Creating a DBN with the Keras Model API

You have now seen how to create a single-layer RBM to generate images; this is the building block required to create a full-fledged DBN. Usually, for a model in TensorFlow 2, we only need to extend tf.keras.Model and define an initialization (where the layers are defined) and a call function (for the forward pass). For our DBN model, we also need a few more custom functions to define its behavior.

First, in the initialization, we need to pass a list of dictionaries that contain the parameters for our RBM layers (number_hidden_units, number_visible_units, learning_rate, cd_steps):

class DBN(tf.keras.Model):
    def __init__(self, rbm_params=None, name='deep_belief_network', 
                 num_epochs=100, tolerance=1e-3, batch_size=32, shuffle_buffer=1024, **kwargs):
        super().__init__(name=name, **kwargs)
        self._rbm_params = rbm_params
        self._rbm_layers = list()
        self._dense_layers = list()
   ...