diff --git a/TrainerClass.py b/TrainerClass.py
index 36c6145..1aba047 100644
--- a/TrainerClass.py
+++ b/TrainerClass.py
@@ -347,7 +347,7 @@ class eNoseTrainer:
             plt.close()
 
     def fit(self):
-        windows = [128,]
+        windows = [64, 128, 256,]
 #        windows = [128, 256, 384]
         total_train_queue = 2*int(1/self.ratio)*(len(self.get_model_train())+len(windows))
         self.logger.info("{:=^60}".format(f'Begin Fit {total_train_queue} Models'))
@@ -518,7 +518,7 @@ class eNoseTrainer:
                 self.logger.debug(f"Y_train_sample: {Y_train_sample.shape}")
                 self.logger.debug(f"Y_test_sample: {Y_test_sample.shape}")
 
-                best_model, study, best_batch_size = self.search_best_conv1D_v1(X_train_sample, X_test_sample, Y_train_sample, Y_test_sample, epochs=10, num_trials=10)
+                best_model, study, best_batch_size = self.search_best_conv1D_v1(X_train_sample, X_test_sample, Y_train_sample, Y_test_sample, epochs=10, num_trials=32)
                 # Save study results to an Excel file
                 trials_data = []
                 for trial in study.trials:
@@ -605,7 +605,7 @@ class eNoseTrainer:
                 self.logger.debug(f"Y_train_sample: {Y_train_sample.shape}")
                 self.logger.debug(f"Y_test_sample: {Y_test_sample.shape}")
 
-                best_model, study, best_batch_size = self.search_best_conv1D_v1(X_train_sample, X_test_sample, Y_train_sample, Y_test_sample, epochs=10, num_trials=10)
+                best_model, study, best_batch_size = self.search_best_conv1D_v1(X_train_sample, X_test_sample, Y_train_sample, Y_test_sample, epochs=10, num_trials=32)
                 # Save study results to an Excel file
                 trials_data = []
                 for trial in study.trials: