Create Unet Error: `padding` should have two elements. Found: (6, 6, 6, 6)

When create the Unet,

with tf.name_scope('unet'):
    pred = UNet().create_model(img_shape, backend='tf', tf_input=img)

The following error occurred

Traceback (most recent call last):
  File "G:/Tensorflow/unet-tensorflow-keras-master/train.py", line 52, in <module>
    pred = UNet().create_model(img_shape, backend='tf', tf_input=img)
  File "G:\Tensorflow\unet-tensorflow-keras-master\model.py", line 84, in create_model
    conv9 = ZeroPadding2D(padding=(ch[0], ch[1], cw[0], cw[1]), dim_ordering=dim_ordering)(conv9)
  File "E:\Program Files\python35\lib\site-packages\keras\legacy\interfaces.py", line 88, in wrapper
    return func(*args, **kwargs)
  File "E:\Program Files\python35\lib\site-packages\keras\layers\convolutional.py", line 1307, in __init__
    'Found: ' + str(padding))
ValueError: `padding` should have two elements. Found: (6, 6, 6, 6)

Besides, there are many warning

build UNet ...
G:\Tensorflow\unet-tensorflow-keras-master\model.py:37: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), name="conv1_1", activation="relu", data_format="channels_last", padding="same")`
  conv1 = Convolution2D(32, 3, 3, activation='relu', border_mode='same', dim_ordering=dim_ordering, name='conv1_1')(inputs)
G:\Tensorflow\unet-tensorflow-keras-master\model.py:38: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), activation="relu", padding="same", data_format="channels_last")`
  conv1 = Convolution2D(32, 3, 3, activation='relu', border_mode='same', dim_ordering=dim_ordering)(conv1)
G:\Tensorflow\unet-tensorflow-keras-master\model.py:39: UserWarning: Update your `MaxPooling2D` call to the Keras 2 API: `MaxPooling2D(data_format="channels_last", pool_size=(2, 2))`
  pool1 = MaxPooling2D(pool_size=(2, 2), dim_ordering=dim_ordering)(conv1)
G:\Tensorflow\unet-tensorflow-keras-master\model.py:40: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), activation="relu", padding="same", data_format="channels_last")`
  conv2 = Convolution2D(64, 3, 3, activation='relu', border_mode='same', dim_ordering=dim_ordering)(pool1)
...

Looking forward to your reply Best wishes to you

zizhaozhang / unet-tensorflow-keras

Create Unet Error: `padding` should have two elements. Found: (6, 6, 6, 6) #3