def log_q_W_global(self, z):
"""
log_q_W samples over q for global vars
"""
mu = self.scale_grad(self.mean)
rho = self.scale_grad(self.rho)
z = z[self.global_slc]
logq = tt.sum(log_normal(z, mu, rho=rho))
return logq
def symbolic_log_q_W_global(self):
"""
log_q_W samples over q for global vars
"""
mu = self.scale_grad(self.mean)
rho = self.scale_grad(self.rho)
z = self.symbolic_random_global_matrix
logq = log_normal(z, mu, rho=rho)
return logq.sum(1)
def symbolic_log_q_W_global(self):
"""
log_q_W samples over q for global vars
"""
mu = self.scale_grad(self.mean)
rho = self.scale_grad(self.rho)
z = self.symbolic_random_global_matrix
logq = log_normal(z, mu, rho=rho)
return logq.sum(1)
def log_q_W_global(self, z):
"""
log_q_W samples over q for global vars
Gradient wrt mu, rho in density parametrization
is set to zero to lower variance of ELBO
"""
mu = self.scale_grad(self.mean)
rho = self.scale_grad(self.rho)
z = z[self.global_slc]
logq = tt.sum(log_normal(z, mu, rho=rho))
return logq