def test_verify_AIS(self):
model = oRBM(input_size=self.input_size,
hidden_size=self.hidden_size,
beta=self.beta)
model.W.set_value(self.W)
model.b.set_value(self.b)
model.c.set_value(self.c)
# Brute force
print "Computing lnZ using brute force (i.e. summing the free energy of all posible $v$)..."
V = theano.shared(
value=cartesian([(0, 1)] * self.input_size, dtype=config.floatX))
brute_force_lnZ = logsumexp(-model.free_energy(V), 0)
f_brute_force_lnZ = theano.function([], brute_force_lnZ)
params_bak = [param.get_value() for param in model.parameters]
print "Approximating lnZ using AIS..."
import time
start = time.time()
try:
ais_working_dir = tempfile.mkdtemp()
result = compute_AIS(model,
M=self.nb_samples,
betas=self.betas,
seed=1234,
ais_working_dir=ais_working_dir,
force=True)
logcummean_Z, logcumstd_Z_down, logcumstd_Z_up = result[
'logcummean_Z'], result['logcumstd_Z_down'], result[
'logcumstd_Z_up']
std_lnZ = result['std_lnZ']
print "{0} sec".format(time.time() - start)
import pylab as plt
plt.gca().set_xmargin(0.1)
plt.errorbar(range(1, self.nb_samples + 1),
logcummean_Z,
yerr=[std_lnZ, std_lnZ],
fmt='or')
plt.errorbar(range(1, self.nb_samples + 1),
logcummean_Z,
yerr=[logcumstd_Z_down, logcumstd_Z_up],
fmt='ob')
plt.plot([1, self.nb_samples], [f_brute_force_lnZ()] * 2, '--g')
plt.ticklabel_format(useOffset=False, axis='y')
plt.show()
AIS_logZ = logcummean_Z[-1]
assert_array_equal(params_bak[0], model.W.get_value())
assert_array_equal(params_bak[1], model.b.get_value())
assert_array_equal(params_bak[2], model.c.get_value())
print np.abs(AIS_logZ - f_brute_force_lnZ())
assert_almost_equal(AIS_logZ, f_brute_force_lnZ(), decimal=2)
finally:
shutil.rmtree(ais_working_dir)
def test_gradients_auto_vs_manual(self):
rng = np.random.RandomState(42)
batch_size = 5
input_size = 10
model = oRBM(input_size=input_size,
hidden_size=32,
CDk=1,
rng=np.random.RandomState(42))
W = rng.rand(model.hidden_size, model.input_size).astype(theano.config.floatX)
model.W = theano.shared(value=W.astype(theano.config.floatX), name='W', borrow=True)
b = rng.rand(model.hidden_size).astype(theano.config.floatX)
model.b = theano.shared(value=b.astype(theano.config.floatX), name='b', borrow=True)
c = rng.rand(model.input_size).astype(theano.config.floatX)
model.c = theano.shared(value=c.astype(theano.config.floatX), name='c', borrow=True)
params = [model.W, model.b, model.c]
chain_start = T.matrix('start')
chain_end = T.matrix('end')
chain_start_value = (rng.rand(batch_size, input_size) > 0.5).astype(theano.config.floatX)
chain_end_value = (rng.rand(batch_size, input_size) > 0.5).astype(theano.config.floatX)
chain_start.tag.test_value = chain_start_value
chain_end.tag.test_value = chain_end_value
### Computing gradients using automatic differentation ###
cost = T.mean(model.free_energy(chain_start)) - T.mean(model.free_energy(chain_end))
gparams_auto = T.grad(cost, params, consider_constant=[chain_end])
### Computing gradients manually ###
h = RBM.sample_h_given_v(model, chain_start, return_probs=True)
_h = RBM.sample_h_given_v(model, chain_end, return_probs=True)
icdf = model.icdf_z_given_v(chain_start)
_icdf = model.icdf_z_given_v(chain_end)
if model.penalty == "softplus_bi":
penalty = model.beta * T.nnet.sigmoid(model.b)
elif self.penalty == "softplus0":
penalty = model.beta * T.nnet.sigmoid(0)
grad_W = (T.dot(chain_end.T, _h*_icdf) - T.dot(chain_start.T, h*icdf)).T / batch_size
grad_b = T.mean((_h-penalty)*_icdf - (h-penalty)*icdf, axis=0)
grad_c = T.mean(chain_end - chain_start, axis=0)
gparams_manual = [grad_W, grad_b, grad_c]
grad_W.name, grad_b.name, grad_c.name = "grad_W", "grad_b", "grad_c"
for gparam_auto, gparam_manual in zip(gparams_auto, gparams_manual):
param1 = gparam_auto.eval({chain_start: chain_start_value, chain_end: chain_end_value})
param2 = gparam_manual.eval({chain_start: chain_start_value, chain_end: chain_end_value})
assert_array_almost_equal(param1, param2, err_msg=gparam_manual.name)
def test_verify_AIS(self):
model = oRBM(input_size=self.input_size,
hidden_size=self.hidden_size,
beta=self.beta)
model.W.set_value(self.W)
model.b.set_value(self.b)
model.c.set_value(self.c)
# Brute force
print "Computing lnZ using brute force (i.e. summing the free energy of all posible $v$)..."
V = theano.shared(value=cartesian([(0, 1)] * self.input_size, dtype=config.floatX))
brute_force_lnZ = logsumexp(-model.free_energy(V), 0)
f_brute_force_lnZ = theano.function([], brute_force_lnZ)
params_bak = [param.get_value() for param in model.parameters]
print "Approximating lnZ using AIS..."
import time
start = time.time()
try:
ais_working_dir = tempfile.mkdtemp()
result = compute_AIS(model, M=self.nb_samples, betas=self.betas, seed=1234, ais_working_dir=ais_working_dir, force=True)
logcummean_Z, logcumstd_Z_down, logcumstd_Z_up = result['logcummean_Z'], result['logcumstd_Z_down'], result['logcumstd_Z_up']
std_lnZ = result['std_lnZ']
print "{0} sec".format(time.time() - start)
import pylab as plt
plt.gca().set_xmargin(0.1)
plt.errorbar(range(1, self.nb_samples+1), logcummean_Z, yerr=[std_lnZ, std_lnZ], fmt='or')
plt.errorbar(range(1, self.nb_samples+1), logcummean_Z, yerr=[logcumstd_Z_down, logcumstd_Z_up], fmt='ob')
plt.plot([1, self.nb_samples], [f_brute_force_lnZ()]*2, '--g')
plt.ticklabel_format(useOffset=False, axis='y')
plt.show()
AIS_logZ = logcummean_Z[-1]
assert_array_equal(params_bak[0], model.W.get_value())
assert_array_equal(params_bak[1], model.b.get_value())
assert_array_equal(params_bak[2], model.c.get_value())
print np.abs(AIS_logZ - f_brute_force_lnZ())
assert_almost_equal(AIS_logZ, f_brute_force_lnZ(), decimal=2)
finally:
shutil.rmtree(ais_working_dir)
def setUp(self):
self.input_size = 4
self.hidden_size = 3
self.beta = 1.01
self.batch_size = 100
rng = np.random.RandomState(42)
self.W = rng.randn(self.hidden_size, self.input_size).astype(config.floatX)
self.b = rng.randn(self.hidden_size).astype(config.floatX)
self.c = rng.randn(self.input_size).astype(config.floatX)
self.model = oRBM(input_size=self.input_size,
hidden_size=self.hidden_size,
beta=self.beta)
self.model.W.set_value(self.W)
self.model.b.set_value(self.b)
self.model.c.set_value(self.c)
def setUp(self):
self.input_size = 4
self.hidden_size = 3
self.beta = 1.01
self.batch_size = 100
rng = np.random.RandomState(42)
self.W = rng.randn(self.hidden_size,
self.input_size).astype(config.floatX)
self.b = rng.randn(self.hidden_size).astype(config.floatX)
self.c = rng.randn(self.input_size).astype(config.floatX)
self.model = oRBM(input_size=self.input_size,
hidden_size=self.hidden_size,
beta=self.beta)
self.model.W.set_value(self.W)
self.model.b.set_value(self.b)
self.model.c.set_value(self.c)
def test_gradients_auto_vs_manual(self):
rng = np.random.RandomState(42)
batch_size = 5
input_size = 10
model = oRBM(input_size=input_size,
hidden_size=32,
CDk=1,
rng=np.random.RandomState(42))
W = rng.rand(model.hidden_size,
model.input_size).astype(theano.config.floatX)
model.W = theano.shared(value=W.astype(theano.config.floatX),
name='W',
borrow=True)
b = rng.rand(model.hidden_size).astype(theano.config.floatX)
model.b = theano.shared(value=b.astype(theano.config.floatX),
name='b',
borrow=True)
c = rng.rand(model.input_size).astype(theano.config.floatX)
model.c = theano.shared(value=c.astype(theano.config.floatX),
name='c',
borrow=True)
params = [model.W, model.b, model.c]
chain_start = T.matrix('start')
chain_end = T.matrix('end')
chain_start_value = (rng.rand(batch_size, input_size) > 0.5).astype(
theano.config.floatX)
chain_end_value = (rng.rand(batch_size, input_size) > 0.5).astype(
theano.config.floatX)
chain_start.tag.test_value = chain_start_value
chain_end.tag.test_value = chain_end_value
### Computing gradients using automatic differentation ###
cost = T.mean(model.free_energy(chain_start)) - T.mean(
model.free_energy(chain_end))
gparams_auto = T.grad(cost, params, consider_constant=[chain_end])
### Computing gradients manually ###
h = RBM.sample_h_given_v(model, chain_start, return_probs=True)
_h = RBM.sample_h_given_v(model, chain_end, return_probs=True)
icdf = model.icdf_z_given_v(chain_start)
_icdf = model.icdf_z_given_v(chain_end)
if model.penalty == "softplus_bi":
penalty = model.beta * T.nnet.sigmoid(model.b)
elif self.penalty == "softplus0":
penalty = model.beta * T.nnet.sigmoid(0)
grad_W = (T.dot(chain_end.T, _h * _icdf) -
T.dot(chain_start.T, h * icdf)).T / batch_size
grad_b = T.mean((_h - penalty) * _icdf - (h - penalty) * icdf, axis=0)
grad_c = T.mean(chain_end - chain_start, axis=0)
gparams_manual = [grad_W, grad_b, grad_c]
grad_W.name, grad_b.name, grad_c.name = "grad_W", "grad_b", "grad_c"
for gparam_auto, gparam_manual in zip(gparams_auto, gparams_manual):
param1 = gparam_auto.eval({
chain_start: chain_start_value,
chain_end: chain_end_value
})
param2 = gparam_manual.eval({
chain_start: chain_start_value,
chain_end: chain_end_value
})
assert_array_almost_equal(param1,
param2,
err_msg=gparam_manual.name)
def model_factory(model_name, input_size, hyperparams):
#Set learning rate method that will be used.
if hyperparams["ConstantLearningRate"] is not None:
infos = hyperparams["ConstantLearningRate"].split()
lr = float(infos[0])
lr_method = ConstantLearningRate(lr=lr)
elif hyperparams["ADAGRAD"] is not None:
infos = hyperparams["ADAGRAD"].split()
lr = float(infos[0])
eps = float(infos[1]) if len(infos) > 1 else 1e-6
lr_method = ADAGRAD(lr=lr, eps=eps)
else:
raise ValueError("The update rule is mandatory!")
#Set regularization method that will be used.
regularization_method = NoRegularization()
if hyperparams["L1Regularization"] is not None and hyperparams[
"L1Regularization"] != 0:
lambda_factor = float(hyperparams["L1Regularization"])
regularization_method = L1Regularization(lambda_factor)
elif hyperparams["L2Regularization"] is not None and hyperparams[
"L2Regularization"] != 0:
lambda_factor = float(hyperparams["L2Regularization"])
regularization_method = L2Regularization(lambda_factor)
#Set contrastive divergence method to use.
CD_method = ContrastiveDivergence()
if hyperparams["PCD"]:
CD_method = PersistentCD(input_size,
nb_particles=hyperparams['batch_size'])
rng = np.random.RandomState(hyperparams["seed"])
#Build model
if model_name == "rbm":
from iRBM.models.rbm import RBM
model = RBM(input_size=input_size,
hidden_size=hyperparams["size"],
learning_rate=lr_method,
regularization=regularization_method,
CD=CD_method,
CDk=hyperparams["cdk"],
rng=rng)
elif model_name == "orbm":
from iRBM.models.orbm import oRBM
model = oRBM(input_size=input_size,
hidden_size=hyperparams["size"],
beta=hyperparams["beta"],
learning_rate=lr_method,
regularization=regularization_method,
CD=CD_method,
CDk=hyperparams["cdk"],
rng=rng)
elif model_name == "irbm":
from iRBM.models.irbm import iRBM
model = iRBM(input_size=input_size,
hidden_size=hyperparams["size"],
beta=hyperparams["beta"],
learning_rate=lr_method,
regularization=regularization_method,
CD=CD_method,
CDk=hyperparams["cdk"],
rng=rng)
return model
def model_factory(model_name, input_size, hyperparams):
#Set learning rate method that will be used.
if hyperparams["ConstantLearningRate"] is not None:
infos = hyperparams["ConstantLearningRate"].split()
lr = float(infos[0])
lr_method = ConstantLearningRate(lr=lr)
elif hyperparams["ADAGRAD"] is not None:
infos = hyperparams["ADAGRAD"].split()
lr = float(infos[0])
eps = float(infos[1]) if len(infos) > 1 else 1e-6
lr_method = ADAGRAD(lr=lr, eps=eps)
else:
raise ValueError("The update rule is mandatory!")
#Set regularization method that will be used.
regularization_method = NoRegularization()
if hyperparams["L1Regularization"] is not None and hyperparams["L1Regularization"] != 0:
lambda_factor = float(hyperparams["L1Regularization"])
regularization_method = L1Regularization(lambda_factor)
elif hyperparams["L2Regularization"] is not None and hyperparams["L2Regularization"] != 0:
lambda_factor = float(hyperparams["L2Regularization"])
regularization_method = L2Regularization(lambda_factor)
#Set contrastive divergence method to use.
CD_method = ContrastiveDivergence()
if hyperparams["PCD"]:
CD_method = PersistentCD(input_size, nb_particles=hyperparams['batch_size'])
rng = np.random.RandomState(hyperparams["seed"])
#Build model
if model_name == "rbm":
from iRBM.models.rbm import RBM
model = RBM(input_size=input_size,
hidden_size=hyperparams["size"],
learning_rate=lr_method,
regularization=regularization_method,
CD=CD_method,
CDk=hyperparams["cdk"],
rng=rng
)
elif model_name == "orbm":
from iRBM.models.orbm import oRBM
model = oRBM(input_size=input_size,
hidden_size=hyperparams["size"],
beta=hyperparams["beta"],
learning_rate=lr_method,
regularization=regularization_method,
CD=CD_method,
CDk=hyperparams["cdk"],
rng=rng
)
elif model_name == "irbm":
from iRBM.models.irbm import iRBM
model = iRBM(input_size=input_size,
hidden_size=hyperparams["size"],
beta=hyperparams["beta"],
learning_rate=lr_method,
regularization=regularization_method,
CD=CD_method,
CDk=hyperparams["cdk"],
rng=rng
)
return model
