
Sx = T.matrix(name='Sx_input') # the data is presented as rasterized images
Fx_inp = T.matrix(name='Fx_inp') # the data is presented as rasterized images
Sx_inp = T.matrix(name='Sx_inp') # the data is presented as rasterized images
if not os.path.isdir(output_folder):
os.makedirs(output_folder)
os.chdir(output_folder)
####################################
# BUILDING THE MODEL NO CORRUPTION #
####################################
rng = numpy.random.RandomState(123)
theano_rng = RandomStreams(rng.randint(2 ** 30))
da = dA(
numpy_rng=rng,
theano_rng=theano_rng,
Fx=Fx,
Sx=Sx,
n_visible=size[0] * size[1],
n_hidden=2000
)
cost, updates = da.get_cost_updates(
learning_rate=learning_rate
)
train_da = theano.function(
[Fx_inp, Sx_inp],
cost,
updates=updates,
givens={
Fx: Fx_inp,
Sx: Sx_inp
}
,mode="DebugMode"
)
start_time = timeit.default_timer()
############
# TRAINING #
############
print "Training Started"
# go through training epochs
for epoch in xrange(training_epochs):
# go through trainng set
c = []
for i in xrange(n_train_batches):
Fx = X_Pairs[i * batch_size: (i + 1) * batch_size]
data_Fx = dataset_loader.load_batch_images(size,1, "F", Fx)
data_Sx = dataset_loader.load_batch_images(size,1, "S", Fx)
print("Trainin on images")
cst=train_da(data_Fx,data_Sx)
print("Cost:")
print(str(cst))
c.append(cst)
print 'Training epoch %d, cost ' % epoch, numpy.mean(c)
end_time = timeit.default_timer()
training_time = (end_time - start_time)
print >> sys.stderr, ('The no corruption code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((training_time) / 60.))
image = Image.fromarray(
tile_raster_images(X=da.W.get_value(borrow=True).T,
img_shape=(size[0], size[1]), tile_shape=(10, 10),
tile_spacing=(1, 1)))
image.save('filters_corruption_0.png')
os.chdir('../')