diff --git a/deeplearning2/vgg16.py b/deeplearning2/vgg16.py
index e880cc6c7..a05d8588c 100644
--- a/deeplearning2/vgg16.py
+++ b/deeplearning2/vgg16.py
@@ -29,8 +29,8 @@ def VGG16(include_top=True, weights='imagenet',
     input_shape = _obtain_input_shape(input_shape,
                                       default_size=224,
                                       min_size=48,
-                                      dim_ordering=K.image_dim_ordering(),
-                                      include_top=include_top)
+                                      data_format=K.image_dim_ordering(),
+                                      require_flatten=include_top)
 
     if input_tensor is None:
         img_input = Input(shape=input_shape)
@@ -40,31 +40,31 @@ def VGG16(include_top=True, weights='imagenet',
         else:
             img_input = input_tensor
     # Block 1
-    x = Convolution2D(64, 3, 3, activation='relu', border_mode='same', name='block1_conv1')(img_input)
-    x = Convolution2D(64, 3, 3, activation='relu', border_mode='same', name='block1_conv2')(x)
+    x = Convolution2D(64, kernel_size=(3,3) activation='relu', padding='same', name='block1_conv1')(img_input)
+    x = Convolution2D(64, kernel_size=(3,3) activation='relu', padding='same', name='block1_conv2')(x)
     x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
 
     # Block 2
-    x = Convolution2D(128, 3, 3, activation='relu', border_mode='same', name='block2_conv1')(x)
-    x = Convolution2D(128, 3, 3, activation='relu', border_mode='same', name='block2_conv2')(x)
+    x = Convolution2D(128, kernel_size=(3,3) activation='relu', padding='same', name='block2_conv1')(x)
+    x = Convolution2D(128, kernel_size=(3,3) activation='relu', padding='same', name='block2_conv2')(x)
     x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
 
     # Block 3
-    x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv1')(x)
-    x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv2')(x)
-    x = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv3')(x)
+    x = Convolution2D(256, kernel_size=(3,3) activation='relu', padding='same', name='block3_conv1')(x)
+    x = Convolution2D(256, kernel_size=(3,3) activation='relu', padding='same', name='block3_conv2')(x)
+    x = Convolution2D(256, kernel_size=(3,3) activation='relu', padding='same', name='block3_conv3')(x)
     x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
 
     # Block 4
-    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv1')(x)
-    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv2')(x)
-    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv3')(x)
+    x = Convolution2D(512, kernel_size=(3,3) activation='relu', padding='same', name='block4_conv1')(x)
+    x = Convolution2D(512, kernel_size=(3,3) activation='relu', padding='same', name='block4_conv2')(x)
+    x = Convolution2D(512, kernel_size=(3,3) activation='relu', padding='same', name='block4_conv3')(x)
     x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
 
     # Block 5
-    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv1')(x)
-    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv2')(x)
-    x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv3')(x)
+    x = Convolution2D(512, kernel_size=(3,3) activation='relu', padding='same', name='block5_conv1')(x)
+    x = Convolution2D(512, kernel_size=(3,3) activation='relu', padding='same', name='block5_conv2')(x)
+    x = Convolution2D(512, kernel_size=(3,3) activation='relu', padding='same', name='block5_conv3')(x)
     x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
 
     if include_top: