def or_infer_with_shift(rbm, vis, x_shift, y_shift, iters, k, beta=1):
n_samples = vis.shape[0]
height = 28
width = 28
xi = vis.copy()[:, 0:height, 0:width]
xf = 1 - xi
yi = vis.copy()[:, y_shift : y_shift + height, x_shift : x_shift + width]
yf = 1 - yi
for i in range(iters):
xs, _ = ml.rbm.gibbs_sample(flatten_samples(xi), k, vis_force=flatten_samples(xf), beta=beta)
xs = unflatten_samples_like(xs, xi)
xr, xrf = or_rest(vis[:, 0:height, 0:width], xs)
yi_by_x = xr[:, y_shift : y_shift + height, x_shift : x_shift + width]
yf_by_x = xrf[:, y_shift : y_shift + height, x_shift : x_shift + width]
yi[:, 0 : height - y_shift, 0 : width - x_shift] = yi_by_x
yf[:, 0 : height - y_shift, 0 : width - x_shift] = yf_by_x
ys, _ = ml.rbm.gibbs_sample(flatten_samples(yi), k, vis_force=flatten_samples(yf), beta=beta)
ys = unflatten_samples_like(ys, yi)
yr, yrf = or_rest(vis[:, y_shift : y_shift + height, x_shift : x_shift + width], ys)
xi_by_y = yr[:, 0 : height - y_shift, 0 : width - x_shift]
xf_by_y = yf[:, 0 : height - y_shift, 0 : width - x_shift]
xi[:, y_shift:, x_shift:] = xi_by_y
return xs, ys
def or_infer_with_shift_iter(rbm, vis, x_shift, y_shift, k, beta=1):
n_samples = vis.shape[0]
x_sx = 0
x_sy = base_y
x_ex = x_sx + width
x_ey = x_sy + height
y_sx = x_shift
y_sy = y_shift
y_ex = y_sx + width
y_ey = y_sy + height
i_sx, i_sy, i_width, i_height = rect_intersection(x_sx, x_sy, width, height,
y_sx, y_sy, width, height)
i_ex = i_sx + i_width
i_ey = i_sy + i_height
#print "intersection: ", i_sx, i_sy, i_width, i_height
#print "fixed"
xi = vis.copy()[:, x_sy:x_ey, x_sx:x_ex]
xf = 1 - xi
yi = vis.copy()[:, y_sy:y_ey, y_sx:y_ex]
yf = 1 - yi
while True:
xs, _ = ml.rbm.gibbs_sample(flatten_samples(xi),
k, vis_force=flatten_samples(xf), beta=beta)
xs = unflatten_samples_like(xs, xi)
xr, xrf = or_rest(vis[:, x_sy:x_ey, x_sx:x_ex], xs)
yi_by_x = xr[:, i_sy-x_sy:i_ey-x_sy, i_sx-x_sx:i_ex-x_sx]
yf_by_x = xrf[:, i_sy-x_sy:i_ey-x_sy, i_sx-x_sx:i_ex-x_sx]
yi[:, i_sy-y_sy:i_ey-y_sy, i_sx-y_sx:i_ex-y_sx] = yi_by_x
yf[:, i_sy-y_sy:i_ey-y_sy, i_sx-y_sx:i_ex-y_sx] = yf_by_x
ys, _ = ml.rbm.gibbs_sample(flatten_samples(yi),
k, vis_force=flatten_samples(yf), beta=beta)
ys = unflatten_samples_like(ys, yi)
yr, yrf = or_rest(vis[:, y_sy:y_ey, y_sx:y_ex], ys)
xi_by_y = yr[:, i_sy-y_sy:i_ey-y_sy, i_sx-y_sx:i_ex-y_sx]
xf_by_y = yrf[:, i_sy-y_sy:i_ey-y_sy, i_sx-y_sx:i_ex-y_sx] #fixed
xi[:, i_sy-x_sy:i_ey-x_sy, i_sx-x_sx:i_ex-x_sx] = xi_by_y
xf[:, i_sy-x_sy:i_ey-x_sy, i_sx-x_sx:i_ex-x_sx] = xf_by_y
# BUG: yf instead of yrf, but worked anyway????
yield xs, ys
def cross_entropy(rbm, vis, points, x_shift, y_shift,
iters, k, beta=1):
assert vis.shape[1] == height+2*base_y and vis.shape[2] == 2*width
infer = or_infer_with_shift_iter(rbm, vis, x_shift, y_shift, k, beta=beta)
H = gp.zeros((vis.shape[0],))
for n in range(points):
for i in range(iters):
xs, ys = infer.next()
H += (rbm.free_energy(flatten_samples(xs), beta=beta) +
ml.rbm.free_energy(flatten_samples(ys), beta=beta))
return H / points
def cross_entropy(rbm, vis, points, x_shift, y_shift, iters, k, beta=1):
n_samples = vis.shape[0]
height = 28
width = 28
xi = vis.copy()[:, 0:height, 0:width]
xf = 1 - xi
yi = vis.copy()[:, y_shift : y_shift + height, x_shift : x_shift + width]
yf = 1 - yi
# print "old cross entropy"
H = 0
for n in range(points):
for i in range(iters):
xs, _ = ml.rbm.gibbs_sample(flatten_samples(xi), k, vis_force=flatten_samples(xf), beta=beta)
xs = unflatten_samples_like(xs, xi)
xr, xrf = or_rest(vis[:, 0:height, 0:width], xs)
yi_by_x = xr[:, y_shift : y_shift + height, x_shift : x_shift + width]
yf_by_x = xrf[:, y_shift : y_shift + height, x_shift : x_shift + width]
yi[:, 0 : height - y_shift, 0 : width - x_shift] = yi_by_x
yf[:, 0 : height - y_shift, 0 : width - x_shift] = yf_by_x
ys, _ = ml.rbm.gibbs_sample(flatten_samples(yi), k, vis_force=flatten_samples(yf), beta=beta)
ys = unflatten_samples_like(ys, yi)
yr, yrf = or_rest(vis[:, y_shift : y_shift + height, x_shift : x_shift + width], ys)
xi_by_y = yr[:, 0 : height - y_shift, 0 : width - x_shift]
xf_by_y = yf[:, 0 : height - y_shift, 0 : width - x_shift]
xi[:, y_shift:, x_shift:] = xi_by_y
xf[:, y_shift:, x_shift:] = xf_by_y
H += rbm.free_energy(flatten_samples(xs), beta=beta) + ml.rbm.free_energy(flatten_samples(ys), beta=beta)
return H