Merge pull request #90 from BerkeleyLab/learn-icar-sat-mr-func

feat(example): train ICAR saturated mixing ratio
BerkeleyLab · Oct 4, 2023 · 967b0da · 967b0da
2 parents aa8c8db + 14a983d
commit 967b0da
Showing 1 changed file with 4 additions and 4 deletions.
diff --git a/example/learn-saturated-mixing-ratio.f90 b/example/learn-saturated-mixing-ratio.f90
@@ -24,7 +24,7 @@ program train_saturated_mixture_ratio
   call system_clock(counter_start, clock_rate)
 
   block
-    integer, parameter :: num_epochs = 10000000, num_mini_batches = 6
+    integer, parameter :: max_num_epochs = 10000000, num_mini_batches = 6
     integer num_pairs ! number of input/output pairs
 
     type(mini_batch_t), allocatable :: mini_batches(:)
@@ -35,7 +35,7 @@ program train_saturated_mixture_ratio
     real, allocatable :: cost(:), random_numbers(:)
     integer io_status, network_unit, plot_unit
     integer, parameter :: io_success=0, diagnostics_print_interval = 1000, network_save_interval = 10000
-    integer, parameter :: nodes_per_layer(*) = [2, 31, 31, 1]
+    integer, parameter :: nodes_per_layer(*) = [2, 72, 1]
     real, parameter :: cost_tolerance = 1.E-08
 
     call random_init(image_distinct=.true., repeatable=.true.)
@@ -81,7 +81,7 @@ program train_saturated_mixture_ratio
         print *, "        Epoch | Cost Function| System_Clock | Nodes per Layer"
         allocate(random_numbers(2:size(input_output_pairs)))
 
-        do e = previous_epoch + 1, previous_epoch + num_epochs
+        do e = previous_epoch + 1, previous_epoch + max_num_epochs
           call random_number(random_numbers)
           call shuffle(input_output_pairs, random_numbers)
           mini_batches = [(mini_batch_t(input_output_pairs(bins(b)%first():bins(b)%last())), b = 1, size(bins))]
@@ -91,7 +91,7 @@ program train_saturated_mixture_ratio
           associate( &
             cost_avg => sum(cost)/size(cost), &
             cumulative_clock_time => previous_clock_time +  real(counter_end - counter_start) / real(clock_rate), &
-            loop_ending => e == previous_epoch + num_epochs &
+            loop_ending => e == previous_epoch + max_num_epochs &
           )
             write_and_exit_if_converged: &
             if (cost_avg < cost_tolerance) then