def setUp(self): self.n_samples = 10 self.layer = NADETop( n_X=8, n_hid=8, ) self.layer.setup()
class TestNADETop(RWSTopLayerTest, unittest.TestCase): def setUp(self): self.n_samples = 10 self.layer = NADETop( n_X=8, n_hid=8, ) self.layer.setup()
def setUp(self): self.n_samples = 10 self.layer = NADETop(n_X=8, n_hid=8) self.layer.setup()
class TestNADETop(RWSTopLayerTest, unittest.TestCase): def setUp(self): self.n_samples = 10 self.layer = NADETop(n_X=8, n_hid=8) self.layer.setup()
from learning.models.nade import NADE, NADETop n_vis = 28 * 28 preproc = PermuteColumns() dataset = CalTechSilhouettes(which_set='train', preproc=[preproc]) valiset = CalTechSilhouettes(which_set='valid', preproc=[preproc]) testset = CalTechSilhouettes(which_set='test', preproc=[preproc]) p_layers = [ NADE( n_X=n_vis, n_Y=150, ), NADETop(n_X=150, ), ] q_layers = [ NADE( n_Y=n_vis, n_X=150, ), ] model = LayerStack( p_layers=p_layers, q_layers=q_layers, ) trainer = Trainer(
n_vis = 28*28 permute = PermuteColumns() dataset = MNIST(fname="mnist_salakhutdinov.pkl.gz", which_set='salakhutdinov_train', preproc=[permute], n_datapoints=50000) valiset = MNIST(fname="mnist_salakhutdinov.pkl.gz", which_set='salakhutdinov_valid', preproc=[permute], n_datapoints=1000) testset = MNIST(fname="mnist_salakhutdinov.pkl.gz", which_set='test', preproc=[permute], n_datapoints=10000) p_layers=[ NADE( n_X=n_vis, n_Y=200, clamp_sigmoid=True, ), NADETop( n_X=200, clamp_sigmoid=True, ), ] q_layers=[ NADE( n_Y=n_vis, n_X=200, clamp_sigmoid=True, ) ] model = LayerStack( p_layers=p_layers, q_layers=q_layers, )