我正在尝试定义一个模型happyModel（）

# GRADED FUNCTION: happyModel

def happyModel():
    """
    Implements the forward propagation for the binary classification model:
    ZEROPAD2D -> CONV2D -> BATCHNORM -> RELU -> MAXPOOL -> FLATTEN -> DENSE

Note that for simplicity and grading purposes, you'll hard-code all the values
such as the stride and kernel (filter) sizes. 
Normally, functions should take these values as function parameters.

Arguments:
None

Returns:
model -- TF Keras model (object containing the information for the entire training process) 
"""
model = tf.keras.Sequential(
    [
        ## ZeroPadding2D with padding 3, input shape of 64 x 64 x 3
        tf.keras.layers.ZeroPadding2D(padding=(3,3), data_format=(64,64,3)),
    
        ## Conv2D with 32 7x7 filters and stride of 1            
        tf.keras.layers.Conv2D(32, (7, 7), strides = (1, 1), name = 'conv0'),
        
        ## BatchNormalization for axis 3
        
        tf.keras.layers.BatchNormalization(axis = 3, name = 'bn0'),
        
        ## ReLU            
        tf.keras.layers.Activation('relu'),
        
        ## Max Pooling 2D with default parameters            
        tf.keras.layers.MaxPooling2D((2, 2), name='max_pool0'),
    
        ## Flatten layer            
        tf.keras.layers.Flatten(),
    
        ## Dense layer with 1 unit for output & 'sigmoid' activation            
        tf.keras.layers.Dense(1, activation='sigmoid', name='fc'),
        
        # YOUR CODE STARTS HERE
        
        
        # YOUR CODE ENDS HERE
    ]
)

return model

下面的代码用于创建上面定义的该模型的对象：

happy_model = happyModel()
# Print a summary for each layer
for layer in summary(happy_model):
    print(layer)
    
output = [['ZeroPadding2D', (None, 70, 70, 3), 0, ((3, 3), (3, 3))],
            ['Conv2D', (None, 64, 64, 32), 4736, 'valid', 'linear', 'GlorotUniform'],
            ['BatchNormalization', (None, 64, 64, 32), 128],
            ['ReLU', (None, 64, 64, 32), 0],
            ['MaxPooling2D', (None, 32, 32, 32), 0, (2, 2), (2, 2), 'valid'],
            ['Flatten', (None, 32768), 0],
            ['Dense', (None, 1), 32769, 'sigmoid']]
    
comparator(summary(happy_model), output)

出现以下错误：

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-67-f33284fd82fe> in <module>
      1 happy_model = happyModel()
      2 # Print a summary for each layer
----> 3 for layer in summary(happy_model):
      4     print(layer)
      5 

~/work/release/W1A2/test_utils.py in summary(model)
     30     result = []
     31     for layer in model.layers:
---> 32         descriptors = [layer.__class__.__name__, layer.output_shape, layer.count_params()]
     33         if (type(layer) == Conv2D):
     34             descriptors.append(layer.padding)

/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py in output_shape(self)
   2177     """
   2178     if not self._inbound_nodes:
-> 2179       raise AttributeError('The layer has never been called '
   2180                            'and thus has no defined output shape.')
   2181     all_output_shapes = set(

AttributeError: The layer has never been called and thus has no defined output shape.

我怀疑我对ZeroPadding2D()的调用不正确。该项目似乎要求ZeroPadding2D()的输入形状为64X64X3。我尝试了许多格式，但无法修复这个问题。有人能给予一个指针吗？非常感谢。