def test_softmax_mf_sample_consistent():
# A test of the Softmax class
# Verifies that the mean field update is consistent with
# the sampling function
# Since a Softmax layer contains only one random variable
# (with n_classes possible values) the mean field assumption
# does not impose any restriction so mf_update simply gives
# the true expected value of h given v.
# We can thus use mf_update to compute the expected value
# of a sample of y conditioned on v, and check that samples
# drawn using the layer's sample method convert to that
# value.
rng = np.random.RandomState([2012, 11, 1, 1154])
theano_rng = MRG_RandomStreams(2012 + 11 + 1 + 1154)
num_samples = 1000
tol = .042
# Make DBM
num_vis = rng.randint(1, 11)
n_classes = rng.randint(1, 11)
v = BinaryVector(num_vis)
v.set_biases(rng.uniform(-1., 1., (num_vis, )).astype(config.floatX))
y = Softmax(n_classes=n_classes, layer_name='y', irange=1.)
y.set_biases(rng.uniform(-1., 1., (n_classes, )).astype(config.floatX))
dbm = DBM(visible_layer=v, hidden_layers=[y], batch_size=1, niter=50)
# Randomly pick a v to condition on
# (Random numbers are generated via dbm.rng)
layer_to_state = dbm.make_layer_to_state(1)
v_state = layer_to_state[v]
y_state = layer_to_state[y]
# Infer P(y | v) using mean field
expected_y = y.mf_update(state_below=v.upward_state(v_state))
expected_y = expected_y[0, :]
expected_y = expected_y.eval()
# copy all the states out into a batch size of num_samples
cause_copy = sharedX(np.zeros((num_samples, ))).dimshuffle(0, 'x')
v_state = v_state[0, :] + cause_copy
y_state = y_state[0, :] + cause_copy
y_samples = y.sample(state_below=v.upward_state(v_state),
theano_rng=theano_rng)
y_samples = function([], y_samples)()
check_multinomial_samples(y_samples, (num_samples, n_classes), expected_y,
tol)
def test_variational_cd():
# Verifies that VariationalCD works well with make_layer_to_symbolic_state
visible_layer = BinaryVector(nvis=100)
hidden_layer = BinaryVectorMaxPool(detector_layer_dim=500,
pool_size=1,
layer_name='h',
irange=0.05,
init_bias=-2.0)
model = DBM(visible_layer=visible_layer,
hidden_layers=[hidden_layer],
batch_size=100,
niter=1)
cost = VariationalCD(num_chains=100, num_gibbs_steps=2)
data_specs = cost.get_data_specs(model)
mapping = DataSpecsMapping(data_specs)
space_tuple = mapping.flatten(data_specs[0], return_tuple=True)
source_tuple = mapping.flatten(data_specs[1], return_tuple=True)
theano_args = []
for space, source in safe_zip(space_tuple, source_tuple):
name = '%s' % (source)
arg = space.make_theano_batch(name=name)
theano_args.append(arg)
theano_args = tuple(theano_args)
nested_args = mapping.nest(theano_args)
grads, updates = cost.get_gradients(model, nested_args)
def make_random_basic_binary_dbm(
rng,
pool_size_1,
num_vis = None,
num_pool_1 = None,
num_pool_2 = None,
pool_size_2 = None,
center = False
):
"""
Makes a DBM with BinaryVector for the visible layer,
and two hidden layers of type BinaryVectorMaxPool.
The weights and biases are initialized randomly with
somewhat large values (i.e., not what you'd want to
use for learning)
rng: A numpy RandomState.
pool_size_1: The size of the pools to use in the first
layer.
"""
if num_vis is None:
num_vis = rng.randint(1,11)
if num_pool_1 is None:
num_pool_1 = rng.randint(1,11)
if num_pool_2 is None:
num_pool_2 = rng.randint(1,11)
if pool_size_2 is None:
pool_size_2 = rng.randint(1,6)
num_h1 = num_pool_1 * pool_size_1
num_h2 = num_pool_2 * pool_size_2
v = BinaryVector(num_vis, center=center)
v.set_biases(rng.uniform(-1., 1., (num_vis,)).astype(config.floatX), recenter=center)
h1 = BinaryVectorMaxPool(
detector_layer_dim = num_h1,
pool_size = pool_size_1,
layer_name = 'h1',
center = center,
irange = 1.)
h1.set_biases(rng.uniform(-1., 1., (num_h1,)).astype(config.floatX), recenter=center)
h2 = BinaryVectorMaxPool(
center = center,
detector_layer_dim = num_h2,
pool_size = pool_size_2,
layer_name = 'h2',
irange = 1.)
h2.set_biases(rng.uniform(-1., 1., (num_h2,)).astype(config.floatX), recenter=center)
dbm = DBM(visible_layer = v,
hidden_layers = [h1, h2],
batch_size = 1,
niter = 50)
return dbm
def __init__(self):
""" gets a small batch of data
sets up a PD-DBM model
"""
self.tol = 1e-5
X = np.random.RandomState([1,2,3]).randn(1000,5)
X -= X.mean()
X /= X.std()
m, D = X.shape
N = 6
N2 = 7
s3c = S3C(nvis = D,
nhid = N,
irange = .1,
init_bias_hid = -1.5,
init_B = 3.,
min_B = 1e-8,
max_B = 1000.,
init_alpha = 1., min_alpha = 1e-8, max_alpha = 1000.,
init_mu = 1., e_step = None,
m_step = Grad_M_Step(),
min_bias_hid = -1e30, max_bias_hid = 1e30,
)
rbm = RBM(nvis = N, nhid = N2, irange = .1, init_bias_vis = -1.5, init_bias_hid = 1.5)
#don't give the model an inference procedure or learning rate so it won't spend years compiling a learn_func
self.model = PDDBM(
dbm = DBM( use_cd = 1,
rbms = [ rbm ]),
s3c = s3c
)
self.model.make_pseudoparams()
self.inference_procedure = InferenceProcedure(
clip_reflections = True,
rho = .5 )
self.inference_procedure.register_model(self.model)
self.X = X
self.N = N
self.N2 = N2
self.m = m
def test_softmax_mf_sample_consistent():
# A test of the Softmax class
# Verifies that the mean field update is consistent with
# the sampling function
# Since a Softmax layer contains only one random variable
# (with n_classes possible values) the mean field assumption
# does not impose any restriction so mf_update simply gives
# the true expected value of h given v.
# We can thus use mf_update to compute the expected value
# of a sample of y conditioned on v, and check that samples
# drawn using the layer's sample method convert to that
# value.
rng = np.random.RandomState([2012,11,1,1154])
theano_rng = MRG_RandomStreams(2012+11+1+1154)
num_samples = 1000
tol = .042
# Make DBM
num_vis = rng.randint(1,11)
n_classes = rng.randint(1, 11)
v = BinaryVector(num_vis)
v.set_biases(rng.uniform(-1., 1., (num_vis,)).astype(config.floatX))
y = Softmax(
n_classes = n_classes,
layer_name = 'y',
irange = 1.)
y.set_biases(rng.uniform(-1., 1., (n_classes,)).astype(config.floatX))
dbm = DBM(visible_layer = v,
hidden_layers = [y],
batch_size = 1,
niter = 50)
# Randomly pick a v to condition on
# (Random numbers are generated via dbm.rng)
layer_to_state = dbm.make_layer_to_state(1)
v_state = layer_to_state[v]
y_state = layer_to_state[y]
# Infer P(y | v) using mean field
expected_y = y.mf_update(
state_below = v.upward_state(v_state))
expected_y = expected_y[0, :]
expected_y = expected_y.eval()
# copy all the states out into a batch size of num_samples
cause_copy = sharedX(np.zeros((num_samples,))).dimshuffle(0,'x')
v_state = v_state[0,:] + cause_copy
y_state = y_state[0,:] + cause_copy
y_samples = y.sample(state_below = v.upward_state(v_state), theano_rng=theano_rng)
y_samples = function([], y_samples)()
check_multinomial_samples(y_samples, (num_samples, n_classes), expected_y, tol)
def test_softmax_mf_energy_consistent_centering():
# A test of the Softmax class
# Verifies that the mean field update is consistent with
# the energy function when using the centering trick
# Since a Softmax layer contains only one random variable
# (with n_classes possible values) the mean field assumption
# does not impose any restriction so mf_update simply gives
# the true expected value of h given v.
# We also know P(h | v)
# = P(h, v) / P( v)
# = P(h, v) / sum_h P(h, v)
# = exp(-E(h, v)) / sum_h exp(-E(h, v))
# So we can check that computing P(h | v) with both
# methods works the same way
rng = np.random.RandomState([2012,11,1,1131])
# Make DBM
num_vis = rng.randint(1,11)
n_classes = rng.randint(1, 11)
v = BinaryVector(num_vis, center=True)
v.set_biases(rng.uniform(-1., 1., (num_vis,)).astype(config.floatX), recenter=True)
y = Softmax(
n_classes = n_classes,
layer_name = 'y',
irange = 1., center=True)
y.set_biases(rng.uniform(-1., 1., (n_classes,)).astype(config.floatX), recenter=True)
dbm = DBM(visible_layer = v,
hidden_layers = [y],
batch_size = 1,
niter = 50)
# Randomly pick a v to condition on
# (Random numbers are generated via dbm.rng)
layer_to_state = dbm.make_layer_to_state(1)
v_state = layer_to_state[v]
y_state = layer_to_state[y]
# Infer P(y | v) using mean field
expected_y = y.mf_update(
state_below = v.upward_state(v_state))
expected_y = expected_y[0, :]
expected_y = expected_y.eval()
# Infer P(y | v) using the energy function
energy = dbm.energy(V = v_state,
hidden = [y_state])
unnormalized_prob = T.exp(-energy)
assert unnormalized_prob.ndim == 1
unnormalized_prob = unnormalized_prob[0]
unnormalized_prob = function([], unnormalized_prob)
def compute_unnormalized_prob(which):
write_y = np.zeros((n_classes,))
write_y[which] = 1.
y_value = y_state.get_value()
y_value[0, :] = write_y
y_state.set_value(y_value)
return unnormalized_prob()
probs = [compute_unnormalized_prob(idx) for idx in xrange(n_classes)]
denom = sum(probs)
probs = [on_prob / denom for on_prob in probs]
# np.asarray(probs) doesn't make a numpy vector, so I do it manually
wtf_numpy = np.zeros((n_classes,))
for i in xrange(n_classes):
wtf_numpy[i] = probs[i]
probs = wtf_numpy
if not np.allclose(expected_y, probs):
print 'mean field expectation of h:',expected_y
print 'expectation of h based on enumerating energy function values:',probs
assert False
def __init__(self, model = None, X = None, tol = 1e-5,
init_H = None, init_S = None, init_G = None):
""" gets a small batch of data
sets up a PD-DBM model
"""
self.tol = tol
if X is None:
X = np.random.RandomState([1,2,3]).randn(1000,5)
X -= X.mean()
X /= X.std()
m, D = X.shape
if model is None:
N = 6
N2 = 7
s3c = S3C(nvis = D,
nhid = N,
irange = .1,
init_bias_hid = -1.5,
init_B = 3.,
min_B = 1e-8,
max_B = 1000.,
init_alpha = 1., min_alpha = 1e-8, max_alpha = 1000.,
init_mu = 1., e_step = None,
m_step = Grad_M_Step(),
min_bias_hid = -1e30, max_bias_hid = 1e30,
)
rbm = RBM(nvis = N, nhid = N2, irange = .5, init_bias_vis = -1.5, init_bias_hid = 1.5)
#don't give the model an inference procedure or learning rate so it won't spend years compiling a learn_func
self.model = PDDBM(
dbm = DBM( use_cd = 1,
rbms = [ rbm ]),
s3c = s3c
)
self.model.make_pseudoparams()
self.inference_procedure = InferenceProcedure(
clip_reflections = True,
rho = .5 )
self.inference_procedure.register_model(self.model)
else:
self.model = model
self.inference_procedure = model.inference_procedure
N = model.s3c.nhid
N2 = model.dbm.rbms[0].nhid
self.X = X
self.N = N
self.N2 = N2
self.m = m
if init_H is None:
self.init_H = np.cast[config.floatX](self.model.rng.uniform(0.,1.,(self.m, self.N)))
self.init_S = np.cast[config.floatX](self.model.rng.uniform(-5.,5.,(self.m, self.N)))
self.init_G = np.cast[config.floatX](self.model.rng.uniform(0.,1.,(self.m,self.N2)))
else:
assert init_S is not None
assert init_G is not None
self.init_H = init_H
self.init_S = init_S
self.init_G = init_G
def test_ais():
"""
Test ais computation by comparing the output of estimate_likelihood to
Russ's code's output for the same parameters.
"""
try:
trainset = MNIST(which_set='train')
testset = MNIST(which_set='test')
except NoDataPathError:
raise SkipTest("PYLEARN2_DATA_PATH environment variable not defined")
nvis = 784
nhid = 20
# Random initialization of RBM parameters
numpy.random.seed(98734)
w_hid = 10 * numpy.cast[theano.config.floatX](numpy.random.randn(nvis,
nhid))
b_vis = 10 * numpy.cast[theano.config.floatX](numpy.random.randn(nvis))
b_hid = 10 * numpy.cast[theano.config.floatX](numpy.random.randn(nhid))
# Initialization of RBM
visible_layer = BinaryVector(nvis)
hidden_layer = BinaryVectorMaxPool(detector_layer_dim=nhid, pool_size=1,
layer_name='h', irange=0.1)
rbm = DBM(100, visible_layer, [hidden_layer], 1)
rbm.visible_layer.set_biases(b_vis)
rbm.hidden_layers[0].set_weights(w_hid)
rbm.hidden_layers[0].set_biases(b_hid)
rbm.nvis = nvis
rbm.nhid = nhid
# Compute real logz and associated train_ll and test_ll using rbm_tools
v_sample = T.matrix('v_sample')
h_sample = T.matrix('h_sample')
W = theano.shared(rbm.hidden_layers[0].get_weights())
hbias = theano.shared(rbm.hidden_layers[0].get_biases())
vbias = theano.shared(rbm.visible_layer.get_biases())
wx_b = T.dot(v_sample, W) + hbias
vbias_term = T.dot(v_sample, vbias)
hidden_term = T.sum(T.log(1 + T.exp(wx_b)), axis=1)
free_energy_v = -hidden_term - vbias_term
free_energy_v_fn = theano.function(inputs=[v_sample],
outputs=free_energy_v)
wh_c = T.dot(h_sample, W.T) + vbias
hbias_term = T.dot(h_sample, hbias)
visible_term = T.sum(T.log(1 + T.exp(wh_c)), axis=1)
free_energy_h = -visible_term - hbias_term
free_energy_h_fn = theano.function(inputs=[h_sample],
outputs=free_energy_h)
real_logz = rbm_tools.compute_log_z(rbm, free_energy_h_fn)
real_ais_train_ll = -rbm_tools.compute_nll(rbm,
trainset.get_design_matrix(),
real_logz, free_energy_v_fn)
real_ais_test_ll = -rbm_tools.compute_nll(rbm, testset.get_design_matrix(),
real_logz, free_energy_v_fn)
# Compute train_ll, test_ll and logz using dbm_metrics
train_ll, test_ll, logz = dbm_metrics.estimate_likelihood([W],
[vbias, hbias],
trainset,
testset,
pos_mf_steps=100)
assert (real_logz - logz) < 2.0
assert (real_ais_train_ll - train_ll) < 2.0
assert (real_ais_test_ll - test_ll) < 2.0
#arg1: layer 1 model
#arg2: layer 2 model
import sys
from pylearn2.utils import serial
if len(sys.argv) == 5:
l1, l2, idx, alpha = sys.argv[1:]
l1 = serial.load(l1)
l2 = serial.load(l2)
from pylearn2.models.dbm import DBM
dbm = DBM(negative_chains=1, monitor_params=0, rbms=[l2])
from galatea.pddbm.pddbm import PDDBM, InferenceProcedure
pddbm = PDDBM(dbm=dbm,
s3c=l1,
inference_procedure=InferenceProcedure(schedule=[['s', 1.],
['h', 1.],
['g', 0],
['h', 0.4],
['s', 0.4],
['h', 0.4],
['g', 0],
['h', 0.4],
['s', 0.4],
['h', 0.4],
['g', 0],
def __init__(self):
""" gets a small batch of data
sets up a PD-DBM model with its DBM weights set to 0
so that it represents the same distribution as an S3C
model
Makes the S3C model it matches
(Note that their learning rules don't match since the
complete partition function of the S3C model is tractable
but the PD-DBM has to approximate the h partition function
via sampling)
"""
self.tol = 1e-5
X = np.random.RandomState([1,2,3]).randn(1000,5)
X -= X.mean()
X /= X.std()
m, D = X.shape
N = 6
N2 = 7
self.X = X
self.N = N
self.N2 = N2
self.m = m
self.D = D
s3c_for_pddbm = self.make_s3c()
self.s3c = self.make_s3c()
self.s3c.W.set_value(s3c_for_pddbm.W.get_value())
rbm = RBM(nvis = N, nhid = N2, irange = .0, init_bias_vis = -1.5, init_bias_hid = 6.)
#don't give the model an inference procedure or learning rate so it won't spend years compiling a learn_func
self.model = PDDBM(
dbm = DBM( use_cd = 1,
rbms = [ rbm ]),
s3c = s3c_for_pddbm
)
self.model.make_pseudoparams()
self.inference_procedure = InferenceProcedure(
schedule = [ ['s',.1], ['h',.1], ['g',0, 0.2], ['s', 0.2], ['h',0.2],
['s',0.3], ['g',0,.3], ['h',0.3], ['s',0.4], ['h',0.4],
['g',0,.4], ['s',0.4], ['h',0.4],
['g',0,.5], ['s',0.5], ['h', 0.5], ['s',0.1],
['h',0.5] ],
clip_reflections = True,
rho = .5 )
self.inference_procedure.register_model(self.model)
self.e_step = make_e_step_from_inference_procedure(self.inference_procedure)
self.e_step.register_model(self.s3c)
self.s3c.make_pseudoparams()
#check that all the parameters match
assert np.abs(self.s3c.W.get_value() - self.model.s3c.W.get_value()).max() == 0.0
assert np.abs(self.s3c.bias_hid.get_value() - self.model.s3c.bias_hid.get_value()).max() == 0.0
assert np.abs(self.s3c.alpha.get_value() - self.model.s3c.alpha.get_value()).max() == 0.0
assert np.abs(self.s3c.mu.get_value() - self.model.s3c.mu.get_value()).max() == 0.0
assert np.abs(self.s3c.B_driver.get_value() - self.model.s3c.B_driver.get_value()).max() == 0.0
#check that the assumptions making these tests valid are met
assert np.abs(self.model.dbm.W[0].get_value()).max() == 0.0
