def main(argv):
if len(argv) < 2:
print 'Usage:', argv[0], '<param_id>', '[experiment]'
print
print ' {0:>3} {1:>7} {2:>5} {3:>5} {4:>5} {5:>5} {6:>5}'.format(
'ID', 'PS', 'OC', 'TI', 'FI', 'LP', 'SC')
for id, params in enumerate(parameters):
print ' {0:>3} {1:>7} {2:>5} {3:>5} {4:>5} {5:>5} {6:>5}'.format(id, *params)
print
print ' ID = parameter set'
print ' PS = patch size'
print ' OC = overcompleteness'
print ' TI = number of training iterations'
print ' FI = number of fine-tuning iterations'
print ' LP = optimize marginal distributions'
print ' SC = initialize with sparse coding'
return 0
seterr(invalid='raise', over='raise', divide='raise')
# start experiment
experiment = Experiment()
# hyperparameters
patch_size, \
overcompleteness, \
max_iter, \
max_iter_ft, \
train_prior, \
sparse_coding = parameters[int(argv[1])]
### DATA PREPROCESSING
# load data, log-transform and center data
data = load('data/vanhateren.{0}.1.npz'.format(patch_size))['data']
data = data[:, :100000]
data = preprocess(data)
# discrete cosine transform and whitening transform
dct = LinearTransform(dim=int(sqrt(data.shape[0])), basis='DCT')
wt = WhiteningTransform(dct(data)[1:], symmetric=True)
### MODEL DEFINITION
isa = ISA(num_visibles=data.shape[0] - 1,
num_hiddens=data.shape[0] * overcompleteness - 1, ssize=1)
# model DC component with a mixture of Gaussians
model = StackedModel(dct,
ConcatModel(MoGaussian(20), StackedModel(wt, isa)))
### MODEL TRAINING
# variables to store in results
experiment['model'] = model
experiment['parameters'] = parameters[int(argv[1])]
def callback(phase, isa, iteration):
"""
Saves intermediate results every few iterations.
"""
if not iteration % 5:
# whitened filters
A = dot(dct.A[1:].T, isa.A)
patch_size = int(sqrt(A.shape[0]) + 0.5)
# save intermediate results
experiment.save('results/vanhateren.{0}/results.{1}.{2}.xpck'.format(argv[1], phase, iteration))
# visualize basis
imsave('results/vanhateren.{0}/basis.{1}.{2:0>3}.png'.format(argv[1], phase, iteration),
stitch(imformat(A.T.reshape(-1, patch_size, patch_size))))
if len(argv) > 2:
# initialize model with trained model
results = Experiment(argv[2])
model = results['model']
isa = model.model[1].model
dct = model.transforms[0]
experiment['model'] = model
else:
# enable regularization of marginals
for gsm in isa.subspaces:
gsm.gamma = 1e-3
gsm.alpha = 2.
gsm.beta = 1.
# train mixture of Gaussians on DC component
model.train(data, 0, max_iter=100)
# initialize filters and marginals
model.initialize(data, 1)
model.initialize(model=1, method='laplace')
experiment.progress(10)
if sparse_coding:
# initialize with sparse coding
if patch_size == '16x16':
model.train(data, 1,
method=('of', {
'max_iter': max_iter,
'noise_var': 0.05,
'var_goal': 1.,
'beta': 10.,
'step_width': 0.01,
'sigma': 0.3,
}),
callback=lambda isa, iteration: callback(0, isa, iteration))
else:
model.train(data, 1,
method=('of', {
'max_iter': max_iter,
'noise_var': 0.1,
'var_goal': 1.,
'beta': 10.,
'step_width': 0.01,
'sigma': 0.5,
}),
callback=lambda isa, iteration: callback(0, isa, iteration))
isa.orthogonalize()
else:
if patch_size == '16x16':
# prevents out-of-memory
mapp.max_processes = 1
# train model using a subset of the data
model.train(data[:, :20000], 1,
max_iter=max_iter,
train_prior=train_prior,
persistent=True,
init_sampling_steps=5,
method=('sgd', {'momentum': 0.8}),
callback=lambda isa, iteration: callback(0, isa, iteration),
sampling_method=('gibbs', {'num_steps': 1}))
experiment.progress(50)
if patch_size == '16x16':
# prevents out-of-memory
mapp.max_processes = 1
# disable regularization
for gsm in isa.subspaces:
gsm.gamma = 0.
# fine-tune model using all the data
model.train(data, 1,
max_iter=max_iter_ft,
train_prior=train_prior,
train_subspaces=False,
persistent=True,
init_sampling_steps=10 if not len(argv) > 2 and (sparse_coding or not train_prior) else 50,
method=('lbfgs', {'max_fun': 50}),
callback=lambda isa, iteration: callback(1, isa, iteration),
sampling_method=('gibbs', {'num_steps': 2}))
experiment.save('results/vanhateren/vanhateren.{0}.{{0}}.{{1}}.xpck'.format(argv[1]))
return 0
