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 sample_z(self, size_M=1, sample=True):
if not self.use_z:
return ones_d((size_M, self.nb_filter)), zeros_d((size_M, ))
qm0 = self.get_params_m()
isample_M = tf.tile(tf.expand_dims(self.qzero_mean, 0), [size_M, 1])
eps = tf.random_normal(tf.stack((size_M, self.nb_filter)))
sample_M = isample_M + tf.sqrt(qm0) * eps if sample else isample_M
logdets = zeros_d((size_M, ))
if self.n_flows_q > 0:
sample_M, logdets = self.flow_q.get_output_for(sample_M,
sample=sample)
return sample_M, logdets
