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 __init__(self, which_set, center = False): #dear pylearn.datasets.MNIST: there is no such thing as the MNIST validation set. quit pretending that there is. orig = i_hate_python.train_valid_test(ntrain=60000,nvalid=0,ntest=10000) Xs = { 'train' : orig.train.x, 'test' : orig.test.x } X = N.cast['float32'](Xs[which_set]) if center: assert False view_converter = dense_design_matrix.DefaultViewConverter((28,28,1)) super(MNIST,self).__init__(X = X, view_converter = view_converter) assert not N.any(N.isnan(self.X))