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,
)
