def main(argv):
# load preprocessed data samples
print 'loading data...\t',
data = load('../../data/vanhateren.npz')
print '[DONE]'
print
# remove DC component (first component)
data_train = data['train'][1:, :]
data_test = data['test'][1:, :]
# create 1st layer
dbn = DBN(GaussianRBM(num_visibles=data_train.shape[0], num_hiddens=100))
# hyperparameters
dbn[0].learning_rate = 5E-3
dbn[0].weight_decay = 1E-2
dbn[0].momentum = 0.9
dbn[0].sigma = 0.65
dbn[0].cd_steps = 1
dbn[0].persistent = True
# train 1st layer
print 'training...\t',
dbn.train(data_train, num_epochs=100, batch_size=100)
print '[DONE]'
# evaluate 1st layer
print 'evaluating...\t',
logptf = dbn.estimate_log_partition_function(num_ais_samples=100, beta_weights=arange(0, 1, 1E-3))
loglik = dbn.estimate_log_likelihood(data_test)
print '[DONE]'
print
print 'estimated log-partf.:\t', logptf
print 'estimated log-loss:\t', -loglik / data_test.shape[0] / log(2)
print
# create 2nd layer
dbn.add_layer(SemiRBM(num_visibles=100, num_hiddens=100))
# initialize parameters
dbn[1].L = dbn[0].W.T * dbn[0].W
dbn[1].b = dbn[0].W.T * dbn[0].b + dbn[0].c + 0.5 * asmatrix(diag(dbn[1].L)).T
dbn[1].L = dbn[1].L - asmatrix(diag(diag(dbn[1].L)))
# hyperparameters
dbn[1].learning_rate = 5E-3
dbn[1].learning_rate_lateral = 5E-4
dbn[1].weight_decay = 5E-3
dbn[1].weight_decay_lateral = 5E-3
dbn[1].momentum = 0.9
dbn[1].momentum_lateral = 0.9
dbn[1].num_lateral_updates = 20
dbn[1].damping = 0.2
dbn[1].cd_steps = 1
dbn[1].persistent = True
# train 2nd layer
print 'training...\t',
dbn.train(data_train, num_epochs=100, batch_size=100)
print '[DONE]'
# evaluate 2nd layer
print 'evaluating...\t',
logptf = dbn.estimate_log_partition_function(num_ais_samples=100, beta_weights=arange(0, 1, 1E-3))
loglik = dbn.estimate_log_likelihood(data_test, num_samples=100)
print '[DONE]'
print
print 'estimated log-partf.:\t', logptf
print 'estimated log-loss:\t', -loglik / data_test.shape[0] / log(2)
print
# fine-tune with wake-sleep
dbn[0].learning_rate /= 4.
dbn[1].learning_rate /= 4.
print 'fine-tuning...\t',
dbn.train_wake_sleep(data_train, num_epochs=10, batch_size=10)
print '[DONE]'
# reevaluate
print 'evaluating...\t',
logptf = dbn.estimate_log_partition_function(num_ais_samples=100, beta_weights=arange(0, 1, 1E-3))
loglik = dbn.estimate_log_likelihood(data_test, num_samples=100)
print '[DONE]'
print
print 'estimated log-partf.:\t', logptf
print 'estimated log-loss:\t', -loglik / data_test.shape[0] / log(2)
return 0
