diff --git a/train.py b/train.py
index 848ff6a..4cc3cbb 100644
--- a/train.py
+++ b/train.py
@@ -277,16 +277,16 @@ def run(_config, n_classes, n_epochs, input_height,
         if (task == "segmentation" or task == "binarization"):
             if not is_loss_soft_dice and not weighted_loss:
                 model.compile(loss='categorical_crossentropy',
-                              optimizer=Adam(lr=learning_rate), metrics=['accuracy'])
+                              optimizer=Adam(learning_rate=learning_rate), metrics=['accuracy'])
             if is_loss_soft_dice:                    
                 model.compile(loss=soft_dice_loss,
-                              optimizer=Adam(lr=learning_rate), metrics=['accuracy'])
+                              optimizer=Adam(learning_rate=learning_rate), metrics=['accuracy'])
             if weighted_loss:
                 model.compile(loss=weighted_categorical_crossentropy(weights),
-                              optimizer=Adam(lr=learning_rate), metrics=['accuracy'])
+                              optimizer=Adam(learning_rate=learning_rate), metrics=['accuracy'])
         elif task == "enhancement":
             model.compile(loss='mean_squared_error',
-                          optimizer=Adam(lr=learning_rate), metrics=['accuracy'])
+                          optimizer=Adam(learning_rate=learning_rate), metrics=['accuracy'])
             
         
         # generating train and evaluation data
@@ -299,7 +299,7 @@ def run(_config, n_classes, n_epochs, input_height,
         ##score_best=[]
         ##score_best.append(0)
         for i in tqdm(range(index_start, n_epochs + index_start)):
-            model.fit_generator(
+            model.fit(
                 train_gen,
                 steps_per_epoch=int(len(os.listdir(dir_flow_train_imgs)) / n_batch) - 1,
                 validation_data=val_gen,
@@ -384,7 +384,7 @@ def run(_config, n_classes, n_epochs, input_height,
 
         #f1score_tot = [0]
         indexer_start = 0
-        opt = SGD(lr=0.01, momentum=0.9)
+        opt = SGD(learning_rate=0.01, momentum=0.9)
         opt_adam = tf.keras.optimizers.Adam(learning_rate=0.0001)
         model.compile(loss="binary_crossentropy",
                             optimizer = opt_adam,metrics=['accuracy'])