def ais_nodata(fname, do_exact=True): rbm_params = load_rbm_params(fname) # ais estimate using tempered models as intermediate distributions t1 = time.time() (logz, log_var_dz), aisobj = rbm_tools.rbm_ais(rbm_params, n_runs=100, seed=123) print 'AIS logZ : %f' % logz print ' log_variance : %f' % log_var_dz print 'Elapsed time: ', time.time() - t1 if do_exact: exact_logz = compute_logz(rbm_params) print 'Exact logZ = %f' % exact_logz numpy.testing.assert_almost_equal(exact_logz, logz, decimal=0)
def ais_nodata(fname, do_exact=True): rbm_params = load_rbm_params(fname) # ais estimate using tempered models as intermediate distributions t1 = time.time() (logz, log_var_dz), aisobj = rbm_tools.rbm_ais(rbm_params, n_runs=100, seed=123) print "AIS logZ : %f" % logz print " log_variance : %f" % log_var_dz print "Elapsed time: ", time.time() - t1 if do_exact: exact_logz = compute_logz(rbm_params) print "Exact logZ = %f" % exact_logz # accept less than 1% error assert abs(exact_logz - logz) < 0.01 * exact_logz
def ais_nodata(fname, do_exact=True, betas=None): rbm_params = load_rbm_params(fname) # ais estimate using tempered models as intermediate distributions t1 = time.time() (logz, log_var_dz), aisobj = \ rbm_tools.rbm_ais(rbm_params, n_runs=100, seed=123, betas=betas) print 'AIS logZ : %f' % logz print ' log_variance : %f' % log_var_dz print 'Elapsed time: ', time.time() - t1 if do_exact: exact_logz = compute_logz(rbm_params) print 'Exact logZ = %f' % exact_logz # accept less than 1% error assert abs(exact_logz - logz) < 0.01*exact_logz
def ais_data(fname, do_exact=True): rbm_params = load_rbm_params(fname) # load data to set visible biases to ML solution from pylearn.datasets import MNIST dataset = MNIST.train_valid_test() data = numpy.asarray(dataset.train.x, dtype=config.floatX) # run ais using B=0 model with ML visible biases t1 = time.time() (logz, log_var_dz), aisobj = rbm_tools.rbm_ais(rbm_params, n_runs=100, seed=123, data=data) print 'AIS logZ : %f' % logz print ' log_variance : %f' % log_var_dz print 'Elapsed time: ', time.time() - t1 if do_exact: exact_logz = compute_logz(rbm_params) print 'Exact logZ = %f' % exact_logz numpy.testing.assert_almost_equal(exact_logz, logz, decimal=0)
def ais_data(fname, do_exact=True): rbm_params = load_rbm_params(fname) # load data to set visible biases to ML solution from pylearn2.datasets.mnist import MNIST dataset = MNIST(which_set='train', one_hot=True) data = numpy.asarray(dataset.X, dtype=config.floatX) # run ais using B=0 model with ML visible biases t1 = time.time() (logz, log_var_dz), aisobj = \ rbm_tools.rbm_ais(rbm_params, n_runs=100, seed=123, data=data) print 'AIS logZ : %f' % logz print ' log_variance : %f' % log_var_dz print 'Elapsed time: ', time.time() - t1 if do_exact: exact_logz = compute_logz(rbm_params) print 'Exact logZ = %f' % exact_logz numpy.testing.assert_almost_equal(exact_logz, logz, decimal=0)
def ais_data(fname, do_exact=True, betas=None): rbm_params = load_rbm_params(fname) # load data to set visible biases to ML solution from pylearn2.datasets.mnist import MNIST dataset = MNIST(which_set='train') data = numpy.asarray(dataset.X, dtype=config.floatX) # run ais using B=0 model with ML visible biases t1 = time.time() (logz, log_var_dz), aisobj = \ rbm_tools.rbm_ais(rbm_params, n_runs=100, seed=123, data=data, betas=betas) print('AIS logZ : %f' % logz) print(' log_variance : %f' % log_var_dz) print('Elapsed time: ', time.time() - t1) if do_exact: exact_logz = compute_logz(rbm_params) print('Exact logZ = %f' % exact_logz) numpy.testing.assert_almost_equal(exact_logz, logz, decimal=0)