diff --git a/Model/automouse_model.py b/Model/automouse_model.py
index 0ad90af..4b9e572 100644
--- a/Model/automouse_model.py
+++ b/Model/automouse_model.py
@@ -1,10 +1,10 @@
 #Libraries
 from keras.models import Model
-from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, merge
+from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, add
 from keras.layers import Dense, Dropout, Activation, Flatten, BatchNormalization
 from metrics import dice_coef, dice_coef_loss
 
-def automouseTKV_model():
+def automouseTKV_model(img_rows, img_cols):
     inputs = Input((1, img_rows, img_cols))
     
     conv1 = Conv2D(32, (7, 7), activation='relu', border_mode='same')(inputs)
@@ -42,31 +42,31 @@ def automouseTKV_model():
     conv6 = BatchNormalization(axis=1)(conv6)
     conv6 = Conv2D(512, (3, 3), activation='relu', border_mode='same')(conv6)
 
-    up7 = merge([UpSampling2D(size=(2, 2))(conv6), conv5], mode='sum', concat_axis=1)
+    up7 = add([UpSampling2D(size=(2, 2))(conv6), conv5], mode='sum', concat_axis=1)
     conv7 = Conv2D(512, (3, 3), activation='relu', border_mode='same')(up7)
     conv7 = Dropout(0.5)(conv7)
     conv7 = BatchNormalization(axis=1)(conv7)
     conv7 = Conv2D(256, (3, 3), activation='relu', border_mode='same')(conv7)
 
-    up8 = merge([UpSampling2D(size=(2, 2))(conv7), conv4], mode='sum', concat_axis=1)
+    up8 = add([UpSampling2D(size=(2, 2))(conv7), conv4], mode='sum', concat_axis=1)
     conv8 = Conv2D(256, (3, 3), activation='relu', border_mode='same')(up8)
     conv8 = Dropout(0.5)(conv8)
     conv8 = BatchNormalization(axis=1)(conv8)
     conv8 = Conv2D(128, (3, 3), activation='relu', border_mode='same')(conv8)
 
-    up9 = merge([UpSampling2D(size=(2, 2))(conv8), conv3], mode='sum', concat_axis=1)
+    up9 = add([UpSampling2D(size=(2, 2))(conv8), conv3], mode='sum', concat_axis=1)
     conv9 = Conv2D(128, (3, 3), activation='relu', border_mode='same')(up9)
     conv9 = Dropout(0.5)(conv9)
     conv9 = BatchNormalization(axis=1)(conv9)
     conv9 = Conv2D(64, (3, 3), activation='relu', border_mode='same')(conv9)
 
-    up10 = merge([UpSampling2D(size=(2, 2))(conv9), conv2], mode='sum', concat_axis=1)
+    up10 = add([UpSampling2D(size=(2, 2))(conv9), conv2], mode='sum', concat_axis=1)
     conv10 = Conv2D(64, (5, 5), activation='relu', border_mode='same')(up10)
     conv10 = Dropout(0.5)(conv10)
     conv10 = BatchNormalization(axis=1)(conv10)
     conv10 = Conv2D(32, (5, 5), activation='relu', border_mode='same')(conv10)
     
-    up11 = merge([UpSampling2D(size=(2, 2))(conv10), conv1], mode='sum', concat_axis=1)
+    up11 = add([UpSampling2D(size=(2, 2))(conv10), conv1], mode='sum', concat_axis=1)
     conv11 = Conv2D(32, (7, 7), activation='relu', border_mode='same')(up11)
     conv11 = Dropout(0.5)(conv11)
     conv11 = BatchNormalization(axis=1)(conv11)
@@ -78,4 +78,4 @@ def automouseTKV_model():
 
     model.compile(optimizer=Adam(lr=0.0001,decay=0.000001), loss=dice_coef_loss, metrics=[dice_coef])
 
-    return model
\ No newline at end of file
+    return model