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],
opt_frq[0:num_filters],
opt_ang[0:num_filters])
# Show histograms of frequencies and angles.
vis.imshow_filter_frequency_angle_histogram(opt_frq=opt_frq,
opt_ang=opt_ang,
max_wavelength=14)
vis.imshow_matrix(matrix=images,
windowtitle='First 100 image patches whitened')
# Show the ICA filters/bases
ica_filters = vis.tile_matrix_rows(matrix=ica.projection_matrix,
tile_width=width,
tile_height=height,
num_tiles_x=width,
num_tiles_y=height,
border_size=1,
normalized=True)
vis.imshow_matrix(matrix=ica_filters,
windowtitle='Filters learned by ICA')
# Get the optimal gabor wavelet frequency and angle for the filters
opt_frq, opt_ang = vis.filter_frequency_and_angle(ica.projection_matrix,
num_of_angles=40)
# Show some tuning curves
num_filters = 20
vis.imshow_filter_tuning_curve(ica.projection_matrix[:,0:num_filters],
num_of_ang=40)
# Show some optima grating
vis.imshow_filter_optimal_gratings(ica.projection_matrix[:,0:num_filters],
opt_frq[0:num_filters],
opt_ang[0:num_filters])
# Show histograms of frequencies and angles.
vis.imshow_filter_frequency_angle_histogram(opt_frq=opt_frq,
opt_ang=opt_ang,
max_wavelength=14)