reduce the iter number

Author: zuhengxu

example/demo_RealNVP.jl

@@ -163,7 +163,7 @@ flow_trained, stats, _ = train_flow(
     flow,
     logp,
     sample_per_iter;
-    max_iters=50_000,
+    max_iters=100,   # change to larger number of iterations (e.g., 50_000) for better results
     optimiser=Optimisers.Adam(5e-4),
     ADbackend=adtype,
     show_progress=true,

example/demo_hamiltonian_flow.jl

@@ -162,7 +162,7 @@ flow_trained, stats, _ = train_flow(
     flow,
     logp_joint,
     sample_per_iter;
-    max_iters=50_000,
+    max_iters=100,   # change to larger number of iterations (e.g., 50_000) for better results
     optimiser=Optimisers.Adam(3e-4),
     ADbackend=adtype,
     show_progress=true,

example/demo_neural_spline_flow.jl

@@ -155,7 +155,7 @@ flow_trained, stats, _ = train_flow(
     flow,
     logp,
     sample_per_iter;
-    max_iters=50_000,
+    max_iters=100,   # change to larger number of iterations (e.g., 50_000) for better results
     optimiser=Optimisers.Adam(5e-5),
     ADbackend=adtype,
     show_progress=true,

example/demo_planar_flow.jl

@@ -35,14 +35,14 @@ flow_untrained = deepcopy(flow)
 ######################################
 # start training
 ######################################
-sample_per_iter = 30
+sample_per_iter = 32
 
 # callback function to log training progress
 cb(iter, opt_stats, re, θ) = (sample_per_iter=sample_per_iter,ad=adtype)
 adtype = ADTypes.AutoMooncake(; config = Mooncake.Config())
 checkconv(iter, stat, re, θ, st) = stat.gradient_norm < one(T)/1000
 flow_trained, stats, _ = train_flow(
-    elbo,
+    elbo_batch,
     flow,
     logp,
     sample_per_iter;

example/demo_radial_flow.jl

@@ -36,14 +36,14 @@ flow_untrained = deepcopy(flow)
 ######################################
 # start training
 ######################################
-sample_per_iter = 30
+sample_per_iter = 32
 
 # callback function to log training progress
 cb(iter, opt_stats, re, θ) = (sample_per_iter=sample_per_iter,ad=adtype)
 adtype = ADTypes.AutoMooncake(; config = Mooncake.Config())
 checkconv(iter, stat, re, θ, st) = stat.gradient_norm < one(T)/1000
 flow_trained, stats, _ = train_flow(
-    elbo,
+    elbo_batch,
     flow,
     logp,
     sample_per_iter;