def setUp(self):
self.n_samples = 10
self.layer = SBN(
n_X=16,
n_Y=8,
)
self.layer.setup()
class TestSBN(RWSLayerTest, unittest.TestCase):
def setUp(self):
self.n_samples = 10
self.layer = SBN(
n_X=16,
n_Y=8,
)
self.layer.setup()
class TestSBN(RWSLayerTest, unittest.TestCase):
def setUp(self):
self.n_samples = 10
self.layer = SBN(
n_X=16,
n_Y=8,
)
self.layer.setup()
def get_toy_model():
from learning.models.rws import LayerStack
from learning.models.sbn import SBN, SBNTop
p_layers = [SBN(n_X=25, n_Y=10), SBNTop(n_X=10, )]
q_layers = [SBN(
n_X=10,
n_Y=25,
)]
model = LayerStack(
p_layers=p_layers,
q_layers=q_layers,
)
return model
def setUp(self):
self.n_samples = 10
self.layer = SBN(
n_X=16,
n_Y=8,
)
self.layer.setup()
def test_FromModel():
D = 5
n_vis = D**2
n_hid = 2 * D
# Ground truth params
W_bars = np.zeros([n_hid, D, D])
for d in xrange(D):
W_bars[d, d, :] = 4.
W_bars[D + d, :, d] = 4.
W_bars = W_bars.reshape((n_hid, n_vis))
P_a = -np.log(D / 2 - 1) * np.ones(n_hid)
P_b = -2 * np.ones(n_vis)
# Instantiate model...
p_layers = [SBN(
n_X=n_vis,
n_Y=n_hid,
), SBNTop(n_X=n_hid)]
q_layers = [SBN(
n_X=n_hid,
n_Y=n_vis,
)]
p_layers[0].set_model_param('W', W_bars)
p_layers[0].set_model_param('b', P_b)
p_layers[1].set_model_param('a', P_a)
model = LayerStack(p_layers=p_layers, q_layers=q_layers)
# ...and generate data
n_datapoints = 1000
data = FromModel(model=model, n_datapoints=n_datapoints)
assert data.X.shape == (n_datapoints, n_vis)
yield check_dtype, data
yield check_range, data
def setUp(self):
p_layers = [
SBN(
n_X=self.n_vis,
n_Y=self.n_hid,
),
SBN(
n_X=self.n_hid,
n_Y=self.n_hid,
),
SBNTop(n_X=self.n_hid)
]
q_layers = [
SBN(
n_Y=self.n_vis,
n_X=self.n_hid,
),
SBN(
n_Y=self.n_hid,
n_X=self.n_hid,
)
]
self.stack = LayerStack(p_layers=p_layers, q_layers=q_layers)
self.stack.setup()
from learning.training import Trainer
from learning.models.rws import LayerStack
from learning.models.sbn import SBN, SBNTop
from learning.models.darn import DARN, DARNTop
from learning.models.nade import NADE, NADETop
n_vis = 28 * 28
dataset = CalTechSilhouettes(which_set='train')
valiset = CalTechSilhouettes(which_set='valid')
testset = CalTechSilhouettes(which_set='test')
p_layers = [
SBN(
n_X=n_vis,
n_Y=500,
),
SBN(
n_X=500,
n_Y=300,
),
SBN(
n_X=300,
n_Y=100,
),
SBN(
n_X=100,
n_Y=50,
),
SBN(
n_X=50,