def kldiv(self):
M, logdets = self.sample_z()
logdets = logdets[0]
M = tf.squeeze(M)
std_mg = self.get_params_W()
qm0 = self.get_params_m()
if len(M.get_shape()) == 0:
Mexp = M
else:
Mexp = tf.expand_dims(M, 1)
Mtilde = Mexp * self.mu_W
Vtilde = tf.square(std_mg)
iUp = outer(tf.exp(self.pvar), ones_d((self.output_dim, )))
logqm = 0.
if self.use_z:
logqm = -tf.reduce_sum(.5 * (tf.log(2 * np.pi) + tf.log(qm0) + 1))
logqm -= logdets
kldiv_w = tf.reduce_sum(.5 * tf.log(iUp) - tf.log(std_mg) +
((Vtilde + tf.square(Mtilde)) /
(2 * iUp)) - .5)
kldiv_bias = tf.reduce_sum(
.5 * self.pvar_bias - .5 * self.logvar_bias +
((tf.exp(self.logvar_bias) + tf.square(self.mu_bias)) /
(2 * tf.exp(self.pvar_bias))) - .5)
if self.use_z:
apvar_M = self.apvar_M
# shared network for hidden layer
mw = tf.matmul(tf.expand_dims(apvar_M, 0), Mtilde)
eps = tf.expand_dims(tf.random_normal((self.output_dim, )), 0)
varw = tf.matmul(tf.square(tf.expand_dims(apvar_M, 0)), Vtilde)
a = tf.nn.tanh(mw + tf.sqrt(varw) * eps)
# split at output layer
if len(tf.squeeze(a).get_shape()) != 0:
w__ = tf.reduce_mean(outer(self.rsr_M, tf.squeeze(a)), axis=1)
wv__ = tf.reduce_mean(outer(self.rsri_M, tf.squeeze(a)),
axis=1)
else:
w__ = self.rsr_M * tf.squeeze(a)
wv__ = self.rsri_M * tf.squeeze(a)
logrm = 0.
if self.flow_r is not None:
M, logrm = self.flow_r.get_output_for(tf.expand_dims(M, 0))
M = tf.squeeze(M)
logrm = logrm[0]
logrm += tf.reduce_sum(-.5 * tf.exp(wv__) * tf.square(M - w__) -
.5 * tf.log(2 * np.pi) + .5 * wv__)
else:
logrm = 0.
return -kldiv_w + logrm - logqm - kldiv_bias
def kldiv(self):
M, logdets = self.sample_z()
logdets = logdets[0]
M = tf.squeeze(M)
std_w = self.get_params_W()
mu = tf.reshape(self.mu_W, [-1, self.nb_filter])
std_w = tf.reshape(std_w, [-1, self.nb_filter])
Mtilde = mu * tf.expand_dims(M, 0)
mbias = self.mu_bias * M
Vtilde = tf.square(std_w)
iUp = outer(tf.exp(self.pvar), ones_d((self.nb_filter, )))
qm0 = self.get_params_m()
logqm = 0.
if self.use_z > 0.:
logqm = -tf.reduce_sum(.5 * (tf.log(2 * np.pi) + tf.log(qm0) + 1))
logqm -= logdets
kldiv_w = tf.reduce_sum(.5 * tf.log(iUp) - .5 * tf.log(Vtilde) +
((Vtilde + tf.square(Mtilde)) /
(2 * iUp)) - .5)
kldiv_bias = tf.reduce_sum(
.5 * self.pvar_bias - .5 * self.logvar_bias +
((tf.exp(self.logvar_bias) + tf.square(mbias)) /
(2 * tf.exp(self.pvar_bias))) - .5)
logrm = 0.
if self.use_z:
apvar_M = self.apvar_M
mw = tf.matmul(Mtilde, tf.expand_dims(apvar_M, 1))
vw = tf.matmul(Vtilde, tf.expand_dims(tf.square(apvar_M), 1))
eps = tf.expand_dims(tf.random_normal((self.input_dim, )), 1)
a = mw + tf.sqrt(vw) * eps
mb = tf.reduce_sum(mbias * apvar_M)
vb = tf.reduce_sum(tf.exp(self.logvar_bias) * tf.square(apvar_M))
a += mb + tf.sqrt(vb) * tf.random_normal(())
w__ = tf.reduce_mean(outer(tf.squeeze(a), self.rsr_M), axis=0)
wv__ = tf.reduce_mean(outer(tf.squeeze(a), self.rsri_M), axis=0)
if self.flow_r is not None:
M, logrm = self.flow_r.get_output_for(tf.expand_dims(M, 0))
M = tf.squeeze(M)
logrm = logrm[0]
logrm += tf.reduce_sum(-.5 * tf.exp(wv__) * tf.square(M - w__) -
.5 * tf.log(2 * np.pi) + .5 * wv__)
return -kldiv_w + logrm - logqm - kldiv_bias
def forward(self, x, mask=None):
# create all pairs of elements
x = torch.cat(utils.outer(x), dim=1)
x = self.conv(x)
# flatten pairs and scale appropriately
n, c, l, _ = x.size()
x = x.view(x.size(0), x.size(1), -1) / l / l
x, _ = self.pool(x)
return x
def uexp(cls, phi):
angle = phi.norm()
if angle < cls.TOL:
return torch.eye(3) + cls.uwedge(phi)
axis = phi / angle
c = angle.cos()
s = angle.sin()
return c * torch.eye(3) + (1 - c) * outer(axis,
axis) + s * cls.uwedge(axis)
def forward(self, x, mask=None):
mask = mask.unsqueeze(1)
# include mask in set features
x = torch.cat([x, mask], dim=1)
# create all pairs of elements
x = torch.cat(utils.outer(x), dim=1)
x = self.conv(x)
# flatten pairs and scale appropriately
n, c, l, _ = x.size()
x = x.view(x.size(0), x.size(1), -1) / l / l
x, _ = x.max(dim=2)
return x