# enter output directory
rbmutil.enter_rbm_plot_directory("mnist", cfg.n_hid, cfg.use_pcd, cfg.n_gibbs_steps,
"prob.txt", clean=False)
# Build RBM
rbm = RestrictedBoltzmannMachine(0, cfg.n_vis, cfg.n_hid, 0)
# load Ruslan's RBM
if use_ruslan:
print "Loading Ruslan's ml.rbm..."
mdata = scipy.io.loadmat("matlab_epoch%d.mat" % (epoch + 1))
ml.rbm.bias_vis = gp.as_garray(mdata['visbiases'][0,:])
ml.rbm.bias_hid = gp.as_garray(mdata['hidbiases'][0,:])
ml.rbm.weights = gp.as_garray(mdata['vishid'])
else:
rbmutil.load_parameters(rbm, "weights-%02i.npz" % epoch)
# load pratition function
if use_ruslan:
filename = "matlab-lpf-%02d.npz" % (epoch+1)
else:
filename = "lpf-%02d.npz" % epoch
print "Loading partition function %s" % filename
lpf = np.load(filename)
rbm.log_pf = lpf['lpf']
# calculate log probability of training set
tr_lp = gp.mean(rbm.normalized_log_p_vis(X))
tst_lp = gp.mean(rbm.normalized_log_p_vis(TX))
print "Average log p(x from training set) = %f" % tr_lp
cfg.n_gibbs_steps)
mr.seed(30)
rbm.weights = 0.01 * (0.5 - mr.rand(rbm.weights.shape))
# load weights
if use_ruslans_start_weights:
filename = "matlab_epoch%d.mat" % (from_epoch-1 + 1)
print "Loading Ruslan's start weights from %s" % filename
mdata = scipy.io.loadmat(filename)
ml.rbm.weights = gp.as_garray(mdata['vishid'])
ml.rbm.bias_vis = gp.as_garray(mdata['visbiases'])
ml.rbm.bias_hid = gp.as_garray(mdata['hidbiases'])
else:
print "Loading python start weights"
filename = "weights-%02d.npz" % (from_epoch-1)
rbmutil.load_parameters(rbm, filename)
# initialize momentums
weights_m1 = 0
bias_vis_m1 = 0
bias_hid_m1 = 0
np.set_printoptions(precision=15)
print "start weights after epoch %d:" % (from_epoch-1)
print ml.rbm.weights[0:5,0:5]
print "random state: ", mr.get_uint32()
# train
for epoch in range(from_epoch, cfg.epochs):
seen_epoch_samples = 0
