Add option to use the ClippedAdam optimizer with centered variance in…

… the Latent Dirichlet Allocation example.

examples/lda.py

@@ -137,7 +137,9 @@ def main(args):
     guide = functools.partial(parametrized_guide, predictor)
     Elbo = JitTraceEnum_ELBO if args.jit else TraceEnum_ELBO
     elbo = Elbo(max_plate_nesting=2)
-    optim = ClippedAdam({"lr": args.learning_rate})
+    optim = ClippedAdam(
+        {"lr": args.learning_rate, "centered_variance": args.centered_variance}
+    )
     svi = SVI(model, guide, optim, elbo)
     logging.info("Step\tLoss")
     for step in range(args.num_steps):
@@ -160,6 +162,7 @@ def main(args):
     parser.add_argument("-n", "--num-steps", default=1000, type=int)
     parser.add_argument("-l", "--layer-sizes", default="100-100")
     parser.add_argument("-lr", "--learning-rate", default=0.01, type=float)
+    parser.add_argument("-cv", "--centered-variance", default=False, type=bool)
     parser.add_argument("-b", "--batch-size", default=32, type=int)
     parser.add_argument("--jit", action="store_true")
     args = parser.parse_args()