def test_translationalinvariance_1d():
Nlayers = 2
Hs = 10
Ht = 10
sList = [MLP(2, Hs, activation=ScalableTanh([2])) for _ in range(Nlayers)]
tList = [MLP(2, Ht, activation=ScalableTanh([2])) for _ in range(Nlayers)]
masktypelist = ['evenodd', 'evenodd'] * (Nlayers // 2)
#assamble RNVP blocks into a TEBD layer
prior = Gaussian([8])
layers = [
RealNVP([2], sList, tList, Gaussian([2]), masktypelist)
for _ in range(6)
]
model = MERA(1, 2, 8, layers, prior)
x = model.sample(10)
xright = Roll(4, 1).forward(x)
xleft = Roll(-4, 1).forward(x)
logp = model.logProbability(x)
assert_array_almost_equal(logp.data.numpy(),
model.logProbability(xleft).data.numpy(),
decimal=6)
assert_array_almost_equal(logp.data.numpy(),
model.logProbability(xright).data.numpy(),
decimal=6)
def test_translationalinvariance():
#RNVP block
Depth = 8
Nlayers = 2
Hs = 10
Ht = 10
sList = [MLP(2, Hs) for _ in range(Nlayers)]
tList = [MLP(2, Ht) for _ in range(Nlayers)]
masktypelist = ['channel', 'channel'] * (Nlayers // 2)
#assamble RNVP blocks into a TEBD layer
prior = Gaussian([8])
layers = [
RealNVP([2], sList, tList, Gaussian([2]), masktypelist)
for _ in range(Depth)
]
model = TEBD(1, 2, Depth, layers, prior)
x = model.sample(10)
xright = Roll(2, 1).forward(x)
xleft = Roll(-2, 1).forward(x)
logp = model.logProbability(x)
assert_array_almost_equal(logp.data.numpy(),
model.logProbability(xleft).data.numpy(),
decimal=6)
assert_array_almost_equal(logp.data.numpy(),
model.logProbability(xright).data.numpy(),
decimal=6)
def test_mera_1d():
masks = [
Variable(torch.ByteTensor([1, 0, 1, 0, 1, 0, 1, 0])),
Variable(torch.ByteTensor([1, 0, 0, 0, 1, 0, 0, 0]))
]
masks_ = [
Variable(torch.ByteTensor([0, 1, 0, 1, 0, 1, 0, 1])),
Variable(torch.ByteTensor([0, 1, 1, 1, 0, 1, 1, 1]))
]
rollList = [
Placeholder(),
Roll(1, 1),
Placeholder(),
Roll(1, 1),
Placeholder(),
Roll(1, 1)
]
maskList = [
Placeholder(2),
Placeholder(2),
Mask(masks[0], masks_[0]),
Mask(masks[0], masks_[0]),
Mask(masks[1], masks_[1]),
Mask(masks[1], masks_[1])
]
Nlayers = 4
Hs = 10
Ht = 10
sList = [MLP(2, Hs) for _ in range(Nlayers)]
tList = [MLP(2, Ht) for _ in range(Nlayers)]
masktypelist = ['channel', 'channel'] * (Nlayers // 2)
#assamble RNVP blocks into a TEBD layer
prior = Gaussian([8])
layers = [
RealNVP([2], sList, tList, Gaussian([2]), masktypelist)
for _ in range(6)
]
model = HierarchyBijector(1, [2 for _ in range(6)], rollList, layers,
maskList, Gaussian([8]))
z = prior(4)
print(z)
x = model.inference(z, True)
print(x)
fLog = model._inferenceLogjac
print(model._inferenceLogjac)
zz = model.generate(x, True)
print(zz)
bLog = model._generateLogjac
print(model._generateLogjac)
print(model.sample(10))
saveDict = model.saveModel({})
torch.save(saveDict, './savetest.testSave')
masksp = [
Variable(torch.ByteTensor([1, 0, 1, 0, 1, 0, 1, 0])),
Variable(torch.ByteTensor([1, 0, 0, 0, 1, 0, 0, 0]))
]
masks_p = [
Variable(torch.ByteTensor([0, 1, 0, 1, 0, 1, 0, 1])),
Variable(torch.ByteTensor([0, 1, 1, 1, 0, 1, 1, 1]))
]
rollListp = [
Placeholder(),
Roll(1, 1),
Placeholder(),
Roll(1, 1),
Placeholder(),
Roll(1, 1)
]
maskListp = [
Placeholder(2),
Placeholder(2),
Mask(masksp[0], masks_p[0]),
Mask(masksp[0], masks_p[0]),
Mask(masksp[1], masks_p[1]),
Mask(masksp[1], masks_p[1])
]
Nlayersp = 4
Hsp = 10
Htp = 10
sListp = [MLP(2, Hsp) for _ in range(Nlayersp)]
tListp = [MLP(2, Htp) for _ in range(Nlayersp)]
masktypelistp = ['channel', 'channel'] * (Nlayersp // 2)
#assamble RNVP blocks into a TEBD layer
priorp = Gaussian([8])
layersp = [
RealNVP([2], sListp, tListp, Gaussian([2]), masktypelistp)
for _ in range(6)
]
modelp = HierarchyBijector(1, [2 for _ in range(6)], rollListp, layersp,
maskListp, Gaussian([8]))
saveDictp = torch.load('./savetest.testSave')
modelp.loadModel(saveDictp)
xp = modelp.inference(z)
print(xp)
assert_array_almost_equal(z.data.numpy(), zz.data.numpy())
assert_array_almost_equal(fLog.data.numpy(), -bLog.data.numpy())
assert_array_almost_equal(xp.data.numpy(), x.data.numpy())
def test_Roll():
#t = np.random.randn(2,4,4)
t = np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]])
r1 = np.roll(t,1,0)
r2 = np.roll(t,1,1)
r3 = np.roll(t,-1,0)
r4 = np.roll(t,-1,1)
tt = torch.from_numpy(t)
tt = tt.view(-1,4,4)
ll1 = Roll(1,1)
l1 = ll1.forward(tt)
l1p = ll1.reverse(l1)
ll2 = Roll(1,2)
l2 = ll2.forward(tt)
l2p = ll2.reverse(l2)
ll3 = Roll(-1,1)
l3 = ll3.forward(tt)
l3p = ll3.reverse(l3)
ll4 = Roll(-1,2)
l4 = ll4.forward(tt)
l4p = ll4.reverse(l4)
#print(tt)
#print(l4)
#print(l4p)
assert_array_almost_equal(r1,l3.numpy()[0])
assert_array_almost_equal(r2,l4.numpy()[0])
assert_array_almost_equal(r3,l1.numpy()[0])
assert_array_almost_equal(r4,l2.numpy()[0])
assert_array_equal(l1p.numpy(),l2p.numpy())
assert_array_equal(l3p.numpy(),l2p.numpy())
assert_array_equal(l4p.numpy(),l2p.numpy())