diff --git a/TrainerClass.py b/TrainerClass.py
index b778d51..7f349f1 100644
--- a/TrainerClass.py
+++ b/TrainerClass.py
@@ -237,7 +237,7 @@ class eNoseTrainer:
             'dropout': tune.choice([0.05, 0.15, 0.3]),
             'lr': tune.choice([0.01, 0.005, 0.001]),
             'batch_size': tune.choice([16, 32, 64]),
-            'epochs': epochssample_space
+            'epochs': epochs
         }
         total_space = (3*3*3*2*3*3*3*3)
 
@@ -492,6 +492,7 @@ class eNoseTrainer:
             self.saveCheckPoint()
 
         sample_size = 50000
+        epochs = 50
         self.loader.smooth = None
         self.loader.reset()
         for window in windows:
@@ -530,10 +531,10 @@ class eNoseTrainer:
                 self.logger.debug(f"Y_train_sample: {Y_train_sample.shape}")
                 self.logger.debug(f"Y_test_sample: {Y_test_sample.shape}")
 
-                optimized_model, model_params = self.search_best_conv1D_v1(X_train_sample, X_test_sample, Y_train_sample, Y_test_sample)
+                optimized_model, model_params = self.search_best_conv1D_v1(X_train_sample, X_test_sample, Y_train_sample, Y_test_sample, epochs=epochs//3)
 
                 self.logger.info(f"Training Model {model_id} with {model_params}")
-                optimized_model.fit(X_train, Y_train, epochs=model_params['epochs'], batch_size=model_params['batch_size'], verbose=1)
+                optimized_model.fit(X_train, Y_train, epochs=epochs, batch_size=model_params['batch_size'], verbose=1)
 
                 Y_train_pred = optimized_model.predict(X_train)
                 Y_test_pred = optimized_model.predict(X_test)
@@ -606,10 +607,10 @@ class eNoseTrainer:
                 self.logger.debug(f"Y_train_sample: {Y_train_sample.shape}")
                 self.logger.debug(f"Y_test_sample: {Y_test_sample.shape}")
 
-                optimized_model, model_params = self.search_best_conv1D_v1(X_train_sample, X_test_sample, Y_train_sample, Y_test_sample)
+                optimized_model, model_params = self.search_best_conv1D_v1(X_train_sample, X_test_sample, Y_train_sample, Y_test_sample, epochs=epochs//3)
 
                 self.logger.info(f"Training Model {model_id} with {model_params}")
-                optimized_model.fit(X_train, Y_train, epochs=model_params['epochs'], batch_size=model_params['batch_size'], verbose=1)
+                optimized_model.fit(X_train, Y_train, epochs=epochs, batch_size=model_params['batch_size'], verbose=1)
 
                 Y_train_pred = optimized_model.predict(X_train)
                 Y_test_pred = optimized_model.predict(X_test)