"W2",
learnable=False)
#V = DeterministicVariable(np.random.normal(0., 1., (100, h_size)), "V", learnable=False)
f = lambda z, W: z + BF.tanh(BF.matmul(W, z))
F = lambda z, W1, W2: f(f(z, W1), W2)
measurement_noise = 2. #1.5
z = [
NormalVariable(np.zeros((h_size, 1)),
np.ones((h_size, 1)),
"z0",
learnable=False)
]
img = [
DeterministicVariable(decoder(BF.reshape(z[0], (h_size, 1, 1))),
"img0",
learnable=False)
]
x = [
NormalVariable(img[0],
measurement_noise * np.ones(
(3, image_size, image_size)),
"x0",
learnable=False)
]
T = 10
t_cond = lambda t: True
driving_noise = 0.05
for t in range(1, T):
images2 = []
images3 = []
for rep in range(N_rep):
h_size = 120
W1 = DeterministicVariable(np.random.normal(0., 0.2, (h_size, h_size)), "W1", learnable=False)
W2 = DeterministicVariable(np.random.normal(0., 0.2, (h_size, h_size)), "W2", learnable=False)
#V = DeterministicVariable(np.random.normal(0., 1., (100, h_size)), "V", learnable=False)
f = lambda z, W: z + BF.tanh(BF.matmul(W, z))
F = lambda z, W1, W2: f(f(z, W1), W2)
measurement_noise = 0.5 #1.5
z = [NormalVariable(np.zeros((h_size, 1)),
np.ones((h_size, 1)),
"z0", learnable=False)]
img = [DeterministicVariable(decoder(BF.reshape(z[0], (h_size, 1, 1))), "img0", learnable=False)]
x = [NormalVariable(img[0],
measurement_noise*np.ones((3, image_size, image_size)),
"x0", learnable=False)]
T = 10
t_cond = lambda t: t < 3 or t > T - 3
driving_noise = 0.05
for t in range(1, T):
z.append(NormalVariable(F(z[-1], W1, W2),
driving_noise*np.ones((h_size, 1)),
"z{}".format(t), learnable=False))
img.append(DeterministicVariable(decoder(BF.reshape(z[-1], (h_size, 1, 1))), "img{}".format(t), learnable=False))
if t_cond(t):
x.append(NormalVariable(img[-1],
measurement_noise*np.ones((3, image_size, image_size)),
out_channels = 10
image_size = 28
Wk = NormalVariable(loc=np.zeros((out_channels, in_channels, 2, 2)),
scale=10 * np.ones(
(out_channels, in_channels, 2, 2)),
name="Wk")
z = DeterministicVariable(BF.mean(BF.relu(BF.conv2d(x, Wk, stride=1)),
(2, 3)),
name="z")
Wl = NormalVariable(loc=np.zeros((num_classes, out_channels)),
scale=10 * np.ones((num_classes, out_channels)),
name="Wl")
b = NormalVariable(loc=np.zeros((num_classes, 1)),
scale=10 * np.ones((num_classes, 1)),
name="b")
reshaped_z = BF.reshape(z, shape=(out_channels, 1))
k = CategoricalVariable(logits=BF.linear(reshaped_z, Wl, b), name="k")
# Probabilistic model
model = ProbabilisticModel([k])
# Observations
k.observe(labels)
# Variational model
#num_particles = 2 #10
wk_locations = [
np.random.normal(0., 0.1, (out_channels, in_channels, 2, 2))
for _ in range(num_particles)
]
wl_locations = [
name="Wk")
z = Normal(BF.conv2d(x, Wk, padding=1), 1., name="z")
num_samples = 6
z.get_sample(num_samples)["z"]
num_classes = 10
Wl = Normal(loc=np.zeros(
(num_classes, image_size * image_size * out_channels)),
scale=1. * np.ones(
(num_classes, image_size * image_size * out_channels)),
name="Wl")
b = Normal(loc=np.zeros((num_classes, 1)),
scale=1. * np.ones((num_classes, 1)),
name="b")
reshaped_z = BF.reshape(z, shape=(image_size * image_size * out_channels, 1))
k = Categorical(logits=BF.linear(reshaped_z, Wl, b), name="k")
k.observe(labels)
from brancher.inference import MAP
from brancher.inference import perform_inference
from brancher.variables import ProbabilisticModel
convolutional_model = ProbabilisticModel([k])
perform_inference(convolutional_model,
inference_method=MAP(),
number_iterations=1,
optimizer="Adam",
lr=0.0025)