Converting TensorFlow Keras model API to model subclassing

For a simple TF2 Object detection CNN architecture defined using Keras's functional API, a batch of data is obtained as:

example, label = next(data_generator(batch_size = 32))
example.keys()
# dict_keys(['image'])
image = example['image']
image.shape
# (32, 144, 144, 3)
label.keys()
# dict_keys(['class_out', 'box_out'])
label['class_out'].shape, label['box_out'].shape
# ((32, 9), (32, 2))

The CNN architecture defined using Keras's functional API is:

input_ = Input(shape = (144, 144, 3), name = 'image')
# name - An optional name string for the Input layer. Should …

api deeplearning keras tensorflow