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

Question

I am trying to define a model 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
and following code is for creating the object of this model defined above:
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)
I got following error:
---------------------------------------------------------------------------
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.
I suspect my calling of ZeroPadding2D() is not right. The project seems to require the input shape of ZeroPadding2D() to be 64X64X3. I tried many formats but could not fix the problem. Anyone can give a pointer? Thanks a lot.

Answer 1

In your model definition, there's an issue with the following layer:
tf.keras.layers.ZeroPadding2D(padding=(3,3), data_format=(64,64,3)),
First, you didn't define any input layer also, the data_format is a string, one of channels_last (default) or channels_first, source. The correct way to define the above model as follows:
def happyModel():
model = tf.keras.Sequential(
[
## ZeroPadding2D with padding 3, input shape of 64 x 64 x 3
tf.keras.layers.ZeroPadding2D(padding=(3,3),
input_shape=(64, 64, 3), data_format="channels_last"),
....
....
happy_model = happyModel()
happy_model.summary()
Model: "sequential_2"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
zero_padding2d_4 (ZeroPaddin (None, 70, 70, 3) 0
_________________________________________________________________
conv0 (Conv2D) (None, 64, 64, 32) 4736
_________________________________________________________________
bn0 (BatchNormalization) (None, 64, 64, 32) 128
_________________________________________________________________
activation_2 (Activation) (None, 64, 64, 32) 0
_________________________________________________________________
max_pool0 (MaxPooling2D) (None, 32, 32, 32) 0
_________________________________________________________________
flatten_16 (Flatten) (None, 32768) 0
_________________________________________________________________
fc (Dense) (None, 1) 32769
=================================================================
Total params: 37,633
Trainable params: 37,569
Non-trainable params: 64

Answer 2

Per the documentation for tf.keras.Sequential() (https://www.tensorflow.org/api_docs/python/tf/keras/Sequential):
"Optionally, the first layer can receive an input_shape argument"
So instead of
tf.keras.layers.ZeroPadding2D(padding=(3,3), data_format=(64,64,3))
if you want to specify input shape it should be
tf.keras.layers.ZeroPadding2D(padding=(3,3), input_shape=(64,64,3))

Answer 3

model = tf.keras.Sequential([
# YOUR CODE STARTS HERE
tf.keras.layers.ZeroPadding2D(padding=(3, 3), input_shape=(64,64,3), data_format="channels_last"),
tf.keras.layers.Conv2D(32, (7, 7), strides = (1, 1)),
tf.keras.layers.BatchNormalization(axis=3),
tf.keras.layers.ReLU(),
tf.keras.layers.MaxPooling2D(),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(1, activation='sigmoid'),
# YOUR CODE ENDS HERE
])
return model
Try it working perfectly......

Answer 4

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=(3,3), input_shape=(64,64,3),data_format="channels_last"),
tfl.Conv2D(32, (7, 7), strides = (1, 1), name = 'conv0'),
tfl.BatchNormalization(axis = 3, name = 'bn0'),
tfl.ReLU(),
tfl.MaxPooling2D((2, 2), name='max_pool0'),
tfl.Flatten(),
tfl.Dense(1, activation='sigmoid', name='fc'),
# YOUR CODE ENDS HERE
])
It is working you can try it.