Update ames-wo-val-hyperparam-optimization-local.py

Reduce some max parameters to prevent exhausting RAM.

Author: david-thrower

ames-wo-val-hyperparam-optimization-local.py

@@ -44,18 +44,18 @@
 
 def objective(trial):
     # Define hyperparameter space
-    minimum_levels = trial.suggest_int('minimum_levels', 1, 8)
-    maximum_levels = trial.suggest_int('maximum_levels', minimum_levels, 8)
-    minimum_units_per_level = trial.suggest_int('minimum_units_per_level', 1, 8)
-    maximum_units_per_level = trial.suggest_int('maximum_units_per_level', minimum_units_per_level, 8)
-    minimum_neurons_per_unit = trial.suggest_int('minimum_neurons_per_unit', 1, 8)
-    maximum_neurons_per_unit = trial.suggest_int('maximum_neurons_per_unit', minimum_neurons_per_unit, 8)
+    minimum_levels = trial.suggest_int('minimum_levels', 1, 5)
+    maximum_levels = trial.suggest_int('maximum_levels', minimum_levels, 5)
+    minimum_units_per_level = trial.suggest_int('minimum_units_per_level', 1, 5)
+    maximum_units_per_level = trial.suggest_int('maximum_units_per_level', minimum_units_per_level, 5)
+    minimum_neurons_per_unit = trial.suggest_int('minimum_neurons_per_unit', 1, 5)
+    maximum_neurons_per_unit = trial.suggest_int('maximum_neurons_per_unit', minimum_neurons_per_unit, 5)
     activation = trial.suggest_categorical('activation', ['relu', 'elu', 'gelu', 'swish', 'softplus'])
     predecessor_level_connection_affinity_factor_first = trial.suggest_loguniform('predecessor_level_connection_affinity_factor_first', 0.1, 40.0)
     predecessor_level_connection_affinity_factor_main = trial.suggest_loguniform('predecessor_level_connection_affinity_factor_main', 0.1, 40.0)
-    max_consecutive_lateral_connections = trial.suggest_int('max_consecutive_lateral_connections', 1, 40)
-    p_lateral_connection = trial.suggest_loguniform('p_lateral_connection', 0.1, 40.0)
-    num_lateral_connection_tries_per_unit = trial.suggest_int('num_lateral_connection_tries_per_unit', 1, 40)
+    max_consecutive_lateral_connections = trial.suggest_int('max_consecutive_lateral_connections', 1, 35)
+    p_lateral_connection = trial.suggest_loguniform('p_lateral_connection', 0.1, 35)
+    num_lateral_connection_tries_per_unit = trial.suggest_int('num_lateral_connection_tries_per_unit', 1, 35)
     learning_rate = trial.suggest_loguniform('learning_rate', 10**-6, 0.6)
     epochs = trial.suggest_int('epochs', 1, 150)
     batch_size = trial.suggest_int('batch_size', 1, 800)