def test_ais_with_dbn_sanity_check(self): dbn = DBN(RBM(5, 20)) dbn.add_layer(RBM(20, 5)) dbn.add_layer(RBM(5, 20)) dbn[0].W = np.matrix(np.random.randn(5, 20)) dbn[0].b = np.matrix(np.random.rand(5, 1) - 0.5) dbn[0].c = np.matrix(np.random.rand(20, 1) - 0.5) dbn[1].W = dbn[0].W.T dbn[1].b = dbn[0].c dbn[1].c = dbn[0].b dbn[2].W = dbn[0].W dbn[2].b = dbn[0].b dbn[2].c = dbn[0].c samples = dbn.sample(100) ais = Estimator(dbn[0]) rbm_logz = ais.estimate_log_partition_function(100, np.sqrt(np.arange(0, 1, 0.001))) rbm_probs = ais.estimate_log_probability(samples) ais = Estimator(dbn) dbn_logz = ais.estimate_log_partition_function(100, np.sqrt(np.arange(0, 1, 0.001))) dbn_probs = ais.estimate_log_probability(samples) self.assertTrue(abs(dbn_logz - rbm_logz) / rbm_logz < 0.02) self.assertTrue((np.exp(dbn_probs) - np.exp(rbm_probs)).mean() < 0.02)
def test_ais_with_dbn_sanity_check(self):
dbn = DBN(RBM(5, 20))
dbn.add_layer(RBM(20, 5))
dbn.add_layer(RBM(5, 20))
dbn[0].W = np.matrix(np.random.randn(5, 20))
dbn[0].b = np.matrix(np.random.rand(5, 1) - 0.5)
dbn[0].c = np.matrix(np.random.rand(20, 1) - 0.5)
dbn[1].W = dbn[0].W.T
dbn[1].b = dbn[0].c
dbn[1].c = dbn[0].b
dbn[2].W = dbn[0].W
dbn[2].b = dbn[0].b
dbn[2].c = dbn[0].c
samples = dbn.sample(100)
ais = Estimator(dbn[0])
rbm_logz = ais.estimate_log_partition_function(
100, np.sqrt(np.arange(0, 1, 0.001)))
rbm_probs = ais.estimate_log_probability(samples)
ais = Estimator(dbn)
dbn_logz = ais.estimate_log_partition_function(
100, np.sqrt(np.arange(0, 1, 0.001)))
dbn_probs = ais.estimate_log_probability(samples)
self.assertTrue(abs(dbn_logz - rbm_logz) / rbm_logz < 0.02)
self.assertTrue((np.exp(dbn_probs) - np.exp(rbm_probs)).mean() < 0.02)
def test_ais_with_dbn(self): dbn = DBN(RBM(10, 10)) dbn.add_layer(RBM(10, 20)) hidden_states = utils.binary_numbers(dbn[0].Y.shape[0]) ais = Estimator(dbn) ais_logz = ais.estimate_log_partition_function(100, np.sqrt(np.arange(0, 1, 0.001))) brf_logz = utils.logsumexp(dbn[1]._ulogprob_vis(hidden_states)) brf_probs = [] dbn_probs = [] dbn_bound = [] for i in range(50): sample = np.matrix(np.random.rand(10, 1)) > 0.5 prob, bound = ais.estimate_log_probability(sample, 200) brf_probs.append(utils.logsumexp(dbn[1]._ulogprob_vis(hidden_states) + dbn[0]._clogprob_vis_hid(sample, hidden_states), 1) - brf_logz) dbn_probs.append(prob) dbn_bound.append(bound) self.assertTrue(np.mean(dbn_bound) < np.mean(dbn_probs)) self.assertTrue(np.abs(np.mean(dbn_probs) - np.mean(brf_probs)) < 0.1)
def test_ais_with_semirbm_dbn(self):
dbn = DBN(RBM(5, 5))
dbn.add_layer(SemiRBM(5, 5))
ais = Estimator(dbn)
ais.estimate_log_partition_function(100,
np.arange(0, 1, 1E-3),
layer=0)
ais.estimate_log_partition_function(10, np.arange(0, 1, 1E-3), layer=1)
dbn[0]._brf_logz = utils.logsumexp(dbn[0]._ulogprob_vis(
utils.binary_numbers(dbn[0].X.shape[0])))
dbn[1]._brf_logz = utils.logsumexp(dbn[1]._ulogprob_vis(
utils.binary_numbers(dbn[1].X.shape[0])))
samples = np.concatenate(
[dbn.sample(25, 100, 20),
np.matrix(np.random.rand(5, 25) > 0.5)], 1)
Y = utils.binary_numbers(dbn[0].Y.shape[0])
X = utils.binary_numbers(dbn[0].X.shape[0])
logRy = dbn[1]._ulogprob_vis(Y)
logQy = utils.logsumexp(dbn[0]._ulogprob(X, Y, all_pairs=True), 0)
log_sum = utils.logsumexp(
dbn[0]._clogprob_hid_vis(samples, Y, all_pairs=True) - logQy +
logRy, 1)
logPx = log_sum + dbn[0]._ulogprob_vis(samples) - dbn[1]._brf_logz
logPx_ = ais.estimate_log_probability(samples)[0]
self.assertTrue(np.abs(logPx_.mean() - logPx.mean()) < 0.1)
def test_ais_with_dbn(self):
dbn = DBN(RBM(10, 10))
dbn.add_layer(RBM(10, 20))
hidden_states = utils.binary_numbers(dbn[0].Y.shape[0])
ais = Estimator(dbn)
ais_logz = ais.estimate_log_partition_function(
100, np.sqrt(np.arange(0, 1, 0.001)))
brf_logz = utils.logsumexp(dbn[1]._ulogprob_vis(hidden_states))
brf_probs = []
dbn_probs = []
dbn_bound = []
for i in range(50):
sample = np.matrix(np.random.rand(10, 1)) > 0.5
prob, bound = ais.estimate_log_probability(sample, 200)
brf_probs.append(
utils.logsumexp(
dbn[1]._ulogprob_vis(hidden_states) +
dbn[0]._clogprob_vis_hid(sample, hidden_states), 1) -
brf_logz)
dbn_probs.append(prob)
dbn_bound.append(bound)
self.assertTrue(np.mean(dbn_bound) < np.mean(dbn_probs))
self.assertTrue(np.abs(np.mean(dbn_probs) - np.mean(brf_probs)) < 0.1)
def test_ais_with_semirbm_dbn(self): dbn = DBN(RBM(5, 5)) dbn.add_layer(SemiRBM(5, 5)) ais = Estimator(dbn) ais.estimate_log_partition_function(100, np.arange(0, 1, 1E-3), layer=0) ais.estimate_log_partition_function(10, np.arange(0, 1, 1E-3), layer=1) dbn[0]._brf_logz = utils.logsumexp(dbn[0]._ulogprob_vis(utils.binary_numbers(dbn[0].X.shape[0]))) dbn[1]._brf_logz = utils.logsumexp(dbn[1]._ulogprob_vis(utils.binary_numbers(dbn[1].X.shape[0]))) samples = np.concatenate([dbn.sample(25, 100, 20), np.matrix(np.random.rand(5, 25) > 0.5)], 1) Y = utils.binary_numbers(dbn[0].Y.shape[0]) X = utils.binary_numbers(dbn[0].X.shape[0]) logRy = dbn[1]._ulogprob_vis(Y) logQy = utils.logsumexp(dbn[0]._ulogprob(X, Y, all_pairs=True), 0) log_sum = utils.logsumexp(dbn[0]._clogprob_hid_vis(samples, Y, all_pairs=True) - logQy + logRy, 1) logPx = log_sum + dbn[0]._ulogprob_vis(samples) - dbn[1]._brf_logz logPx_ = ais.estimate_log_probability(samples)[0] self.assertTrue(np.abs(logPx_.mean() - logPx.mean()) < 0.1)
