Error in implementing happy model()

Course Q&A Deep Learning Specialization Convolutional Neural Networks
1

Here is the code I wrote as per the instructions, but I am getting an error. Please help.

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
        
        ## Conv2D with 32 7x7 filters and stride of 1
        
        ## BatchNormalization for axis 3
        
        ## ReLU
        
        ## Max Pooling 2D with default parameters
        
        ## Flatten layer
        
        ## Dense layer with 1 unit for output & 'sigmoid' activation
        
        # YOUR CODE STARTS HERE
        tfl.ZeroPadding2D(padding=3),
        tfl.Conv2D(32,7,strides=1),
        tfl.BatchNormalization(axis=3),
        tfl.ReLU(),
        tfl.MaxPool2D(pool_size=(2,2),padding= 'valid'),
        tfl.Flatten(data_format= None),
        tfl.Dense(1,activation= 'sigmoid'),
        
        # YOUR CODE ENDS HERE
    
     ])

return model

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)

ERROR message is:

AttributeError Traceback (most recent call last)
in
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.

2

Remove the trailing comma from the line that defines the Dense layer.

3

Other comments:
In ZeroPadding2D, you need to specify the shape.
In MaxPool2D and Flatten, you don’t need to specify any arguments, because the default values are correct.

1 Like
4

according to the documentation of keras, specifying one int value is enough for symmetric padding. And I removed the arguments from the other 2 functions. But I’m still geting the same error.

1 Like
5

According to the assignment instructions, you need to specify the shape.

1 Like
6

Thank you, I got it.