def encode(self, h_enc, batch_size):
z_mu = self.z_mu(h_enc)
z_logvar = self.z_logvar(h_enc)
z_eps = self.samples(batch_size)
z = z_mu + ex.exp(0.5 * z_logvar) * z_eps
kld = KLDStandardNormal()(z_mu, z_logvar)
return z, kld
def encode(self, h_enc, batch_size):
z_mu = self.z_mu(h_enc)
z_logvar = self.z_logvar(h_enc)
z_eps = self.samples(batch_size)
z = z_mu + ex.exp(0.5 * z_logvar) * z_eps
kld = KLDStandardNormal()(z_mu, z_logvar)
return z, kld
def encode(self, h_enc, batch_size, phase=None):
z_mu = self.z_mu(h_enc)
z_log_sigma = self.z_log_sigma(h_enc)
if phase is None:
phase = self.phase
if phase == 'test':
z = z_mu
else:
z_eps = self.samples(batch_size)
z = z_mu + expr.exp(0.5 * z_log_sigma) * z_eps
kld = KLDivergence()(z_mu, z_log_sigma)
return z, kld
def __call__(self, h_enc):
z_mu = self.z_mu(h_enc)
z_log_sigma = self.z_log_sigma(h_enc)
eps = expr.random.normal(size=(self.batch_size, self.n_hidden))
z = z_mu + expr.exp(0.5 * z_log_sigma) * eps
return z, z_mu, z_log_sigma
def __call__(self, h_enc):
z_mu = self.z_mu(h_enc)
z_log_sigma = self.z_log_sigma(h_enc)
eps = expr.random.normal(size=(self.batch_size, self.n_hidden))
z = z_mu + expr.exp(0.5 * z_log_sigma) * eps
return z, z_mu, z_log_sigma
