def model_spec(x):
xs = nn.int_shape(x)
sum_log_det_jacobians = tf.zeros(xs[0])
# model logit instead of the x itself
y = x
alpha = 1e-5
y = y * (1 - alpha) + alpha * 0.5
jac = tf.reduce_sum(-tf.log(y) - tf.log(1 - y), [1, 2, 3])
y = tf.log(y) - tf.log(1 - y)
sum_log_det_jacobians += jac
if len(layers) == 0:
construct_model_spec()
# construct forward pass
z = None
jac = sum_log_det_jacobians
# TODO change for CIFAR-10
#y=x
for layer in layers:
y, jac, z = layer.forward_and_jacobian(y, jac, z)
if z is None:
z = y
else:
z = tf.concat([z, y], axis=3)
# record dimension of the final variable
global final_latent_dimension
final_latent_dimension = nn.int_shape(z)
return z, jac
def model_spec(x):
counters = {}
xs = nn.int_shape(x)
sum_log_det_jacobians = tf.zeros(xs[0])
# corrupt data (Tapani Raiko's dequantization)
y = x * 0.5 + 0.5
y = y * 255.0
corruption_level = 1.0
y = y + corruption_level * tf.random_uniform(xs)
y = y / (255.0 + corruption_level)
#model logit instead of the x itself
alpha = 1e-5
y = y * (1 - alpha) + alpha * 0.5
jac = tf.reduce_sum(-tf.log(y) - tf.log(1 - y), [1, 2, 3])
y = tf.log(y) - tf.log(1 - y)
sum_log_det_jacobians += jac
if len(layers) == 0:
construct_model_spec()
# construct forward pass
z = None
jac = sum_log_det_jacobians
for layer in layers:
y, jac, z = layer.forward_and_jacobian(y, jac, z)
z = tf.concat([z, y], 3)
# record dimension of the final variable
global final_latent_dimension
final_latent_dimension = nn.int_shape(z)
return z, jac
def model_spec(x,
reuse=True,
model_type="nice",
train=False,
alpha=1e-7,
init_type="uniform",
hidden_layers=1000,
no_of_layers=1,
batch_norm_adaptive=0):
counters = {}
xs = nn.int_shape(x)
sum_log_det_jacobians = tf.zeros(xs[0])
# corrupt data (Tapani Raiko's dequantization)
y = x
y = y * 255.0
corruption_level = 1.0
y = y + corruption_level * tf.random_uniform(xs)
y = y / (255.0 + corruption_level)
#model logit instead of the x itself
jac = 0
y = y * (1 - 2 * alpha) + alpha
if model_type == "nice":
jac = tf.reduce_sum(-tf.log(y) - tf.log(1 - y) + tf.log(1 - 2 * alpha),
[1])
else:
jac = tf.reduce_sum(-tf.log(y) - tf.log(1 - y) + tf.log(1 - 2 * alpha),
[1, 2, 3])
y = tf.log(y) - tf.log(1 - y)
sum_log_det_jacobians += jac
if len(layers) == 0:
if model_type == "nice":
construct_nice_spec(init_type=init_type,
hidden_layers=hidden_layers,
no_of_layers=no_of_layers)
else:
construct_model_spec(no_of_layers=no_of_layers,
add_scaling=(batch_norm_adaptive != 0))
# construct forward pass
z = None
jac = sum_log_det_jacobians
for layer in layers:
y, jac, z = layer.forward_and_jacobian(y,
jac,
z,
reuse=reuse,
train=train)
if model_type == "nice":
z = y
else:
z = tf.concat(axis=3, values=[z, y])
# record dimension of the final variable
global final_latent_dimension
final_latent_dimension = nn.int_shape(z)
return z, jac