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