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_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_rbm(self): rbm = RBM(5, 20) rbm.W = np.matrix(np.random.randn(5, 20)) rbm.b = np.matrix(np.random.randn(5, 1)) rbm.c = np.matrix(np.random.randn(20, 1)) ais = Estimator(rbm) ais_logz = ais.estimate_log_partition_function(100, np.arange(0, 1, 0.001)) brf_logz = utils.logsumexp(rbm._ulogprob_vis(utils.binary_numbers(rbm.X.shape[0]))) lower = np.log(np.exp(ais_logz) - 4. * np.sqrt(rbm._ais_var)) upper = np.log(np.exp(ais_logz) + 4. * np.sqrt(rbm._ais_var)) self.assertTrue(upper - lower < 1.) self.assertTrue(lower < brf_logz and brf_logz < upper)
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 main(argv): num_visibles = 28 * 28 num_hiddens = [1000, 1000] num_epochs = 50 batch_size = 100 # load data samples data = load('../data/mnist.npz')['train'] / 255. # train 1st layer dbn = DBN(RBM(num_visibles, num_hiddens[0])) dbn.train(data, num_epochs, batch_size) # train 2nd layer dbn.add_layer(RBM(num_hiddens[0], num_hiddens[1])) dbn.train(data, num_epochs, batch_size, [1E-1, 1E-2]) return 0
def test_probabilities(self): srbm = SemiRBM(9, 12) srbm.W = np.matrix(np.random.randn(srbm.X.shape[0], srbm.Y.shape[0])) srbm.b = np.matrix(np.random.rand(srbm.X.shape[0], 1)) srbm.c = np.matrix(np.random.randn(srbm.Y.shape[0], 1)) srbm.L = np.matrix(np.random.randn(srbm.X.shape[0], srbm.X.shape[0])) / 2. srbm.L = np.triu(srbm.L) + np.triu( srbm.L).T - 2. * np.diag(np.diag(srbm.L)) examples_vis = np.matrix(np.random.rand(srbm.X.shape[0], 100) < 0.5) examples_hid = np.matrix(np.random.rand(srbm.Y.shape[0], 100) < 0.5) states_vis = utils.binary_numbers(srbm.X.shape[0]) states_hid = utils.binary_numbers(srbm.Y.shape[0]) # check that conditional probabilities are normalized logprobs = srbm._clogprob_hid_vis(examples_vis, states_hid, all_pairs=True) self.assertTrue(np.all(utils.logsumexp(logprobs, 1) < 1E-10)) # test for consistency logprobs1 = srbm._ulogprob(examples_vis, examples_hid, all_pairs=True) logprobs3 = srbm._clogprob_hid_vis(examples_vis, examples_hid, all_pairs=True) \ + srbm._ulogprob_vis(examples_vis).T self.assertTrue(np.all(np.abs(logprobs1 - logprobs3) < 1E-10)) rbm = RBM(srbm.X.shape[0], srbm.Y.shape[0]) rbm.W = srbm.W rbm.b = srbm.b rbm.c = srbm.c srbm.L *= 0 logprobs1 = rbm._ulogprob_vis(examples_vis) logprobs2 = srbm._ulogprob_vis(examples_vis) self.assertTrue(np.all(np.abs(logprobs1 - logprobs2) < 1E-10)) logprobs1 = rbm._clogprob_hid_vis(examples_vis, examples_hid) logprobs2 = srbm._clogprob_hid_vis(examples_vis, examples_hid) self.assertTrue(np.all(np.abs(logprobs1 - logprobs2) < 1E-10))
def test_ais_with_rbm(self): rbm = RBM(5, 20) rbm.W = np.matrix(np.random.randn(5, 20)) rbm.b = np.matrix(np.random.randn(5, 1)) rbm.c = np.matrix(np.random.randn(20, 1)) ais = Estimator(rbm) ais_logz = ais.estimate_log_partition_function(100, np.arange(0, 1, 0.001)) brf_logz = utils.logsumexp( rbm._ulogprob_vis(utils.binary_numbers(rbm.X.shape[0]))) lower = np.log(np.exp(ais_logz) - 4. * np.sqrt(rbm._ais_var)) upper = np.log(np.exp(ais_logz) + 4. * np.sqrt(rbm._ais_var)) self.assertTrue(upper - lower < 1.) self.assertTrue(lower < brf_logz and brf_logz < upper)
def test_probabilities(self): srbm = SemiRBM(9, 12) srbm.W = np.matrix(np.random.randn(srbm.X.shape[0], srbm.Y.shape[0])) srbm.b = np.matrix(np.random.rand(srbm.X.shape[0], 1)) srbm.c = np.matrix(np.random.randn(srbm.Y.shape[0], 1)) srbm.L = np.matrix(np.random.randn(srbm.X.shape[0], srbm.X.shape[0])) / 2. srbm.L = np.triu(srbm.L) + np.triu(srbm.L).T - 2. * np.diag(np.diag(srbm.L)) examples_vis = np.matrix(np.random.rand(srbm.X.shape[0], 100) < 0.5) examples_hid = np.matrix(np.random.rand(srbm.Y.shape[0], 100) < 0.5) states_vis = utils.binary_numbers(srbm.X.shape[0]) states_hid = utils.binary_numbers(srbm.Y.shape[0]) # check that conditional probabilities are normalized logprobs = srbm._clogprob_hid_vis(examples_vis, states_hid, all_pairs=True) self.assertTrue(np.all(utils.logsumexp(logprobs, 1) < 1E-10)) # test for consistency logprobs1 = srbm._ulogprob(examples_vis, examples_hid, all_pairs=True) logprobs3 = srbm._clogprob_hid_vis(examples_vis, examples_hid, all_pairs=True) \ + srbm._ulogprob_vis(examples_vis).T self.assertTrue(np.all(np.abs(logprobs1 - logprobs3) < 1E-10)) rbm = RBM(srbm.X.shape[0], srbm.Y.shape[0]) rbm.W = srbm.W rbm.b = srbm.b rbm.c = srbm.c srbm.L *= 0 logprobs1 = rbm._ulogprob_vis(examples_vis) logprobs2 = srbm._ulogprob_vis(examples_vis) self.assertTrue(np.all(np.abs(logprobs1 - logprobs2) < 1E-10)) logprobs1 = rbm._clogprob_hid_vis(examples_vis, examples_hid) logprobs2 = srbm._clogprob_hid_vis(examples_vis, examples_hid) self.assertTrue(np.all(np.abs(logprobs1 - logprobs2) < 1E-10))
def test_probs(self): rbm = RBM(9, 11) rbm.W = np.matrix(np.random.randn(rbm.X.shape[0], rbm.Y.shape[0])) rbm.b = np.matrix(np.random.rand(rbm.X.shape[0], 1)) rbm.c = np.matrix(np.random.randn(rbm.Y.shape[0], 1)) examples_vis = np.matrix(np.random.rand(rbm.X.shape[0], 100) < 0.5) examples_hid = np.matrix(np.random.rand(rbm.Y.shape[0], 100) < 0.5) states_vis = utils.binary_numbers(rbm.X.shape[0]) states_hid = utils.binary_numbers(rbm.Y.shape[0]) # check that conditional probabilities are normalized logprobs = rbm._clogprob_vis_hid(states_vis, examples_hid, all_pairs=True) self.assertTrue(np.all(utils.logsumexp(logprobs, 0) < 1E-10)) logprobs = rbm._clogprob_hid_vis(examples_vis, states_hid, all_pairs=True) self.assertTrue(np.all(utils.logsumexp(logprobs, 1) < 1E-10)) # test for consistency logprobs1 = rbm._ulogprob(examples_vis, examples_hid, all_pairs=True) logprobs2 = rbm._clogprob_vis_hid(examples_vis, examples_hid, all_pairs=True) \ + rbm._ulogprob_hid(examples_hid) logprobs3 = rbm._clogprob_hid_vis(examples_vis, examples_hid, all_pairs=True) \ + rbm._ulogprob_vis(examples_vis).T self.assertTrue(np.all(np.abs(logprobs1 - logprobs2) < 1E-10)) self.assertTrue(np.all(np.abs(logprobs1 - logprobs3) < 1E-10))
def test_all_pairs(self): rbm = RBM(10, 20) rbm.W = np.matrix(np.random.randn(rbm.X.shape[0], rbm.Y.shape[0])) rbm.b = np.matrix(np.random.rand(rbm.X.shape[0], 1)) rbm.c = np.matrix(np.random.randn(rbm.Y.shape[0], 1)) examples_vis = np.matrix(np.random.rand(rbm.X.shape[0], 100) < 0.5) examples_hid = np.matrix(np.random.rand(rbm.Y.shape[0], 100) < 0.5) logprob1 = rbm._ulogprob(examples_vis, examples_hid) logprob2 = np.diag(rbm._ulogprob(examples_vis, examples_hid, all_pairs=True)) self.assertTrue(np.abs(logprob1 - logprob2).sum() < 1E-10) logprob1 = rbm._ulogprob(examples_vis[:, 1], examples_hid) logprob2 = rbm._ulogprob(examples_vis, examples_hid, all_pairs=True)[1, :] self.assertTrue(np.abs(logprob1 - logprob2).sum() < 1E-10) logprob1 = rbm._ulogprob(examples_vis, examples_hid[:, 1]) logprob2 = rbm._ulogprob(examples_vis, examples_hid, all_pairs=True)[:, 1].T self.assertTrue(np.abs(logprob1 - logprob2).sum() < 1E-10) logprob1 = rbm._clogprob_vis_hid(examples_vis, examples_hid) logprob2 = np.diag(rbm._clogprob_vis_hid(examples_vis, examples_hid, all_pairs=True)) self.assertTrue(np.abs(logprob1 - logprob2).sum() < 1E-10) logprob1 = rbm._clogprob_vis_hid(examples_vis[:, 1], examples_hid) logprob2 = rbm._clogprob_vis_hid(examples_vis, examples_hid, all_pairs=True)[1, :] self.assertTrue(np.abs(logprob1 - logprob2).sum() < 1E-10) logprob1 = rbm._clogprob_vis_hid(examples_vis, examples_hid[:, 1]) logprob2 = rbm._clogprob_vis_hid(examples_vis, examples_hid, all_pairs=True)[:, 1].T self.assertTrue(np.abs(logprob1 - logprob2).sum() < 1E-10) logprob1 = rbm._clogprob_hid_vis(examples_vis, examples_hid) logprob2 = np.diag(rbm._clogprob_hid_vis(examples_vis, examples_hid, all_pairs=True)) self.assertTrue(np.abs(logprob1 - logprob2).sum() < 1E-10) logprob1 = rbm._clogprob_hid_vis(examples_vis[:, 1], examples_hid) logprob2 = rbm._clogprob_hid_vis(examples_vis, examples_hid, all_pairs=True)[1, :] self.assertTrue(np.abs(logprob1 - logprob2).sum() < 1E-10) logprob1 = rbm._clogprob_hid_vis(examples_vis, examples_hid[:, 1]) logprob2 = rbm._clogprob_hid_vis(examples_vis, examples_hid, all_pairs=True)[:, 1].T self.assertTrue(np.abs(logprob1 - logprob2).sum() < 1E-10)
def test_all_pairs(self): rbm = RBM(10, 20) rbm.W = np.matrix(np.random.randn(rbm.X.shape[0], rbm.Y.shape[0])) rbm.b = np.matrix(np.random.rand(rbm.X.shape[0], 1)) rbm.c = np.matrix(np.random.randn(rbm.Y.shape[0], 1)) examples_vis = np.matrix(np.random.rand(rbm.X.shape[0], 100) < 0.5) examples_hid = np.matrix(np.random.rand(rbm.Y.shape[0], 100) < 0.5) logprob1 = rbm._ulogprob(examples_vis, examples_hid) logprob2 = np.diag( rbm._ulogprob(examples_vis, examples_hid, all_pairs=True)) self.assertTrue(np.abs(logprob1 - logprob2).sum() < 1E-10) logprob1 = rbm._ulogprob(examples_vis[:, 1], examples_hid) logprob2 = rbm._ulogprob(examples_vis, examples_hid, all_pairs=True)[1, :] self.assertTrue(np.abs(logprob1 - logprob2).sum() < 1E-10) logprob1 = rbm._ulogprob(examples_vis, examples_hid[:, 1]) logprob2 = rbm._ulogprob(examples_vis, examples_hid, all_pairs=True)[:, 1].T self.assertTrue(np.abs(logprob1 - logprob2).sum() < 1E-10) logprob1 = rbm._clogprob_vis_hid(examples_vis, examples_hid) logprob2 = np.diag( rbm._clogprob_vis_hid(examples_vis, examples_hid, all_pairs=True)) self.assertTrue(np.abs(logprob1 - logprob2).sum() < 1E-10) logprob1 = rbm._clogprob_vis_hid(examples_vis[:, 1], examples_hid) logprob2 = rbm._clogprob_vis_hid(examples_vis, examples_hid, all_pairs=True)[1, :] self.assertTrue(np.abs(logprob1 - logprob2).sum() < 1E-10) logprob1 = rbm._clogprob_vis_hid(examples_vis, examples_hid[:, 1]) logprob2 = rbm._clogprob_vis_hid(examples_vis, examples_hid, all_pairs=True)[:, 1].T self.assertTrue(np.abs(logprob1 - logprob2).sum() < 1E-10) logprob1 = rbm._clogprob_hid_vis(examples_vis, examples_hid) logprob2 = np.diag( rbm._clogprob_hid_vis(examples_vis, examples_hid, all_pairs=True)) self.assertTrue(np.abs(logprob1 - logprob2).sum() < 1E-10) logprob1 = rbm._clogprob_hid_vis(examples_vis[:, 1], examples_hid) logprob2 = rbm._clogprob_hid_vis(examples_vis, examples_hid, all_pairs=True)[1, :] self.assertTrue(np.abs(logprob1 - logprob2).sum() < 1E-10) logprob1 = rbm._clogprob_hid_vis(examples_vis, examples_hid[:, 1]) logprob2 = rbm._clogprob_hid_vis(examples_vis, examples_hid, all_pairs=True)[:, 1].T self.assertTrue(np.abs(logprob1 - logprob2).sum() < 1E-10)