AttributeError: 'tuple' object has no attribute 'lower' while object creation of a Model - tuples

Question

While Defining a model 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.
model -- TF Keras model (object containing the information for the entire training process)
def happyModel():
model = tf.keras.Sequential(
[
## ZeroPadding2D with padding 3, input shape of 64 x 64 x 3
tfl.ZeroPadding2D(padding=(3,3), data_format=(64,64,3)),
## Conv2D with 32 7x7 filters and stride of 1
tfl.Conv2D(32, (7, 7), strides = (1, 1), name = 'conv0'),
## BatchNormalization for axis 3
tfl.BatchNormalization(axis = 3, name = 'bn0'),
## ReLU
tfl.Activation('relu'),
## Max Pooling 2D with default parameters
tfl.MaxPooling2D((2, 2), name='max_pool0'),
## Flatten layer
tfl.Flatten(),
## Dense layer with 1 unit for output & 'sigmoid' activation
tfl.Dense(1, activation='sigmoid', name='fc'),
# YOUR CODE STARTS HERE
# YOUR CODE ENDS HERE
]
)
return model
Object Creation of the defined 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)
The error I am getting "AttributeError: 'tuple' object has no attribute 'lower'"
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-50-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
<ipython-input-49-b5fc98b1ebba> in happyModel()
21
22 ## ZeroPadding2D with padding 3, input shape of 64 x 64 x 3
---> 23 tfl.ZeroPadding2D(padding=(3,3), data_format=(64,64,3)),
24
25 ## Conv2D with 32 7x7 filters and stride of 1
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/layers/convolutional.py in
__init__(self, padding, data_format, **kwargs)
2800 def __init__(self, padding=(1, 1), data_format=None, **kwargs):
2801 super(ZeroPadding2D, self).__init__(**kwargs)
-> 2802 self.data_format = conv_utils.normalize_data_format(data_format)
2803 if isinstance(padding, int):
2804 self.padding = ((padding, padding), (padding, padding))
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/utils/conv_utils.py in
normalize_data_format(value)
190 if value is None:
191 value = backend.image_data_format()
--> 192 data_format = value.lower()
193 if data_format not in {'channels_first', 'channels_last'}:
194 raise ValueError('The `data_format` argument must be one of '
AttributeError: 'tuple' object has no attribute 'lower'
What is the reason behind it? Can anyone suggest me any solutions! TIA

Answer 1

I think the problem was in the parameter of the function definition. The following code works for me and solves the problem.
def happyModel():
Implements the forward propagation for the binary classification model:
ZEROPAD2D -> CONV2D -> BATCHNORM -> RELU -> MAXPOOL -> FLATTEN -> DENSE
model = tf.keras.Sequential([
## ZeroPadding2D with padding 3, input shape of 64 x 64 x 3
tfl.ZeroPadding2D(padding=(3,3), input_shape=(64, 64, 3), data_format=None),
## Conv2D with 32 7x7 filters and stride of 1
tfl.Conv2D(filters=32, kernel_size=7, strides=1,padding='valid'),
## BatchNormalization for axis 3
tfl.BatchNormalization(axis = 3, name = 'bn0'),
## ReLU
tfl.ReLU(max_value=None, negative_slope=0, threshold=0),
## Max Pooling 2D with default parameters
tfl.MaxPool2D(pool_size=(2, 2), strides=None, padding='valid',data_format=None),
## Flatten layer
tfl.Flatten(data_format=None),
## Dense layer with 1 unit for output & 'sigmoid' activation
tfl.Dense(1, activation='sigmoid', name='fc'),
])
return model