diff --git a/EEG_Tensorflow_models/Models/DMTL_BCI.py b/EEG_Tensorflow_models/Models/DMTL_BCI.py
index 7baee95..1d3acef 100644
--- a/EEG_Tensorflow_models/Models/DMTL_BCI.py
+++ b/EEG_Tensorflow_models/Models/DMTL_BCI.py
@@ -5,7 +5,7 @@
 from tensorflow.keras.layers import Input, Flatten
 from tensorflow.keras.constraints import max_norm
 from tensorflow.keras import backend as K
-from tensorflow.keras.layers.experimental.preprocessing import Resizing
+from tensorflow.keras.layers import Resizing
 from tensorflow.keras.losses import CategoricalCrossentropy
 from tensorflow.keras.regularizers import l1_l2
 
diff --git a/EEG_Tensorflow_models/Models/MTVAE.py b/EEG_Tensorflow_models/Models/MTVAE.py
index 0788768..054e76c 100644
--- a/EEG_Tensorflow_models/Models/MTVAE.py
+++ b/EEG_Tensorflow_models/Models/MTVAE.py
@@ -5,7 +5,7 @@
 from tensorflow.keras.layers import Input, Flatten, Reshape
 from tensorflow.keras.constraints import max_norm
 from tensorflow.keras import backend as K
-from tensorflow.keras.layers.experimental.preprocessing import Resizing
+from tensorflow.keras.layers import Resizing
 from tensorflow.keras.losses import CategoricalCrossentropy
 from tensorflow.keras.regularizers import l1_l2
 import tensorflow as tf
diff --git a/EEG_Tensorflow_models/Models/MTVAE_1conv2d.py b/EEG_Tensorflow_models/Models/MTVAE_1conv2d.py
index 8042c1e..692ad2f 100644
--- a/EEG_Tensorflow_models/Models/MTVAE_1conv2d.py
+++ b/EEG_Tensorflow_models/Models/MTVAE_1conv2d.py
@@ -5,7 +5,7 @@
 from tensorflow.keras.layers import Input, Flatten, Reshape
 from tensorflow.keras.constraints import max_norm
 from tensorflow.keras import backend as K
-from tensorflow.keras.layers.experimental.preprocessing import Resizing
+from tensorflow.keras.layers import Resizing
 from tensorflow.keras.losses import CategoricalCrossentropy
 from tensorflow.keras.regularizers import l1_l2
 import tensorflow as tf
@@ -56,4 +56,4 @@ def MTVAE_1conv2d(nb_classes, Chans = 22, Samples = 250, dropoutRate = 0.5, l1 =
     
     KL = -0.5 * tf.keras.backend.sum( 1 + var_flat - tf.keras.backend.exp(var_flat) - tf.keras.backend.square(mu_flat),axis=-1)
     model.add_loss(tf.keras.backend.mean(KL)/var_flat.shape[-1])#Chans*Samples)
-    return model
\ No newline at end of file
+    return model
diff --git a/EEG_Tensorflow_models/Models/Shallownet_1conv2d.py b/EEG_Tensorflow_models/Models/Shallownet_1conv2d.py
index 655c701..d037ae6 100644
--- a/EEG_Tensorflow_models/Models/Shallownet_1conv2d.py
+++ b/EEG_Tensorflow_models/Models/Shallownet_1conv2d.py
@@ -5,7 +5,7 @@
 from tensorflow.keras.layers import Input, Flatten
 from tensorflow.keras.constraints import max_norm
 from tensorflow.keras import backend as K
-from tensorflow.keras.layers.experimental.preprocessing import Resizing
+from tensorflow.keras.layers import Resizing
 from tensorflow.keras.losses import CategoricalCrossentropy
 from tensorflow.keras.regularizers import l1_l2
 
diff --git a/EEG_Tensorflow_models/Models/Shallownet_1conv2d_rff.py b/EEG_Tensorflow_models/Models/Shallownet_1conv2d_rff.py
index 51549ba..d27e32b 100644
--- a/EEG_Tensorflow_models/Models/Shallownet_1conv2d_rff.py
+++ b/EEG_Tensorflow_models/Models/Shallownet_1conv2d_rff.py
@@ -6,7 +6,7 @@
 from tensorflow.keras.layers import Input, Flatten
 from tensorflow.keras.constraints import max_norm
 from tensorflow.keras import backend as K
-from tensorflow.keras.layers.experimental.preprocessing import Resizing
+from tensorflow.keras.layers import Resizing
 from tensorflow.keras.losses import CategoricalCrossentropy
 from tensorflow.keras.regularizers import l1_l2