我从https://github.com/FengleiFan/QAE中获取代码，并更改了数据集。我用tensorflow 1.x版本在google colab上执行代码，但我得到了我无法解决的错误。
下面提到的代码是二次自动编码器的架构。而不是Conv2D，他们使用二次卷积函数。
def CTcnn（shape=（SIZE，SIZE，1））：

x_input = Input(shape=(SIZE,SIZE,1))
encode_conv1 = Quad_conv_layer_same(x_input,shape=[3,3,1,15]) 
encode_conv2 = Quad_conv_layer_same(encode_conv1,shape=[3,3,15,15])
encode_conv3 = Quad_conv_layer_same(encode_conv2,shape=[3,3,15,15])
encode_conv4 = Quad_conv_layer_same(encode_conv3,shape=[3,3,15,15])
encode_conv5 = Quad_conv_layer_valid(encode_conv4,shape=[3,3,15,15])
decode_conv4 = tf.nn.relu(Quad_deconv_layer_valid_linear(encode_conv5,shape=[3, 3,15,15],outputshape=tf.shape(encode_conv4))+encode_conv4)
decode_conv3 = Quad_deconv_layer_same(decode_conv4,shape=[3, 3,15,15],outputshape=tf.shape(encode_conv3))
decode_conv2 = tf.nn.relu(Quad_deconv_layer_same_linear(decode_conv3,shape=[3, 3,15,15],outputshape=tf.shape(encode_conv2))+encode_conv2)
decode_conv1 = Quad_deconv_layer_same(decode_conv2,shape=[3, 3,15,15],outputshape=tf.shape(encode_conv1))
x_output = tf.nn.relu(Quad_deconv_layer_same_linear(decode_conv1,shape=[3, 3,1,15],outputshape=tf.shape(x_input))+x_input)    
model=tf.keras.Model(inputs=x_input, outputs=x_output, name='CTcnn')
return model
**model = CTcnn(shape=(SIZE,SIZE,1))**  #getting error in this line
model.compile(optimizer='adam', loss='mean_squared_error')
model.summary()

以下是完整的错误：

FailedPreconditionError                   Traceback (most recent call last)
<ipython-input-49-07fdc66cd455> in <module>()
----> 1 model = CTcnn(shape=(SIZE,SIZE,1))
      2 model.compile(optimizer='adam', loss='mean_squared_error')
      3 model.summary()
<ipython-input-48-503bfc463612> in CTcnn(shape)
     16   x_output = tf.nn.relu(Quad_deconv_layer_same_linear(decode_conv1,shape=[3, 3,1,15],outputshape=tf.shape(x_input))+x_input)
     17 
---> 18   model=tf.keras.Model(inputs=x_input, outputs=x_output, name='CTcnn')
     19   return model
     20
/tensorflow-1.15.2/python3.6/tensorflow_core/python/keras/engine/training.py in __init__(self, *args, **kwargs)
    145 
    146   def __init__(self, *args, **kwargs):
--> 147     super(Model, self).__init__(*args, **kwargs)
    148     _keras_api_gauge.get_cell('model').set(True)
    149     # initializing _distribution_strategy here since it is possible to call
/tensorflow-1.15.2/python3.6/tensorflow_core/python/keras/engine/network.py in __init__(self, *args, **kwargs)
    162         'inputs' in kwargs and 'outputs' in kwargs):
    163       # Graph network
--> 164       self._init_graph_network(*args, **kwargs)
    165     else:
    166       # Subclassed network
/tensorflow-1.15.2/python3.6/tensorflow_core/python/training/tracking/base.py in _method_wrapper(self, *args, **kwargs)
    455     self._self_setattr_tracking = False  # pylint: disable=protected-access
    456     try:
--> 457       result = method(self, *args, **kwargs)
    458     finally:
    459       self._self_setattr_tracking = previous_value  # pylint: disable=protected-access
/tensorflow-1.15.2/python3.6/tensorflow_core/python/keras/engine/network.py in _init_graph_network(self, inputs, outputs, name, **kwargs)
    265
    266     if any(not hasattr(tensor, '_keras_history') for tensor in self.outputs):
--> 267       base_layer_utils.create_keras_history(self._nested_outputs)
    268 
    269     self._base_init(name=name, **kwargs)
/tensorflow-1.15.2/python3.6/tensorflow_core/python/keras/engine/base_layer_utils.py in create_keras_history(tensors)
    182     keras_tensors: The Tensors found that came from a Keras Layer.
    183   """
--> 184   _, created_layers = _create_keras_history_helper(tensors, set(), [])
    185   return created_layers
    186
/tensorflow-1.15.2/python3.6/tensorflow_core/python/keras/engine/base_layer_utils.py in _create_keras_history_helper(tensors, processed_ops, created_layers)
    229               constants[i] = backend.function([], op_input)([])
    230       processed_ops, created_layers = _create_keras_history_helper(
--> 231           layer_inputs, processed_ops, created_layers)
    232       name = op.name
    233       node_def = op.node_def.SerializeToString()
/tensorflow-1.15.2/python3.6/tensorflow_core/python/keras/engine/base_layer_utils.py in _create_keras_history_helper(tensors, processed_ops, created_layers)
    229               constants[i] = backend.function([], op_input)([])
    230       processed_ops, created_layers = _create_keras_history_helper(
--> 231           layer_inputs, processed_ops, created_layers)
    232       name = op.name
    233       node_def = op.node_def.SerializeToString()
/tensorflow-1.15.2/python3.6/tensorflow_core/python/keras/engine/base_layer_utils.py in _create_keras_history_helper(tensors, processed_ops, created_layers)
    227           else:
    228             with ops.init_scope():
--> 229               constants[i] = backend.function([], op_input)([])
    230       processed_ops, created_layers = _create_keras_history_helper(
    231           layer_inputs, processed_ops, created_layers)
/tensorflow-1.15.2/python3.6/tensorflow_core/python/keras/backend.py in __call__(self, inputs)
   3474
   3475     fetched = self._callable_fn(*array_vals,
-> 3476                                 run_metadata=self.run_metadata)
   3477     self._call_fetch_callbacks(fetched[-len(self._fetches):])
   3478     output_structure = nest.pack_sequence_as(
/tensorflow-1.15.2/python3.6/tensorflow_core/python/client/session.py in __call__(self, *args, **kwargs)
   1470         ret = tf_session.TF_SessionRunCallable(self._session._session,
   1471                                                self._handle, args,
-> 1472                                                run_metadata_ptr)
   1473         if run_metadata:
   1474           proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)        
FailedPreconditionError: 2 root error(s) found.
  (0) Failed precondition: Attempting to use uninitialized value Variable_179
     [[{{node Variable_179/read}}]]
     [[Variable_179/read/_1]]
  (1) Failed precondition: Attempting to use uninitialized value Variable_179
     [[{{node Variable_179/read}}]]
0 successful operations.
0 derived errors ignored.

有什么办法可以补救吗？