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))
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))
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)
