VIS.tile_matrix_rows(wl1.weights,
v11,
v12,
v21,
v22,
border_size=1,
normalized=False), 'Weights 1')
VIS.imshow_matrix(
VIS.tile_matrix_rows(numx.dot(wl1.weights, wl2.weights),
v11,
v12,
v31,
v32,
border_size=1,
normalized=False), 'Weights 2')
# # Samplesome steps
chain_m = [
numx.float64(numx.random.rand(10 * batch_size, v11 * v12) < 0.5),
numx.float64(numx.random.rand(10 * batch_size, v21 * v22) < 0.5),
numx.float64(numx.random.rand(10 * batch_size, v31 * v32) < 0.5)
]
model.sample(chain_m, 100, [False, False, False], True)
# GEt probabilities
samples = l1.activation(None, chain_m[1])[0]
VIS.imshow_matrix(
VIS.tile_matrix_columns(samples, v11, v12, 10, batch_size, 1, False),
'Samples')
VIS.show()
vis.figure(0, figsize=[7, 7])
vis.title("Data with estimated principal components")
vis.plot_2d_data(data)
vis.plot_2d_weights(scale_factor*pca.projection_matrix)
vis.axis('equal')
vis.axis([-4, 4, -4, 4])
# Figure 2 - Data with estimated principal components in projected space
vis.figure(2, figsize=[7, 7])
vis.title("Data with estimated principal components in projected space")
vis.plot_2d_data(data_pca)
vis.plot_2d_weights(scale_factor*pca.project(pca.projection_matrix.T))
vis.axis('equal')
vis.axis([-4, 4, -4, 4])
# PCA with whitening
pca = PCA(data.shape[1], whiten=True)
pca.train(data)
data_pca = pca.project(data)
# Figure 3 - Data with estimated principal components in whitened space
vis.figure(3, figsize=[7, 7])
vis.title("Data with estimated principal components in whitened space")
vis.plot_2d_data(data_pca)
vis.plot_2d_weights(pca.project(pca.projection_matrix.T).T)
vis.axis('equal')
vis.axis([-4, 4, -4, 4])
# Show all windows
vis.show()
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()