def main():
parser = OptionParser()
parser.add_option("-d",
"--data",
dest="dataset",
default="toy",
help="specify the dataset, either cifar10 or toy")
(options, args) = parser.parse_args()
global SAVE_MODEL
if options.dataset == 'toy':
trainset, testset = get_dataset_toy()
SAVE_MODEL = False
elif options.dataset == 'cifar10':
trainset, testset, = get_dataset_cifar10()
SAVE_MODEL = True
design_matrix = trainset.get_design_matrix()
n_input = design_matrix.shape[1]
# build layers
layers = []
structure = [[n_input, 400], [400, 50], [50, 100], [100, 2]]
# layer 0: gaussianRBM
layers.append(get_grbm(structure[0]))
# layer 1: denoising AE
layers.append(get_denoising_autoencoder(structure[1]))
# layer 2: AE
layers.append(get_autoencoder(structure[2]))
# layer 3: logistic regression used in supervised training
layers.append(get_logistic_regressor(structure[3]))
#construct training sets for different layers
trainset = [
trainset,
TransformerDataset(raw=trainset, transformer=layers[0]),
TransformerDataset(raw=trainset,
transformer=StackedBlocks(layers[0:2])),
TransformerDataset(raw=trainset,
transformer=StackedBlocks(layers[0:3]))
]
# construct layer trainers
layer_trainers = []
layer_trainers.append(get_layer_trainer_sgd_rbm(layers[0], trainset[0]))
layer_trainers.append(
get_layer_trainer_sgd_autoencoder(layers[1], trainset[1]))
layer_trainers.append(
get_layer_trainer_sgd_autoencoder(layers[2], trainset[2]))
layer_trainers.append(get_layer_trainer_logistic(layers[3], trainset[3]))
#unsupervised pretraining
for layer_trainer in layer_trainers[0:3]:
layer_trainer.main_loop()
#supervised training
layer_trainers[-1].main_loop()
def build_stacked_RBM(nvis,
nhids,
batch_size,
vis_type='binary',
input_mean_vis=None,
irange=1e-3,
rng=None):
"""
Note from IG:
This method doesn't seem to work correctly with Gaussian RBMs.
In general, this is a difficult function to support, because it
needs to pass the write arguments to the constructor of many kinds
of RBMs. It would probably be better to just construct an instance
of pylearn2.models.mlp.MLP with its hidden layers set to instances
of pylearn2.models.mlp.RBM_Layer. If anyone is working on this kind
of problem, a PR replacing this function with a helper function to
make such an MLP would be very welcome.
Allocate a StackedBlocks containing RBMs.
The visible units of the input RBM can be either binary or gaussian,
the other ones are all binary.
"""
#TODO: not sure this is the right way of dealing with mean_vis.
layers = []
assert vis_type in ['binary', 'gaussian']
if vis_type == 'binary':
assert input_mean_vis is None
elif vis_type == 'gaussian':
assert input_mean_vis in (True, False)
# The number of visible units in each layer is the initial input
# size and the first k-1 hidden unit sizes.
nviss = [nvis] + nhids[:-1]
seq = izip(
xrange(len(nhids)),
nhids,
nviss,
)
for k, nhid, nvis in seq:
if k == 0 and vis_type == 'gaussian':
rbm = GaussianBinaryRBM(nvis=nvis,
nhid=nhid,
batch_size=batch_size,
irange=irange,
rng=rng,
mean_vis=input_mean_vis)
else:
rbm = RBM(nvis - nvis,
nhid=nhid,
batch_size=batch_size,
irange=irange,
rng=rng)
layers.append(rbm)
# Create the stack
return StackedBlocks(layers)
def build_stacked_ae(nvis,
nhids,
act_enc,
act_dec,
tied_weights=False,
irange=1e-3,
rng=None,
corruptor=None,
contracting=False):
"""Allocate a stack of autoencoders."""
if not hasattr(rng, 'randn'):
rng = numpy.random.RandomState(rng)
layers = []
final = {}
# "Broadcast" arguments if they are singular, or accept sequences if
# they are the same length as nhids
for c in [
'corruptor', 'contracting', 'act_enc', 'act_dec', 'tied_weights',
'irange'
]:
if type(locals()[c]) is not str and hasattr(locals()[c], '__len__'):
assert len(nhids) == len(locals()[c])
final[c] = locals()[c]
else:
final[c] = [locals()[c]] * len(nhids)
# The number of visible units in each layer is the initial input
# size and the first k-1 hidden unit sizes.
nviss = [nvis] + nhids[:-1]
seq = izip(
nhids,
nviss,
final['act_enc'],
final['act_dec'],
final['corruptor'],
final['contracting'],
final['tied_weights'],
final['irange'],
)
# Create each layer.
for (nhid, nvis, act_enc, act_dec, corr, cae, tied, ir) in seq:
args = (nvis, nhid, act_enc, act_dec, tied, ir, rng)
if cae and corr is not None:
raise ValueError("Can't specify denoising and contracting "
"objectives simultaneously")
elif cae:
autoenc = ContractiveAutoencoder(*args)
elif corr is not None:
autoenc = DenoisingAutoencoder(corr, *args)
else:
autoenc = Autoencoder(*args)
layers.append(autoenc)
# Create the stack
return StackedBlocks(layers)
def build_stacked_RBM(nvis,
nhids,
batch_size,
vis_type='binary',
input_mean_vis=None,
irange=1e-3,
rng=None):
"""
Allocate a StackedBlocks containing RBMs.
The visible units of the input RBM can be either binary or gaussian,
the other ones are all binary.
"""
#TODO: not sure this is the right way of dealing with mean_vis.
layers = []
assert vis_type in ['binary', 'gaussian']
if vis_type == 'binary':
assert input_mean_vis is None
elif vis_type == 'gaussian':
assert input_mean_vis in (True, False)
# The number of visible units in each layer is the initial input
# size and the first k-1 hidden unit sizes.
nviss = [nvis] + nhids[:-1]
seq = izip(
xrange(len(nhids)),
nhids,
nviss,
)
for k, nhid, nvis in seq:
if k == 0 and vis_type == 'gaussian':
rbm = GaussianBinaryRBM(nvis=nvis,
nhid=nhid,
batch_size=batch_size,
irange=irange,
rng=rng,
mean_vis=input_mean_vis)
else:
rbm = RBM(nvis - nvis,
nhid=nhid,
batch_size=batch_size,
irange=irange,
rng=rng)
layers.append(rbm)
# Create the stack
return StackedBlocks(layers)
def construct_ae(structure):
# some settings
irange = 0.1
layers = []
for vsize, hsize in zip(structure[:-1], structure[1:]):
# DenoisingAutoencoder / ContractiveAutoencoder / HigherOrderContractiveAutoencoder
layers.append(
autoencoder.DenoisingAutoencoder(
nvis=vsize,
nhid=hsize,
tied_weights=True,
act_enc='sigmoid',
act_dec='sigmoid',
irange=irange,
# for DenoisingAutoencoder / HigherOrderContractiveAutoencoder:
corruptor=BinomialCorruptor(0.5),
# for HigherOrderContractiveAutoencoder:
# num_corruptions=6
))
return StackedBlocks(layers)
def construct_ae(structure):
# some settings
irange = 0.05
layers = []
for vsize, hsize in zip(structure[:-1], structure[1:]):
# DenoisingAutoencoder?, ContractiveAutoencoder?, HigherOrderContractiveAutoencoder?
layers.append(
autoencoder.ContractiveAutoencoder(
# DenoisingAutoencoder
#corruptor=BinomialCorruptor(0.5),
# HigherOrderContractiveAutoencoder
#corruptor=GaussianCorruptor(0.5),
#num_corruptions=8,
nvis=vsize,
nhid=hsize,
tied_weights=True,
act_enc='sigmoid',
act_dec='sigmoid',
#act_enc=Rectify(),
#act_dec=Rectify(),
irange=irange))
return StackedBlocks(layers)
def main():
parser = OptionParser()
parser.add_option("-d",
"--data",
dest="dataset",
default="toy",
help="specify the dataset, either cifar10, mnist or toy")
(options, args) = parser.parse_args()
if options.dataset == 'toy':
trainset, testset = get_dataset_toy()
n_output = 2
elif options.dataset == 'cifar10':
trainset, testset, = get_dataset_cifar10()
n_output = 10
elif options.dataset == 'mnist':
trainset, testset, = get_dataset_mnist()
n_output = 10
else:
NotImplementedError()
design_matrix = trainset.get_design_matrix()
n_input = design_matrix.shape[1]
# build layers
layers = []
structure = [[n_input, 10], [10, 50], [50, 100], [100, n_output]]
# layer 0: gaussianRBM
layers.append(get_grbm(structure[0]))
# layer 1: denoising AE
layers.append(get_denoising_autoencoder(structure[1]))
# layer 2: AE
layers.append(get_autoencoder(structure[2]))
# layer 3: logistic regression used in supervised training
layers.append(get_logistic_regressor(structure[3]))
#construct training sets for different layers
trainset = [
trainset,
TransformerDataset(raw=trainset, transformer=layers[0]),
TransformerDataset(raw=trainset,
transformer=StackedBlocks(layers[0:2])),
TransformerDataset(raw=trainset,
transformer=StackedBlocks(layers[0:3]))
]
# construct layer trainers
layer_trainers = []
layer_trainers.append(get_layer_trainer_sgd_rbm(layers[0], trainset[0]))
layer_trainers.append(
get_layer_trainer_sgd_autoencoder(layers[1], trainset[1]))
layer_trainers.append(
get_layer_trainer_sgd_autoencoder(layers[2], trainset[2]))
layer_trainers.append(get_layer_trainer_logistic(layers[3], trainset[3]))
#unsupervised pretraining
for i, layer_trainer in enumerate(layer_trainers[0:3]):
print '-----------------------------------'
print ' Unsupervised training layer %d, %s' % (i, layers[i].__class__)
print '-----------------------------------'
layer_trainer.main_loop()
print '\n'
print '------------------------------------------------------'
print ' Unsupervised training done! Start supervised training...'
print '------------------------------------------------------'
print '\n'
#supervised training
layer_trainers[-1].main_loop()
def main():
trainset, validset, testset, extraset = get_dataset_icml()
#trainset,testset = get_dataset_mnist()
design_matrix = trainset.get_design_matrix()
n_input = design_matrix.shape[1]
n_output = 9 #10
# build layers
layers = []
structure = [[n_input, 1000], [1000,1000],[1000,1000], [1000, n_output]]
#layers.append(get_grbm(structure[0]))
# layer 0: denoising AE
layers.append(get_grbm(structure[0]))
# layer 1: denoising AE
layers.append(get_grbm(structure[1]))
# layer 1: denoising AE
layers.append(get_grbm(structure[2]))
# layer 2: logistic regression used in supervised training
#layers.append(get_logistic_regressor(structure[3]))
#construct training sets for different layers
traindata = [ extraset ,
TransformerDataset( raw = extraset, transformer = layers[0] ),
TransformerDataset( raw = extraset, transformer = StackedBlocks( layers[0:2] )),
TransformerDataset( raw = extraset, transformer = StackedBlocks( layers[0:3] )) ]
#valid = TransformerDataset( raw = validset, transformer = StackedBlocks( layers[0:2] ))
#valid = trainset
# construct layer trainers
layer_trainers = []
#layer_trainers.append(get_layer_trainer_sgd_rbm(layers[0], trainset[0]))
layer_trainers.append(get_layer_trainer_sgd_rbm(layers[0], traindata[0],'db1.pkl'))
layer_trainers.append(get_layer_trainer_sgd_rbm(layers[1], traindata[1],'db2.pkl'))
layer_trainers.append(get_layer_trainer_sgd_rbm(layers[2], traindata[2],'db3.pkl'))
#layer_trainers.append(get_layer_trainer_logistic(layers[2], trainset[2], valid))
#unsupervised pretraining
for i, layer_trainer in enumerate(layer_trainers[0:3]):
print '-----------------------------------'
print ' Unsupervised training (pretraining) layer %d, %s'%(i, layers[i].__class__)
print '-----------------------------------'
layer_trainer.main_loop()
print '\n'
print '------------------------------------------------------'
print ' Unsupervised training done! Start supervised training (fine-tuning)...'
print '------------------------------------------------------'
print '\n'
mlp_layers = []
mlp_layers.append(PretrainedLayer(layer_name = 'h0', layer_content = serial.load('db1.pkl')))
mlp_layers.append(PretrainedLayer(layer_name = 'h1', layer_content = serial.load('db2.pkl')))
mlp_layers.append(PretrainedLayer(layer_name = 'h2', layer_content = serial.load('db3.pkl')))
#supervised training
#layer_trainers[-1].main_loop()
mlp_model = get_layer_MLP(mlp_layers,trainset,validset)
mlp_model.main_loop()
pca1 = create_pca(conf, layer1, data, model=layer1['name'])
data = [
utils.sharedX(pca1.function()(set.get_value(borrow=True)), borrow=True)
for set in data
]
# Second layer : train or load a DAE or CAE
ae = create_ae(conf, layer2, data, model=layer2['name'])
data = [
utils.sharedX(ae.function()(set.get_value(borrow=True)), borrow=True)
for set in data
]
# Third layer : train or load a PCA
pca2 = create_pca(conf, layer3, data, model=layer3['name'])
data = [
utils.sharedX(pca2.function()(set.get_value(borrow=True)), borrow=True)
for set in data
]
# Compute the ALC for example with labels
if conf['transfer']:
data_train, label_train = utils.filter_labels(data[0], label)
alc = embed.score(data_train, label_train)
print '... resulting ALC on train is', alc
conf['train_alc'] = alc
# Stack both layers and create submission file
block = StackedBlocks([pca1, ae, pca2])
utils.create_submission(conf, block.function())