From eb5ce3b1c2d175a69df048bd5992fd6a5aca2277 Mon Sep 17 00:00:00 2001 From: Leandro Soares <98189939+SoaresLMB@users.noreply.github.com> Date: Wed, 14 Aug 2024 11:27:04 -0300 Subject: [PATCH] Update model_builders.py --- builders/model_builders.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/builders/model_builders.py b/builders/model_builders.py index 27202d770..de47216f2 100644 --- a/builders/model_builders.py +++ b/builders/model_builders.py @@ -239,7 +239,7 @@ def objective(trial,input_shape,X_train,y_train,X_val,y_val,neural_network_type, num_layers = trial.suggest_int('num_layers', 2, 4) num_dense_layers = trial.suggest_int('num_dense_layers', 1, 3) dense_neurons = trial.suggest_int('dense_neurons', 60, 320, log=True) - dropout = trial.suggest_float('dropout', 0.5, 0.9, step=0.1) + dropout = trial.suggest_float('dropout', 0.1, 0.5, step=0.1) learning_rate = trial.suggest_categorical('learning_rate', [0.0001, 0.0003, 0.0006, 0.001, 0.003, 0.006, 0.01]) decision_threshold = trial.suggest_float('decision_threshold', 0.5, 0.9,step=0.1) @@ -266,7 +266,7 @@ def objective(trial,input_shape,X_train,y_train,X_val,y_val,neural_network_type, num_layers = trial.suggest_int('num_layers', 1, 5) dense_neurons = trial.suggest_int('dense_neurons', 20, 4000, log=True) - dropout = trial.suggest_float('dropout', 0.5, 0.9, step=0.1) + dropout = trial.suggest_float('dropout', 0.1, 0.5, step=0.1) learning_rate = trial.suggest_categorical('learning_rate', [0.001, 0.003, 0.005, 0.007, 0.01, 0.03, 0.05, 0.07]) decision_threshold = trial.suggest_float('decision_threshold', 0.5, 0.9,step=0.1) @@ -305,7 +305,7 @@ def objective(trial,input_shape,X_train,y_train,X_val,y_val,neural_network_type, def create_study_object(objective, input_shape, X_train, y_train, X_val, y_val, neural_network_type,neural_network_results_dir,number_of_labels): study = optuna.create_study(direction="maximize") - study.optimize(lambda trial: objective(trial, input_shape, X_train, y_train, X_val, y_val, neural_network_type,neural_network_results_dir,number_of_labels), n_trials=100) + study.optimize(lambda trial: objective(trial, input_shape, X_train, y_train, X_val, y_val, neural_network_type,neural_network_results_dir,number_of_labels), n_trials=20) best_trial = study.best_trial best_params = best_trial.params