logZ = estimator.annealed_importance_sampling(rbm)[0]
LL_train = numx.mean(estimator.log_likelihood_v(rbm, logZ, train_data))
LL_test = numx.mean(estimator.log_likelihood_v(rbm, logZ, test_data))
print 'AIS: \t%0.5f \t%0.5f' % (LL_train, LL_test)
# Approximate partition function by reverse AIS (tends to underestimate)
logZ = estimator.reverse_annealed_importance_sampling(rbm)[0]
LL_train = numx.mean(estimator.log_likelihood_v(rbm, logZ, train_data))
LL_test = numx.mean(estimator.log_likelihood_v(rbm, logZ, test_data))
print 'reverse AIS \t%0.5f \t%0.5f' % (LL_train, LL_test)
# Reorder RBM features by average activity decreasingly
rbmReordered = vis.reorder_filter_by_hidden_activation(rbm, train_data)
# Display RBM parameters
vis.imshow_standard_rbm_parameters(rbmReordered, v1, v2, h1, h2)
# Sample some steps and show results
samples = vis.generate_samples(rbm, train_data[0:30], 30, 1, v1, v2, False,
None)
vis.imshow_matrix(samples, 'Samples')
# Get the optimal gabor wavelet frequency and angle for the filters
opt_frq, opt_ang = vis.filter_frequency_and_angle(rbm.w, num_of_angles=40)
# Show some tuning curves
num_filters = 20
vis.imshow_filter_tuning_curve(rbm.w[:, 0:num_filters], num_of_ang=40)
# Show some optima grating
vis.imshow_filter_optimal_gratings(rbm.w[:, 0:num_filters],
batch = train_data[b:b + batch_size, :]
trainer.train(data=batch, epsilon=0.1, regL2Norm= 0.001)
# Calculate Log-Likelihood, reconstruction error and expected end time every 10th epoch
if (epoch % 10 == 0):
RE = numx.mean(ESTIMATOR.reconstruction_error(rbm, train_data))
print '%d\t\t%8.6f\t\t' % (epoch, RE),
print measurer.get_expected_end_time(epoch , epochs),
print
measurer.end()
# Print end time
print
print 'End-time: \t', measurer.get_end_time()
print 'Training time:\t', measurer.get_interval()
# Reorder RBM features by average activity decreasingly
reordered_rbm = STATISTICS.reorder_filter_by_hidden_activation(rbm, train_data)
# Display RBM parameters
VISUALIZATION.imshow_standard_rbm_parameters(reordered_rbm, v1, v2, h1, h2)
# Sample some steps and show results
samples = STATISTICS.generate_samples(rbm, train_data[0:30], 30, 1, v1, v2, False, None)
VISUALIZATION.imshow_matrix(samples, 'Samples')
VISUALIZATION.show()