#epoch = 9
use_ruslan = False
# load dataset
X, TX = rbmutil.load_mnist(False)
# load ruslan's training set
mdata = scipy.io.loadmat("mnist.mat")
X = gp.as_garray(mdata['fbatchdata'])
# 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:
rbm = RestrictedBoltzmannMachine(0, cfg.n_vis, cfg.n_hid, 0)
rbmutil.load_parameters(rbm, "weights-%02i.npz" % epoch)
# init AIS estimator
print "Calculating base RBM biases using %d samples with %d Gibbs steps " \
"inbetween..." % (ais_base_samples, ais_base_gibbs_steps_between_samples)
ais = AnnealedImportanceSampler(rbm, ais_base_samples, ais_base_chains,
ais_base_gibbs_steps_between_samples)
print "Saving base RBM biases to %s..." % filename
np.savez_compressed(filename,
base_bias_vis=gp.as_numpy_array(ais.base_bias_vis))
print "Base RBM log partition function: %f" % ais.base_log_partition_function()
# check base rbm log partition function
if check_base_rbm_partition_function:
baserbm = RestrictedBoltzmannMachine(0, cfg.n_vis, cfg.n_hid, 0)
baserbm.weights = gp.zeros(baserbm.weights.shape)
baserbm.bias_hid = gp.zeros(baserbm.bias_hid.shape)
baserbm.bias_vis = ais.base_bias_vis
print "Base RBM log partition function using partition_func: %f" % baserbm.partition_function(20, 50).ln()
# perform estimation of partition function
print "Estimating partition function using %dx %d AIS runs with %d intermediate "\
"RBMs and %d Gibbs steps..." % (ais_iterations, ais_runs, len(ais_betas), ais_gibbs_steps)
with open("ais_iterations.csv", 'w') as outfile:
outfile.write("iterations\tlog Z\n")
lpfs = []
for i in range(ais_iterations):
lpf, lpf_m_3s, lpf_p_3s = ais.log_partition_function(ais_betas, ais_runs,
ais_gibbs_steps)
#np.seterr(all='raise')
#gp.acceptable_number_types = 'no nans or infs'
# parameters
epoch = 14
do_sampling = True
# load dataset
X, VX, TX = rbmutil.load_mnist()
# enter output directory
rbmutil.enter_rbm_plot_directory("mnist", cfg.n_hid, cfg.use_pcd, cfg.n_gibbs_steps,
clean=False)
# Build RBM
rbm = RestrictedBoltzmannMachine(0, cfg.n_vis, cfg.n_hid, 0)
rbmutil.load_parameters(rbm, "weights-%02i.npz" % epoch)
#rbmutil.load_parameters("../../../DeepLearningTutorials/code/rbm_plots/GPU-PCD/weights.npz")
#epoch = 99
# calculate statistics
seen_epoch_samples = 0
pl_bit = 0
pl_sum = 0
rc_sum = 0
for x in util.draw_slices(X, cfg.batch_size, kind='sequential',
samples_are='rows', stop=True):
print "%d / %d \r" % (seen_epoch_samples, X.shape[0]),
seen_epoch_samples += cfg.batch_size
# parameters
use_ruslan = True
#epoch = cfg.epochs - 1
epoch = 9
batch = 0
#X, TX = rbmutil.load_mnist(False)
mdata = scipy.io.loadmat("mnist.mat")
X = mdata['fbatchdata']
# enter output directory
rbmutil.enter_rbm_plot_directory("mnist", cfg.n_hid, cfg.use_pcd, cfg.n_gibbs_steps,
"sample.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..."
epoch = 99
mdata = scipy.io.loadmat("mnistvh.mat")
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)
# compute hidden activations
v = X[batch*cfg.batch_size:(batch+1)*cfg.batch_size]
hid_act = gp.as_numpy_array(rbm.p_hid_given_vis(v))
cfg.epochs = 2
# load dataset
X, TX = rbmutil.load_mnist(False)
# load ruslan's training set
mdata = scipy.io.loadmat("mnist.mat")
X = gp.as_garray(mdata['fbatchdata'])
# create output directory
rbmutil.enter_rbm_plot_directory("mnist", cfg.n_hid, cfg.use_pcd,
cfg.n_gibbs_steps, "training_from.txt",
clean=False)
# Build RBM
rbm = RestrictedBoltzmannMachine(cfg.batch_size, cfg.n_vis, cfg.n_hid,
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)
